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26 * @license React
27 * react-dom.production.js
28 *
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30 *
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34 * @license React
35 * react-dom-client.production.js
36 *
37 * Copyright (c) Meta Platforms, Inc. and affiliates.
38 *
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L=c.memoizedProps,Q=Uc(p,L);S.props=Q;var re=S.context,de=p.contextType;N=Au,typeof de=="object"&&de!==null&&(N=Ln(de));var fe=p.getDerivedStateFromProps;de=typeof fe=="function"||typeof S.getSnapshotBeforeUpdate=="function",L=c.pendingProps!==L,de||typeof S.UNSAFE_componentWillReceiveProps!="function"&&typeof S.componentWillReceiveProps!="function"||(L||re!==N)&&aR(c,S,g,N),sl=!1;var oe=c.memoizedState;S.state=oe,Nm(c,g,S,w),Rm(),re=c.memoizedState,L||oe!==re||sl?(typeof fe=="function"&&(g2(c,p,fe,g),re=c.memoizedState),(Q=sl||nR(c,p,Q,g,oe,re,N))?(de||typeof S.UNSAFE_componentWillMount!="function"&&typeof S.componentWillMount!="function"||(typeof S.componentWillMount=="function"&&S.componentWillMount(),typeof S.UNSAFE_componentWillMount=="function"&&S.UNSAFE_componentWillMount()),typeof S.componentDidMount=="function"&&(c.flags|=4194308)):(typeof S.componentDidMount=="function"&&(c.flags|=4194308),c.memoizedProps=g,c.memoizedState=re),S.props=g,S.state=re,S.context=N,g=Q):(typeof S.componentDidMount=="function"&&(c.flags|=4194308),g=!1)}else{S=c.stateNode,Yk(s,c),N=c.memoizedProps,de=Uc(p,N),S.props=de,fe=c.pendingProps,oe=S.context,re=p.contextType,Q=Au,typeof re=="object"&&re!==null&&(Q=Ln(re)),L=p.getDerivedStateFromProps,(re=typeof L=="function"||typeof S.getSnapshotBeforeUpdate=="function")||typeof S.UNSAFE_componentWillReceiveProps!="function"&&typeof S.componentWillReceiveProps!="function"||(N!==fe||oe!==Q)&&aR(c,S,g,Q),sl=!1,oe=c.memoizedState,S.state=oe,Nm(c,g,S,w),Rm();var ce=c.memoizedState;N!==fe||oe!==ce||sl||s!==null&&s.dependencies!==null&&Eb(s.dependencies)?(typeof L=="function"&&(g2(c,p,L,g),ce=c.memoizedState),(de=sl||nR(c,p,de,g,oe,ce,Q)||s!==null&&s.dependencies!==null&&Eb(s.dependencies))?(re||typeof S.UNSAFE_componentWillUpdate!="function"&&typeof S.componentWillUpdate!="function"||(typeof S.componentWillUpdate=="function"&&S.componentWillUpdate(g,ce,Q),typeof S.UNSAFE_componentWillUpdate=="function"&&S.UNSAFE_componentWillUpdate(g,ce,Q)),typeof S.componentDidUpdate=="function"&&(c.flags|=4),typeof S.getSnapshotBeforeUpdate=="function"&&(c.flags|=1024)):(typeof S.componentDidUpdate!="function"||N===s.memoizedProps&&oe===s.memoizedState||(c.flags|=4),typeof S.getSnapshotBeforeUpdate!="function"||N===s.memoizedProps&&oe===s.memoizedState||(c.flags|=1024),c.memoizedProps=g,c.memoizedState=ce),S.props=g,S.state=ce,S.context=Q,g=de):(typeof S.componentDidUpdate!="function"||N===s.memoizedProps&&oe===s.memoizedState||(c.flags|=4),typeof S.getSnapshotBeforeUpdate!="function"||N===s.memoizedProps&&oe===s.memoizedState||(c.flags|=1024),g=!1)}return S=g,Kb(s,c),g=(c.flags&128)!==0,S||g?(S=c.stateNode,p=g&&typeof p.getDerivedStateFromError!="function"?null:S.render(),c.flags|=1,s!==null&&g?(c.child=zc(c,s.child,null,w),c.child=zc(c,null,p,w)):Un(s,c,p,w),c.memoizedState=S.state,s=c.child):s=Qo(s,c,w),s}function bR(s,c,p,g){return Nc(),c.flags|=256,Un(s,c,p,g),c.child}var w2={dehydrated:null,treeContext:null,retryLane:0,hydrationErrors:null};function k2(s){return{baseLanes:s,cachePool:s8()}}function j2(s,c,p){return s=s!==null?s.childLanes&~p:0,c&&(s|=ai),s}function vR(s,c,p){var g=c.pendingProps,w=!1,S=(c.flags&128)!==0,N;if((N=S)||(N=s!==null&&s.memoizedState===null?!1:(cn.current&2)!==0),N&&(w=!0,c.flags&=-129),N=(c.flags&32)!==0,c.flags&=-33,s===null){if(ut){if(w?dl(c):ul(),(s=Bt)?(s=TN(s,Mi),s=s!==null&&s.data!=="&"?s:null,s!==null&&(c.memoizedState={dehydrated:s,treeContext:al!==null?{id:to,overflow:no}:null,retryLane:536870912,hydrationErrors:null},p=Z6(s),p.return=c,c.child=p,zn=c,Bt=null)):s=null,s===null)throw rl(c);return rj(s)?c.lanes=32:c.lanes=536870912,null}var L=g.children;return g=g.fallback,w?(ul(),w=c.mode,L=Xb({mode:"hidden",children:L},w),g=Rc(g,w,p,null),L.return=c,g.return=c,L.sibling=g,c.child=L,g=c.child,g.memoizedState=k2(p),g.childLanes=j2(s,N,p),c.memoizedState=w2,Wm(null,g)):(dl(c),S2(c,L))}var Q=s.memoizedState;if(Q!==null&&(L=Q.dehydrated,L!==null)){if(S)c.flags&256?(dl(c),c.flags&=-257,c=C2(s,c,p)):c.memoizedState!==null?(ul(),c.child=s.child,c.flags|=128,c=null):(ul(),L=g.fallback,w=c.mode,g=Xb({mode:"visible",children:g.children},w),L=Rc(L,w,p,null),L.flags|=2,g.return=c,L.return=c,g.sibling=L,c.child=g,zc(c,s.child,null,p),g=c.child,g.memoizedState=k2(p),g.childLanes=j2(s,N,p),c.memoizedState=w2,c=Wm(null,g));else if(dl(c),rj(L)){if(N=L.nextSibling&&L.nextSibling.dataset,N)var re=N.dgst;N=re,g=Error(i(419)),g.stack="",g.digest=N,Cm({value:g,source:null,stack:null}),c=C2(s,c,p)}else if(fn||Eu(s,c,p,!1),N=(p&s.childLanes)!==0,fn||N){if(N=Pt,N!==null&&(g=dr(N,p),g!==0&&g!==Q.retryLane))throw 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c=Xb({mode:"visible",children:c},s.mode),c.return=s,s.child=c}function Xb(s,c){return s=Ja(22,s,null,c),s.lanes=0,s}function C2(s,c,p){return zc(c,s.child,null,p),s=S2(c,c.pendingProps.children),s.flags|=2,c.memoizedState=null,s}function xR(s,c,p){s.lanes|=c;var g=s.alternate;g!==null&&(g.lanes|=c),Uk(s.return,c,p)}function A2(s,c,p,g,w,S){var N=s.memoizedState;N===null?s.memoizedState={isBackwards:c,rendering:null,renderingStartTime:0,last:g,tail:p,tailMode:w,treeForkCount:S}:(N.isBackwards=c,N.rendering=null,N.renderingStartTime=0,N.last=g,N.tail=p,N.tailMode=w,N.treeForkCount=S)}function wR(s,c,p){var g=c.pendingProps,w=g.revealOrder,S=g.tail;g=g.children;var N=cn.current,L=(N&2)!==0;if(L?(N=N&1|2,c.flags|=128):N&=1,K(cn,N),Un(s,c,g,p),g=ut?Sm:0,!L&&s!==null&&(s.flags&128)!==0)e:for(s=c.child;s!==null;){if(s.tag===13)s.memoizedState!==null&&xR(s,p,c);else if(s.tag===19)xR(s,p,c);else if(s.child!==null){s.child.return=s,s=s.child;continue}if(s===c)break 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Error(i(153));if(c.child!==null){for(s=c.child,p=Go(s,s.pendingProps),c.child=p,p.return=c;s.sibling!==null;)s=s.sibling,p=p.sibling=Go(s,s.pendingProps),p.return=c;p.sibling=null}return c.child}function T2(s,c){return(s.lanes&c)!==0?!0:(s=s.dependencies,!!(s!==null&&Eb(s)))}function nJ(s,c,p){switch(c.tag){case 3:le(c,c.stateNode.containerInfo),ol(c,pn,s.memoizedState.cache),Nc();break;case 27:case 5:ve(c);break;case 4:le(c,c.stateNode.containerInfo);break;case 10:ol(c,c.type,c.memoizedProps.value);break;case 31:if(c.memoizedState!==null)return c.flags|=128,Zk(c),null;break;case 13:var g=c.memoizedState;if(g!==null)return g.dehydrated!==null?(dl(c),c.flags|=128,null):(p&c.child.childLanes)!==0?vR(s,c,p):(dl(c),s=Qo(s,c,p),s!==null?s.sibling:null);dl(c);break;case 19:var w=(s.flags&128)!==0;if(g=(p&c.childLanes)!==0,g||(Eu(s,c,p,!1),g=(p&c.childLanes)!==0),w){if(g)return wR(s,c,p);c.flags|=128}if(w=c.memoizedState,w!==null&&(w.rendering=null,w.tail=null,w.lastEffect=null),K(cn,cn.current),g)break;return null;case 22:return c.lanes=0,pR(s,c,p,c.pendingProps);case 24:ol(c,pn,s.memoizedState.cache)}return Qo(s,c,p)}function kR(s,c,p){if(s!==null)if(s.memoizedProps!==c.pendingProps)fn=!0;else{if(!T2(s,p)&&(c.flags&128)===0)return fn=!1,nJ(s,c,p);fn=(s.flags&131072)!==0}else fn=!1,ut&&(c.flags&1048576)!==0&&e8(c,Sm,c.index);switch(c.lanes=0,c.tag){case 16:e:{var g=c.pendingProps;if(s=Dc(c.elementType),c.type=s,typeof s=="function")Nk(s)?(g=Uc(s,g),c.tag=1,c=yR(null,c,s,g,p)):(c.tag=0,c=x2(null,c,s,g,p));else{if(s!=null){var w=s.$$typeof;if(w===C){c.tag=11,c=dR(null,c,s,g,p);break e}else if(w===R){c.tag=14,c=uR(null,c,s,g,p);break e}}throw c=D(s)||s,Error(i(306,c,""))}}return c;case 0:return x2(s,c,c.type,c.pendingProps,p);

48case 1:return g=c.type,w=Uc(g,c.pendingProps),yR(s,c,g,w,p);case 3:e:{if(le(c,c.stateNode.containerInfo),s===null)throw Error(i(387));g=c.pendingProps;var S=c.memoizedState;w=S.element,Yk(s,c),Nm(c,g,null,p);var N=c.memoizedState;if(g=N.cache,ol(c,pn,g),g!==S.cache&&Bk(c,[pn],p,!0),Rm(),g=N.element,S.isDehydrated)if(S={element:g,isDehydrated:!1,cache:N.cache},c.updateQueue.baseState=S,c.memoizedState=S,c.flags&256){c=bR(s,c,g,p);break e}else if(g!==w){w=Ai(Error(i(424)),c),Cm(w),c=bR(s,c,g,p);break e}else{switch(s=c.stateNode.containerInfo,s.nodeType){case 9:s=s.body;break;default:s=s.nodeName==="HTML"?s.ownerDocument.body:s}for(Bt=Ri(s.firstChild),zn=c,ut=!0,il=null,Mi=!0,p=p8(c,null,g,p),c.child=p;p;)p.flags=p.flags&-3|4096,p=p.sibling}else{if(Nc(),g===w){c=Qo(s,c,p);break e}Un(s,c,g,p)}c=c.child}return c;case 26:return Kb(s,c),s===null?(p=ON(c.type,null,c.pendingProps,null))?c.memoizedState=p:ut||(p=c.type,s=c.pendingProps,g=hv(ge.current).createElement(p),g[Wn]=c,g[Ea]=s,Bn(g,p,s),Tn(g),c.stateNode=g):c.memoizedState=ON(c.type,s.memoizedProps,c.pendingProps,s.memoizedState),null;case 27:return ve(c),s===null&&ut&&(g=c.stateNode=EN(c.type,c.pendingProps,ge.current),zn=c,Mi=!0,w=Bt,vl(c.type)?(oj=w,Bt=Ri(g.firstChild)):Bt=w),Un(s,c,c.pendingProps.children,p),Kb(s,c),s===null&&(c.flags|=4194304),c.child;case 5:return s===null&&ut&&((w=g=Bt)&&(g=NJ(g,c.type,c.pendingProps,Mi),g!==null?(c.stateNode=g,zn=c,Bt=Ri(g.firstChild),Mi=!1,w=!0):w=!1),w||rl(c)),ve(c),w=c.type,S=c.pendingProps,N=s!==null?s.memoizedProps:null,g=S.children,tj(w,S)?g=null:N!==null&&tj(w,N)&&(c.flags|=32),c.memoizedState!==null&&(w=e2(s,c,YZ,null,null,p),Zm._currentValue=w),Kb(s,c),Un(s,c,g,p),c.child;case 6:return s===null&&ut&&((s=p=Bt)&&(p=OJ(p,c.pendingProps,Mi),p!==null?(c.stateNode=p,zn=c,Bt=null,s=!0):s=!1),s||rl(c)),null;case 13:return vR(s,c,p);case 4:return le(c,c.stateNode.containerInfo),g=c.pendingProps,s===null?c.child=zc(c,null,g,p):Un(s,c,g,p),c.child;case 11:return dR(s,c,c.type,c.pendingProps,p);case 7:return Un(s,c,c.pendingProps,p),c.child;case 8:return Un(s,c,c.pendingProps.children,p),c.child;case 12:return Un(s,c,c.pendingProps.children,p),c.child;case 10:return g=c.pendingProps,ol(c,c.type,g.value),Un(s,c,g.children,p),c.child;case 9:return w=c.type._context,g=c.pendingProps.children,Pc(c),w=Ln(w),g=g(w),c.flags|=1,Un(s,c,g,p),c.child;case 14:return uR(s,c,c.type,c.pendingProps,p);case 15:return hR(s,c,c.type,c.pendingProps,p);case 19:return wR(s,c,p);case 31:return tJ(s,c,p);case 22:return pR(s,c,p,c.pendingProps);case 24:return Pc(c),g=Ln(pn),s===null?(w=Fk(),w===null&&(w=Pt,S=qk(),w.pooledCache=S,S.refCount++,S!==null&&(w.pooledCacheLanes|=p),w=S),c.memoizedState={parent:g,cache:w},Hk(c),ol(c,pn,w)):((s.lanes&p)!==0&&(Yk(s,c),Nm(c,null,null,p),Rm()),w=s.memoizedState,S=c.memoizedState,w.parent!==g?(w={parent:g,cache:g},c.memoizedState=w,c.lanes===0&&(c.memoizedState=c.updateQueue.baseState=w),ol(c,pn,g)):(g=S.cache,ol(c,pn,g),g!==w.cache&&Bk(c,[pn],p,!0))),Un(s,c,c.pendingProps.children,p),c.child;case 29:throw c.pendingProps}throw Error(i(156,c.tag))}function Zo(s){s.flags|=4}function _2(s,c,p,g,w){if((c=(s.mode&32)!==0)&&(c=!1),c){if(s.flags|=16777216,(w&335544128)===w)if(s.stateNode.complete)s.flags|=8192;else if(KR())s.flags|=8192;else throw Wc=Pb,Gk}else s.flags&=-16777217}function jR(s,c){if(c.type!=="stylesheet"||(c.state.loading&4)!==0)s.flags&=-16777217;else if(s.flags|=16777216,!zN(c))if(KR())s.flags|=8192;else throw Wc=Pb,Gk}function Qb(s,c){c!==null&&(s.flags|=4),s.flags&16384&&(c=s.tag!==22?Ot():536870912,s.lanes|=c,qu|=c)}function zm(s,c){if(!ut)switch(s.tailMode){case"hidden":c=s.tail;for(var p=null;c!==null;)c.alternate!==null&&(p=c),c=c.sibling;p===null?s.tail=null:p.sibling=null;break;case"collapsed":p=s.tail;for(var g=null;p!==null;)p.alternate!==null&&(g=p),p=p.sibling;g===null?c||s.tail===null?s.tail=null:s.tail.sibling=null:g.sibling=null}}function qt(s){var c=s.alternate!==null&&s.alternate.child===s.child,p=0,g=0;if(c)for(var w=s.child;w!==null;)p|=w.lanes|w.childLanes,g|=w.subtreeFlags&65011712,g|=w.flags&65011712,w.return=s,w=w.sibling;else for(w=s.child;w!==null;)p|=w.lanes|w.childLanes,g|=w.subtreeFlags,g|=w.flags,w.return=s,w=w.sibling;return s.subtreeFlags|=g,s.childLanes=p,c}function aJ(s,c,p){var g=c.pendingProps;switch(Dk(c),c.tag){case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return qt(c),null;case 1:return qt(c),null;case 3:return p=c.stateNode,g=null,s!==null&&(g=s.memoizedState.cache),c.memoizedState.cache!==g&&(c.flags|=2048),Vo(pn),ee(),p.pendingContext&&(p.c

48ontext=p.pendingContext,p.pendingContext=null),(s===null||s.child===null)&&(Mu(c)?Zo(c):s===null||s.memoizedState.isDehydrated&&(c.flags&256)===0||(c.flags|=1024,zk())),qt(c),null;case 26:var w=c.type,S=c.memoizedState;return s===null?(Zo(c),S!==null?(qt(c),jR(c,S)):(qt(c),_2(c,w,null,g,p))):S?S!==s.memoizedState?(Zo(c),qt(c),jR(c,S)):(qt(c),c.flags&=-16777217):(s=s.memoizedProps,s!==g&&Zo(c),qt(c),_2(c,w,s,g,p)),null;case 27:if(Se(c),p=ge.current,w=c.type,s!==null&&c.stateNode!=null)s.memoizedProps!==g&&Zo(c);else{if(!g){if(c.stateNode===null)throw Error(i(166));return qt(c),null}s=ne.current,Mu(c)?n8(c):(s=EN(w,g,p),c.stateNode=s,Zo(c))}return qt(c),null;case 5:if(Se(c),w=c.type,s!==null&&c.stateNode!=null)s.memoizedProps!==g&&Zo(c);else{if(!g){if(c.stateNode===null)throw Error(i(166));return qt(c),null}if(S=ne.current,Mu(c))n8(c);else{var N=hv(ge.current);switch(S){case 1:S=N.createElementNS("http://www.w3.org/2000/svg",w);break;case 2:S=N.createElementNS("http://www.w3.org/1998/Math/MathML",w);break;default:switch(w){case"svg":S=N.createElementNS("http://www.w3.org/2000/svg",w);break;case"math":S=N.createElementNS("http://www.w3.org/1998/Math/MathML",w);break;case"script":S=N.createElement("div"),S.innerHTML="<script><\/script>",S=S.removeChild(S.firstChild);break;case"select":S=typeof g.is=="string"?N.createElement("select",{is:g.is}):N.createElement("select"),g.multiple?S.multiple=!0:g.size&&(S.size=g.size);break;default:S=typeof g.is=="string"?N.createElement(w,{is:g.is}):N.createElement(w)}}S[Wn]=c,S[Ea]=g;e:for(N=c.child;N!==null;){if(N.tag===5||N.tag===6)S.appendChild(N.stateNode);else if(N.tag!==4&&N.tag!==27&&N.child!==null){N.child.return=N,N=N.child;continue}if(N===c)break e;for(;N.sibling===null;){if(N.return===null||N.return===c)break e;N=N.return}N.sibling.return=N.return,N=N.sibling}c.stateNode=S;e:switch(Bn(S,w,g),w){case"button":case"input":case"select":case"textarea":g=!!g.autoFocus;break e;case"img":g=!0;break e;default:g=!1}g&&Zo(c)}}return qt(c),_2(c,c.type,s===null?null:s.memoizedProps,c.pendingProps,p),null;case 6:if(s&&c.stateNode!=null)s.memoizedProps!==g&&Zo(c);else{if(typeof g!="string"&&c.stateNode===null)throw Error(i(166));if(s=ge.current,Mu(c)){if(s=c.stateNode,p=c.memoizedProps,g=null,w=zn,w!==null)switch(w.tag){case 27:case 5:g=w.memoizedProps}s[Wn]=c,s=!!(s.nodeValue===p||g!==null&&g.suppressHydrationWarning===!0||vN(s.nodeValue,p)),s||rl(c,!0)}else s=hv(s).createTextNode(g),s[Wn]=c,c.stateNode=s}return qt(c),null;case 31:if(p=c.memoizedState,s===null||s.memoizedState!==null){if(g=Mu(c),p!==null){if(s===null){if(!g)throw Error(i(318));if(s=c.memoizedState,s=s!==null?s.dehydrated:null,!s)throw Error(i(557));s[Wn]=c}else Nc(),(c.flags&128)===0&&(c.memoizedState=null),c.flags|=4;qt(c),s=!1}else p=zk(),s!==null&&s.memoizedState!==null&&(s.memoizedState.hydrationErrors=p),s=!0;if(!s)return c.flags&256?(ti(c),c):(ti(c),null);if((c.flags&128)!==0)throw Error(i(558))}return qt(c),null;case 13:if(g=c.memoizedState,s===null||s.memoizedState!==null&&s.memoizedState.dehydrated!==null){if(w=Mu(c),g!==null&&g.dehydrated!==null){if(s===null){if(!w)throw Error(i(318));if(w=c.memoizedState,w=w!==null?w.dehydrated:null,!w)throw Error(i(317));w[Wn]=c}else Nc(),(c.flags&128)===0&&(c.memoizedState=null),c.flags|=4;qt(c),w=!1}else w=zk(),s!==null&&s.memoizedState!==null&&(s.memoizedState.hydrationErrors=w),w=!0;if(!w)return c.flags&256?(ti(c),c):(ti(c),null)}return ti(c),(c.flags&128)!==0?(c.lanes=p,c):(p=g!==null,s=s!==null&&s.memoizedState!==null,p&&(g=c.child,w=null,g.alternate!==null&&g.alternate.memoizedState!==null&&g.alternate.memoizedState.cachePool!==null&&(w=g.alternate.memoizedState.cachePool.pool),S=null,g.memoizedState!==null&&g.memoizedState.cachePool!==null&&(S=g.memoizedState.cachePool.pool),S!==w&&(g.flags|=2048)),p!==s&&p&&(c.child.flags|=8192),Qb(c,c.updateQueue),qt(c),null);case 4:return ee(),s===null&&X2(c.stateNode.containerInfo),qt(c),null;case 10:return Vo(c.type),qt(c),null;case 19:if(F(cn),g=c.memoizedState,g===null)return qt(c),null;if(w=(c.flags&128)!==0,S=g.rendering,S===null)if(w)zm(g,!1);else{if(nn!==0||s!==null&&(s.flags&128)!==0)for(s=c.child;s!==null;){if(S=zb(s),S!==null){for(c.flags|=128,zm(g,!1),s=S.updateQueue,c.updateQueue=s,Qb(c,s),c.subtreeFlags=0,s=p,p=c.child;p!==null;)Q6(p,s),p=p.sibling;return K(cn,cn.current&1|2),ut&&Ho(c,g.treeForkCount),c.child}s=s.sibling}g.tail!==null&&Qt()>nv&&(c.flags|=128,w=!0,zm(g,!1),c.lanes=4194304)}else{if(!w)if(s=zb(S),s!==null){if(c.flags|=128,w=!0,s=s.updateQueue,c.updateQueue=s,Qb(c,s),zm(g,!0),g.tail===null&&g.tailMode==="hidden"&&!S.alternate&&!ut)return qt(c),null}else 2*Qt()-g.renderingStartTime>nv&&p!==536870912&&(c.flags|=128,w=!0,zm(g,!1),c.lanes=4194304);g.isBackwards?(S.sibling=c.child,c.child=S):(s=g.last,s!==null?s.sibling=S:c.child=S,g.last=S)}return g.tail!==null?(s=g.tail,g.rendering=s,g.tail=s.sibling,g.renderingStartTime=Qt(),s.sibling=null,p=cn.current,K(cn,w?p&1|2:p&1),ut&&Ho(c,g.treeForkCount),s):(qt(c),null);case 22:case 23:return ti(c),Qk(),g=c.memoizedState!==null,s!==null?s.memoizedState!==null!==g&&(c.flags|=8192):g&&(c.flags|=8192),g?(p&536870912)!==0&&(c.flags&128)===0&&(qt(c),c.subtreeFlags&6&&(c.flags|=8192)):qt(c),p=c.updateQueue,p!==null&&Qb(c,p.retryQueue),p=null,s!==null&&s.memoizedState!==null&&s.memoizedState.cachePool!==null&&(p=s.memoizedState.cachePool.pool),g=null,c.memoizedState!==null&&c.memoizedState.cachePool!==null&&(g=c.memoizedState.cachePool.pool),g!==p&&

48(c.flags|=2048),s!==null&&F(Ic),null;case 24:return p=null,s!==null&&(p=s.memoizedState.cache),c.memoizedState.cache!==p&&(c.flags|=2048),Vo(pn),qt(c),null;case 25:return null;case 30:return null}throw Error(i(156,c.tag))}function iJ(s,c){switch(Dk(c),c.tag){case 1:return s=c.flags,s&65536?(c.flags=s&-65537|128,c):null;case 3:return Vo(pn),ee(),s=c.flags,(s&65536)!==0&&(s&128)===0?(c.flags=s&-65537|128,c):null;case 26:case 27:case 5:return Se(c),null;case 31:if(c.memoizedState!==null){if(ti(c),c.alternate===null)throw Error(i(340));Nc()}return s=c.flags,s&65536?(c.flags=s&-65537|128,c):null;case 13:if(ti(c),s=c.memoizedState,s!==null&&s.dehydrated!==null){if(c.alternate===null)throw Error(i(340));Nc()}return s=c.flags,s&65536?(c.flags=s&-65537|128,c):null;case 19:return F(cn),null;case 4:return ee(),null;case 10:return Vo(c.type),null;case 22:case 23:return ti(c),Qk(),s!==null&&F(Ic),s=c.flags,s&65536?(c.flags=s&-65537|128,c):null;case 24:return Vo(pn),null;case 25:return null;default:return null}}function SR(s,c){switch(Dk(c),c.tag){case 3:Vo(pn),ee();break;case 26:case 27:case 5:Se(c);break;case 4:ee();break;case 31:c.memoizedState!==null&&ti(c);break;case 13:ti(c);break;case 19:F(cn);break;case 10:Vo(c.type);break;case 22:case 23:ti(c),Qk(),s!==null&&F(Ic);break;case 24:Vo(pn)}}function Lm(s,c){try{var p=c.updateQueue,g=p!==null?p.lastEffect:null;if(g!==null){var w=g.next;p=w;do{if((p.tag&s)===s){g=void 0;var S=p.create,N=p.inst;g=S(),N.destroy=g}p=p.next}while(p!==w)}}catch(L){St(c,c.return,L)}}function hl(s,c,p){try{var g=c.updateQueue,w=g!==null?g.lastEffect:null;if(w!==null){var S=w.next;g=S;do{if((g.tag&s)===s){var N=g.inst,L=N.destroy;if(L!==void 0){N.destroy=void 0,w=c;var Q=p,re=L;try{re()}catch(de){St(w,Q,de)}}}g=g.next}while(g!==S)}}catch(de){St(c,c.return,de)}}function CR(s){var c=s.updateQueue;if(c!==null){var p=s.stateNode;try{f8(c,p)}catch(g){St(s,s.return,g)}}}function 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50 * @license lucide-react v0.469.0 - ISC
51 *
52 * This source code is licensed under the ISC license.
53 * See the LICENSE file in the root directory of this source tree.
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55 * @license lucide-react v0.469.0 - ISC
56 *
57 * This source code is licensed under the ISC license.
58 * See the LICENSE file in the root directory of this source tree.
59 */var gte={xmlns:"http://www.w3.org/2000/svg",width:24,height:24,viewBox:"0 0 24 24",fill:"none",stroke:"currentColor",strokeWidth:2,strokeLinecap:"round",strokeLinejoin:"round"};/**
60 * @license lucide-react v0.469.0 - ISC
61 *
62 * This source code is licensed under the ISC license.
63 * See the LICENSE file in the root directory of this source tree.

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65 * @license lucide-react v0.469.0 - ISC
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67 * This source code is licensed under the ISC license.
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75 * @license lucide-react v0.469.0 - ISC
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77 * This source code is licensed under the ISC license.
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80 * @license lucide-react v0.469.0 - ISC
81 *
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83 * See the LICENSE file in the root directory of this source tree.
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86 *
87 * This source code is licensed under the ISC license.
88 * See the LICENSE file in the root directory of this source tree.
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90 * @license lucide-react v0.469.0 - ISC
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92 * This source code is licensed under the ISC license.
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95 * @license lucide-react v0.469.0 - ISC
96 *
97 * This source code is licensed under the ISC license.
98 * See the LICENSE file in the root directory of this source tree.
99 */const wte=je("BookOpen",[["path",{d:"M12 7v14",key:"1akyts"}],["path",{d:"M3 18a1 1 0 0 1-1-1V4a1 1 0 0 1 1-1h5a4 4 0 0 1 4 4 4 4 0 0 1 4-4h5a1 1 0 0 1 1 1v13a1 1 0 0 1-1 1h-6a3 3 0 0 0-3 3 3 3 0 0 0-3-3z",key:"ruj8y"}]]);/**
100 * @license lucide-react v0.469.0 - ISC
101 *
102 * This source code is licensed under the ISC license.
103 * See the LICENSE file in the root directory of this source tree.
104 */const rM=je("Bot",[["path",{d:"M12 8V4H8",key:"hb8ula"}],["rect",{width:"16",height:"12",x:"4",y:"8",rx:"2",key:"enze0r"}],["path",{d:"M2 14h2",key:"vft8re"}],["path",{d:"M20 14h2",key:"4cs60a"}],["path",{d:"M15 13v2",key:"1xurst"}],["path",{d:"M9 13v2",key:"rq6x2g"}]]);/**
105 * @license lucide-react v0.469.0 - ISC
106 *
107 * This source code is licensed under the ISC license.
108 * See the LICENSE file in the root directory of this source tree.
109 */const kte=je("Box",[["path",{d:"M21 8a2 2 0 0 0-1-1.73l-7-4a2 2 0 0 0-2 0l-7 4A2 2 0 0 0 3 8v8a2 2 0 0 0 1 1.73l7 4a2 2 0 0 0 2 0l7-4A2 2 0 0 0 21 16Z",key:"hh9hay"}],["path",{d:"m3.3 7 8.7 5 8.7-5",key:"g66t2b"}],["path",{d:"M12 22V12",key:"d0xqtd"}]]);/**
110 * @license lucide-react v0.469.0 - ISC
111 *
112 * This source code is licensed under the ISC license.
113 * See the LICENSE file in the root directory of this source tree.
114 */const jte=je("Boxes",[["path",{d:"M2.97 12.92A2 2 0 0 0 2 14.63v3.24a2 2 0 0 0 .97 1.71l3 1.8a2 2 0 0 0 2.06 0L12 19v-5.5l-5-3-4.03 2.42Z",key:"lc1i9w"}],["path",{d:"m7 16.5-4.74-2.85",key:"1o9zyk"}],["path",{d:"m7 16.5 5-3",key:"va8pkn"}],["path",{d:"M7 16.5v5.17",key:"jnp8gn"}],["path",{d:"M12 13.5V19l3.97 2.38a2 2 0 0 0 2.06 0l3-1.8a2 2 0 0 0 .97-1.71v-3.24a2 2 0 0 0-.97-1.71L17 10.5l-5 3Z",key:"8zsnat"}],["path",{d:"m17 16.5-5-3",key:"8arw3v"}],["path",{d:"m17 16.5 4.74-2.85",key:"8rfmw"}],["path",{d:"M17 16.5v5.17",key:"k6z78m"}],["path",{d:"M7.97 4.42A2 2 0 0 0 7 6.13v4.37l5 3 5-3V6.13a2 2 0 0 0-.97-1.71l-3-1.8a2 2 0 0 0-2.06 0l-3 1.8Z",key:"1xygjf"}],["path",{d:"M12 8 7.26 5.15",key:"1vbdud"}],["path",{d:"m12 8 4.74-2.85",key:"3rx089"}],["path",{d:"M12 13.5V8",key:"1io7kd"}]]);/**
115 * @license lucide-react v0.469.0 - ISC
116 *
117 * This source code is licensed under the ISC license.
118 * See the LICENSE file in the root directory of this source tree.
119 */const Ste=je("Brain",[["path",{d:"M12 5a3 3 0 1 0-5.997.125 4 4 0 0 0-2.526 5.77 4 4 0 0 0 .556 6.588A4 4 0 1 0 12 18Z",key:"l5xja"}],["path",{d:"M12 5a3 3 0 1 1 5.997.125 4 4 0 0 1 2.526 5.77 4 4 0 0 1-.556 6.588A4 4 0 1 1 12 18Z",key:"ep3f8r"}],["path",{d:"M15 13a4.5 4.5 0 0 1-3-4 4.5 4.5 0 0 1-3 4",key:"1p4c4q"}],["path",{d:"M17.599 6.5a3 3 0 0 0 .399-1.375",key:"tmeiqw"}],["path",{d:"M6.003 5.125A3 3 0 0 0 6.401 6.5",key:"105sqy"}],["path",{d:"M3.477 10.896a4 4 0 0 1 .585-.396",key:"ql3yin"}],["path",{d:"M19.938 10.5a4 4 0 0 1 .585.396",key:"1qfode"}],["path",{d:"M6 18a4 4 0 0 1-1.967-.516",key:"2e4loj"}],["path",{d:"M19.967 17.484A4 4 0 0 1 18 18",key:"159ez6"}]]);/**
120 * @license lucide-react v0.469.0 - ISC
121 *
122 * This source code is licensed under the ISC license.
123 * See the LICENSE file in the root directory of this source tree.
124 */const k$=je("Building2",[["path",{d:"M6 22V4a2 2 0 0 1 2-2h8a2 2 0 0 1 2 2v18Z",key:"1b4qmf"}],["path",{d:"M6 12H4a2 2 0 0 0-2 2v6a2 2 0 0 0 2 2h2",key:"i71pzd"}],["path",{d:"M18 9h2a2 2 0 0 1 2 2v9a2 2 0 0 1-2 2h-2",key:"10jefs"}],["path",{d:"M10 6h4",key:"1itunk"}],["path",{d:"M10 10h4",key:"tcdvrf"}

124],["path",{d:"M10 14h4",key:"kelpxr"}],["path",{d:"M10 18h4",key:"1ulq68"}]]);/**
125 * @license lucide-react v0.469.0 - ISC
126 *
127 * This source code is licensed under the ISC license.
128 * See the LICENSE file in the root directory of this source tree.
129 */const Cte=je("Cable",[["path",{d:"M17 21v-2a1 1 0 0 1-1-1v-1a2 2 0 0 1 2-2h2a2 2 0 0 1 2 2v1a1 1 0 0 1-1 1",key:"10bnsj"}],["path",{d:"M19 15V6.5a1 1 0 0 0-7 0v11a1 1 0 0 1-7 0V9",key:"1eqmu1"}],["path",{d:"M21 21v-2h-4",key:"14zm7j"}],["path",{d:"M3 5h4V3",key:"z442eg"}],["path",{d:"M7 5a1 1 0 0 1 1 1v1a2 2 0 0 1-2 2H4a2 2 0 0 1-2-2V6a1 1 0 0 1 1-1V3",key:"ebdjd7"}]]);/**
130 * @license lucide-react v0.469.0 - ISC
131 *
132 * This source code is licensed under the ISC license.
133 * See the LICENSE file in the root directory of this source tree.
134 */const Ate=je("Calculator",[["rect",{width:"16",height:"20",x:"4",y:"2",rx:"2",key:"1nb95v"}],["line",{x1:"8",x2:"16",y1:"6",y2:"6",key:"x4nwl0"}],["line",{x1:"16",x2:"16",y1:"14",y2:"18",key:"wjye3r"}],["path",{d:"M16 10h.01",key:"1m94wz"}],["path",{d:"M12 10h.01",key:"1nrarc"}],["path",{d:"M8 10h.01",key:"19clt8"}],["path",{d:"M12 14h.01",key:"1etili"}],["path",{d:"M8 14h.01",key:"6423bh"}],["path",{d:"M12 18h.01",key:"mhygvu"}],["path",{d:"M8 18h.01",key:"lrp35t"}]]);/**
135 * @license lucide-react v0.469.0 - ISC
136 *
137 * This source code is licensed under the ISC license.
138 * See the LICENSE file in the root directory of this source tree.
139 */const j$=je("CalendarDays",[["path",{d:"M8 2v4",key:"1cmpym"}],["path",{d:"M16 2v4",key:"4m81vk"}],["rect",{width:"18",height:"18",x:"3",y:"4",rx:"2",key:"1hopcy"}],["path",{d:"M3 10h18",key:"8toen8"}],["path",{d:"M8 14h.01",key:"6423bh"}],["path",{d:"M12 14h.01",key:"1etili"}],["path",{d:"M16 14h.01",key:"1gbofw"}],["path",{d:"M8 18h.01",key:"lrp35t"}],["path",{d:"M12 18h.01",key:"mhygvu"}],["path",{d:"M16 18h.01",key:"kzsmim"}]]);/**
140 * @license lucide-react v0.469.0 - ISC
141 *
142 * This source code is licensed under the ISC license.
143 * See the LICENSE file in the root directory of this source tree.
144 */const S$=je("Calendar",[["path",{d:"M8 2v4",key:"1cmpym"}],["path",{d:"M16 2v4",key:"4m81vk"}],["rect",{width:"18",height:"18",x:"3",y:"4",rx:"2",key:"1hopcy"}],["path",{d:"M3 10h18",key:"8toen8"}]]);/**
145 * @license lucide-react v0.469.0 - ISC
146 *
147 * This source code is licensed under the ISC license.
148 * See the LICENSE file in the root directory of this source tree.
149 */const Tte=je("ChartColumn",[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"M18 17V9",key:"2bz60n"}],["path",{d:"M13 17V5",key:"1frdt8"}],["path",{d:"M8 17v-3",key:"17ska0"}]]);/**
150 * @license lucide-react v0.469.0 - ISC
151 *
152 * This source code is licensed under the ISC license.
153 * See the LICENSE file in the root directory of this source tree.
154 */const oM=je("ChartLine",[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"m19 9-5 5-4-4-3 3",key:"2osh9i"}]]);/**
155 * @license lucide-react v0.469.0 - ISC
156 *
157 * This source code is licensed under the ISC license.
158 * See the LICENSE file in the root directory of this source tree.
159 */const _te=je("ChartPie",[["path",{d:"M21 12c.552 0 1.005-.449.95-.998a10 10 0 0 0-8.953-8.951c-.55-.055-.998.398-.998.95v8a1 1 0 0 0 1 1z",key:"pzmjnu"}],["path",{d:"M21.21 15.89A10 10 0 1 1 8 2.83",key:"k2fpak"}]]);/**
160 * @license lucide-react v0.469.0 - ISC
161 *
162 * This source code is licensed under the ISC license.
163 * See the LICENSE file in the root directory of this source tree.
164 */const C$=je("Check",[["path",{d:"M20 6 9 17l-5-5",key:"1gmf2c"}]]);/**
165 * @license lucide-react v0.469.0 - ISC
166 *
167 * This source code is licensed under the ISC license.
168 * See the LICENSE file in the root directory of this source tree.
169 */const Mte=je("CircleCheck",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"m9 12 2 2 4-4",key:"dzmm74"}]]);/**
170 * @license lucide-react v0.469.0 - ISC
171 *
172 * This source code is licensed under the ISC license.
173 * See the LICENSE file in the root directory of this source tree.
174 */const Ete=je("ClipboardList",[["rect",{width:"8",height:"4",x:"8",y:"2",rx:"1",ry:"1",key:"tgr4d6"}],["path",{d:"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2",key:"116196"}],["path",{d:"M12 11h4",key:"1jrz19"}],["path",{d:"M12 16h4",key:"n85exb"}],["path",{d:"M8 11h.01",key:"1dfujw"}],["path",{d:"M8 16h.01",key:"18s6g9"}]]);/**
175 * @license lucide-react v0.469.0 - ISC
176 *
177 * This source code is licensed under the ISC license.
178 * See the LICENSE file in the root directory of this source tree.
179 */const Rte=je("Clock",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["polyline",{points:"12 6 12 12 16 14",key:"68esgv"}]]);/**
180 * @license lucide-react v0.469.0 - ISC
181 *
182 * This source code is licensed under the ISC license.
183 * See the LICENSE file in the root directory of this source tree.
184 */const Nte=je("Code",[["polyline",{points:"16 18 22 12 16 6",key:"z7tu5w"}],["polyline",{points:"8 6 2 12 8 18",key:"1eg1df"}]]);/**
185 * @license lucide-react v0.469.0 - ISC
186 *
187 * This source code is licensed under the ISC license.
188 * See the LICENSE file in the root directory of this source tree.
189 */const Ote=je("Columns3",[["rect",{width:"18",height:"18",x:"3",y:"3",rx:"2",key:"afitv7"}],["path",{d:"M9 3v18",key:"fh3hqa"}],["path",{d:"M15 3v18",key:"14nvp0"}]]);/**
190 * @license lucide-react v0.469.0 - ISC
191 *
192 * This source code is licensed under the ISC license.
193 * See the LICENSE file in the root directory of this source tree.
194 */const Pte=je("Compass",[["path",{d:"m16.24 7.76-1.804 5.411a2 2 0 0 1-1.265 1.265L7.76 16.24l1.804-5.411a2 2 0 0 1 1.265-1.265z",ke

194y:"9ktpf1"}],["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}]]);/**
195 * @license lucide-react v0.469.0 - ISC
196 *
197 * This source code is licensed under the ISC license.
198 * See the LICENSE file in the root directory of this source tree.
199 */const A$=je("Crosshair",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["line",{x1:"22",x2:"18",y1:"12",y2:"12",key:"l9bcsi"}],["line",{x1:"6",x2:"2",y1:"12",y2:"12",key:"13hhkx"}],["line",{x1:"12",x2:"12",y1:"6",y2:"2",key:"10w3f3"}],["line",{x1:"12",x2:"12",y1:"22",y2:"18",key:"15g9kq"}]]);/**
200 * @license lucide-react v0.469.0 - ISC
201 *
202 * This source code is licensed under the ISC license.
203 * See the LICENSE file in the root directory of this source tree.
204 */const Lw=je("Database",[["ellipse",{cx:"12",cy:"5",rx:"9",ry:"3",key:"msslwz"}],["path",{d:"M3 5V19A9 3 0 0 0 21 19V5",key:"1wlel7"}],["path",{d:"M3 12A9 3 0 0 0 21 12",key:"mv7ke4"}]]);/**
205 * @license lucide-react v0.469.0 - ISC
206 *
207 * This source code is licensed under the ISC license.
208 * See the LICENSE file in the root directory of this source tree.
209 */const Ite=je("Download",[["path",{d:"M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4",key:"ih7n3h"}],["polyline",{points:"7 10 12 15 17 10",key:"2ggqvy"}],["line",{x1:"12",x2:"12",y1:"15",y2:"3",key:"1vk2je"}]]);/**
210 * @license lucide-react v0.469.0 - ISC
211 *
212 * This source code is licensed under the ISC license.
213 * See the LICENSE file in the root directory of this source tree.
214 */const Dte=je("ExternalLink",[["path",{d:"M15 3h6v6",key:"1q9fwt"}],["path",{d:"M10 14 21 3",key:"gplh6r"}],["path",{d:"M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6",key:"a6xqqp"}]]);/**
215 * @license lucide-react v0.469.0 - ISC
216 *
217 * This source code is licensed under the ISC license.
218 * See the LICENSE file in the root directory of this source tree.
219 */const sM=je("Eye",[["path",{d:"M2.062 12.348a1 1 0 0 1 0-.696 10.75 10.75 0 0 1 19.876 0 1 1 0 0 1 0 .696 10.75 10.75 0 0 1-19.876 0",key:"1nclc0"}],["circle",{cx:"12",cy:"12",r:"3",key:"1v7zrd"}]]);/**
220 * @license lucide-react v0.469.0 - ISC
221 *
222 * This source code is licensed under the ISC license.
223 * See the LICENSE file in the root directory of this source tree.
224 */const Wte=je("FileCheck",[["path",{d:"M15 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V7Z",key:"1rqfz7"}],["path",{d:"M14 2v4a2 2 0 0 0 2 2h4",key:"tnqrlb"}],["path",{d:"m9 15 2 2 4-4",key:"1grp1n"}]]);/**
225 * @license lucide-react v0.469.0 - ISC
226 *
227 * This source code is licensed under the ISC license.
228 * See the LICENSE file in the root directory of this source tree.
229 */const T$=je("FileText",[["path",{d:"M15 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V7Z",key:"1rqfz7"}],["path",{d:"M14 2v4a2 2 0 0 0 2 2h4",key:"tnqrlb"}],["path",{d:"M10 9H8",key:"b1mrlr"}],["path",{d:"M16 13H8",key:"t4e002"}],["path",{d:"M16 17H8",key:"z1uh3a"}]]);/**
230 * @license lucide-react v0.469.0 - ISC
231 *
232 * This source code is licensed under the ISC license.
233 * See the LICENSE file in the root directory of this source tree.
234 */const zte=je("Filter",[["polygon",{points:"22 3 2 3 10 12.46 10 19 14 21 14 12.46 22 3",key:"1yg77f"}]]);/**
235 * @license lucide-react v0.469.0 - ISC
236 *
237 * This source code is licensed under the ISC license.
238 * See the LICENSE file in the root directory of this source tree.
239 */const Lte=je("Gauge",[["path",{d:"m12 14 4-4",key:"9kzdfg"}],["path",{d:"M3.34 19a10 10 0 1 1 17.32 0",key:"19p75a"}]]);/**
240 * @license lucide-react v0.469.0 - ISC
241 *
242 * This source code is licensed under the ISC license.
243 * See the LICENSE file in the root directory of this source tree.
244 */const Ute=je("GitBranch",[["line",{x1:"6",x2:"6",y1:"3",y2:"15",key:"17qcm7"}],["circle",{cx:"18",cy:"6",r:"3",key:"1h7g24"}],["circle",{cx:"6",cy:"18",r:"3",key:"fqmcym"}],["path",{d:"M18 9a9 9 0 0 1-9 9",key:"n2h4wq"}]]);/**
245 * @license lucide-react v0.469.0 - ISC
246 *
247 * This source code is licensed under the ISC license.
248 * See the LICENSE file in the root directory of this source tree.
249 */const Bte=je("GitMerge",[["circle",{cx:"18",cy:"18",r:"3",key:"1xkwt0"}],["circle",{cx:"6",cy:"6",r:"3",key:"1lh9wr"}],["path",{d:"M6 21V9a9 9 0 0 0 9 9",key:"7kw0sc"}]]);/**
250 * @license lucide-react v0.469.0 - ISC
251 *
252 * This source code is licensed under the ISC license.
253 * See the LICENSE file in the root directory of this source tree.
254 */const _$=je("Globe",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"M12 2a14.5 14.5 0 0 0 0 20 14.5 14.5 0 0 0 0-20",key:"13o1zl"}],["path",{d:"M2 12h20",key:"9i4pu4"}]]);/**
255 * @license lucide-react v0.469.0 - ISC
256 *
257 * This source code is licensed under the ISC license.
258 * See the LICENSE file in the root directory of this source tree.
259 */const qte=je("Handshake",[["path",{d:"m11 17 2 2a1 1 0 1 0 3-3",key:"efffak"}],["path",{d:"m14 14 2.5 2.5a1 1 0 1 0 3-3l-3.88-3.88a3 3 0 0 0-4.24 0l-.88.88a1 1 0 1 1-3-3l2.81-2.81a5.79 5.79 0 0 1 7.06-.87l.47.28a2 2 0 0 0 1.42.25L21 4",key:"9pr0kb"}],["path",{d:"m21 3 1 11h-2",key:"1tisrp"}],["path",{d:"M3 3 2 14l6.5 6.5a1 1 0 1 0 3-3",key:"1uvwmv"}],["path",{d:"M3 4h8",key:"1ep09j"}]]);/**
260 * @license lucide-react v0.469.0 - ISC
261 *
262 * This source code is licensed under the ISC license.
263 * See the LICENSE file in the root directory of this source tree.
264 */const $te=je("HeartPulse",[["path",{d:"M19 14c1.49-1.46 3-3.21 3-5.5A5.5 5.5 0 0 0 16.5 3c-1.76 0-3 .5-4.5 2-1.5-1.5-2.74-2-4.5-2A5.5 5.5 0 0 0 2 8.5c0 2.3 1.5 4.05 3 5.5l7 7Z",key:"c3ymky"}],["path",{d:"M3.22 12H9.5l.5-1 2 4.5 2-7 1.5 3.5h5.27",key:"1uw2ng"}]]);/**
265 * @license lucide-react v0.469.0 - ISC
266 *
267 * This source code is licensed under the ISC license.
268 * See the LICENSE file in the root directory of this source tree.
269 */const Fte=je("Layers",[["path",{d:"M12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83z",key:"zw3jo"}],["path",{d:"M2 12a1 1 0 0 0 .58.91l8.6 3.91a2 2 0 0 0 1.65 0l8.58-3.9A1 1 0 0 0 22 12",key:"1wduqc"}],["path",{d:"M2 17a1 1 0 0 0 .58.91l8.6 3.91a2 2 0 0 0 1.65 0l8.58-3.9A1 1 0 0 0 22 17",key:"kqbvx6"}]]);/**
270 * @license lucide-react v0.469.0 - ISC
271 *
272 * This source code is licensed under the ISC license.
273 * See the LICENSE file in the root directory of this source tree.

274 */const M$=je("LayoutDashboard",[["rect",{width:"7",height:"9",x:"3",y:"3",rx:"1",key:"10lvy0"}],["rect",{width:"7",height:"5",x:"14",y:"3",rx:"1",key:"16une8"}],["rect",{width:"7",height:"9",x:"14",y:"12",rx:"1",key:"1hutg5"}],["rect",{width:"7",height:"5",x:"3",y:"16",rx:"1",key:"ldoo1y"}]]);/**
275 * @license lucide-react v0.469.0 - ISC
276 *
277 * This source code is licensed under the ISC license.
278 * See the LICENSE file in the root directory of this source tree.
279 */const E$=je("Library",[["path",{d:"m16 6 4 14",key:"ji33uf"}],["path",{d:"M12 6v14",key:"1n7gus"}],["path",{d:"M8 8v12",key:"1gg7y9"}],["path",{d:"M4 4v16",key:"6qkkli"}]]);/**
280 * @license lucide-react v0.469.0 - ISC
281 *
282 * This source code is licensed under the ISC license.
283 * See the LICENSE file in the root directory of this source tree.
284 */const Gte=je("Link2",[["path",{d:"M9 17H7A5 5 0 0 1 7 7h2",key:"8i5ue5"}],["path",{d:"M15 7h2a5 5 0 1 1 0 10h-2",key:"1b9ql8"}],["line",{x1:"8",x2:"16",y1:"12",y2:"12",key:"1jonct"}]]);/**
285 * @license lucide-react v0.469.0 - ISC
286 *
287 * This source code is licensed under the ISC license.
288 * See the LICENSE file in the root directory of this source tree.
289 */const Hte=je("ListChecks",[["path",{d:"m3 17 2 2 4-4",key:"1jhpwq"}],["path",{d:"m3 7 2 2 4-4",key:"1obspn"}],["path",{d:"M13 6h8",key:"15sg57"}],["path",{d:"M13 12h8",key:"h98zly"}],["path",{d:"M13 18h8",key:"oe0vm4"}]]);/**
290 * @license lucide-react v0.469.0 - ISC
291 *
292 * This source code is licensed under the ISC license.
293 * See the LICENSE file in the root directory of this source tree.
294 */const Uw=je("Lock",[["rect",{width:"18",height:"11",x:"3",y:"11",rx:"2",ry:"2",key:"1w4ew1"}],["path",{d:"M7 11V7a5 5 0 0 1 10 0v4",key:"fwvmzm"}]]);/**
295 * @license lucide-react v0.469.0 - ISC
296 *
297 * This source code is licensed under the ISC license.
298 * See the LICENSE file in the root directory of this source tree.
299 */const xy=je("Mail",[["rect",{width:"20",height:"16",x:"2",y:"4",rx:"2",key:"18n3k1"}],["path",{d:"m22 7-8.97 5.7a1.94 1.94 0 0 1-2.06 0L2 7",key:"1ocrg3"}]]);/**
300 * @license lucide-react v0.469.0 - ISC
301 *
302 * This source code is licensed under the ISC license.
303 * See the LICENSE file in the root directory of this source tree.
304 */const Yte=je("MapPin",[["path",{d:"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0",key:"1r0f0z"}],["circle",{cx:"12",cy:"10",r:"3",key:"ilqhr7"}]]);/**
305 * @license lucide-react v0.469.0 - ISC
306 *
307 * This source code is licensed under the ISC license.
308 * See the LICENSE file in the root directory of this source tree.
309 */const R$=je("Megaphone",[["path",{d:"m3 11 18-5v12L3 14v-3z",key:"n962bs"}],["path",{d:"M11.6 16.8a3 3 0 1 1-5.8-1.6",key:"1yl0tm"}]]);/**
310 * @license lucide-react v0.469.0 - ISC
311 *
312 * This source code is licensed under the ISC license.
313 * See the LICENSE file in the root directory of this source tree.
314 */const Vte=je("MessageSquare",[["path",{d:"M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z",key:"1lielz"}]]);/**
315 * @license lucide-react v0.469.0 - ISC
316 *
317 * This source code is licensed under the ISC license.
318 * See the LICENSE file in the root directory of this source tree.
319 */const Kte=je("Microscope",[["path",{d:"M6 18h8",key:"1borvv"}],["path",{d:"M3 22h18",key:"8prr45"}],["path",{d:"M14 22a7 7 0 1 0 0-14h-1",key:"1jwaiy"}],["path",{d:"M9 14h2",key:"197e7h"}],["path",{d:"M9 12a2 2 0 0 1-2-2V6h6v4a2 2 0 0 1-2 2Z",key:"1bmzmy"}],["path",{d:"M12 6V3a1 1 0 0 0-1-1H9a1 1 0 0 0-1 1v3",key:"1drr47"}]]);/**
320 * @license lucide-react v0.469.0 - ISC
321 *
322 * This source code is licensed under the ISC license.
323 * See the LICENSE file in the root directory of this source tree.
324 */const Xte=je("Milestone",[["path",{d:"M12 13v8",key:"1l5pq0"}],["path",{d:"M12 3v3",key:"1n5kay"}],["path",{d:"M4 6a1 1 0 0 0-1 1v5a1 1 0 0 0 1 1h13a2 2 0 0 0 1.152-.365l3.424-2.317a1 1 0 0 0 0-1.635l-3.424-2.318A2 2 0 0 0 17 6z",key:"1btarq"}]]);/**
325 * @license lucide-react v0.469.0 - ISC
326 *
327 * This source code is licensed under the ISC license.
328 * See the LICENSE file in the root directory of this source tree.
329 */const N$=je("Network",[["rect",{x:"16",y:"16",width:"6",height:"6",rx:"1",key:"4q2zg0"}],["rect",{x:"2",y:"16",width:"6",height:"6",rx:"1",key:"8cvhb9"}],["rect",{x:"9",y:"2",width:"6",height:"6",rx:"1",key:"1egb70"}],["path",{d:"M5 16v-3a1 1 0 0 1 1-1h12a1 1 0 0 1 1 1v3",key:"1jsf9p"}],["path",{d:"M12 12V8",key:"2874zd"}]]);/**
330 * @license lucide-react v0.469.0 - ISC
331 *
332 * This source code is licensed under the ISC license.
333 * See the LICENSE file in the root directory of this source tree.
334 */const Qte=je("Pencil",[["path",{d:"M21.174 6.812a1 1 0 0 0-3.986-3.987L3.842 16.174a2 2 0 0 0-.5.83l-1.321 4.352a.5.5 0 0 0 .623.622l4.353-1.32a2 2 0 0 0 .83-.497z",key:"1a8usu"}],["path",{d:"m15 5 4 4",key:"1mk7zo"}]]);/**
335 * @license lucide-react v0.469.0 - ISC
336 *
337 * This source code is licensed under the ISC license.
338 * See the LICENSE file in the root directory of this source tree.
339 */const Bw=je("Phone",[["path",{d:"M22 16.92v3a2 2 0 0 1-2.18 2 19.79 19.79 0 0 1-8.63-3.07 19.5 19.5 0 0 1-6-6 19.79 19.79 0 0 1-3.07-8.67A2 2 0 0 1 4.11 2h3a2 2 0 0 1 2 1.72 12.84 12.84 0 0 0 .7 2.81 2 2 0 0 1-.45 2.11L8.09 9.91a16 16 0 0 0 6 6l1.27-1.27a2 2 0 0 1 2.11-.45 12.84 12.84 0 0 0 2.81.7A2 2 0 0 1 22 16.92z",key:"foiqr5"}]]);/**
340 * @license lucide-react v0.469.0 - ISC
341 *
342 * This source code is licensed under the ISC license.
343 * See the LICENSE file in the root directory of this source tree.
344 */const O$=je("Plug",[["path",{d:"M12 22v-5",key:"1ega77"}

344],["path",{d:"M9 8V2",key:"14iosj"}],["path",{d:"M15 8V2",key:"18g5xt"}],["path",{d:"M18 8v5a4 4 0 0 1-4 4h-4a4 4 0 0 1-4-4V8Z",key:"osxo6l"}]]);/**
345 * @license lucide-react v0.469.0 - ISC
346 *
347 * This source code is licensed under the ISC license.
348 * See the LICENSE file in the root directory of this source tree.
349 */const Zte=je("Presentation",[["path",{d:"M2 3h20",key:"91anmk"}],["path",{d:"M21 3v11a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V3",key:"2k9sn8"}],["path",{d:"m7 21 5-5 5 5",key:"bip4we"}]]);/**
350 * @license lucide-react v0.469.0 - ISC
351 *
352 * This source code is licensed under the ISC license.
353 * See the LICENSE file in the root directory of this source tree.
354 */const P$=je("Radio",[["path",{d:"M4.9 19.1C1 15.2 1 8.8 4.9 4.9",key:"1vaf9d"}],["path",{d:"M7.8 16.2c-2.3-2.3-2.3-6.1 0-8.5",key:"u1ii0m"}],["circle",{cx:"12",cy:"12",r:"2",key:"1c9p78"}],["path",{d:"M16.2 7.8c2.3 2.3 2.3 6.1 0 8.5",key:"1j5fej"}],["path",{d:"M19.1 4.9C23 8.8 23 15.1 19.1 19",key:"10b0cb"}]]);/**
355 * @license lucide-react v0.469.0 - ISC
356 *
357 * This source code is licensed under the ISC license.
358 * See the LICENSE file in the root directory of this source tree.
359 */const Jte=je("RefreshCw",[["path",{d:"M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8",key:"v9h5vc"}],["path",{d:"M21 3v5h-5",key:"1q7to0"}],["path",{d:"M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16",key:"3uifl3"}],["path",{d:"M8 16H3v5",key:"1cv678"}]]);/**
360 * @license lucide-react v0.469.0 - ISC
361 *
362 * This source code is licensed under the ISC license.
363 * See the LICENSE file in the root directory of this source tree.
364 */const ene=je("Repeat",[["path",{d:"m17 2 4 4-4 4",key:"nntrym"}],["path",{d:"M3 11v-1a4 4 0 0 1 4-4h14",key:"84bu3i"}],["path",{d:"m7 22-4-4 4-4",key:"1wqhfi"}],["path",{d:"M21 13v1a4 4 0 0 1-4 4H3",key:"1rx37r"}]]);/**
365 * @license lucide-react v0.469.0 - ISC
366 *
367 * This source code is licensed under the ISC license.
368 * See the LICENSE file in the root directory of this source tree.
369 */const tne=je("Route",[["circle",{cx:"6",cy:"19",r:"3",key:"1kj8tv"}],["path",{d:"M9 19h8.5a3.5 3.5 0 0 0 0-7h-11a3.5 3.5 0 0 1 0-7H15",key:"1d8sl"}],["circle",{cx:"18",cy:"5",r:"3",key:"gq8acd"}]]);/**
370 * @license lucide-react v0.469.0 - ISC
371 *
372 * This source code is licensed under the ISC license.
373 * See the LICENSE file in the root directory of this source tree.
374 */const nne=je("Search",[["circle",{cx:"11",cy:"11",r:"8",key:"4ej97u"}],["path",{d:"m21 21-4.3-4.3",key:"1qie3q"}]]);/**
375 * @license lucide-react v0.469.0 - ISC
376 *
377 * This source code is licensed under the ISC license.
378 * See the LICENSE file in the root directory of this source tree.
379 */const ane=je("Send",[["path",{d:"M14.536 21.686a.5.5 0 0 0 .937-.024l6.5-19a.496.496 0 0 0-.635-.635l-19 6.5a.5.5 0 0 0-.024.937l7.93 3.18a2 2 0 0 1 1.112 1.11z",key:"1ffxy3"}],["path",{d:"m21.854 2.147-10.94 10.939",key:"12cjpa"}]]);/**
380 * @license lucide-react v0.469.0 - ISC
381 *
382 * This source code is licensed under the ISC license.
383 * See the LICENSE file in the root directory of this source tree.
384 */const ine=je("Settings",[["path",{d:"M12.22 2h-.44a2 2 0 0 0-2 2v.18a2 2 0 0 1-1 1.73l-.43.25a2 2 0 0 1-2 0l-.15-.08a2 2 0 0 0-2.73.73l-.22.38a2 2 0 0 0 .73 2.73l.15.1a2 2 0 0 1 1 1.72v.51a2 2 0 0 1-1 1.74l-.15.09a2 2 0 0 0-.73 2.73l.22.38a2 2 0 0 0 2.73.73l.15-.08a2 2 0 0 1 2 0l.43.25a2 2 0 0 1 1 1.73V20a2 2 0 0 0 2 2h.44a2 2 0 0 0 2-2v-.18a2 2 0 0 1 1-1.73l.43-.25a2 2 0 0 1 2 0l.15.08a2 2 0 0 0 2.73-.73l.22-.39a2 2 0 0 0-.73-2.73l-.15-.08a2 2 0 0 1-1-1.74v-.5a2 2 0 0 1 1-1.74l.15-.09a2 2 0 0 0 .73-2.73l-.22-.38a2 2 0 0 0-2.73-.73l-.15.08a2 2 0 0 1-2 0l-.43-.25a2 2 0 0 1-1-1.73V4a2 2 0 0 0-2-2z",key:"1qme2f"}],["circle",{cx:"12",cy:"12",r:"3",key:"1v7zrd"}]]);/**
385 * @license lucide-react v0.469.0 - ISC
386 *
387 * This source code is licensed under the ISC license.
388 * See the LICENSE file in the root directory of this source tree.
389 */const rne=je("Share2",[["circle",{cx:"18",cy:"5",r:"3",key:"gq8acd"}],["circle",{cx:"6",cy:"12",r:"3",key:"w7nqdw"}],["circle",{cx:"18",cy:"19",r:"3",key:"1xt0gg"}],["line",{x1:"8.59",x2:"15.42",y1:"13.51",y2:"17.49",key:"47mynk"}],["line",{x1:"15.41",x2:"8.59",y1:"6.51",y2:"10.49",key:"1n3mei"}]]);/**
390 * @license lucide-react v0.469.0 - ISC
391 *
392 * This source code is licensed under the ISC license.
393 * See the LICENSE file in the root directory of this source tree.
394 */const I$=je("ShieldCheck",[["path",{d:"M20 13c0 5-3.5 7.5-7.66 8.95a1 1 0 0 1-.67-.01C7.5 20.5 4 18 4 13V6a1 1 0 0 1 1-1c2 0 4.5-1.2 6.24-2.72a1.17 1.17 0 0 1 1.52 0C14.51 3.81 17 5 19 5a1 1 0 0 1 1 1z",ke

394y:"oel41y"}],["path",{d:"m9 12 2 2 4-4",key:"dzmm74"}]]);/**
395 * @license lucide-react v0.469.0 - ISC
396 *
397 * This source code is licensed under the ISC license.
398 * See the LICENSE file in the root directory of this source tree.
399 */const one=je("Shield",[["path",{d:"M20 13c0 5-3.5 7.5-7.66 8.95a1 1 0 0 1-.67-.01C7.5 20.5 4 18 4 13V6a1 1 0 0 1 1-1c2 0 4.5-1.2 6.24-2.72a1.17 1.17 0 0 1 1.52 0C14.51 3.81 17 5 19 5a1 1 0 0 1 1 1z",key:"oel41y"}]]);/**
400 * @license lucide-react v0.469.0 - ISC
401 *
402 * This source code is licensed under the ISC license.
403 * See the LICENSE file in the root directory of this source tree.
404 */const sne=je("Sigma",[["path",{d:"M18 7V5a1 1 0 0 0-1-1H6.5a.5.5 0 0 0-.4.8l4.5 6a2 2 0 0 1 0 2.4l-4.5 6a.5.5 0 0 0 .4.8H17a1 1 0 0 0 1-1v-2",key:"wuwx1p"}]]);/**
405 * @license lucide-react v0.469.0 - ISC
406 *
407 * This source code is licensed under the ISC license.
408 * See the LICENSE file in the root directory of this source tree.
409 */const lne=je("SlidersHorizontal",[["line",{x1:"21",x2:"14",y1:"4",y2:"4",key:"obuewd"}],["line",{x1:"10",x2:"3",y1:"4",y2:"4",key:"1q6298"}],["line",{x1:"21",x2:"12",y1:"12",y2:"12",key:"1iu8h1"}],["line",{x1:"8",x2:"3",y1:"12",y2:"12",key:"ntss68"}],["line",{x1:"21",x2:"16",y1:"20",y2:"20",key:"14d8ph"}],["line",{x1:"12",x2:"3",y1:"20",y2:"20",key:"m0wm8r"}],["line",{x1:"14",x2:"14",y1:"2",y2:"6",key:"14e1ph"}],["line",{x1:"8",x2:"8",y1:"10",y2:"14",key:"1i6ji0"}],["line",{x1:"16",x2:"16",y1:"18",y2:"22",key:"1lctlv"}]]);/**
410 * @license lucide-react v0.469.0 - ISC
411 *
412 * This source code is licensed under the ISC license.
413 * See the LICENSE file in the root directory of this source tree.
414 */const L0=je("Sparkles",[["path",{d:"M9.937 15.5A2 2 0 0 0 8.5 14.063l-6.135-1.582a.5.5 0 0 1 0-.962L8.5 9.936A2 2 0 0 0 9.937 8.5l1.582-6.135a.5.5 0 0 1 .963 0L14.063 8.5A2 2 0 0 0 15.5 9.937l6.135 1.581a.5.5 0 0 1 0 .964L15.5 14.063a2 2 0 0 0-1.437 1.437l-1.582 6.135a.5.5 0 0 1-.963 0z",key:"4pj2yx"}],["path",{d:"M20 3v4",key:"1olli1"}],["path",{d:"M22 5h-4",key:"1gvqau"}],["path",{d:"M4 17v2",key:"vumght"}],["path",{d:"M5 18H3",key:"zchphs"}]]);/**
415 * @license lucide-react v0.469.0 - ISC
416 *
417 * This source code is licensed under the ISC license.
418 * See the LICENSE file in the root directory of this source tree.
419 */const cne=je("Table",[["path",{d:"M12 3v18",key:"108xh3"}],["rect",{width:"18",height:"18",x:"3",y:"3",rx:"2",key:"afitv7"}],["path",{d:"M3 9h18",key:"1pudct"}],["path",{d:"M3 15h18",key:"5xshup"}]]);/**
420 * @license lucide-react v0.469.0 - ISC
421 *
422 * This source code is licensed under the ISC license.
423 * See the LICENSE file in the root directory of this source tree.
424 */const dne=je("Tag",[["path",{d:"M12.586 2.586A2 2 0 0 0 11.172 2H4a2 2 0 0 0-2 2v7.172a2 2 0 0 0 .586 1.414l8.704 8.704a2.426 2.426 0 0 0 3.42 0l6.58-6.58a2.426 2.426 0 0 0 0-3.42z",key:"vktsd0"}],["circle",{cx:"7.5",cy:"7.5",r:".5",fill:"currentColor",key:"kqv944"}]]);/**
425 * @license lucide-react v0.469.0 - ISC
426 *
427 * This source code is licensed under the ISC license.
428 * See the LICENSE file in the root directory of this source tree.
429 */const une=je("Tags",[["path",{d:"m15 5 6.3 6.3a2.4 2.4 0 0 1 0 3.4L17 19",key:"1cbfv1"}],["path",{d:"M9.586 5.586A2 2 0 0 0 8.172 5H3a1 1 0 0 0-1 1v5.172a2 2 0 0 0 .586 1.414L8.29 18.29a2.426 2.426 0 0 0 3.42 0l3.58-3.58a2.426 2.426 0 0 0 0-3.42z",key:"135mg7"}],["circle",{cx:"6.5",cy:"9.5",r:".5",fill:"currentColor",key:"5pm5xn"}]]);/**
430 * @license lucide-react v0.469.0 - ISC
431 *
432 * This source code is licensed under the ISC license.
433 * See the LICENSE file in the root directory of this source tree.
434 */const D$=je("Target",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["circle",{cx:"12",cy:"12",r:"6",key:"1vlfrh"}],["circle",{cx:"12",cy:"12",r:"2",key:"1c9p78"}]]);/**
435 * @license lucide-react v0.469.0 - ISC
436 *
437 * This source code is licensed under the ISC license.
438 * See the LICENSE file in the root directory of this source tree.
439 */const hne=je("Terminal",[["polyline",{points:"4 17 10 11 4 5",key:"akl6gq"}],["line",{x1:"12",x2:"20",y1:"19",y2:"19",key:"q2wloq"}]]);/**
440 * @license lucide-react v0.469.0 - ISC
441 *
442 * This source code is licensed under the ISC license.
443 * See the LICENSE file in the root directory of this source tree.

444 */const pne=je("Timer",[["line",{x1:"10",x2:"14",y1:"2",y2:"2",key:"14vaq8"}],["line",{x1:"12",x2:"15",y1:"14",y2:"11",key:"17fdiu"}],["circle",{cx:"12",cy:"14",r:"8",key:"1e1u0o"}]]);/**
445 * @license lucide-react v0.469.0 - ISC
446 *
447 * This source code is licensed under the ISC license.
448 * See the LICENSE file in the root directory of this source tree.
449 */const mne=je("Upload",[["path",{d:"M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4",key:"ih7n3h"}],["polyline",{points:"17 8 12 3 7 8",key:"t8dd8p"}],["line",{x1:"12",x2:"12",y1:"3",y2:"15",key:"widbto"}]]);/**
450 * @license lucide-react v0.469.0 - ISC
451 *
452 * This source code is licensed under the ISC license.
453 * See the LICENSE file in the root directory of this source tree.
454 */const fne=je("UserPlus",[["path",{d:"M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2",key:"1yyitq"}],["circle",{cx:"9",cy:"7",r:"4",key:"nufk8"}],["line",{x1:"19",x2:"19",y1:"8",y2:"14",key:"1bvyxn"}],["line",{x1:"22",x2:"16",y1:"11",y2:"11",key:"1shjgl"}]]);/**
455 * @license lucide-react v0.469.0 - ISC
456 *
457 * This source code is licensed under the ISC license.
458 * See the LICENSE file in the root directory of this source tree.
459 */const W$=je("Users",[["path",{d:"M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2",key:"1yyitq"}],["circle",{cx:"9",cy:"7",r:"4",key:"nufk8"}],["path",{d:"M22 21v-2a4 4 0 0 0-3-3.87",key:"kshegd"}],["path",{d:"M16 3.13a4 4 0 0 1 0 7.75",key:"1da9ce"}]]);/**
460 * @license lucide-react v0.469.0 - ISC
461 *
462 * This source code is licensed under the ISC license.
463 * See the LICENSE file in the root directory of this source tree.
464 */const gne=je("Webhook",[["path",{d:"M18 16.98h-5.99c-1.1 0-1.95.94-2.48 1.9A4 4 0 0 1 2 17c.01-.7.2-1.4.57-2",key:"q3hayz"}],["path",{d:"m6 17 3.13-5.78c.53-.97.1-2.18-.5-3.1a4 4 0 1 1 6.89-4.06",key:"1go1hn"}],["path",{d:"m12 6 3.13 5.73C15.66 12.7 16.9 13 18 13a4 4 0 0 1 0 8",key:"qlwsc0"}]]);/**
465 * @license lucide-react v0.469.0 - ISC
466 *
467 * This source code is licensed under the ISC license.
468 * See the LICENSE file in the root directory of this source tree.
469 */const z$=je("Workflow",[["rect",{width:"8",height:"8",x:"3",y:"3",rx:"2",key:"by2w9f"}],["path",{d:"M7 11v4a2 2 0 0 0 2 2h4",key:"xkn7yn"}],["rect",{width:"8",height:"8",x:"13",y:"13",rx:"2",key:"1cgmvn"}]]);/**
470 * @license lucide-react v0.469.0 - ISC
471 *
472 * This source code is licensed under the ISC license.
473 * See the LICENSE file in the root directory of this source tree.
474 */const yne=je("Wrench",[["path",{d:"M14.7 6.3a1 1 0 0 0 0 1.4l1.6 1.6a1 1 0 0 0 1.4 0l3.77-3.77a6 6 0 0 1-7.94 7.94l-6.91 6.91a2.12 2.12 0 0 1-3-3l6.91-6.91a6 6 0 0 1 7.94-7.94l-3.76 3.76z",key:"cbrjhi"}]]);/**
475 * @license lucide-react v0.469.0 - ISC
476 *
477 * This source code is licensed under the ISC license.
478 * See the LICENSE file in the root directory of this source tree.
479 */const L$=je("X",[["path",{d:"M18 6 6 18",key:"1bl5f8"}],["path",{d:"m6 6 12 12",key:"d8bk6v"}]]);/**
480 * @license lucide-react v0.469.0 - ISC
481 *
482 * This source code is licensed under the ISC license.
483 * See the LICENSE file in the root directory of this source tree.
484 */const bne=je("Zap",[["path",{d:"M4 14a1 1 0 0 1-.78-1.63l9.9-10.2a.5.5 0 0 1 .86.46l-1.92 6.02A1 1 0 0 0 13 10h7a1 1 0 0 1 .78 1.63l-9.9 10.2a.5.5 0 0 1-.86-.46l1.92-6.02A1 1 0 0 0 11 14z",key:"1xq2db"}]]),lM="-",vne=e=>{const t=wne(e),{conflictingClassGroups:a,conflictingClassGroupModifiers:i}=e;return{getClassGroupId:l=>{const d=l.split(lM);return d[0]===""&&d.length!==1&&d.shift(),U$(d,t)||xne(l)},getConflictingClassGroupIds:(l,d)=>{const u=a[l]||[];return d&&i[l]?[...u,...i[l]]:u}}},U$=(e,t)=>{var l;if(e.length===0)return t.classGroupId;const a=e[0],i=t.nextPart.get(a),r=i?U$(e.slice(1),i):void 0;if(r)return r;if(t.validators.length===0)return;const o=e.join(lM);return(l=t.validators.find(({validator:d})=>d(o)))==null?void 0:l.classGroupId},gO=/^\[(.+)\]$/,xne=e=>{if(gO.test(e)){const t=gO.exec(e)[1],a=t==null?void 0:t.substring(0,t.indexOf(":"));if(a)return"arbitrary.."+a}},wne=e=>{const{theme:t,prefix:a}=e,i={nextPart:new Map,validators:[]};return jne(Object.entries(e.classGroups),a).forEach(([o,l])=>{p4(l,i,o,t)}),i},p4=(e,t,a,i)=>{e.forEach(r=>{if(typeof r=="string"){const o=r===""?t:yO(t,r);o.classGroupId=a;return}if(typeof r=="function"){if(kne(r)){p4(r(i),t,a,i);return}t.validators.push({validator:r,classGroupId:a});return}Object.entries(r).forEach(([o,l])=>

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485return{...i,error:o,errorUpdateCount:i.errorUpdateCount+1,errorUpdatedAt:Date.now(),fetchFailureCount:i.fetchFailureCount+1,fetchFailureReason:o,fetchStatus:"idle",status:"error",isInvalidated:!0};case"invalidate":return{...i,isInvalidated:!0};case"setState":return{...i,...t.state}}};this.state=a(this.state),pa.batch(()=>{this.observers.forEach(i=>{i.onQueryUpdate()}),ue(this,Li).notify({query:this,type:"updated",action:t})})},Uq);function zre(e,t){return{fetchFailureCount:0,fetchFailureReason:null,fetchStatus:EF(t.networkMode)?"fetching":"paused",...e===void 0&&{error:null,status:"pending"}}}function WO(e,t){return{data:e,dataUpdatedAt:t??Date.now(),error:null,isInvalidated:!1,status:"success"}}function zO(e){const t=typeof e.initialData=="function"?e.initialData():e.initialData,a=t!==void 0,i=a?typeof e.initialDataUpdatedAt=="function"?e.initialDataUpdatedAt():e.initialDataUpdatedAt:0;return{data:t,dataUpdateCount:0,dataUpdatedAt:a?i??Date.now():0,error:null,errorUpdateCount:0,errorUpdatedAt:0,fetchFailureCount:0,fetchFailureReason:null,fetchMeta:null,isInvalidated:!1,status:a?"success":"pending",fetchStatus:"idle"}}var gy,lo,Zn,vd,co,Rl,Bq,Lre=(Bq=class extends NF{constructor(t){super();ct(this,co);ct(this,gy);ct(this,lo);ct(this,Zn);ct(this,vd);He(this,gy,t.client),this.mutationId=t.mutationId,He(this,Zn,t.mutationCache),He(this,lo,[]),this.state=t.state||Ure(),this.setOptions(t.options),this.scheduleGc()}setOptions(t){this.options=t,this.updateGcTime(this.options.gcTime)}get meta(){return this.options.meta}addObserver(t){ue(this,lo).includes(t)||(ue(this,lo).push(t),this.clearGcTimeout(),ue(this,Zn).notify({type:"observerAdded",mutation:this,observer:t}))}removeObserver(t){He(this,lo,ue(this,lo).filter(a=>a!==t)),this.scheduleGc(),ue(this,Zn).notify({type:"observerRemoved",mutation:this,observer:t})}optionalRemove(){ue(this,lo).length||(this.state.status==="pending"?this.scheduleGc():ue(this,Zn).remove(this))}continue(){var t;return((t=ue(this,vd))==null?void 0:t.continue())??this.execute(this.state.variables)}async execute(t){var l,d,u,h,m,f,y,b,x,k,v,j,T,A,C,_,E,R;const a=()=>{qn(this,co,Rl).call(this,{type:"continue"})},i={client:ue(this,gy),meta:this.options.meta,mutationKey:this.options.mutationKey};He(this,vd,RF({fn:()=>this.options.mutationFn?this.options.mutationFn(t,i):Promise.reject(new Error("No mutationFn found")),onFail:(W,z)=>{qn(this,co,Rl).call(this,{type:"failed",failureCount:W,error:z})},onPause:()=>{qn(this,co,Rl).call(this,{type:"pause"})},onContinue:a,retry:this.options.retry??0,retryDelay:this.options.retryDelay,networkMode:this.options.networkMode,canRun:()=>ue(this,Zn).canRun(this)}));const r=this.state.status==="pending",o=!ue(this,vd).canStart();try{if(r)a();else{qn(this,co,Rl).call(this,{type:"pending",variables:t,isPaused:o}),ue(this,Zn).config.onMutate&&await ue(this,Zn).config.onMutate(t,this,i);const z=await((d=(l=this.options).onMutate)==null?void 0:d.call(l,t,i));z!==this.state.context&&qn(this,co,Rl).call(this,{type:"pending",context:z,variables:t,isPaused:o})}const W=await ue(this,vd).start();return await((h=(u=ue(this,Zn).config).onSuccess)==null?void 0:h.call(u,W,t,this.state.context,this,i)),await((f=(m=this.options).onSuccess)==null?void 0:f.call(m,W,t,this.state.context,i)),await((b=(y=ue(this,Zn).config).onSettled)==null?void 0:b.call(y,W,null,this.state.variables,this.state.context,this,i)),await((k=(x=this.options).onSettled)==null?void 0:k.call(x,W,null,t,this.state.context,i)),qn(this,co,Rl).call(this,{type:"success",data:W}),W}catch(W){try{await((j=(v=ue(this,Zn).config).onError)==null?void 0:j.call(v,W,t,this.state.context,this,i))}catch(z){Promise.reject(z)}try{await((A=(T=this.options).onError)==null?void 0:A.call(T,W,t,this.state.context,i))}catch(z){Promise.reject(z)}try{await((_=(C=ue(this,Zn).config).onSettled)==null?void 0:_.call(C,void 0,W,this.state.variables,this.state.context,this,i))}catch(z){Promise.reject(z)}try{await((R=(E=this.options).onSettled)==null?void 0:R.call(E,void 0,W,t,this.state.context,i))}catch(z){Promise.reject(z)}throw qn(this,co,Rl).call(this,{type:"error",error:W}),W}finally{ue(this,Zn).runNext(this)}}},gy=new WeakMap,lo=new WeakMap,Zn=new WeakMap,vd=new WeakMap,co=new WeakSet,Rl=function(t){const a=i=>{switch(t.type){case"failed":return{...i,failureCount:t.failureCount,failureReason:t.error};case"pause":return{...i,isPaused:!0};case"continue":return{...i,isPaused:!1};

485case"pending":return{...i,context:t.context,data:void 0,failureCount:0,failureReason:null,error:null,isPaused:t.isPaused,status:"pending",variables:t.variables,submittedAt:Date.now()};case"success":return{...i,data:t.data,failureCount:0,failureReason:null,error:null,status:"success",isPaused:!1};case"error":return{...i,data:void 0,error:t.error,failureCount:i.failureCount+1,failureReason:t.error,isPaused:!1,status:"error"}}};this.state=a(this.state),pa.batch(()=>{ue(this,lo).forEach(i=>{i.onMutationUpdate(t)}),ue(this,Zn).notify({mutation:this,type:"updated",action:t})})},Bq);function Ure(){return{context:void 0,data:void 0,error:null,failureCount:0,failureReason:null,isPaused:!1,status:"idle",variables:void 0,submittedAt:0}}var fs,yr,yy,qq,Bre=(qq=class extends Vw{constructor(t={}){super();ct(this,fs);ct(this,yr);ct(this,yy);this.config=t,He(this,fs,new Set),He(this,yr,new Map),He(this,yy,0)}build(t,a,i){const r=new Lre({client:t,mutationCache:this,mutationId:++Av(this,yy)._,options:t.defaultMutationOptions(a),state:i});return this.add(r),r}add(t){ue(this,fs).add(t);const a=Nv(t);if(typeof a=="string"){const i=ue(this,yr).get(a);i?i.push(t):ue(this,yr).set(a,[t])}this.notify({type:"added",mutation:t})}remove(t){if(ue(this,fs).delete(t)){const a=Nv(t);if(typeof a=="string"){const i=ue(this,yr).get(a);if(i)if(i.length>1){const r=i.indexOf(t);r!==-1&&i.splice(r,1)}else i[0]===t&&ue(this,yr).delete(a)}}this.notify({type:"removed",mutation:t})}canRun(t){const a=Nv(t);if(typeof a=="string"){const i=ue(this,yr).get(a),r=i==null?void 0:i.find(o=>o.state.status==="pending");return!r||r===t}else return!0}runNext(t){var i;const a=Nv(t);if(typeof a=="string"){const r=(i=ue(this,yr).get(a))==null?void 0:i.find(o=>o!==t&&o.state.isPaused);return(r==null?void 0:r.continue())??Promise.resolve()}else return Promise.resolve()}clear(){pa.batch(()=>{ue(this,fs).forEach(t=>{this.notify({type:"removed",mutation:t})}),ue(this,fs).clear(),ue(this,yr).clear()})}getAll(){return Array.from(ue(this,fs))}find(t){const a={exact:!0,...t};return this.getAll().find(i=>OO(a,i))}findAll(t={}){return this.getAll().filter(a=>OO(t,a))}notify(t){pa.batch(()=>{this.listeners.forEach(a=>{a(t)})})}resumePausedMutations(){const t=this.getAll().filter(a=>a.state.isPaused);return pa.batch(()=>Promise.all(t.map(a=>a.continue().catch(gr))))}},fs=new WeakMap,yr=new WeakMap,yy=new WeakMap,qq);function Nv(e){var t;return(t=e.options.scope)==null?void 0:t.id}var uo,$q,qre=($q=class extends Vw{constructor(t={}){super();ct(this,uo);this.config=t,He(this,uo,new Map)}build(t,a,i){const r=a.queryKey,o=a.queryHash??yM(r,a);let l=this.get(o);return l||(l=new Wre({client:t,queryKey:r,queryHash:o,options:t.defaultQueryOptions(a),state:i,defaultOptions:t.getQueryDefaults(r)}),this.add(l)),l}add(t){ue(this,uo).has(t.queryHash)||(ue(this,uo).set(t.queryHash,t),this.notify({type:"added",query:t}))}remove(t){const a=ue(this,uo).get(t.queryHash);a&&(t.destroy(),a===t&&ue(this,uo).delete(t.queryHash),this.notify({type:"removed",query:t}))}clear(){pa.batch(()=>{this.getAll().forEach(t=>{this.remove(t)})})}get(t){return ue(this,uo).get(t)}getAll(){return[...ue(this,uo).values()]}find(t){const a={exact:!0,...t};return this.getAll().find(i=>NO(a,i))}findAll(t={}){const a=this.getAll();return Object.keys(t).length>0?a.filter(i=>NO(t,i)):a}notify(t){pa.batch(()=>{this.listeners.forEach(a=>{a(t)})})}onFocus(){pa.batch(()=>{this.getAll().forEach(t=>{t.onFocus()})})}onOnline(){pa.batch(()=>{this.getAll().forEach(t=>{t.onOnline()})})}},uo=new WeakMap,$q),an,Bl,ql,Bh,qh,$l,$h,Fh,Fq,$re=(Fq=class{constructor(e={}){ct(this,an);ct(this,Bl);ct(this,ql);ct(this,Bh);ct(this,qh);ct(this,$l);ct(this,$h);ct(this,Fh);He(this,an,e.queryCache||new qre),He(this,Bl,e.mutationCache||new Bre),He(this,ql,e.defaultOptions||{}),He(this,Bh,new Map),He(this,qh,new Map),He(this,$l,0)}mount(){Av(this,$l)._++,ue(this,$l)===1&&(He(this,$h,AF.subscribe(async e=>{e&&(await this.resumePausedMutations(),ue(this,an).onFocus())})),He(this,Fh,F0.subscribe(async e=>{e&&(await this.resumePausedMutations(),ue(this,an).onOnline())})))}unmount(){var e,t;Av(this,$l)._--,ue(this,$l)===0&&((e=ue(this,$h))==null||e.call(this),He(this,$h,void 0),(t=ue(this,Fh))==null||t.call(this),He(this,Fh,void 0))}isFetching(e){return ue(this,an).findAll({...e,fetchStatus:"fetching"}).length}isMutating(e){return ue(this,Bl).findAll({...e,status:"pending"}).length}getQueryData(e){var a;const t=this.defaultQueryOptions({queryKey:e});return(a=ue(this,an).get(t.queryHash))==null?void 0:a.state.data}ensureQueryData(e){const t=this.defaultQueryOptions(e),a=ue(this,an).build(this,t),i=a.state.data;return i===void 0?this.fetchQuery(e):(e.revalidateIfStale&&a.isStaleByTime(x4(t.staleTime,a))&&this.prefetchQuery(t),Promise.resolve(i))}getQueriesData(e){return ue(this,an).findAll(e).map(({queryKey:t,state:a})=>{const i=a.data;return[t,i]})}setQueryData(e,t,a){const i=this.defaultQueryOptions({queryKey:e}),r=ue(this,an).get(i.queryHash),o=r==null?void 0:r.state.data,l=xre(t,o);if(l!==void 0)return ue(this,an).build(this,i).setData(l,{...a,manual:!0})}

485setQueriesData(e,t,a){return pa.batch(()=>ue(this,an).findAll(e).map(({queryKey:i})=>[i,this.setQueryData(i,t,a)]))}getQueryState(e){var a;const t=this.defaultQueryOptions({queryKey:e});return(a=ue(this,an).get(t.queryHash))==null?void 0:a.state}removeQueries(e){const t=ue(this,an);pa.batch(()=>{t.findAll(e).forEach(a=>{t.remove(a)})})}resetQueries(e,t){const a=ue(this,an);return pa.batch(()=>(a.findAll(e).forEach(i=>{i.reset()}),this.refetchQueries({type:"active",...e},t)))}cancelQueries(e,t={}){const a={revert:!0,...t},i=pa.batch(()=>ue(this,an).findAll(e).map(r=>r.cancel(a)));return Promise.all(i).then(gr).catch(gr)}invalidateQueries(e,t={}){return pa.batch(()=>(ue(this,an).findAll(e).forEach(a=>{a.invalidate()}),(e==null?void 0:e.refetchType)==="none"?Promise.resolve():this.refetchQueries({...e,type:(e==null?void 0:e.refetchType)??(e==null?void 0:e.type)??"active"},t)))}refetchQueries(e,t={}){const a={...t,cancelRefetch:t.cancelRefetch??!0},i=pa.batch(()=>ue(this,an).findAll(e).filter(r=>!r.isDisabled()&&!r.isStatic()).map(r=>{let o=r.fetch(void 0,a);return a.throwOnError||(o=o.catch(gr)),r.state.fetchStatus==="paused"?Promise.resolve():o}));return Promise.all(i).then(gr)}fetchQuery(e){const t=this.defaultQueryOptions(e);t.retry===void 0&&(t.retry=!1);const a=ue(this,an).build(this,t);return a.isStaleByTime(x4(t.staleTime,a))?a.fetch(t):Promise.resolve(a.state.data)}prefetchQuery(e){return this.fetchQuery(e).then(gr).catch(gr)}fetchInfiniteQuery(e){return e._type="infinite",this.fetchQuery(e)}prefetchInfiniteQuery(e){return this.fetchInfiniteQuery(e).then(gr).catch(gr)}ensureInfiniteQueryData(e){return e._type="infinite",this.ensureQueryData(e)}resumePausedMutations(){return F0.isOnline()?ue(this,Bl).resumePausedMutations():Promise.resolve()}getQueryCache(){return ue(this,an)}getMutationCache(){return ue(this,Bl)}getDefaultOptions(){return ue(this,ql)}setDefaultOptions(e){He(this,ql,e)}setQueryDefaults(e,t){ue(this,Bh).set(gg(e),{queryKey:e,defaultOptions:t})}getQueryDefaults(e){const t=[...ue(this,Bh).values()],a={};return t.forEach(i=>{yg(e,i.queryKey)&&Object.assign(a,i.defaultOptions)}),a}setMutationDefaults(e,t){ue(this,qh).set(gg(e),{mutationKey:e,defaultOptions:t})}getMutationDefaults(e){const t=[...ue(this,qh).values()],a={};return t.forEach(i=>{yg(e,i.mutationKey)&&Object.assign(a,i.defaultOptions)}),a}defaultQueryOptions(e){if(e._defaulted)return e;const t={...ue(this,ql).queries,...this.getQueryDefaults(e.queryKey),...e,_defaulted:!0};return t.queryHash||(t.queryHash=yM(t.queryKey,t)),t.refetchOnReconnect===void 0&&(t.refetchOnReconnect=t.networkMode!=="always"),t.throwOnError===void 0&&(t.throwOnError=!!t.suspense),!t.networkMode&&t.persister&&(t.networkMode="offlineFirst"),t.queryFn===bM&&(t.enabled=!1),t}defaultMutationOptions(e){return e!=null&&e._defaulted?e:{...ue(this,ql).mutations,...(e==null?void 0:e.mutationKey)&&this.getMutationDefaults(e.mutationKey),...e,_defaulted:!0}}clear(){ue(this,an).clear(),ue(this,Bl).clear()}},an=new WeakMap,Bl=new WeakMap,ql=new WeakMap,Bh=new WeakMap,qh=new WeakMap,$l=new WeakMap,$h=new WeakMap,Fh=new WeakMap,Fq),Fre=M.createContext(void 0),Gre=({client:e,children:t})=>(M.useEffect(()=>(e.mount(),()=>{e.unmount()}),[e]),n.jsx(Fre.Provider,{value:e,children:t}));/**
486 * react-router v8.3.0
487 *
488 * Copyright (c) Remix Software Inc.
489 *
490 * This source code is licensed under the MIT license found in the
491 * LICENSE.md file in the root directory of this source tree.
492 *
493 * @license MIT
494 */const vM=/^(?:[a-z][a-z0-9+.-]*:|[\\/]{2})/i,PF=/^[\\/]{2}/;function Hre(e,t){return t+e.replace(/\\/g,"/")}/**
495 * react-router v8.3.0
496 *
497 * Copyright (c) Remix Software Inc.
498 *
499 * This source code is licensed under the MIT license found in the
500 * LICENSE.md file in the root directory of this source tree.
501 *
502 * @license MIT
503 */const LO="popstate";function UO(e){return typeof e=="object"&&e!=null&&"pathname"in e&&"search"in e&&"hash"in e&&"state"in e&&"key"in e}function Yre(e={}){function t(i,r){var h;let o=(h=r.state)==null?void 0:h.masked,{pathname:l,search:d,hash:u}=o||i.location;return j4("",{pathname:l,search:d,hash:u},r.state&&r.state.usr||null,r.state&&r.state.key||"default",o?{pathname:i.location.pathname,search:i.location.search,hash:i.location.hash}:void 0)}function a(i,r){return typeof r=="string"?r:bg(r)}return Kre(t,a,null,e)}function Xt(e,t){if(e===!1||e===null||typeof e>"u")throw new Error(t)}function Do(e,t){if(!e){typeof console<"u"&&console.warn(t);try{throw new Error(t)}catch{}}}function Vre(){return Math.random().toString(36).substring(2,10)}function BO(e,t){return{usr:e.state,key:e.key,idx:t,masked:e.mask?{pathname:e.pathname,search:e.search,hash:e.hash}:void 0}}function j4(e,t,a=null,i,r){return{pathname:typeof e=="string"?e:e.pathname,search:"",hash:"",...typeof t=="string"?Vp(t):t,state:a,key:t&&t.key||i||Vre(),mask:r}}function bg({pathname:e="/",search:t="",hash:a=""}){return t&&t!=="?"&&(e+=t.charAt(0)==="?"?t:"?"+t),a&&a!=="#"&&(e+=a.charAt(0)==="#"?a:"#"+a),e}function Vp(e){let t={};if(e){let a=e.indexOf("#");a>=0&&(t.hash=e.substring(a),e=e.substring(0,a));let i=e.indexOf("?");i>=0&&(t.search=e.substring(i),e=e.substring(0,i)),e&&(t.pathname=e)}return t}function Kre(e,t,a,i={}){let{window:r=document.defaultView,v5Compat:o=!1}=i,l=r.history,d="POP",u=null,h=m();h==null&&(h=0,l.replaceState({...l.state,idx:h},""));function m(){return(l.state||{idx:null}).idx}function f(){d="POP";let v=m(),j=v==null?null:v-h;h=v,u&&u({action:d,location:k.location,delta:j})}function y(v,j){d="PUSH";

503let T=UO(v)?v:j4(k.location,v,j);h=m()+1;let A=BO(T,h),C=k.createHref(T.mask||T);try{l.pushState(A,"",C)}catch(_){if(_ instanceof DOMException&&_.name==="DataCloneError")throw _;r.location.assign(C)}o&&u&&u({action:d,location:k.location,delta:1})}function b(v,j){d="REPLACE";let T=UO(v)?v:j4(k.location,v,j);h=m();let A=BO(T,h),C=k.createHref(T.mask||T);l.replaceState(A,"",C),o&&u&&u({action:d,location:k.location,delta:0})}function x(v){return Xre(r,v)}let k={get action(){return d},get location(){return e(r,l)},listen(v){if(u)throw new Error("A history only accepts one active listener");return r.addEventListener(LO,f),u=v,()=>{r.removeEventListener(LO,f),u=null}},createHref(v){return t(r,v)},createURL:x,encodeLocation(v){let j=x(v);return{pathname:j.pathname,search:j.search,hash:j.hash}},push:y,replace:b,go(v){return l.go(v)}};return k}function Xre(e,t,a=!1){let i="http://localhost";e&&(i=e.location.origin!=="null"?e.location.origin:e.location.href),Xt(i,"No window.location.(origin|href) available to create URL");let r=typeof t=="string"?t:bg(t);return r=r.replace(/ $/,"%20"),!a&&PF.test(r)&&(r=i+r),new URL(r,i)}/**
504 * react-router v8.3.0
505 *
506 * Copyright (c) Remix Software Inc.
507 *
508 * This source code is licensed under the MIT license found in the
509 * LICENSE.md file in the root directory of this source tree.
510 *
511 * @license MIT
512 */function IF(e,t,a="/"){return Qre(e,t,a,!1)}function Qre(e,t,a,i,r){let o=lc((typeof t=="string"?Vp(t):t).pathname||"/",a);if(o==null)return null;let l=Zre(e),d=null,u=uoe(o);for(let h=0;d==null&&h<l.length;++h)d=doe(l[h],u,i);return d}function Zre(e){let t=DF(e);return Jre(t),t}function DF(e,t=[],a=[],i="",r=!1){let o=(l,d,u=r,h)=>{let m={relativePath:h===void 0?l.path||"":h,caseSensitive:l.caseSensitive===!0,childrenIndex:d,route:l};if(m.relativePath.startsWith("/")){if(!m.relativePath.startsWith(i)&&u)return;Xt(m.relativePath.startsWith(i),`Absolute route path "${m.relativePath}" nested under path "${i}" is not valid. An absolute child route path must start with the combined path of all its parent routes.`),m.relativePath=m.relativePath.slice(i.length)}let f=Rr([i,m.relativePath]),y=a.concat(m);l.children&&l.children.length>0&&(Xt(l.index!==!0,`Index routes must not have child routes. Please remove all child routes from route path "${f}".`),DF(l.children,t,y,f,u)),!(l.path==null&&!l.index)&&t.push({path:f,score:loe(f,l.index),routesMeta:y.map((b,x)=>{let[k,v]=LF(b.relativePath,b.caseSensitive,x===y.length-1);return{...b,matcher:k,compiledParams:v}})})};return e.forEach((l,d)=>{var u;if(l.path===""||!((u=l.path)!=null&&u.includes("?")))o(l,d);else for(let h of WF(l.path))o(l,d,!0,h)}),t}function WF(e){let t=e.split("/");if(t.length===0)return[];let[a,...i]=t,r=a.endsWith("?"),o=a.replace(/\?$/,"");if(i.length===0)return r?[o,""]:[o];let l=WF(i.join("/")),d=[];return d.push(...l.map(u=>u===""?o:[o,u].join("/"))),r&&d.push(...l),d.map(u=>e.startsWith("/")&&u===""?"/":u)}function Jre(e){e.sort((t,a)=>t.score!==a.score?a.score-t.score:coe(t.routesMeta.map(i=>i.childrenIndex),a.routesMeta.map(i=>i.childrenIndex)))}const eoe=/^:[\w-]+$/,toe=/^:[\w-]+/,noe=3.5,aoe=3,ioe=2,roe=1,ooe=10,soe=-2,qO=e=>e==="*";function loe(e,t){let a=e.split("/"),i=a.length;return a.some(qO)&&(i+=soe),t&&(i+=ioe),a.filter(r=>!qO(r)).reduce((r,o)=>r+(eoe.test(o)?aoe:toe.test(o)?noe:o===""?roe:ooe),i)}function coe(e,t){return e.length===t.length&&e.slice(0,-1).every((a,i)=>a===t[i])?e[e.length-1]-t[t.length-1]:0}function doe(e,t,a=!1){let{routesMeta:i}=e,r={},o="/",l=[];for(let d=0;d<i.length;++d){let u=i[d],h=d===i.length-1,m=o==="/"?t:t.slice(o.length)||"/",f={path:u.relativePath,caseSensitive:u.caseSensitive,end:h},y=u.matcher&&u.compiledParams?zF(f,m,u.matcher,u.compiledParams):G0(f,m),b=u.route;if(!y&&h&&a&&!i[i.length-1].route.index&&(y=G0({path:u.relativePath,caseSensitive:u.caseSensitive,end:!1},m)),!y)return null;

512Object.assign(r,y.params),l.push({params:r,pathname:Rr([o,y.pathname]),pathnameBase:moe(Rr([o,y.pathnameBase])),route:b}),y.pathnameBase!=="/"&&(o=Rr([o,y.pathnameBase]))}return l}function G0(e,t){typeof e=="string"&&(e={path:e,caseSensitive:!1,end:!0});let[a,i]=LF(e.path,e.caseSensitive,e.end);return zF(e,t,a,i)}function zF(e,t,a,i){let r=t.match(a);if(!r)return null;let o=r[0],l=o.replace(/(.)\/+$/,"$1"),d=r.slice(1);return{params:i.reduce((u,{paramName:h,isOptional:m},f)=>{if(h==="*"){let b=d[f]||"";l=o.slice(0,o.length-b.length).replace(/(.)\/+$/,"$1")}const y=d[f];return m&&!y?u[h]=void 0:u[h]=(y||"").replace(/%2F/g,"/"),u},{}),pathname:o,pathnameBase:l,pattern:e}}function LF(e,t=!1,a=!0){Do(e==="*"||!e.endsWith("*")||e.endsWith("/*"),`Route path "${e}" will be treated as if it were "${e.replace(/\*$/,"/*")}" because the \`*\` character must always follow a \`/\` in the pattern. To get rid of this warning, please change the route path to "${e.replace(/\*$/,"/*")}".`);let i=[],r="^"+e.replace(/\/*\*?$/,"").replace(/^\/*/,"/").replace(/[\\.*+^${}|()[\]]/g,"\\$&").replace(/\/:([\w-]+)(\?)?/g,(o,l,d,u,h)=>{if(i.push({paramName:l,isOptional:d!=null}),d){let m=h.charAt(u+o.length);return m&&m!=="/"?"/([^\\/]*)":"(?:/([^\\/]*))?"}return"/([^\\/]+)"}).replace(/\/([\w-]+)\?(?=\/|$|\()/g,"(?:/$1)?");return e.endsWith("*")?(i.push({paramName:"*"}),r+=e==="*"||e==="/*"?"(.*)$":"(?:\\/(.+)|\\/*)$"):a?r+="\\/*$":e!==""&&e!=="/"&&(r+="(?:(?=\\/|$))"),[new RegExp(r,t?void 0:"i"),i]}function uoe(e){try{return e.split("/").map(t=>decodeURIComponent(t).replace(/\//g,"%2F")).join("/")}catch(t){return Do(!1,`The URL path "${e}" could not be decoded because it is a malformed URL segment. This is probably due to a bad percent encoding (${t}).`),e}}function lc(e,t){if(t==="/")return e;if(!e.toLowerCase().startsWith(t.toLowerCase()))return null;let a=t.endsWith("/")?t.length-1:t.length,i=e.charAt(a);return i&&i!=="/"?null:e.slice(a)||"/"}function hoe(e,t="/"){let{pathname:a,search:i="",hash:r=""}=typeof e=="string"?Vp(e):e,o;return a?(a=UF(a),a.startsWith("/")?o=$O(a.substring(1),"/"):o=$O(a,t)):o=t,{pathname:o,search:foe(i),hash:goe(r)}}function $O(e,t){let a=BF(t).split("/");return e.split("/").forEach(i=>{i===".."?a.length>1&&a.pop():i!=="."&&a.push(i)}),a.length>1?a.join("/"):"/"}function Ej(e,t,a,i){return`Cannot include a '${e}' character in a manually specified \`to.${t}\` field [${JSON.stringify(i)}].  Please separate it out to the \`to.${a}\` field. Alternatively you may provide the full path as a string in <Link to="..."> and the router will parse it for you.`}function poe(e){return e.filter((t,a)=>a===0||t.route.path&&t.route.path.length>0)}function xM(e){let t=poe(e);return t.map((a,i)=>i===t.length-1?a.pathname:a.pathnameBase)}function Kw(e,t,a,i=!1){let r;typeof e=="string"?r=Vp(e):(r={...e},Xt(!r.pathname||!r.pathname.includes("?"),Ej("?","pathname","search",r)),Xt(!r.pathname||!r.pathname.includes("#"),Ej("#","pathname","hash",r)),Xt(!r.search||!r.search.includes("#"),Ej("#","search","hash",r)));let o=e===""||r.pathname==="",l=o?"/":r.pathname,d;if(l==null)d=a;else{let f=t.length-1;if(!i&&l.startsWith("..")){let y=l.split("/");for(;y[0]==="..";)y.shift(),f-=1;r.pathname=y.join("/")}d=f>=0?t[f]:"/"}let u=hoe(r,d),h=l&&l!=="/"&&l.endsWith("/"),m=(o||l===".")&&a.endsWith("/");return!u.pathname.endsWith("/")&&(h||m)&&(u.pathname+="/"),u}const UF=e=>e.replace(/[\\/]{2,}/g,"/"),Rr=e=>

512UF(e.join("/")),BF=e=>e.replace(/\/+$/,""),moe=e=>BF(e).replace(/^\/*/,"/"),foe=e=>!e||e==="?"?"":e.startsWith("?")?e:"?"+e,goe=e=>!e||e==="#"?"":e.startsWith("#")?e:"#"+e;var yoe=class{constructor(e,t,a,i=!1){ha(this,"status");ha(this,"statusText");ha(this,"data");ha(this,"error");ha(this,"internal");this.status=e,this.statusText=t||"",this.internal=i,a instanceof Error?(this.data=a.toString(),this.error=a):this.data=a}};function boe(e){return e!=null&&typeof e.status=="number"&&typeof e.statusText=="string"&&typeof e.internal=="boolean"&&"data"in e}function voe(e){return Rr(e.map(t=>t.route.path).filter(Boolean))||"/"}const qF=typeof window<"u"&&typeof window.document<"u"&&typeof window.document.createElement<"u";function $F(e,t){let a=e;if(typeof a!="string"||!vM.test(a))return{absoluteURL:void 0,isExternal:!1,to:a};let i=a,r=!1;if(qF)try{let o=new URL(window.location.href),l=PF.test(a)?new URL(Hre(a,o.protocol)):new URL(a),d=lc(l.pathname,t);l.origin===o.origin&&d!=null?a=d+l.search+l.hash:r=!0}catch{Do(!1,`<Link to="${a}"> contains an invalid URL which will probably break when clicked - please update to a valid URL path.`)}return{absoluteURL:i,isExternal:r,to:a}}/**
513 * react-router v8.3.0
514 *
515 * Copyright (c) Remix Software Inc.
516 *
517 * This source code is licensed under the MIT license found in the
518 * LICENSE.md file in the root directory of this source tree.
519 *
520 * @license MIT
521 */const FF=["POST","PUT","PATCH","DELETE"];new Set(FF);const xoe=["GET",...FF];new Set(xoe);const woe=["about:","blob:","chrome:","chrome-untrusted:","content:","data:","devtools:","file:","filesystem:","javascript:"];function koe(e){try{return woe.includes(new URL(e).protocol)}catch{return!1}}/**
522 * react-router v8.3.0
523 *
524 * Copyright (c) Remix Software Inc.
525 *
526 * This source code is licensed under the MIT license found in the
527 * LICENSE.md file in the root directory of this source tree.
528 *
529 * @license MIT
530 */const Kp=M.createContext(null);Kp.displayName="DataRouter";const Xw=M.createContext(null);Xw.displayName="DataRouterState";const GF=M.createContext(!1);function joe(){return M.useContext(GF)}const HF=M.createContext({isTransitioning:!1});HF.displayName="ViewTransition";const Soe=M.createContext(new Map);Soe.displayName="Fetchers";const Coe=M.createContext(null);Coe.displayName="Await";const rr=M.createContext(null);rr.displayName="Navigation";const ky=M.createContext(null);ky.displayName="Location";const Gr=M.createContext({outlet:null,matches:[],isDataRoute:!1});Gr.displayName="Route";const wM=M.createContext(null);wM.displayName="RouteError";/**
531 * react-router v8.3.0
532 *
533 * Copyright (c) Remix Software Inc.
534 *
535 * This source code is licensed under the MIT license found in the
536 * LICENSE.md file in the root directory of this source tree.
537 *
538 * @license MIT
539 */const YF="REACT_ROUTER_ERROR",Aoe="REDIRECT",Toe="ROUTE_ERROR_RESPONSE";function _oe(e){if(e.startsWith(`${YF}:${Aoe}:{`))try{let t=JSON.parse(e.slice(28));if(typeof t=="object"&&t&&typeof t.status=="number"&&typeof t.statusText=="string"&&typeof t.location=="string"&&typeof t.reloadDocument=="boolean"&&typeof t.replace=="boolean")return t}catch{}}function Moe(e){if(e.startsWith(`${YF}:${Toe}:{`))try{let t=JSON.parse(e.slice(40));if(typeof t=="object"&&t&&typeof t.status=="number"&&typeof t.statusText=="string")return new yoe(t.status,t.statusText,t.data)}catch{}}/**
540 * react-router v8.3.0
541 *
542 * Copyright (c) Remix Software Inc.
543 *
544 * This source code is licensed under the MIT license found in the
545 * LICENSE.md file in the root directory of this source tree.
546 *
547 * @license MIT
548 */function Eoe(e,{relative:t}={}){Xt(Xp(),"useHref() may be used only in the context of a <Router> component.");let{basename:a,navigator:i}=M.useContext(rr),{hash:r,pathname:o,search:l}=jy(e,{relative:t}),d=o;return a!=="/"&&(d=o==="/"?a:Rr([a,o])),i.createHref({pathname:d,search:l,hash:r})}function Xp(){return M.useContext(ky)!=null}function Kn(){return Xt(Xp(),"useLocation() may be used only in the context of a <Router> component."),M.useContext(ky).location}const VF="You should call navigate() in a React.useEffect(), not when your component is first rendered.";function gc(){let{isDataRoute:e}=M.useContext(Gr);return e?Foe():Roe()}function Roe(){Xt(Xp(),"useNavigate() may be used only in the context of a <Router> component.");let e=M.useContext(Kp),{basename:t,navigator:a}=M.useContext(rr),{matches:i}=M.useContext(Gr),{pathname:r}=Kn(),o=JSON.stringify(xM(i)),l=M.useRef(!1);return M.useLayoutEffect(()=>{l.current=!0}),M.useCallback((d,u={})=>

548{if(Do(l.current,VF),!l.current)return;if(typeof d=="number"){a.go(d);return}let h=Kw(d,JSON.parse(o),r,u.relative==="path");e==null&&t!=="/"&&(h.pathname=h.pathname==="/"?t:Rr([t,h.pathname])),(u.replace?a.replace:a.push)(h,u.state,u)},[t,a,o,r,e])}M.createContext(null);function Ta(){var t;let{matches:e}=M.useContext(Gr);return((t=e[e.length-1])==null?void 0:t.params)??{}}function jy(e,{relative:t}={}){let{matches:a}=M.useContext(Gr),{pathname:i}=Kn(),r=JSON.stringify(xM(a));return M.useMemo(()=>Kw(e,JSON.parse(r),i,t==="path"),[e,r,i,t])}function Noe(e,t){return KF(e,t)}function KF(e,t,a){var x;Xt(Xp(),"useRoutes() may be used only in the context of a <Router> component.");let{navigator:i}=M.useContext(rr),{matches:r}=M.useContext(Gr),o=r[r.length-1],l=o?o.params:{};o&&o.pathname;let d=o?o.pathnameBase:"/";o&&o.route;let u=Kn(),h;if(t){let k=typeof t=="string"?Vp(t):t;Xt(d==="/"||((x=k.pathname)==null?void 0:x.startsWith(d)),`When overriding the location using \`<Routes location>\` or \`useRoutes(routes, location)\`, the location pathname must begin with the portion of the URL pathname that was matched by all parent routes. 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548=i,x=f.route.loader&&!y.hasOwnProperty(f.route.id)&&(!b||b[f.route.id]===void 0);if(f.route.lazy||x){a.isStatic&&(l=!0),d>=0?r=r.slice(0,d+1):r=[r[0]];break}}}}let u=a==null?void 0:a.onError,h=i&&u?(m,f)=>{var y,b;u(m,{location:i.location,params:((b=(y=i.matches)==null?void 0:y[0])==null?void 0:b.params)??{},pattern:voe(i.matches),errorInfo:f})}:void 0;return r.reduceRight((m,f,y)=>{let b,x=!1,k=null,v=null;i&&(b=o&&f.route.id?o[f.route.id]:void 0,k=f.route.errorElement||Poe,l&&(d<0&&y===0?(Goe("route-fallback",!1,"No `HydrateFallback` element provided to render during initial hydration"),x=!0,v=null):d===y&&(x=!0,v=f.route.hydrateFallbackElement||null)));let j=t.concat(r.slice(0,y+1)),T=()=>{let A;return b?A=k:x?A=v:f.route.Component?A=M.createElement(f.route.Component,null):f.route.element?A=f.route.element:A=m,M.createElement(Woe,{match:f,routeContext:{outlet:m,matches:j,isDataRoute:i!=null},children:A})};return i&&(f.route.ErrorBoundary||f.route.errorElement||y===0)?M.createElement(Ioe,{location:i.location,revalidation:i.revalidation,component:k,error:b,children:T(),routeContext:{outlet:null,matches:j,isDataRoute:!0},onError:h}):T()},null)}function kM(e){return`${e} must be used within a data router.  See https://reactrouter.com/en/main/routers/picking-a-router.`}function Loe(e){let t=M.useContext(Kp);return Xt(t,kM(e)),t}function Uoe(e){let t=M.useContext(Xw);return Xt(t,kM(e)),t}function Boe(e){let t=M.useContext(Gr);return Xt(t,kM(e)),t}function jM(e){let t=Boe(e),a=t.matches[t.matches.length-1];return Xt(a.route.id,`${e} can only be used on routes that contain a unique "id"`),a.route.id}function qoe(){return jM("useRouteId")}function $oe(){var i;let e=M.useContext(wM),t=Uoe("useRouteError"),a=jM("useRouteError");return e!==void 0?e:(i=t.errors)==null?void 0:i[a]}function Foe(){let{router:e}=Loe("useNavigate"),t=jM("useNavigate"),a=M.useRef(!1);return M.useLayoutEffect(()=>{a.current=!0}),M.useCallback(async(i,r={})=>{Do(a.current,VF),a.current&&(typeof i=="number"?await e.navigate(i):await e.navigate(i,{fromRouteId:t,...r}))},[e,t])}const FO={};function Goe(e,t,a){FO[e]||(FO[e]=!0,Do(!1,a))}/**
549 * react-router v8.3.0
550 *
551 * Copyright (c) Remix Software Inc.
552 *
553 * This source code is licensed under the MIT license found in the
554 * LICENSE.md file in the root directory of this source tree.
555 *
556 * @license MIT
557 */M.memo(Hoe);function Hoe({routes:e,manifest:t,future:a,state:i,isStatic:r,onError:o}){return KF(e,void 0,{manifest:t,state:i,isStatic:r,onError:o})}function yt({to:e,replace:t,state:a,relative:i}){Xt(Xp(),"<Navigate> may be used only in the context of a <Router> component.");let{static:r}=M.useContext(rr);Do(!r,"<Navigate> must not be used on the initial render in a <StaticRouter>. This is a no-op, but you should modify your code so the <Navigate> is only ever rendered in response to some user interaction or state change.");let{matches:o}=M.useContext(Gr),{pathname:l}=Kn(),d=gc(),u=Kw(e,xM(o),l,i==="path"),h=JSON.stringify(u);return M.useEffect(()=>{d(JSON.parse(h),{replace:t,state:a,relative:i})},[d,h,i,t,a]),null}function we(e){Xt(!1,"A <Route> is only ever to be used as the child of <Routes> element, never rendered directly. Please wrap your <Route> in a <Routes>.")}function Yoe({basename:e="/",children:t=null,location:a,navigationType:i="POP",navigator:r,static:o=!1,useTransitions:l}){Xt(!Xp(),"You cannot render a <Router> inside another <Router>. You should never have more than one in your app.");let d=e.replace(/^\/*/,"/"),u=M.useMemo(()=>({basename:d,navigator:r,static:o,useTransitions:l,future:{}}),[d,r,o,l]);typeof a=="string"&&(a=Vp(a));let{pathname:h="/",search:m="",hash:f="",state:y=null,key:b="default",mask:x}=a,k=M.useMemo(()=>{let v=lc(h,d);return v==null?null:{location:{pathname:v,search:m,hash:f,state:y,key:b,mask:x},navigationType:i}},[d,h,m,f,y,b,i,x]);return Do(k!=null,`<Router basename="${d}"> is not able to match the URL "${h}${m}${f}" because it does not start with the basename, so the <Router> won't render anything.`),k==null?null:M.createElement(rr.Provider,{value:u},M.createElement(ky.Provider,{children:t,value:k}))}function Voe({children:e,location:t}){return Noe(S4(e),t)}function S4(e,t=[]){let a=[];

557return M.Children.forEach(e,(i,r)=>{if(!M.isValidElement(i))return;let o=[...t,r];if(i.type===M.Fragment){a.push.apply(a,S4(i.props.children,o));return}Xt(i.type===we,`[${typeof i.type=="string"?i.type:i.type.name}] is not a <Route> component. All component children of <Routes> must be a <Route> or <React.Fragment>`);let l=i.props;Xt(!l.index||!l.children,"An index route cannot have child routes.");let d={id:l.id||o.join("-"),caseSensitive:l.caseSensitive,element:l.element,Component:l.Component,index:l.index,path:l.path,middleware:l.middleware,loader:l.loader,action:l.action,hydrateFallbackElement:l.hydrateFallbackElement,HydrateFallback:l.HydrateFallback,errorElement:l.errorElement,ErrorBoundary:l.ErrorBoundary,shouldRevalidate:l.shouldRevalidate,handle:l.handle,lazy:l.lazy};l.children&&(d.children=S4(l.children,o)),a.push(d)}),a}/**
558 * react-router v8.3.0
559 *
560 * Copyright (c) Remix Software Inc.
561 *
562 * This source code is licensed under the MIT license found in the
563 * LICENSE.md file in the root directory of this source tree.
564 *
565 * @license MIT
566 */const w0="application/x-www-form-urlencoded";function Qw(e){return typeof HTMLElement<"u"&&e instanceof HTMLElement}function Koe(e){return Qw(e)&&e.tagName.toLowerCase()==="button"}function Xoe(e){return Qw(e)&&e.tagName.toLowerCase()==="form"}function Qoe(e){return Qw(e)&&e.tagName.toLowerCase()==="input"}function Zoe(e){return!!(e.metaKey||e.altKey||e.ctrlKey||e.shiftKey)}function Joe(e,t){return e.button===0&&(!t||t==="_self")&&!Zoe(e)}function C4(e=""){return new URLSearchParams(typeof e=="string"||Array.isArray(e)||e instanceof URLSearchParams?e:Object.keys(e).reduce((t,a)=>{let i=e[a];return t.concat(Array.isArray(i)?i.map(r=>[a,r]):[[a,i]])},[]))}function ese(e,t){let a=C4(e);return t&&t.forEach((i,r)=>{a.has(r)||t.getAll(r).forEach(o=>{a.append(r,o)})}),a}let Ov=null;function tse(){if(Ov===null)try{new FormData(document.createElement("form"),0),Ov=!1}catch{Ov=!0}return Ov}const nse=new Set(["application/x-www-form-urlencoded","multipart/form-data","text/plain"]);function Nj(e){return e!=null&&!nse.has(e)?(Do(!1,`"${e}" is not a valid \`encType\` for \`<Form>\`/\`<fetcher.Form>\` and will default to "${w0}"`),null):e}function ase(e,t){let a,i,r,o,l;if(Xoe(e)){let d=e.getAttribute("action");i=d?lc(d,t):null,a=e.getAttribute("method")||"get",r=Nj(e.getAttribute("enctype"))||w0,o=new FormData(e)}else if(Koe(e)||Qoe(e)&&(e.type==="submit"||e.type==="image")){let d=e.form;if(d==null)throw new Error('Cannot submit a <button> or <input type="submit"> without a <form>');let u=e.getAttribute("formaction")||d.getAttribute("action");if(i=u?lc(u,t):null,a=e.getAttribute("formmethod")||d.getAttribute("method")||"get",r=Nj(e.getAttribute("formenctype"))||Nj(d.getAttribute("enctype"))||w0,o=new FormData(d,e),!tse()){let{name:h,type:m,value:f}=e;if(m==="image"){let y=h?`${h}.`:"";o.append(`${y}x`,"0"),o.append(`${y}y`,"0")}else h&&o.append(h,f)}}else{if(Qw(e))throw new Error('Cannot submit element that is not <form>, <button>, or <input type="submit|image">');a="get",i=null,r=w0,l=e}return o&&r==="text/plain"&&(l=o,o=void 0),{action:i,method:a.toLowerCase(),encType:r,formData:o,body:l}}/**
567 * react-router v8.3.0
568 *
569 * Copyright (c) Remix Software Inc.
570 *
571 * This source code is licensed under the MIT license found in the
572 * LICENSE.md file in the root directory of this source tree.
573 *
574 * @license MIT
575 */function SM(e,t){if(e===!1||e===null||typeof e>"u")throw new Error(t)}/**
576 * react-router v8.3.0
577 *
578 * Copyright (c) Remix Software Inc.
579 *
580 * This source code is licensed under the MIT license found in the
581 * LICENSE.md file in the root directory of this source tree.
582 *
583 * @license MIT
584 */function XF(e,t){let a=typeof e=="string"?new URL(e,typeof window>"u"?"server://singlefetch/":window.location.origin):e;return a.pathname.endsWith("/")?a.pathname=`${a.pathname}_.${t}`:a.pathname=`${a.pathname}.${t}`,a}/**
585 * react-router v8.3.0
586 *
587 * Copyright (c) Remix Software Inc.
588 *
589 * This source code is licensed under the MIT license found in the
590 * LICENSE.md file in the root directory of this source tree.
591 *
592 * @license MIT
593 */async function ise(e,t){if(e.id in t)return t[e.id];try{let a=await import(e.module);return t[e.id]=a,a}catch(a){return console.error(`Error loading route module \`${e.module}\`, reloading page...`),console.error(a),window.__reactRouterContext&&window.__reactRouterContext.

593isSpaMode,window.location.reload(),new Promise(()=>{})}}/**
594 * react-router v8.3.0
595 *
596 * Copyright (c) Remix Software Inc.
597 *
598 * This source code is licensed under the MIT license found in the
599 * LICENSE.md file in the root directory of this source tree.
600 *
601 * @license MIT
602 */function rse(e){return e==null?!1:e.href==null?e.rel==="preload"&&typeof e.imageSrcSet=="string"&&typeof e.imageSizes=="string":typeof e.rel=="string"&&typeof e.href=="string"}async function ose(e,t,a){return dse((await Promise.all(e.map(async i=>{let r=t.routes[i.route.id];if(r){let o=await ise(r,a);return o.links?o.links():[]}return[]}))).flat(1).filter(rse).filter(i=>i.rel==="stylesheet"||i.rel==="preload").map(i=>i.rel==="stylesheet"?{...i,rel:"prefetch",as:"style"}:{...i,rel:"prefetch"}))}function GO(e,t,a,i,r,o){let l=(u,h)=>a[h]?u.route.id!==a[h].route.id:!0,d=(u,h)=>{var m;return a[h].pathname!==u.pathname||((m=a[h].route.path)==null?void 0:m.endsWith("*"))&&a[h].params["*"]!==u.params["*"]};return o==="assets"?t.filter((u,h)=>l(u,h)||d(u,h)):o==="data"?t.filter((u,h)=>{var f;let m=i.routes[u.route.id];if(!m||!m.hasLoader)return!1;if(l(u,h)||d(u,h))return!0;if(u.route.shouldRevalidate){let y=u.route.shouldRevalidate({currentUrl:new URL(r.pathname+r.search+r.hash,window.origin),currentParams:((f=a[0])==null?void 0:f.params)||{},nextUrl:new URL(e,window.origin),nextParams:u.params,defaultShouldRevalidate:!0});if(typeof y=="boolean")return y}return!0}):[]}function sse(e,t,{includeHydrateFallback:a}={}){return lse(e.map(i=>{let r=t.routes[i.route.id];if(!r)return[];let o=[r.module];return r.clientActionModule&&(o=o.concat(r.clientActionModule)),r.clientLoaderModule&&(o=o.concat(r.clientLoaderModule)),a&&r.hydrateFallbackModule&&(o=o.concat(r.hydrateFallbackModule)),r.imports&&(o=o.concat(r.imports)),o}).flat(1))}function lse(e){return[...new Set(e)]}function cse(e){let t={},a=Object.keys(e).sort();for(let i of a)t[i]=e[i];return t}function dse(e,t){let a=new Set;return new Set(t),e.reduce((i,r)=>{let o=JSON.stringify(cse(r));return a.has(o)||(a.add(o),i.push({key:o,link:r})),i},[])}/**
603 * react-router v8.3.0
604 *
605 * Copyright (c) Remix Software Inc.
606 *
607 * This source code is licensed under the MIT license found in the
608 * LICENSE.md file in the root directory of this source tree.
609 *
610 * @license MIT
611 */function use(){let e=M.useContext(Kp);return SM(e,"You must render this element inside a <DataRouterContext.Provider> element"),e}function hse(){let e=M.useContext(Xw);return SM(e,"You must render this element inside a <DataRouterStateContext.Provider> element"),e}const CM=M.createContext(void 0);CM.displayName="FrameworkContext";function AM(){let e=M.useContext(CM);return SM(e,"You must render this element inside a <HydratedRouter> element"),e}function pse(e,t){let a=M.useContext(CM),[i,r]=M.useState(!1),[o,l]=M.useState(!1),{onFocus:d,onBlur:u,onMouseEnter:h,onMouseLeave:m,onTouchStart:f}=t,y=M.useRef(null);M.useEffect(()=>{if(e==="render"&&l(!0),e==="viewport"){let k=j=>{j.forEach(T=>{l(T.isIntersecting)})},v=new IntersectionObserver(k,{threshold:.5});return y.current&&v.observe(y.current),()=>{v.disconnect()}}},[e]),M.useEffect(()=>{if(i){let k=setTimeout(()=>{l(!0)},100);return()=>{clearTimeout(k)}}},[i]);let b=()=>{r(!0)},x=()=>{r(!1),l(!1)};return a?e!=="intent"?[o,y,{}]:[o,y,{onFocus:sf(d,b),onBlur:sf(u,x),onMouseEnter:sf(h,b),onMouseLeave:sf(m,x),onTouchStart:sf(f,b)}]:[!1,y,{}]}function sf(e,t){return a=>{e&&e(a),a.defaultPrevented||t(a)}}function mse({page:e,...t}){let a=joe(),{nonce:i}=AM(),{router:r}=use(),o=M.useMemo(()=>IF(r.routes,e,r.basename),[r.routes,e,r.basename]);return o?(t.nonce==null&&i&&(t={...t,nonce:i}),a?M.createElement(gse,{page:e,matches:o,...t}):M.createElement(yse,{page:e,matches:o,...t})):null}function fse(e){let{manifest:t,routeModules:a}=AM(),[i,r]=M.useState([]);return M.useEffect(()=>{let o=!1;return ose(e,t,a).then(l=>{o||r(l)}),()=>{o=!0}},[e,t,a]),i}function gse({page:e,matches:t,...a}){let i=Kn(),r=M.useMemo(()=>{if(e===i.pathname+i.search+i.hash)return[];let o=XF(e,"rsc"),l=!1,d=[];for(let u of t)typeof u.route.shouldRevalidate=="function"?l=!0:d.push(u.route.id);return l&&d.length>0&&o.searchParams.set("_routes",d.join(",")),[o.pathname+o.search]},[e,i,t]);return M.createElement(M.Fragment,null,r.map(o=>M.createElement("link",{key:o,rel:"prefetch",as:"fetch",href:o,...a}

611)))}function yse({page:e,matches:t,...a}){let i=Kn(),{manifest:r,routeModules:o}=AM(),{loaderData:l,matches:d}=hse(),u=M.useMemo(()=>GO(e,t,d,r,i,"data"),[e,t,d,r,i]),h=M.useMemo(()=>GO(e,t,d,r,i,"assets"),[e,t,d,r,i]),m=M.useMemo(()=>{if(e===i.pathname+i.search+i.hash)return[];let b=new Set,x=!1;if(t.forEach(v=>{var T;let j=r.routes[v.route.id];!j||!j.hasLoader||(!u.some(A=>A.route.id===v.route.id)&&v.route.id in l&&((T=o[v.route.id])!=null&&T.shouldRevalidate)||j.hasClientLoader?x=!0:b.add(v.route.id))}),b.size===0)return[];let k=XF(e,"data");return x&&b.size>0&&k.searchParams.set("_routes",t.filter(v=>b.has(v.route.id)).map(v=>v.route.id).join(",")),[k.pathname+k.search]},[l,i,r,u,t,e,o]),f=M.useMemo(()=>sse(h,r),[h,r]),y=fse(h);return M.createElement(M.Fragment,null,m.map(b=>M.createElement("link",{key:b,rel:"prefetch",as:"fetch",href:b,...a})),f.map(b=>M.createElement("link",{key:b,rel:"modulepreload",href:b,...a})),y.map(({key:b,link:x})=>M.createElement("link",{key:b,nonce:a.nonce,...x,crossOrigin:x.crossOrigin??a.crossOrigin})))}function bse(...e){return t=>{e.forEach(a=>{typeof a=="function"?a(t):a!=null&&(a.current=t)})}}/**
612 * react-router v8.3.0
613 *
614 * Copyright (c) Remix Software Inc.
615 *
616 * This source code is licensed under the MIT license found in the
617 * LICENSE.md file in the root directory of this source tree.
618 *
619 * @license MIT
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621
622Can you explain what Upside actually does, who it's for, and what I'd get out of it day to day? I'm especially interested in how it fits with the tools we already use (our CRM, email, and call recordings) and how it works with AI tools and agents.
623
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637   on /platform/mcp draw the same line or the design has drifted.
638
639   The color is pinned rather than inherited. It used to be currentColor, which
640   sounds like the tidy choice but meant the line took whatever green each
641   surface happened to set on its label — measured across the site that was
642   #2d6a4f on 11 routes and #509A79 on 6, which reads as two different-looking
643   underlines even though both measured 1.5px. Every CardCta sits inside
644   .pf-card (always white), so one pinned green is safe on all of them; if a
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651   sweep and then snaps thin when the layer is dropped. clip-path leaves the
652   element at its true size throughout, so the thickness is constant.
653   WHY 2px AND NOT 1.5px
654   The line's vertical position is the sum of everything stacked above it —
655   heroes, clamp() headings, SVG visuals — so it is fractional, and the
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658   A fractional position means the line straddles the device-pixel grid, and
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660   thicker line just loses proportionally less of its solid core:
661
662     1.5px = 3 device px @2x -> 3 solid rows aligned, 2 at half-phase
663     2px   = 4 device px @2x -> 4 solid rows aligned, 3 at half-phase
664
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696  .pf-grid.hero-wide { grid-template-columns: 1fr; }
697  .pf-grid.flip .pf-grid-copy { order: 1; } .pf-grid.flip .pf-grid-art { order: 2; }
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701.pf-hero-secondary { transition: border-color .15s ease, background .15s ease; }
702.pf-hero-secondary:hover { border-color: #b9c5be; background: #f7f9f8; }
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705.pf-callout { display: none; }
706.pf-blurbs { display: flex; flex-direction: column; gap: 0.85rem; margin-top: 1.1rem; }
707/* Green badge eyebrow — the canonical section/stage kicker (matches homepage .plat-badge). */
708.pf-badge, .pf-stage-label { display: inline-flex; align-items: center; font-family: Geist, sans-serif; font-weight: 600; font-size: 0.75rem; letter-spacing: 0.06em; text-transform: uppercase; color: #509A79; background: rgba(80,154,121,0.10); border-radius: 999px; padding: 0.42rem 0.78rem; margin-bottom: 1.1rem; }
709/* Icon lead-in blurbs (Figma stage pattern): small ink icon + bold heading line, muted body below. */
710.pf-iblurbs { display: flex; flex-direction: column; gap: 1.4rem; margin-top: 1.6rem; }
711.pf-iblurb-head { display: flex; align-items: center; gap: 0.55rem; margin: 0; }
712/* Green final-CTA band (mirrors the homepage .hcta 1:1, scoped to platform pages). */
713.pf-fcta { max-width: 1440px; margin: 0 auto; box-sizing: border-box; display: flex; flex-direction: column; align-items: center; gap: 40px; padding: 96px 72px; background: #fafafa; border-top: 1px solid #e6ebe7; border-bottom: 1px solid #e6ebe7; }
714.pf-fcta-avatar { width: 96px; height: 96px; border-radius: 24px; display: flex; align-items: center; justify-content: center; background: rgba(255,255,255,0.95); box-shadow: 0 0 0 1px rgba(0,0,0,0.075), 0 2px 2px rgba(0,0,0,0.04); }
715.pf-fcta-avatar img { display: block; }
716.pf-fcta-copy { display: flex; flex-direction: column; align-items: center; gap: 20px; }
717.pf-fcta-heading { margin: 0; max-width: 600px; text-align: center; color: #1b1f1c; font-size: 48px; line-height: 55.2px; font-weight: 500; letter-spacing: -1.92px; }
718.pf-fcta-sub { margin: 0; max-width: 640px; text-align: center; color: #4f5654; font-size: 20px; line-height: 30px; font-weight: 400; }
719.pf-fcta-actions { display: flex; gap: 20px; justify-content: center; flex-wrap: wrap; align-items: center; }
720.pf-fcta-btn { display: inline-flex; align-items: center; gap: 8px; border-radius: 999px; padding: 14px 20px; font-size: 16px; line-height: 20.8px; font-weight: 500; text-decoration: none; transition: background 0.15s ease, color 0.15s ease, border-color 0.15s ease; }
721.pf-fcta-btn--primary { background: #509a79; color: #fff; }
722.pf-fcta-btn--primary:hover { background: #427f64; }
723.pf-fcta-btn--secondary { background: #fff; color: #1b1f1c; border: 1.5px solid #d6ddd8; }
724.pf-fcta-btn--secondary:hover { background: #f2f5f3; }
725.pf-fcta-arrow { font-size: 15px; line-height: 1; }
726@media (max-width: 860px) {
727  .pf-fcta { padding: 56px 24px; gap: 32px; }
728  .pf-fcta-heading { font-size: 32px; line-height: 38px; letter-spacing: -1px; }
729  .pf-fcta-sub { font-size: 17px;

729 line-height: 26px; }
730}
731/* Visual captions were wireframe annotations (illustrative / sample / "Hooli demo
732   org" / "marker sketch") — agency directives, not page copy. Hidden site-wide. */
733.pf-cap { display: none; }
734/* Two-column FAQ (Figma): intro/header on the left, accordion on the right. */
735.pf-faq-split { display: grid; grid-template-columns: minmax(0, 0.92fr) minmax(0, 1.32fr); gap: clamp(2rem, 5vw, 4.5rem); align-items: start; }
736.pf-faq-split .pf-faq-list { margin-top: 0; }
737@media (max-width: 880px) { .pf-faq-split { grid-template-columns: 1fr; gap: 1.75rem; } }
738.pf-faq { border: 1px solid #e6ebe7; border-radius: 12px; background: #fff; transition: border-color .15s ease, box-shadow .15s ease; }
739.pf-faq:hover { border-color: #cdd8d1; }
740.pf-faq[open] { border-color: #cdd8d1; box-shadow: 0 4px 16px rgba(27,31,28,0.05); }
741.pf-faq > summary { display: flex; align-items: center; justify-content: space-between; gap: 1.25rem; cursor: pointer; list-style: none; padding: 1.15rem 1.35rem; }
742.pf-faq > summary::-webkit-details-marker { display: none; }
743.pf-faq-chev { flex-shrink: 0; color: #509A79; transition: transform .2s ease; }
744.pf-faq[open] .pf-faq-chev { transform: rotate(180deg); }
745.pf-faq-a { padding: 0 1.35rem 1.2rem; }
746/* Smoothly expand/collapse native <details> instead of jumping. interpolate-size
747   lets block-size animate to/from auto; allow-discrete fades content-visibility.
748   Browsers without ::details-content just toggle instantly (graceful). */
749:root { interpolate-size: allow-keywords; }
750.pf-faq::details-content {
751  block-size: 0;
752  overflow: clip;
753  opacity: 0;
754  transition: block-size .3s ease, opacity .2s ease, content-visibility .3s allow-discrete;
755}
756.pf-faq[open]::details-content { block-size: auto; opacity: 1; }
757
758/* Subdivided blurb lattice (Vercel-style): one bordered box split by hairlines,
759   not a scatter of separate cards. The inner grid is shifted -1px so each cell's
760   top/left borders form the internal dividers while the outer frame is the
761   container border (clipped by overflow:hidden) — tolerates partial last rows. */
762.pf-blurbgrid { margin-top: 1.5rem; border: 1px solid #e6ebe7; border-radius: 16px; overflow: hidden; background: #fff; }
763.pf-blurbgrid-inner { display: grid; grid-template-columns: repeat(var(--pf-bg-cols, 2), minmax(0, 1fr)); margin: -1px 0 0 -1px; }
764.pf-blurb { border-top: 1px solid #e6ebe7; border-left: 1px solid #e6ebe7; padding: 1.45rem 1.6rem; min-width: 0; }
765@media (max-width: 700px) {
766  .pf-blurbgrid-inner { grid-template-columns: 1fr; }
767  .pf-blurb-filler { display: none; }
768}
769/* Customer case-study cards (CaseCards). Logos ship in mixed colors (some are
770   white wordmarks built for dark backgrounds), so normalize them all to a
771   uniform dark ink that reaches full strength on hover.
772   Hover lift/shadow/arrow now come from the shared .pf-card model above; only
773   the resting shadow and the logo treatment are specific to these cards. The
774   resting shadow lives here, not inline, so the hover shadow can override it. */
775.cs-card { box-shadow: 0 1px 2px rgba(27,31,28,0.04); }
776.cs-logo { filter: brightness(0); opacity: .68; transition: opacity .2s ease; }
777.cs-card:hover .cs-logo { opacity: 1; }
778@media (prefers-reduced-motion: reduce) { .cs-logo { transition: none; } }
779/* Six-item card grids lay out 2x3 (not 4 + a sparse second row), collapsing
780   responsively. Applied by CardRow/SpineCards when there are exactly 6 cards. */
781.pf-cards-3 { grid-template-columns: repeat(3, minmax(0, 1fr)); }
782@media (max-width: 900px) { .pf-cards-3 { grid-template-columns: repeat(2, minmax(0, 1fr)); } }
783@media (max-width: 560px) { .pf-cards-3 { grid-template-columns: 1fr; } }
784/* "Boxed" layout (Vercel/Attio): content sits in a centered column with hairline
785   side rails + bottom divider on a uniform canvas. */
786.pf-boxed { position: relative; background: #fff; border-left: 1px solid #e6ebe7; border-right: 1px solid #e6ebe7; border-bottom: 1px solid #e6ebe7; }
787`;function Wo({seo:e,children:t,navVariant:a="light"}){return n.jsxs(Qe,{navVariant:a,children:[n.jsx(Je,{title:e.title,description:e.description,canonicalUrl:e.canonical,ogImage:e.ogImage}),n.jsx("style",{children:fG}),t]})}function Ce({children:e,alt:t=!1,center:a=!1,artBg:i,id:r}){return n.jsx("section",{id:r,style:{background:se.bgSoft,...i?{"--pf-art

787-bg":i}:{}},children:n.jsx("div",{className:"pf-boxed",style:{maxWidth:"1440px",margin:"0 auto",padding:"clamp(3rem, 6vw, 4.75rem) clamp(1.25rem, 5vw, 4.5rem)",textAlign:a?"center":"left",background:t?se.bgSoft:"#fff"},children:e})})}function De({children:e,style:t}){return n.jsx("h2",{className:"it-title",style:{color:se.text,margin:"0 0 0.85rem 0",...t},children:e})}function We({children:e,style:t}){return n.jsx("p",{className:"it-lead",style:{color:se.muted,margin:0,maxWidth:"70ch",...t},children:e})}function gs({head:e,eyebrow:t,sub:a,visual:i,flip:r=!1,alt:o=!1,artBg:l,artFit:d=!1,children:u}){return n.jsxs(Ce,{alt:o,artBg:l,children:[n.jsxs("div",{className:`pf-grid${r?" flip":""}`,children:[n.jsxs("div",{className:"pf-grid-copy",style:{minWidth:0},children:[t&&n.jsx(Ie,{children:t}),n.jsx(De,{style:t?{margin:"0.9rem 0 0.85rem"}:void 0,children:e}),a&&n.jsx(We,{style:{marginTop:"0.9rem"},children:a})]}),n.jsx("div",{className:"pf-grid-art",style:{display:"flex",justifyContent:"center",minWidth:0},children:n.jsx("div",{className:`pf-art${d?" pf-art-fit":""}`,children:i})})]}),u]})}const aP={display:"inline-flex",alignItems:"center",gap:"0.4rem",color:se.green,fontWeight:600,textDecoration:"none",background:"none",border:"none",padding:0,margin:0,cursor:"pointer",font:"inherit",fontSize:"1.0625rem",lineHeight:1.2};function IM(e){return e.replace(/[\s→↓↗↘↙➜»]+$/u,"")}function Yh({label:e,href:t,onClick:a,onDark:i=!1}){const r=IM(e),o=i?{...aP,color:"#fff"}:aP,l=n.jsx("span",{"aria-hidden":"true",style:{fontWeight:700},children:"→"});if(a&&!t)return n.jsxs("button",{type:"button",style:o,onClick:a,children:[r," ",l]});const d=u=>{if(t&&t.startsWith("#")){const h=document.getElementById(t.slice(1));h&&(u.preventDefault(),h.scrollIntoView({behavior:"smooth"}),window.history.pushState(null,"",t))}};return n.jsxs("a",{href:t,style:o,onClick:d,children:[r," ",l]})}function Vs({eyebrow:e,headline:t,sub:a,secondary:i,extraCta:r,visual:o,wideVisual:l,breadcrumbRoot:d={label:"Platform",href:"/platform"}}){return n.jsx("section",{style:{marginTop:"-65px",padding:"clamp(8.5rem, 9vw, 9rem) clamp(1.25rem, 5vw, 4.5rem) clamp(3rem, 6vw, 5rem)",background:"#fff",borderBottom:`1px solid ${se.border}`},children:n.jsx("div",{style:{maxWidth:"1440px",margin:"0 auto"},children:n.jsxs("div",{className:`pf-grid${l?" hero-wide":""}`,children:[n.jsxs("div",{className:"pf-grid-copy",style:{minWidth:0},children:[n.jsxs("nav",{"aria-label":"Breadcrumb",style:{display:"flex",alignItems:"center",gap:"0.5rem",marginBottom:"1.15rem",fontFamily:"Geist, sans-serif",fontSize:"0.9rem",fontWeight:500},children:[n.jsx(J,{to:d.href,style:{color:se.muted,textDecoration:"none"},children:d.label}),n.jsx("span",{"aria-hidden":"true",style:{color:"#c1c9c4"},children:"/"}),n.jsx("span",{style:{color:se.muted},children:e})]}),n.jsx("h1",{className:"it-display",style:{color:se.text,margin:"0 0 1.1rem 0"},children:t}),n.jsx("p",{className:"it-lead",style:{color:se.muted,margin:"0 0 1.75rem 0",maxWidth:"60ch"},children:a}),n.jsxs("div",{style:{display:"flex",gap:"1rem",flexWrap:"wrap",alignItems:"center"},children:[n.jsx(J,{to:Wt,className:"cta cc-large w-button",children:"Request demo"}),i&&n.jsx(gG,{label:i.label,href:i.href}),r]})]}),n.jsx("div",{className:"pf-grid-art",style:{display:"flex",justifyContent:"center",minWidth:0},children:n.jsx("div",{className:"pf-art",children:o})})]})})})}function yc({src:e,alt:t}){return n.jsx("img",{src:e,alt:t,loading:"eager",style:{width:"100%",height:"auto",display:"block",borderRadius:"16px"}})}function gG({label:e,href:t}){const a=IM(e),i=r=>{if(t.startsWith("#")){const o=document.getElementById(t.slice(1));o&&(r.preventDefault(),o.scrollIntoView({behavior:"smooth"}),window.history.pushState(null,"",t))}};return n.jsxs("a",{href:t,onClick:i,className:"pf-hero-secondary",style:{display:"inline-flex",alignItems:"center",gap:"0.5rem",background:"#fff",color:se.text,border:"1.5px solid #d6ddd8",borderRadius:"999px",padding:"0.75rem 1.15rem",fontFamily:"Geist, sans-serif",fontSize:"1rem",fontWeight:500,textDecoration:"none",lineHeight:1.3},children:[a,n.jsx("span",{"aria-hidden":"true",style:{fontSize:"0.9rem"},children:"→"})]})}function kle({items:e}){return e.some(a=>a.icon)?n.jsx("div",{className:"pf-iblurbs",children:e.map((a,i)=>{const r=a.icon?ca(a.icon):null;return n.jsxs("div",{children:[n.jsxs("p",{className:"it-body pf-iblurb-head",style:{color:se.text},children:[r&&n.jsx(r,{size:20,strokeWidth:1.9,"aria-hidden":"true",style:{color:se.text,flexShrink:0}}),n.jsx("span",{className:"it-leadin",children:a.lead})]}),n.jsx("p",{className:"it-body",style:{color:se.muted,margin:"0.35rem 0 0 0"},children:a.rest})]},i)})}):n.jsx("div",{className:"pf-blurbs",children:e.map((a,i)=>n.jsxs("p",{className:"it-body",style:{color:se.text,margin:0},children:[n.jsx("span",{className:"it-leadin",children:a.lead})," ",n.jsx("span",{style:{color:se.muted},children:a.rest})]},i))})}function Ie({children:e}){return n.jsx("span",{className:"pf-badge",children:e})}function jle({pills:e}){return n.jsx("div",{className:"pf-pill-row",children:e.map(t=>n.jsx("span",{className:"pf-pill",children:t},t))})}function Sle({children:e}){return n.jsx("div",{className:"pf-callout",children:e})}function If({label:e,heading:t,lead:a,blurbs:i,pills:r,callout:o,visual:l,caption:d,flip:u=!1,alt:h=!1,artBg:m,id:f}){return n.jsx(Ce,{alt:h,artBg:m,id:f,children:n.jsxs("div",{className:`pf-grid${u?" flip":""}`,children:[n.jsxs("div",{className:"pf-grid-copy",style:{minWidth:0},children:[n.jsx("span",{className:"pf-stage-label",children:e}),n.jsx("h2",{className:"it-title-sm",style:{color:se.text,margin:"0 0 0.6rem 0"},children:t}),n.jsx("p",{className:"it-lead",style:{color:se.muted,margin:0,maxWidth:"52ch"},children:a}),n.jsx(kle,{items:i}),r&&n.jsx(jle,{pills:r}),o&&n.jsx(Sle,{children:o})]}),n.jsxs("div",{className:"pf-grid-art",style:{display:"flex",flexDirection:"column",minWidth:0},children:[n.jsx("div",{className:"pf-art",children:l}),d&&n.jsx("div",{className:"pf-cap",children:d})]})]})})}function Ws({cards:e}){const t=e.some(i=>i.visual),a=e.length===6;return n.jsx("div",{className:a?"pf-cards-3":void 0,style:{display:"grid",...a?{}:{gridTemplateColumns:"repeat(auto-fit, minmax(240px, 1fr))"},gap:"1rem",marginTop:"1.75rem",alignItems:"stretch"},children:e.map((i,r)=>{const o={display:"flex",flexDirection:"column",borderRadius:"16px",padding:"1.35rem 1.5rem"},l=n.jsxs(n.Fragment,{children:[i.logo&&n.jsx("img",{src:i.logo.src,alt:i.logo.alt,className:"cs-logo",style:{height:i.logo.height??28,width:"auto",maxWidth:"200px",display:"block",marginBottom:"0.9rem"}}),i.num&&n.jsx("div",{className:"it-label",style:{color:se.greenSoft,marginBottom:"0.5rem"},children:i.num}),n.jsx("div",{className:"it-card-title",style:{color:se.text,margin:"0 0 0.5rem 0"},children:i.title}),n.jsx("p",{className:"it-body-sm",style:{color:se.muted,margin:0},children:i.body}),t&&n.jsx("div",{style:{marginTop:"auto",paddingTop:"1.2rem"},children:i.visual})]});return i.href?n.jsxs(J,{to:i.href,className:"pf-card pf-card--nav pf-card__hit",style:{...o,textDecoration:"none"},children:[l,n.jsx("span",{style:{marginTop:"auto",paddingTop:"1.1rem",color:se.greenSoft,fontWeight:600,fontSize:"0.9rem"},children:n.jsx(Ds,{label:"Explore"})})]},r):n.jsx("div",{className:"pf-card",style:o,children:l},r)})})}function N4({label:e="COMING"}){return n.jsx("span",{style:{marginLeft:"0.5rem",fontFamily:"Geist, sans-serif",fontWeight:700,fontSize:"0.62rem",letterSpacing:"0.04em",color:"#a1560b",background:"#FEF3C7",borderRadius:"999px",padding:"0.1rem 0.5rem",verticalAlign:"middle"},children:e})}function Rn({items:e}){const t=e.length,a=t<=2?t:t%3===0?3:t%2===0?2:3,i=(a-t%a)%a;return n.jsx("div",{className:"pf-blurbgrid",children:n.jsxs("div",{className:"pf-blurbgrid-inner",style:{"--pf-bg-cols":a},children:[e.map((r,o)=>{const l=r.icon?ca(r.icon):null;return n.jsxs("div",{className:"pf-blurb",children:[l&&n.jsx(l,{size:20,strokeWidth:1.9,"aria-hidden":"true",style:{color:se.greenSoft,display:"block",marginBottom:"0.7rem"}}),n.jsxs("p",{className:"it-body",style:{color:se.text,margin:0},children:[n.jsx("span",{className:"it-leadin",children:r.lead}),r.coming&&n.jsx(N4,{})," ",n.jsx("span",{style:{color:se.muted},children:r.rest})]})]},o)}),Array.from({length:i}).map((r,o)=>n.jsx("div",{className:"pf-blurb pf-blurb-filler","aria-hidden":"true"},`filler-${o}`))]})})}function aa({visual:e,caption:t,artBg:a}

787){return n.jsxs("div",{style:{marginTop:"2rem",...a?{"--pf-art-bg":a}:{}},children:[n.jsx("div",{className:"pf-art",style:{maxWidth:"640px",margin:"0 auto"},children:e}),t&&n.jsx("div",{className:"pf-cap",style:{maxWidth:"640px",margin:"0.85rem auto 0",textAlign:"center"},children:t})]})}function Jw({headers:e,rows:t}){return n.jsxs("div",{style:{marginTop:"1.75rem",border:`1px solid ${se.border}`,borderRadius:"16px",overflow:"hidden",background:"#fff",boxShadow:"0 4px 16px rgba(27,31,28,0.05)"},children:[n.jsxs("div",{style:{display:"grid",gridTemplateColumns:"1fr 1fr"},children:[n.jsx("div",{className:"it-card-title",style:{color:se.text,padding:"1rem 1.25rem",background:"#fff",borderBottom:`1px solid ${se.border}`,borderRight:`1px solid ${se.border}`},children:e[0]}),n.jsx("div",{className:"it-card-title",style:{color:se.green,padding:"1rem 1.25rem",background:se.greenLight,borderBottom:`1px solid ${se.border}`},children:e[1]})]}),t.map(([a,i],r)=>n.jsxs("div",{style:{display:"grid",gridTemplateColumns:"1fr 1fr"},children:[n.jsxs("div",{className:"it-body-sm",style:{display:"flex",gap:"0.5rem",color:se.muted,padding:"0.9rem 1.25rem",borderBottom:r===t.length-1?"none":`1px solid ${se.border}`,borderRight:`1px solid ${se.border}`},children:[n.jsx("span",{style:{color:"#a33",fontWeight:700,flexShrink:0},children:"✕"}),n.jsx("span",{children:a})]}),n.jsxs("div",{className:"it-body-sm",style:{display:"flex",gap:"0.5rem",color:se.text,padding:"0.9rem 1.25rem",borderBottom:r===t.length-1?"none":`1px solid ${se.border}`},children:[n.jsx("span",{style:{color:se.green,fontWeight:700,flexShrink:0},children:"✓"}),n.jsx("span",{children:i})]})]},r))]})}function Cle({children:e,style:t}){return n.jsx("div",{style:{marginTop:"1.5rem",background:se.bgSoft,border:`1px solid ${se.border}`,borderRadius:"16px",padding:"1.35rem 1.5rem",...t},children:e})}const zs={graphite:{src:"/images/graphite_logo_upside_customer.svg",alt:"Graphite is an Upside Customer",height:26},cresta:{src:"/images/cresta_logo_upside_customer.svg",alt:"Cresta is an Upside Customer",height:26},assembled:{src:"/images/assembled_logo_upside_customer.svg",alt:"Assembled is an Upside Customer",height:26},syndio:{src:"/images/syndio_logo_upside_customer.svg",alt:"Syndio is an Upside Customer",height:28},dscout:{src:"/images/dscout_logo_upside_customer.svg",alt:"dscout is an Upside Customer",height:27},comply:{src:"/images/comply-logo.png",alt:"Comply",height:52},servicetitan:{src:"/images/servicetitan_logo_upside_customer.png",alt:"ServiceTitan is an Upside Customer",height:34}};function yG({children:e,logo:t}){return n.jsxs("div",{style:{marginTop:"1.75rem",maxWidth:"640px",border:`1px solid ${se.border}`,borderRadius:"12px",padding:"1.35rem 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838h_attribution-sucks-the-darn-sales-rep-wont-activity-7422327955277783040-DUEs",ss="https://www.linkedin.com/posts/dahmadi_activity-7477773014265196544",lP="https://www.linkedin.com/posts/madalina_activity-7477772390165323776",Iv="https://www.linkedin.com/posts/alexdbauer_activity-7477771580954267648",cP={x:[],g2:[{text:"Upside provides one of the clearest 360-degree views of our marketing activities and their impact.",name:"Carl Gunlefinger",role:"Senior Marketing Operations Manager",company:"Comply",image:"/images/mentions/g2/carl-g.jpg"},{text:"The MCP lets me drill into the data — or zoom back out — in almost any way I can imagine.",name:"Carl Gunlefinger",role:"Senior Marketing Operations Manager",company:"Comply",image:"/images/mentions/g2/carl-g.jpg"},{text:"It finally gives me the ability to build the kinds of views I've wanted for years but never had the infrastructure to create.",name:"Carl Gunlefinger",role:"Senior Marketing Operations Manager",company:"Comply",image:"/images/mentions/g2/carl-g.jpg"},{text:"Upside has become an integral part of our team's workflow.",name:"Carl Gunlefinger",role:"Senior Marketing Operations Manager",company:"Comply",image:"/images/mentions/g2/carl-g.jpg"},{text:"The missing number that changed how we think about paid media: influenced pipeline.",name:"Cameron Tomczak",role:"Marketing Campaign Specialist",company:"Dscout",image:"/images/mentions/g2/cameron-t.jpg"},{text:"Upside gave us that missing number: influenced pipeline — actual opportunities where paid media showed up in the buying journey.",name:"Cameron Tomczak",role:"Marketing Campaign Specialist",company:"Dscout",image:"/images/mentions/g2/cameron-t.jpg"},{text:"We can now walk into a leadership meeting and show influenced pipeline broken down by account, campaign, and month.",name:"Cameron Tomczak",role:"Marketing Campaign Specialist",company:"Dscout",image:"/images/mentions/g2/cameron-t.jpg"},{text:"Reliable, accurate, 3-layers deep marketing attribution.",name:"Caitlin Maloney",role:"Chief Growth Officer",company:"Graphite",image:"/images/mentions/g2/caitlin-m.jpg"},{text:"Finally understanding the true ROI of our marketing campaigns across channels.",name:"Caitlin Maloney",role:"Chief Growth Officer",company:"Graphite",image:"/images/mentions/g2/caitlin-m.jpg"},{text:"Purpose-built for our GTM layer.",name:"Stephen Daniels",role:"VP, GTM & Strategic Operations",company:"Cresta",image:"/images/mentions/g2/stephen-d.webp"},{text:"The team handled everything — spun up an instance in just a day.",name:"Stephen Daniels",role:"VP, GTM & Strategic Operations",company:"Cresta",image:"/images/mentions/g2/stephen-d.webp"},{text:"Consultative, intelligent, and extremely accurate. Valuable for our company strategy.",name:"Stephen Daniels",role:"VP, GTM & Strategic Operations",company:"Cresta",image:"/images/mentions/g2/stephen-d.webp"},{text:"I use Upside to fill in messy data gaps and understand business trends.",name:"Stephen Daniels",role:"VP, GTM & Strategic Operations",company:"Cresta",image:"/images/mentions/g2/stephen-d.webp"},{text:"It helps with attribution — showing what generates leads.",name:"Stephen Daniels",role:"VP, GTM & Strategic Operations",company:"Cresta",image:"/images/mentions/g2/stephen-d.webp"},{text:"Fast setup, detailed attribution, flexible to slice data as needed.",name:"Kate Johnson",role:"CMO",company:"Dscout",image:"/images/mentions/g2/kate-j.jpg"},{text:"Other platforms have you spend months assigning rules, scores, and weights.",name:"Kate Johnson",role:"CMO",company:"Dscout",image:"/images/mentions/g2/kate-j.jpg"},{text:"Upside just ingested the data really quickly.",name:"Kate Johnson",role:"CMO",company:"Dscout",image:"/images/mentions/g2/kate-j.jpg"},{text:"Whenever there's something we wish the platform had, we tell Upside and they build it within a couple days.",name:"Kate Johnson",role:"CMO",company:"Dscout",image:"/images/mentions/g2/kate-j.jpg"},{text:"Connects to HubSpot, Salesforce, Gong, and Outreach.",name:"Kate Johnson",role:"CMO",company:"Dscout",image:"/images/mentions/g2/kate-j.jpg"},{text:"Complete picture all in one place of what marketing and sales are doing to create and close pipeline.",name:"Kate Johnson",role:"CMO",company:"Dscout",image:"/images/mentions/g2/kate-j.jpg"},{text:"Upside's integrations bring all of our data into one place.",name:"Mariah Olive",role:"Performance Marketing Manager",company:"Comply",image:"/images/mentions/g2/mariah-o.jpg"},{text:"A unique, customizable view across our entire stack.",name:"Mariah Olive",role:"Performance Marketing Manager",company:"Comply",image:"/images/mentions/g2/mariah-o.jpg"},{text:"The MCP, paired with Claude to create miniapps around specific tactics, is a game changer for reporting.",name:"Mariah Olive",role:"Performance Marketing Manager",company:"Comply",image:"/images/mentions/g2/mariah-o.jpg"},{text:"The app performs well at surfacing specific campaigns and showing exactly how members are engaging.",name:"Mariah Olive",role:"Performance Marketing Manager",company:"Comply",image:"/images/mentions/g2/mariah-o.jpg"},{text:"AI insights help diagnose what has gone wrong — or right — across the campaigns we're running.",name:"Mariah Olive",role:"Performance Marketing Manager",company:"Comply",image:"/images/mentions/g2/mariah-o.jpg"},{text:"The team is always willing to dig in and add to their own product roadmap.",name:"Mariah Olive",role:"Performance Marketing Manager",company:"Comply",image:"/images/mentions/g2/mariah-o.jpg"},{text:"Understanding the full funnel of our leads, from initial touchpoint all the way to closed won or lost.",name:"Nicolas Yahni",role:"Senior Customer Success Manager",company:"Graphite",image:"/images/mentions/g2/nicolas-y.webp"},{text:"The MCP feature has helped my team find insights to themes we didn't even have in our scope.",name:"Nicolas Yahni",role:"Senior Customer Success Manager",company:"Graphite",image:"/images/mentions/g2/nicolas-y.webp"},{text:"It helps us see where we need to double down and what's working best.",name:"Nicolas Yahni",role:"Senior Customer Success Manager",company:"Graphite",image:"/images/mentions/g2/nicolas-y.webp"},{text:"The Upside team was great to work with through every step of setup.",name:"Nicolas Yahni",role:"Senior Customer Success Manager",company:"Graphite",image:"/images/mentions/g2/nicolas-y.webp"},{text:"Upside's flexibility — and its ability to stitch the buyer journey together accurately.",name:"Sam Zolnor",role:"Sr. Digital Marketing Manager",company:"Adaptive Planning",image:"/images/mentions/g2/sam-z.png"},{text:"A true view into a multi-touch experience — and the gaps in our processes.",name:"Sam Zolnor",role:"Sr. Digital Marketing Manager",company:"Adaptive Planning",image:"/images/mentions/g2/sam-z.png"},{text:"AI features that helped add richer insights to our buyer journey.",name:"Sam Zolnor",role:"Sr. Digital Marketing Manager",company:"Adaptive Planning",image:"/images/mentions/g2/sam-z.png"},{text:"The tool was great and the team was a pleasure to work with.",name:"Sam Zolnor",role:"Sr. Digital Marketing Manager",company:"Adaptive Planning",image:"/images/mentions/g2/sam-z.png"},{text:"The support the Upside team offers when we have queries on data points has been crucial.",name:"Mariah Olive",role:"Performance Marketing Manager",company:"Comply",image:"/images/mentions/g2/mariah-o.jpg"},{text:"The team digs in to diagnose and solve any issue we surface.",name:"Mariah Olive",role:"Performance Marketing Manager",company:"Comply",image:"/images/mentions/g2/mariah-o.jpg"},{text:"Upside makes the un-attributable attributable.",name:"Darshan Advani",role:"Sr. Enterprise Growth Marketing",company:"Exa",image:"/images/mentions/g2/darshan-a.jpg"},{text:"It connects everything from call intelligence to our campaigns and marketing channels in one place, so we can query information with ease.",name:"Darshan Advani",role:"Sr. Enterprise Growth Marketing",company:"Exa",image:"/images/mentions/g2/darshan-a.jpg"},{text:"You can connect LinkedIn ad engagements directly to closed-won pipeline — or even connect billboard mentions in calls to the campaign you deployed.",name:"Darshan Advani",role:"Sr. Enterprise Growth Marketing",company:"Exa",image:"/images/mentions/g2/darshan-a.jpg"},{text:"Every time I meet with the Upside team, something really creative comes out of it.",n

838ame:"Darshan Advani",role:"Sr. Enterprise Growth Marketing",company:"Exa",image:"/images/mentions/g2/darshan-a.jpg"},{text:"It really makes me feel like the opportunity for complete attribution can exist.",name:"Darshan Advani",role:"Sr. Enterprise Growth Marketing",company:"Exa",image:"/images/mentions/g2/darshan-a.jpg"},{text:"Effortless multi-touch attribution across all our tools.",name:"Cassandra Stumer",role:"Product Marketing",company:"Assembled"},{text:"Easiest way to build a dashboard (miniapp) ever.",name:"Cassandra Stumer",role:"Product Marketing",company:"Assembled"},{text:"Their team and customer support are phenomenal — they always go above and beyond.",name:"Cassandra Stumer",role:"Product Marketing",company:"Assembled"},{text:"Easily spinning up custom reports with natural language makes showing the value of marketing easy.",name:"Cassandra Stumer",role:"Product Marketing",company:"Assembled"},{text:"We were fully integrated with all our data populated in just two to three days — compared to a previous competitor that took over a year to set up.",name:"Lisa Cole",role:"Chief Marketing & AI Officer",company:"2X",image:"/images/mentions/case/lisa-cole.jpg"},"Anything we've had notes for has been ticketed and quickly updated in the platform.","Updates land in a way that aligns to the best experience of all their customers.","Unique insights I haven't been able to get from any other tool.","Goes beyond multi-touch attribution — it uncovers what actually influenced the outcome.","Some of the best teachers and minds in this space. We learn from them every time we meet.","A trusted data source with immediate, easily filterable results.","Their product expands and improves at a rapid pace.","Upside lets us easily build custom apps on top of their data and get answers in real time.","Visibility into massive data — without hiring another human to solve big data problems.","Helps me get a much better idea of what our enterprise deal motion looks like.","We use Claude to access GTM data integrated with Salesforce, Hubspot, and Gong.","We can quickly and accurately pull attribution reporting for our programs, campaigns, and channels.","Helps uncover what actually influenced an outcome — whether they were referred or had been a user at a previous company.","We're able to build miniapps with LLMs that benefit our sales teams and leaders.","Using Upside through Claude makes it so much easier to get answers conversationally — without spinning up a data request every time."],linkedin:[{text:"We have been diving in and geeking out with Upside!",name:"Kaycee McAdams",role:"Sr. Director",company:"Bandwidth",image:"/images/mentions/linkedin/kaycee-mcadams.jpg",href:"https://www.linkedin.com/feed/update/urn:li:activity:7481412709360525312/"},{text:"Upside dashboards have created such valuable, unbiased, and actionable conversations among our GTM teams.",name:"Kaycee McAdams",role:"Sr. Director",company:"Bandwidth",image:"/images/mentions/linkedin/kaycee-mcadams.jpg",href:"https://www.linkedin.com/feed/update/urn:li:activity:7481412709360525312/"},{text:"Love what the team at Upside is doing with the AI data layer — the need for a semantic / context layer is rising fast.",name:"Andy Mowat",role:"GTM Operator & Advisor",company:"4x Unicorn Exec",image:"/images/mentions/linkedin/andy-mowat.jpg",href:"https://www.linkedin.com/feed/update/urn:li:activity:7481412709360525312/"},{text:"Huge fan of Upside — great products and even better people.",name:"Chris Perpignani",role:"Enterprise Growth & Sales Development",company:"Exa",image:"/images/mentions/linkedin/chris-perpignani.jpg",href:"https://www.linkedin.com/feed/update/urn:li:activity:7481412709360525312/"},{text:"This was something I've never seen before.",name:"Lindsey Marymont",role:"Head of Demand Generation",company:"Assembled",image:"/images/mentions/linkedin/lindsey-marymont.jpg"},{text:"Excited to be working with the team.",name:"Lindsey Marymont",role:"Head of Demand Generation",company:"Assembled",image:"/images/mentions/linkedin/lindsey-marymont.jpg"},{text:"The value we've seen already is huge. Can't wait for more.",name:"Lindsey Marymont",role:"Head of Demand Generation",company:"Assembled",image:"/images/mentions/linkedin/lindsey-marymont.jpg"},{text:"Give Mada and Dan at Upside a follow if you don't already.",name:"Kate Johnson",role:"CMO",company:"Dscout",image:"/images/mentions/linkedin/kate-johnson.jpg"},{text:"They are actually doing the transformative stuff.",name:"Kate Johnson",role:"CMO",company:"Dscout",image:"/images/mentions/linkedin/kate-johnson.jpg"},{text:"We were fortunate to start working with Upside as a design partner early on. Blown away at what they've built.",name:"Wendy Werve",role:"Chief Market Officer",company:"Comply",image:"/images/mentions/linkedin/wendy-werve.png"},{text:"Their team has been the BEST partners. The platform just keeps getting better — definitely game changing for us.",name:"Wendy Werve",role:"Chief Market Officer",company:"Comply",image:"/images/mentions/linkedin/wendy-werve.png"},{text:"Love Upside for visibility into the prospect/customer lifecycle.",name:"Sam Zolnor",role:"Sr. Digital Marketing Manager",company:"Workday",image:"/images/mentions/linkedin/sam-zolnor.png"},{text:"It pulls all of your data across platforms — including your CRM — and turns it into actionable information.",name:"Sam Zolnor",role:"Sr. Digital Marketing Manager",company:"Workday",image:"/images/mentions/linkedin/sam-zolnor.png"},{text:"Their AI-powered deep research tool helped me surface so much to my broader team — beyond just what marketing programs were working.",name:"Sam Zolnor",role:"Sr. Digital Marketing Manager",company:"Workday",image:"/images/mentions/linkedin/sam-zolnor.png"},{text:"The whole Upside team couldn't be nicer. Such supportive partners and wonderful to work with.",name:"Sam Zolnor",role:"Sr. Digital Marketing Manager",company:"Workday",image:"/images/mentions/linkedin/sam-zolnor.png"}

838,{text:"Upside is pretty impressive.",name:"Kacie Jenkins",role:"Marketing Leader",company:"Former SVP Marketing",image:"/images/mentions/linkedin/kacie-jenkins.jpg"},{text:"It can take over a huge chunk of the time most marketing leaders spend digging through the CRM doing dot-connecting detective work.",name:"Kacie Jenkins",role:"Marketing Leader",company:"Former SVP Marketing",image:"/images/mentions/linkedin/kacie-jenkins.jpg"},{text:"You'd love the way Mada and Alex think.",name:"Kacie Jenkins",role:"Marketing Leader",company:"Former SVP Marketing",image:"/images/mentions/linkedin/kacie-jenkins.jpg"},{text:"This is incredible.",name:"Jason Schleweis",role:"Senior Director, Marketing Technology & Automation",company:"Commvault",image:"/images/mentions/linkedin/jason-s.png"},{text:"This is probably the most impressive product demo I've ever seen.",name:"Jason Schleweis",role:"Senior Director, Marketing Technology & Automation",company:"Commvault",image:"/images/mentions/linkedin/jason-s.png"},{text:"That stat about AI discovery leading to higher win rates is huge.",name:"James Nolle",role:"GTM Leader",company:"",image:"/images/mentions/linkedin/james-nolle.jpg"},{text:"Lots of talk about GTMEs these days — but these folks are doing it best.",name:"Camille Ricketts",role:"Storyteller, Marketer, Investor",company:"",image:"/images/mentions/linkedin/camille-ricketts.jpg",href:"https://www.linkedin.com/posts/madalina_activity-7468461846069624832"},{text:"They're quickly becoming the data layer for GTME.",name:"Camille Ricketts",role:"Storyteller, Marketer, Investor",company:"",image:"/images/mentions/linkedin/camille-ricketts.jpg",href:"https://www.linkedin.com/posts/madalina_activity-7468461846069624832"},{text:"Jaw dropping micro-site — some of the best marketing, in service to the ecosystem, I've seen this year.",name:"Camille Ricketts",role:"Storyteller, Marketer, Investor",company:"",image:"/images/mentions/linkedin/camille-ricketts.jpg",href:"https://www.linkedin.com/posts/madalina_activity-7468461846069624832"},{text:"Having the best time with the Upside MCP in Claude.",name:"Balqis Osman",role:"Head of Marketing Operations, Corporate Marketing",company:"2X",image:"/images/mentions/linkedin/balqis-osman.jpg",href:"https://www.linkedin.com/posts/dahmadi_activity-7474983285874642945"},{text:"The Upside team is the best!",name:"Tina Staroselsky",role:"Vice President, Revenue Marketing",company:"Ironclad",image:"/images/mentions/linkedin/tina-staroselsky.jpg",href:ro},{text:"I'm a BIG fan of Mada Seghete and Alex Bauer and what they're building at Upside!",name:"Brandon Redlinger",role:"Fractional VP Marketing",company:"",image:"/images/mentions/linkedin/brandon-redlinger.jpg",href:ro},{text:"Big fan of the team over at Upside!!",name:"Drew Smith",role:"Founder",company:"Attributa",image:"/images/mentions/linkedin/drew-smith.jpg",href:ro},{text:"Mada and the Upside team are amazing!",name:"Felicia Curcuru",role:"Co-founder & CEO",company:"Binti",image:"/images/mentions/linkedin/felicia-curcuru.jpg",href:ro},{text:"I love Mada and her team!!",name:"Tracy Lawrence",role:"Founder Coach",company:"",image:"/images/mentions/linkedin/tracy-lawrence.jpg",href:ro},{text:"Very excited about what Upside is building.",name:"Peter Bregman",role:"Vice President, Marketing & Sales Development",company:"Uber Freight",image:"/images/mentions/linkedin/peter-bregman.jpg",href:ro},{text:"Love this — such a clear explanation of something complex I've been trying to articulate for a while.",name:"Lauren Alt Kishpaugh",role:"Interim VP, Revenue Marketing",company:"Pendo",image:"/images/mentions/linkedin/lauren-alt-kishpaugh.jpg",href:ro},{text:"Love the problem space and Upside's approach.",name:"Andre Vanier",role:"Head of Orchestration Strategy",company:"Zapier",image:"/images/mentions/linkedin/andre-vanier.jpg",href:ro},{text:"AI has turned the attribution game Upside down — pun unintended — and Upside is a pioneer that rocks!",name:"Nalini Mohan",role:"Founding Marketer",company:"",image:"/images/mentions/linkedin/nalini-mohan.jpg",href:ro},{text:"Incredible.",name:"Dave Fontenot",role:"HF0 Residency",company:"",image:"/images/mentions/linkedin/dave-fontenot.jpg",href:ro},{text:"Get a demo of this product ASAP. It will blow your mind.",name:"Wendy Werve",role:"Chief Market Officer",company:"Comply",image:"/images/mentions/linkedin/wendy-werve.png",href:ss},{text:"We started with a goal of better attribution, but Upside went well beyond — the granularity and visibility into our prospect and customer journeys is incredible.",name:"Wendy Werve",role:"Chief Market Officer",company:"Comply",image:"/images/mentions/linkedin/wendy-werve.png",href:ss},{text:"Upside is nothing short of amazing! It powers incredible insights and views into our data in minutes that previously would have taken weeks or months to build.",name:"Carl Gunlefinger",role:"Senior Marketing Operations Manager",company:"Comply",image:"/images/mentions/g2/carl-g.jpg",href:ss},{text:"I've loved getting the keys to the proverbial Upside car.",name:"Zach Kunkel",role:"Product Marketing Leader",company:"Bandwidth",image:"/images/mentions/linkedin/zach-kunkel.jpg",href:ss},{text:"We used to start every question by asking how much we could really trust the data. Not the case with Upside anymore.",name:"Zach Kunkel",role:"Product Marketing Leader",company:"Bandwidth",image:"/images/mentions/linkedin/zach-kunkel.jpg",href:ss},{text:"Our team are big fans of Upside — it's been pivotal in unlocking our data for us.",name:"Balqis Osman",role:"Head of Marketing Operations, Corporate Marketing",company:"2X",image:"/images/mentions/linkedin/balqis-osman.jpg",href:ss},{text:"We got value from Upside so quickly.",name:"Kate Johnson",role:"CMO",company:"Dscout",image:"/images/mentions/linkedin/kate-johnson.jpg",href:lP},{text:"Dan and the Upside team are building something genuinely revolutionary for attribution.",name:"Darshan Advani",role:"Sr. Enterprise Growth Marketing",company:"Exa",image:"/images/mentions/linkedin/darshan-advani.jpg",href:ss},{text:"They're doing legendary work at Upside. Definitely check them out.",name:"Darshan Advani",role:"Sr. Enterprise Growth Marketing",company:"Exa",image:"/images/mentions/linkedin/darshan-advani.jpg",href:ss},{text:"The Upside team figured out the hard piece — before AI can drive GTM workflows, the data underneath has to be usable.",name:"Chris H.",role:"Revenue Technology",company:"Cresta",image:"/images/mentions/linkedin/chris-h.jpg",href:ss},{text:"Fan of Upside and the rise of growth engineers.",name:"Rish Gupta",role:"Co-founder & CEO",company:"Spot AI",image:"/images/mentions/linkedin/rish-gupta.jpg",href:lP},{text:"What you're building is amazing and unlike anything else in the market. I've not been this excited about a GTM product or team in a very long time.",name:"Wendy Werve",role:"Chief Market Officer",company:"Springbrook Software",image:"/images/mentions/linkedin/wendy-werve.png",href:Iv},{text:"This is absolutely what the market and every CMO needs.",name:"Wendy Werve",role:"Chief Market Officer",company:"Springbrook Software",image:"/images/mentions/linkedin/wendy-werve.png",href:Iv},{text:"You need a clean data layer to make agents useful at scale. I feel like every team out there needs Upside.",name:"Brandon Redlinger",role:"Fractional VP of Marketing",company:"B2B SaaS + AI",image:"/images/mentions/linkedin/brandon-redlinger.jpg",href:Iv},{text:'Everyone is saying "clean up your data" to prepare for AI, but the details on how are pretty thin. 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I finally understood what was possible.",name:"Matt Grebow",role:"Director of Enterprise Marketing",company:"ServiceTitan",image:"/images/mentions/case/matt-grebow.png",href:"https://www.youtube.com/watch?v=uz8KsgeFi1k&t=501s"}

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862        .ent-sub { margin: 0 0 20px 0; font-size: 18px; line-height: 27px; font-weight: 400; color: rgba(255,255,255,0.6); }
863        .ent-cta { display: inline-flex; align-items: center; gap: 8px; padding: 12px 16px; border: 1px solid rgba(255,255,255,0.3); border-radius: 999px; font-size: 14px; font-weight: 500; line-height: 1; color: #fff; text-decoration: none; }
864        .ent-cta img { display: block; }
865        .ent-grid { display: grid; gap: 1px; background: rgba(255,255,255,0.1); border-top: 1px solid rgba(255,255,255,0.1); }
866        .ent-grid--3 { grid-template-columns: 72px 1fr 1fr 1fr 72px; }
867        .ent-grid--4 { grid-template-columns: 72px 1fr 1fr 1fr 1fr 72px; }
868        .ent-spacer { background: #101010; }
869        .ent-cell { background: #101010; }
870        .ent-cell--seal { display: flex; flex-direction: column; gap: 24px; padding: 48px 40px; }
871        .ent-cell--ctrl { display: flex; flex-direction: column; gap: 8px; padding: 40px; }
872        .ent-cell--empty { min-height: 72px; }
873        .ent-seal { display: block; width: 68px; height: 68px; }
874        .ent-cell__body { display: flex; flex-direction: column; gap: 8px; }
875        .ent-cell__title--lg { font-size: 20px; line-height: 30px; font-weight: 500; color: #fff; }
876        .ent-cell__blurb--lg { margin: 0; font-size: 16px; line-height: 24px; font-weight: 400; color: rgba(255,255,255,0.6); max-width: 270px; }
877        .ent-ctrl__title-wrap { display: flex; flex-direction: column; gap: 16px; }
878        .ent-ic { display: block; width: 24px; height: 24px; }
879        .ent-cell__title-row { display: flex; align-items: center; gap: 12px; }
880        .ent-cell__title { font-size: 16px; line-height: 24px; font-weight: 500; color: #fff; }
881        .ent-cell__blurb { margin: 0; font-size: 14px; line-height: 21px; font-weight: 400; color: rgba(255,255,255,0.6); max-width: 220px; }
882        .ent-soon { font-size: 10px; font-weight: 600; letter-spacing: 0.4px; text-transform: uppercase; color: rgba(255,255,255,0.85); background: rgba(255,255,255,0.1); border: 1px solid rgba(255,255,255,0.18); border-radius: 6px; padding: 4px 8px; line-height: 1; white-space: nowrap; }
883        /* ── Onboarding (1:1 Figma node 2751:45316) ── */
884        .onb-band { background: linear-gradient(180deg, rgba(255,255,255,0.9), rgba(255,255,255,0.7)); border-top: 1px solid rgba(12,30,32,0.1); border-bottom: 1px solid rgba(12,30,32,0.1); padding: 72px; }
885        .onb-inner { max-width: 1296px; margin: 0 auto; display: flex; flex-direction: column; gap: 72px; align-items: center; }
886        .onb-head { display: flex; flex-direction: column; align-items: center; gap: 16px; text-align: center; }
887        .onb-badge { font-size: 12px; font-weight: 600; line-height: 12px; letter-spacing: 0.6px; text-transform: uppercase; color: #509a79; background: rgba(80,154,121,0.1); border-radius: 999px; padding: 8px 12px; }
888        .onb-heading { margin: 0; font-size: 40px; line-height: 44px; font-weight: 500; letter-spacing: -1.2px; color: #0c1e20; }
889        .onb-sub { margin: 0; font-size: 18px; line-height: 27px; font-weight: 400; color: rgba(12,30,32,0.6); max-width: 700px; }
890        .onb-flow { width: 100%; display: flex; flex-direction: column; gap: 64px; }
891        .onb-timeline { padding: 0 64px; }
892        .onb-track { position: relative; display: flex; align-items: center; padding-top: 40px; }
893        .onb-node { position: relative; flex: 0 0 14px; width: 14px; height: 14px; }
894        .onb-dot { width: 14px; height: 14px; border-radius: 999px; box-sizing: border-box; }
895        .onb-dot--hollow { border: 1.5px solid rgba(12,30,32,0.2); background: #fafafa; }
896        .onb-dot--fill { background: #509a79; border: 1.5px solid #509a79; }
897        .onb-line { flex: 1 1 auto; height: 1px; background: rgba(12,30,32,0.2); }
898        .onb-cap { position: absolute; bottom: calc(100% + 12px); left: 50%; transform: translateX(-50%); white-space: nowrap; font-size: 16px; line-height: 16px; font-weight: 500; border-radius: 8px; padding: 8px 10px; }
899        .onb-cap--solid { border: 1px solid rgba(12,30,32,0.2); color: #0c1e20; background: #fafafa; }
900        .onb-cap--dash { border: 1px dashed rgba(12,30,32,0.2); color: #0c1e20; background: #fafafa; }
901        .onb-cap--green { color: #509a79; background: rgba(80,154,121,0.1); }
902        .onb-grid { display: grid; grid-template-columns: repeat(3, 1fr); gap: 24px; }
903        .onb-card { border-radius: 24px; padding: 32px; display: flex; flex-direction: column; gap: 24px; }
904        .onb-card--plain { background: #fff; box-shadow: 0 1px 2px rgba(12,30,32,0.04), 0 8px 24px rgba(12,30,32,0.04); }
905        .onb-card--dash { background: #fff; border: 1px dashed rgba(12,30,32,0.2); }
906        .onb-card--green { background: #509a79; }
907        .onb-card__icon { width: 28px; height: 28px; display: block; }
908        .onb-card__title { font-size: 20px; line-height: 21px; font-weight: 500; color: #0c1e20; }
909        .onb-card--green .onb-card__title { color: #fff; }
910        .onb-list { display: flex; flex-direction: column; gap: 12px; }
911        .onb-item { display: flex; align-items: flex-start; gap: 16px; }
912        .onb-item > span { font-size: 16px; line-height: 24px; color: rgba(12,30,32,0.7); }
913        .onb-card--green .onb-item > span { color: #fff; }
914        .onb-check { flex: 0 0 16px; width: 16px; height: 16px; margin-top: 4px; border-radius: 999px; display: inline-flex; align-items: center; justify-content: center; background: #509a79; }
915        .onb-card--green .onb-check { background: #fff; }
916        .onb-cta { display: inline-flex; align-items: center; gap: 12px; background: #509a79; color: #fff; border-radius: 999px; padding: 14px 20px; font-size: 16px; line-height: 21px; font-weight: 500; text-decoration: none; }
917        .onb-cta:hover { background: #1f5238; }
918
919        @media (max-width: 860px) {
920          .home-theme-grid { grid-template-columns: 1fr; }
921          .home-theme-copy { min-height: 0; }
922          .home-theme-copy .pl-block { margin-top: 0.5rem; }
923          .home-theme-grid.flip .home-theme-copy { order: 1; }
924          .home-theme-grid.flip .home-theme-art { order: 2; }
925          .onb-band { padding: 40px 24px; }
926          .onb-inner { gap: 48px; }
927          .onb-heading { font-size: 30px; line-height: 36px; }
928          .onb-timeline { padding: 0; }
929          .onb-grid { grid-template-columns: 1fr; }
930          .ent-head { flex-direction: column; align-items: flex-start; gap: 32px; padding: 40px 24px; }
931          .ent-head__right { flex: 1 1 auto; max-width: none; }
932          .ent-grid--3, .ent-grid--4 { grid-template-columns: 1fr 1fr; }
933          .ent-grid--3 .ent-spacer, .ent-grid--4 .ent-spacer { display: none; }
934          .ent-cell--seal, .ent-cell--ctrl { padding: 32px 24px; }
935          .ent-grid--empty { display: none; }
936        }
937        @media (max-width: 560px) {
938          .ent-grid--3, .ent-grid--4 { grid-template-columns: 1fr; }
939        }
940
941        /* ── Miniapp examples carousel ── */
942        .mac { max-width: 960px; margin: 0 auto; }
943        /* Vertical padding gives the card's drop shadow + hover lift room to render
944           (overflow:hidden clips at the padding box); horizontal padding stays 0 so
945           off-screen slides are clipped hard at the edge with no shadow sliver. The
946           hover shadow below is sized to fit within this padding so nothing clips. */
947        .mac-track { overflow: hidden; border-radius: 16px;

947 padding: 18px 0 30px; }
948        .mac-slides { display: flex; transition: transform .42s cubic-bezier(.22,.61,.36,1); }
949        .mac-slide { flex: 0 0 100%; min-width: 0; text-decoration: none; color: inherit; }
950        .mac-card { display: grid; grid-template-columns: 1fr 1.12fr; align-items: stretch; background: #fff; border: 1px solid ${Ae.border}; border-radius: 16px; overflow: hidden; box-shadow: 0 2px 12px rgba(12,30,32,0.06); transition: box-shadow .18s ease, transform .18s ease; }
951        .mac-card:hover { box-shadow: 0 8px 22px rgba(12,30,32,0.14); transform: translateY(-2px); }
952        /* Left cell is a soft category-tinted stage (bg set inline per slide); the
953           floating thumbnail (outer bg stripped) sits on it at native 16:10. */
954        .mac-thumb { display: flex; align-items: center; justify-content: center; padding: clamp(20px, 2.8vw, 34px); }
955        .mac-thumb img { width: 100%; max-width: 360px; height: auto; aspect-ratio: 16 / 10; object-fit: contain; display: block; }
956        .mac-body { padding: clamp(18px, 2.4vw, 28px); display: flex; flex-direction: column; justify-content: center; }
957        .mac-chip { align-self: flex-start; font-family: ${Fc}; font-weight: 700; font-size: 0.62rem; letter-spacing: 0.05em; text-transform: uppercase; border-radius: 999px; padding: 4px 10px; margin-bottom: 0.7rem; }
958        .mac-title { font-family: ${Fc}; font-weight: 600; font-size: clamp(1.06rem, 1.9vw, 1.3rem); line-height: 1.18; letter-spacing: -0.01em; color: ${Ae.text}; margin: 0 0 0.55rem; text-wrap: balance; }
959        .mac-desc { color: ${Ae.muted}; font-size: 0.84rem; line-height: 1.5; margin: 0 0 0.8rem; }
960        .mac-tags { display: flex; flex-wrap: wrap; gap: 6px; }
961        .mac-tag { font-size: 0.69rem; font-weight: 500; color: ${Ae.muted}; background: ${Ae.bgSoft}; border: 1px solid ${Ae.border}; border-radius: 999px; padding: 3px 9px; }
962        .mac-open { margin-top: 0.9rem; font-family: ${Fc}; font-weight: 600; font-size: 0.84rem; color: ${Ae.green}; display: inline-flex; align-items: center; gap: 6px; transition: gap .16s ease; }
963        .mac-card:hover .mac-open { gap: 10px; }
964        .mac-all { display: flex; flex-direction: column; align-items: center; justify-content: center; text-align: center; gap: 0.75rem; min-height: 100%; background: linear-gradient(160deg, ${Ae.greenLight}, #fff); border: 1px solid ${Ae.border}; border-radius: 16px; padding: 2.4rem 1.6rem; }
965        .mac-all-big { font-family: ${Fc}; font-weight: 700; font-size: clamp(1.6rem, 4vw, 2.25rem); line-height: 1; color: ${Ae.green}; letter-spacing: -0.02em; }
966        .mac-all-h { font-family: ${Fc}; font-weight: 600; font-size: 1.25rem; color: ${Ae.text}; margin: 0; }
967        .mac-all-p { color: ${Ae.muted}; font-size: 0.84rem; max-width: 46ch; margin: 0; }
968        .mac-btn { margin-top: 0.4rem; font-family: ${Fc}; font-weight: 600; font-size: 0.875rem; color: #fff; background: ${Ae.green}; border: none; border-radius: 999px; padding: 0.7rem 1.4rem; cursor: pointer; text-decoration: none; display: inline-flex; gap: 8px; transition: background .16s ease, transform .16s ease; }
969        .mac-btn:hover { background: ${Ae.greenHover}; transform: translateY(-2px); }
970        /* Single control row under the card */
971        .mac-ctl { display: flex; align-items: center; justify-content: center; gap: 0.9rem; margin-top: 0.6rem; }
972        .mac-arrow { width: 36px; height: 36px; border-radius: 999px; cursor: pointer; background: #fff; border: 1px solid ${Ae.border}; box-shadow: 0 2px 12px rgba(12,30,32,0.06); color: ${Ae.text}; display: inline-flex; align-items: center; justify-content: center; font-size: 17px; transition: transform .16s ease, box-shadow .16s ease, opacity .16s ease; }
973        .mac-arrow:hover { box-shadow: 0 14px 36px rgba(12,30,32,0.13); transform: scale(1.06); }
974        .mac-arrow:focus-visible { outline: 3px solid ${Ae.greenSoft}; outline-offset: 2px; }
975        .mac-arrow:disabled { opacity: 0.35; cursor: default; box-shadow: none; }
976        .mac-arrow:disabled:hover { transform: none; }
977        .mac-dots { display: flex; align-items: center; gap: 8px; }
978        .mac-dot { width: 7px; height: 7px; border-radius: 999px; border: none; padding: 0; cursor: pointer; background: ${Ae.border}; transition: width .2s ease, background .2s ease; }
979        .mac-dot.active { width: 24px; background: ${Ae.green}; }
980        .mac-dot.all.active { background: ${Ae.greenSoft}; }
981        .mac-dot:focus-visible { outline: 2px solid ${Ae.greenSoft}; outline-offset: 3px; }
982        .mac-seeall { font-family: ${Fc}; font-weight: 600; font-size: 0.82rem; color: ${Ae.green}; text-decoration: none; white-space: nowrap; margin-left: 0.4rem; }
983        .mac-seeall:hover { color: ${Ae.greenHover}; }
984        @media (max-width: 720px) {
985          .mac-card { grid-template-columns: 1fr; }
986          .mac-thumb img { max-width: 460px; margin: 0 auto; }
987          .mac-ctl { flex-wrap: wrap; }
988        }
989        @media (prefers-reduced-motion: reduce) {

990          .mac-slides { transition: none; }
991          .mac-card, .mac-arrow, .mac-btn, .mac-open { transition: none; }
992        }
993      `}),n.jsx("section",{className:"hero",style:{marginTop:"-65px"},children:n.jsxs("div",{className:"hero__container",style:{position:"relative",overflow:"hidden"},children:[n.jsx(Ole,{}),n.jsxs("div",{style:{position:"relative",zIndex:2,maxWidth:"var(--_layout---max-widths--l)",marginLeft:"auto",marginRight:"auto",width:"100%"},children:[n.jsx("div",{style:{display:"flex",justifyContent:"center",marginTop:"-0.9rem",marginBottom:"2.625rem"},children:n.jsxs(J,{to:"/gtme",className:"gtme-pill",style:{display:"inline-flex",alignItems:"center",gap:"0.6rem",textDecoration:"none",fontSize:"0.95rem",fontWeight:500,lineHeight:1,color:"var(--_neutral-colors---white--100)",background:"var(--_neutral-colors---white--10)",border:"1px solid var(--_neutral-colors---white--20)",borderRadius:"999px",padding:"0.5rem 0.5rem 0.5rem 1.1rem",backdropFilter:"blur(4px)",WebkitBackdropFilter:"blur(4px)"},children:[n.jsxs("span",{children:["The GTM Engineer Community",n.jsx("span",{className:"gtme-pill-extra",children:": jobs, events, peers"})]}),n.jsx("span",{"aria-hidden":"true",style:{display:"inline-flex",alignItems:"center",justifyContent:"center",background:"var(--_neutral-colors---white--20)",borderRadius:"999px",padding:"0.22rem 0.6rem",fontSize:"0.95rem",lineHeight:1},children:"→"})]})}),n.jsx("div",{className:"hero__content",style:{position:"relative",zIndex:2,flexDirection:"column",justifyContent:"center",textAlign:"center"},children:n.jsxs("div",{className:"hero__header",style:{alignItems:"center",minWidth:0,width:"100%",maxWidth:"780px",marginLeft:"auto",marginRight:"auto",textAlign:"center"},children:[n.jsxs("div",{className:"hero__header-content",style:{alignItems:"center"},children:[n.jsx("h1",{className:"heading-xxl",children:"The data layer for the age of agentic GTM."}),n.jsx("div",{className:"hero__header-paragraph-wrapper",style:{maxWidth:"560px",marginLeft:"auto",marginRight:"auto"},children:n.jsx("p",{className:"body-l",children:"Upside reconstructs what actually happened in every account, then makes that record available to your team, your AI agents, and the apps you ship."})})]}),n.jsxs("div",{className:"hero__buttons",style:{justifyContent:"center"},children:[n.jsx(J,{to:Wt,className:"cta cc-large w-button",children:"Request demo"}),n.jsx(J,{to:"/platform",className:"cta cc-hero-secondary cc-large w-inline-block",children:"Explore the platform →"})]})]})})]}),n.jsxs("div",{className:"hero__social-proof",style:{position:"relative",zIndex:2},children:[n.jsx("div",{className:"hero__social-proof-copy",children:n.jsx("p",{className:"body-m",children:"For go-to-market teams with large, complex deals"})}),n.jsx("div",{className:"hero__client-logos",children:n.jsx("div",{className:"hero__client-logos-track",children:[...$j,...$j].map((e,t)=>{const a=t>=$j.length;return n.jsxs("div",{className:"hero__client-logo-wrapper",children:[n.jsx("div",{className:"hero__client-logo-slot",children:n.jsx("img",{src:e.src,loading:"lazy",alt:a?"":Ple(e.name),"aria-hidden":a||void 0,className:"hero__client-logo",style:{"--hero-logo-h":`${e.height}px`,"--hero-logo-nudge":`${e.nudge??0}px`}})}),e.href?n.jsx(J,{to:e.href,className:"hero__case-study-link body-xs","aria-hidden":a||void 0,tabIndex:a?-1:0,children:"Case study →"}):n.jsx("span",{className:"hero__case-study-link hero__case-study-link--placeholder body-xs","aria-hidden":"true",children:"Case study →"})]},`${e.name}-${t}`)})})})]})]})}),n.jsx("div",{style:{background:Ae.bgSoft},children:n.jsxs("div",{className:"pf-boxed",style:{maxWidth:"1440px",margin:"0 auto",background:"transparent"},children:[n.jsx("section",{style:{padding:"clamp(3rem, 6vw, 4.5rem) clamp(1.25rem, 5vw, 4rem)",background:Ae.bgSoft,borderBottom:`1px solid ${Ae.border}`},children:n.jsxs("div",{style:{maxWidth:"1296px",margin:"0 auto"},children:[n.jsxs("div",{style:{textAlign:"center",marginBottom:"2rem"},children:[n.jsx(P4,{children:"Why now"}),n.jsx("h2",{className:"it-title",style:{margin:"0 0 0.85rem 0",color:Ae.text},children:"Get your GTM data ready for AI."}),n.jsx("p",{className:"it-lead",style:{color:Ae.muted,maxWidth:"720px",margin:"0 auto"},children:"Point AI at your CRM today and it answers with total confidence over data that was never complete, missing the moments that actually moved each deal."})]}),n.jsx("img",{src:"/images/home/why-now.png",alt:"A reconstructed account timeline: logged CRM events plus touchpoints recovered from email and calls, with inferred influence",loading:"lazy",style:{width:"100%",height:"auto",display:"block"}})]})}),uce.map((e,t)=>n.jsx(xce,{theme:e,index:t},e.heading)),n.jsx("section",{style:{padding:"clamp(3rem, 6vw, 4.5rem) clamp(1.25rem, 5vw, 4rem)",background:"#fff",borderBottom:`1px solid ${Ae.border}`},children:n.jsxs("div",{style:{maxWidth:"1200px",margin:"0 auto"},children:[n.jsxs("div",{style:{textAlign:"center",marginBottom:"2rem"},children:[n.jsx(P4,{children:"Built on Upside"}),n.jsx("h2",{className:"it-title",style:{margin:"0 0 0.85rem 0",color:Ae.text},children:"See what teams build on Upside"}),n.jsx("p",{className:"it-lead",style:{color:Ae.muted,maxWidth:"640px",margin:"0 auto"}

993,children:"Examples of real miniapps our customers built on their own go-to-market data."})]}),n.jsx(wce,{})]})}),n.jsxs("section",{className:"ent-band",style:{background:"#101010",color:"#fafafa",borderTop:`1px solid ${Ae.border}`,borderBottom:`1px solid ${Ae.border}`},children:[n.jsxs("div",{className:"ent-head",children:[n.jsxs("div",{className:"ent-head__left",children:[n.jsx("span",{className:"ent-eyebrow",children:"Enterprise-Ready"}),n.jsx("h2",{className:"ent-heading",children:"Built to clear your security review."})]}),n.jsxs("div",{className:"ent-head__right",children:[n.jsx("p",{className:"ent-sub",children:"Everything the security review will ask about, answered up front. Upside works with your most sensitive GTM data, so the controls your security team will ask about are already in place."}),n.jsxs(J,{to:"/security",className:"ent-cta",children:["Review security ",n.jsx("img",{src:"/images/home/enterprise/ic-arrow.svg",alt:"",width:18,height:18,"aria-hidden":"true"})]})]})]}),n.jsxs("div",{className:"ent-grid ent-grid--3",children:[n.jsx("div",{className:"ent-spacer","aria-hidden":"true"}),pce.map(e=>n.jsxs("div",{className:"ent-cell ent-cell--seal",children:[n.jsx("img",{className:"ent-seal",src:e.icon,alt:"",width:68,height:68,"aria-hidden":"true"}),n.jsxs("div",{className:"ent-cell__body",children:[n.jsx("div",{className:"ent-cell__title ent-cell__title--lg",children:e.label}),n.jsx("p",{className:"ent-cell__blurb ent-cell__blurb--lg",children:e.blurb})]})]},e.label)),n.jsx("div",{className:"ent-spacer","aria-hidden":"true"})]}),n.jsxs("div",{className:"ent-grid ent-grid--4",children:[n.jsx("div",{className:"ent-spacer","aria-hidden":"true"}),mce.map(e=>n.jsxs("div",{className:"ent-cell ent-cell--ctrl",children:[n.jsxs("div",{className:"ent-ctrl__title-wrap",children:[n.jsx("img",{className:"ent-ic",src:e.icon,alt:"",width:24,height:24,"aria-hidden":"true"}),n.jsxs("div",{className:"ent-cell__title-row",children:[n.jsx("span",{className:"ent-cell__title",children:e.label}),e.comingSoon&&n.jsx("span",{className:"ent-soon",children:"Coming Soon"})]})]}),n.jsx("p",{className:"ent-cell__blurb",children:e.blurb})]},e.label)),n.jsx("div",{className:"ent-spacer","aria-hidden":"true"})]}),n.jsxs("div",{className:"ent-grid ent-grid--4 ent-grid--empty","aria-hidden":"true",children:[n.jsx("div",{className:"ent-spacer"}),n.jsx("div",{className:"ent-cell ent-cell--empty"}),n.jsx("div",{className:"ent-cell ent-cell--empty"}),n.jsx("div",{className:"ent-cell ent-cell--empty"}),n.jsx("div",{className:"ent-cell ent-cell--empty"}),n.jsx("div",{className:"ent-spacer"})]})]}),n.jsx("div",{className:"onb-band",children:n.jsxs("div",{className:"onb-inner",children:[n.jsxs("div",{className:"onb-head",children:[n.jsx("span",{className:"onb-badge",children:"Onboarding"}),n.jsx("h2",{className:"onb-heading",children:"Live in a day, not a quarter."}),n.jsx("p",{className:"onb-sub",children:"No setup project, no data cleanup. 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996          .plat-badge {
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1000            border-radius: 999px; padding: 8px 12px;
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1002          .plat-head { display: flex; justify-content: space-between; align-items: flex-end; gap: 32px; }
1003          .plat-heading { margin: 0; font-size: 40px; line-height: 44px; font-weight: 500; letter-spacing: -1.2px; color: #0c1e20; }
1004          .plat-sub { margin: 0; max-width: 420px; text-align: right; font-size: 18px; line-height: 27px; color: rgba(12,30,32,0.6); }
1005          .plat-group { margin-top: 48px; }
1006          .plat-label { display: block; margin-bottom: 16px; font-size: 12px; font-weight: 600; letter-spacing: 0.6px; text-transform: uppercase; color: rgba(12,30,32,0.5); }
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1023          .plat-card__title { font-size: 17px; line-height: 22px; font-weight: 500; color: #0c1e20; }
1024          .plat-card__blurb { font-size: 14px; line-height: 21px; color: rgba(12,30,32,0.6); }
1025          .plat-card__more { margin-top: 6px; font-size: 14px; font-weight: 500; color: #509a79; display: inline-flex; align-items: center; gap: 6px; }
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1029             half pixel shifts the entire "By use case" group below it onto a
1030             half-pixel offset — and its card underlines then rasterize
1031             differently from the identical ones above, which is exactly the
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1033             element added to the end of a .plat-group. */
1034          .plat-more-btn {
1035            display: inline-flex; align-items: center; gap: 8px; margin-top: 24px;
1036            border: 1px solid ${Ae.border}; border-radius: 999px; padding: 12px 18px;
1037            font-size: 15px; line-height: 22px; font-weight: 500; color: #0c1e20; background: #fff; text-decoration: none;
1038            transition: border-color 0.15s ease;
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1040          .plat-more-btn:hover { border-color: rgba(12,30,32,0.25); }
1041          @media (max-width: 860px) {
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1043            .plat-heading { font-size: 30px; line-height: 36px; }
1044            .plat-sub { text-align: left; }
1045            .plat-grid { grid-template-columns: 1fr; }
1046          }
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1051            display: flex;
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1053            align-items: center;
1054            gap: 40px;
1055            padding: 96px 72px;
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1060            height: 96px;
1061            border-radius: 24px;
1062            display: flex;
1063            align-items: center;
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1065            background: rgba(255,255,255,0.95);
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1072            align-items: center;
1073            gap: 20px;
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1075          .hcta-heading {
1076            margin: 0;
1077            max-width: 600px;
1078            text-align: center;
1079            color: #fff;
1080            font-size: 48px;
1081            line-height: 55.2px;
1082            font-weight: 500;
1083            letter-spacing: -1.92px;
1084          }
1085          .hcta-sub {
1086            margin: 0;
1087            max-width: 640px;
1088            text-align: center;
1089            color: rgba(255,255,255,0.6);
1090            font-size: 20px;
1091            line-height: 30px;
1092            font-weight: 400;
1093          }
1094          .hcta-actions {
1095            display: flex;
1096            gap: 20px;
1097            justify-content: center;
1098            flex-wrap: wrap;
1099          }
1100          .hcta-btn {
1101            display: inline-flex;
1102            align-items: center;
1103            gap: 8px;
1104            border-radius: 999px;
1105            padding: 14px 20px;
1106            font-size: 16px;
1107            line-height: 20.8px;
1108            font-weight: 500;
1109            text-decoration: none;
1110            transition: background 0.15s ease, color 0.15s ease, border-color 0.15s ease;
1111          }
1112          .hcta-btn--primary { background: #fff; color: #509a79; }
1113          .hcta-btn--primary:hover { background: rgba(255,255,255,0.9); }
1114          .hcta-btn--secondary { background: transparent; color: #fff; border: 1.75px solid #fff; }
1115          .hcta-btn--secondary:hover { background: rgba(255,255,255,0.12); }
1116          .hcta-btn__arrow { font-size: 15px; line-height: 1; }
1117          @media (max-width: 860px) {
1118            .hcta { padding: 56px 24px; gap: 32px; }
1119            .hcta-heading { font-size: 32px; line-height: 38px; letter-spacing: -1px; }
1120            .hcta-sub { font-size: 17px;

1120 line-height: 26px; }
1121          }
1122        `})]})]}),P4=({children:e})=>n.jsx("span",{className:"it-eyebrow",style:{display:"inline-flex",alignItems:"center",alignSelf:"flex-start",width:"auto",fontSize:"0.75rem",letterSpacing:"0.06em",color:Ae.greenSoft,borderRadius:"999px",padding:"0.42rem 0.78rem",marginBottom:"1rem",background:"rgba(80,154,121,0.10)"},children:e}),vce=({label:e,surfaces:t})=>n.jsxs("div",{className:"pl-block",children:[n.jsx("span",{className:"it-label",style:{display:"block",color:"#8a93a0",marginBottom:"0.85rem"},children:e}),t.map(a=>{const i=ca(a.iconName),r=!a.comingSoon&&!!a.href,o=n.jsxs("div",{style:{display:"flex",gap:"0.9rem",alignItems:"flex-start",padding:"0.95rem 0",borderTop:`1px solid ${Ae.border}`},children:[n.jsx("span",{style:{flex:"0 0 30px",height:30,borderRadius:"8px",background:a.comingSoon?"#f1f3f1":Ae.greenLight,display:"inline-flex",alignItems:"center",justifyContent:"center",color:a.comingSoon?"#9aa0a6":Ae.green,opacity:a.comingSoon?.7:1},children:i&&n.jsx(i,{size:16,strokeWidth:1.9})}),n.jsxs("span",{style:{flex:1,minWidth:0,lineHeight:1.45},children:[n.jsxs("span",{className:"it-card-title",style:{display:"block",fontWeight:600,color:a.comingSoon?"#9aa0a6":Ae.text},children:[a.label,a.comingSoon&&n.jsx("span",{style:{marginLeft:"0.5rem",fontSize:"0.55rem",fontWeight:700,letterSpacing:"0.05em",textTransform:"uppercase",color:Ae.blue,background:"#eaf2fc",border:"1px solid #cfe0f7",borderRadius:"5px",padding:"0.05rem 0.4rem",verticalAlign:"middle"},children:"Coming Soon"})]}),n.jsx("span",{className:"it-body-sm",style:{display:"block",color:Ae.muted,marginTop:"0.25rem"},children:a.blurb})]}),r&&n.jsx("span",{className:"pl-arrow","aria-hidden":"true",style:{flex:"0 0 auto",alignSelf:"center",color:Ae.green,fontWeight:700,fontSize:"1.05rem",lineHeight:1},children:"→"})]});return r?n.jsx(J,{to:a.href,className:"pl-link",style:{textDecoration:"none",color:"inherit",display:"block"},children:o},a.label):n.jsx("div",{children:o},a.label)})]}),xce=({theme:e,index:t})=>n.jsx("section",{style:{padding:"clamp(4rem, 7vw, 6rem) clamp(1.25rem, 5vw, 4rem)",background:t%2===0?"#fff":Ae.bgSoft,borderBottom:`1px solid ${Ae.border}`},children:n.jsxs("div",{style:{maxWidth:"1296px",margin:"0 auto"},children:[n.jsxs("div",{className:`home-theme-grid${e.flip?" flip":""}`,children:[n.jsxs("div",{className:"home-theme-copy",style:{minWidth:0},children:[n.jsx(P4,{children:e.eyebrow}),n.jsx("h2",{className:"it-title-sm",style:{margin:"0 0 0.75rem 0",color:Ae.text},children:e.heading}),n.jsx("p",{className:"it-body",style:{color:Ae.muted,margin:"0 0 1.75rem 0",maxWidth:"420px"},children:e.body}),n.jsx(vce,{label:e.poweredLabel,surfaces:e.powered})]}),n.jsx("div",{className:"home-theme-art",style:{display:"flex",justifyContent:"center",minWidth:0},children:n.jsx("div",{className:"pf-art",children:n.jsx("div",{style:{background:t%2===0?Ae.bgSoft:"#fff",border:`1px solid ${Ae.border}`,borderRadius:"24px",boxShadow:"0 8px 30px rgba(27,31,28,0.05)",overflow:"hidden",width:"100%"},children:e.visual})})})]}),n.jsxs("figure",{style:{position:"relative",maxWidth:"720px",margin:"clamp(1.75rem, 3vw, 2.5rem) auto 0",background:t%2===0?Ae.bgSoft:"#fff",border:`1px solid ${Ae.border}`,borderRadius:"16px",padding:"1.4rem 1.6rem 1.5rem"},children:[n.jsx("span",{"aria-hidden":"true",style:{position:"absolute",top:"-0.6rem",left:"1.1rem",fontFamily:"Georgia, 'Times New Roman', serif",fontSize:"3rem",lineHeight:1,color:Ae.greenSoft,opacity:.32},children:"“"}),n.jsx("blockquote",{style:{position:"relative",margin:0,padding:0,border:"none",fontFamily:"Geist, sans-serif",fontWeight:500,fontSize:"1.0625rem",color:Ae.text,lineHeight:1.5},children:e.quote.text}),n.jsx("figcaption",{className:"it-quote-cite",style:{display:"block",color:Ae.muted,marginTop:"0.8rem",fontSize:"0.92rem"},children:e.quote.attribution})]})]})}),gP={Attribution:{tint:Ae.blueSoft,ink:Ae.blue},Sales:{tint:Ae.greenLight,ink:Ae.green},Marketing:{tint:Ae.cream,ink:"#a5701c"}},wce=()=>{const[e,t]=M.useState(0),a=hce.flatMap(l=>{const d=_M(l.slug);return d?[{app:l,ex:d}]:[]}),i=a.length,r=a.length+1,o=l=>t(Math.max(0,Math.min(r-1,l)));return n.jsxs("div",{className:"mac",children:[n.jsx("div",{className:"mac-track",children:n.jsxs("div",{className:"mac-slides",style:{transform:`translateX(-${e*100}%)`},children:[a.map(({app:l,ex:d},u)=>{const h=gP[d.category]??gP.Sales,m=u!==e;return n.jsx("a",{href:xa(l.slug),className:"mac-slide","aria-hidden":m||void 0,tabIndex:m?-1:void 0,children:n.jsxs("div",{className:"mac-card",children:[n.jsx("div",{className:"mac-thumb",style:{background:h.tint},children:n.jsx("img",{src:d.thumbnail.replace("thumbnail.svg","thumbnail-floating.svg"),alt:"",loading:"lazy",onError:f=>{const y=f.currentTarget;

1122y.src.includes("/thumbnail-floating.svg")&&(y.src=d.thumbnail)}})}),n.jsxs("div",{className:"mac-body",children:[n.jsx("span",{className:"mac-chip",style:{background:h.tint,color:h.ink},children:d.category}),n.jsx("h3",{className:"mac-title",children:d.title}),n.jsx("p",{className:"mac-desc",children:l.description}),n.jsx("div",{className:"mac-tags",children:l.tags.map(f=>n.jsx("span",{className:"mac-tag",children:f},f))}),n.jsxs("span",{className:"mac-open",children:["Open example",n.jsx("span",{"aria-hidden":"true",children:"→"})]})]})]})},l.slug)}),n.jsx("div",{className:"mac-slide","aria-hidden":e!==i||void 0,children:n.jsxs("div",{className:"mac-all",children:[n.jsxs("div",{className:"mac-all-big",children:[Ed.length," miniapps"]}),n.jsx("h3",{className:"mac-all-h",children:"See the whole library"}),n.jsx("p",{className:"mac-all-p",children:"Dashboards, heatmaps, trackers, and analyses across sales, marketing, and attribution, all built on Upside."}),n.jsxs(J,{to:xa(),className:"mac-btn",tabIndex:e!==i?-1:void 0,children:["Browse all miniapps ",n.jsx("span",{"aria-hidden":"true",children:"→"})]})]})})]})}),n.jsxs("div",{className:"mac-ctl",children:[n.jsx("button",{className:"mac-arrow",onClick:()=>o(e-1),disabled:e===0,"aria-label":"Previous miniapp",children:"‹"}),n.jsx("div",{className:"mac-dots",children:Array.from({length:r}).map((l,d)=>n.jsx("button",{className:`mac-dot${d===e?" active":""}${d===i?" all":""}`,onClick:()=>o(d),"aria-label":d===i?"Go to the see-all slide":`Go to miniapp ${d+1}`,"aria-current":d===e?"true":void 0},d))}),n.jsx("button",{className:"mac-arrow",onClick:()=>o(e+1),disabled:e===r-1,"aria-label":"Next miniapp",children:"›"}),n.jsxs(J,{to:xa(),className:"mac-seeall",children:["See all ",Ed.length," →"]})]})]})},kce=({green:e})=>n.jsx("svg",{width:"10",height:"10",viewBox:"0 0 24 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84px",textAlign:"center"},children:[a&&n.jsx("div",{style:{fontSize:"12px",fontWeight:500,letterSpacing:"0.14em",textTransform:"uppercase",color:Pe.greenSoft},children:a}),n.jsx("h1",{style:{margin:a?"20px auto 0":0,fontSize:"clamp(34px, 6vw, 62px)",fontWeight:600,letterSpacing:"-0.03em",lineHeight:1.04,color:Pe.text,maxWidth:"760px",textWrap:"balance"},children:e}),n.jsx("p",{style:{fontSize:"19px",lineHeight:1.6,color:Pe.muted,margin:"24px auto 0",maxWidth:"600px",textWrap:"pretty"},children:t})]})}),jce=[{image:"/images/alex-bauer.jpg",name:"Alex Bauer",role:"Product",bio:"Branch early employee + Head of Product",linkedin:"https://www.linkedin.com/in/alexdbauer"},{image:"/images/dan-ahmadi.png",name:"Dan Ahmadi",role:"GTM & Ops",bio:"12+ years in B2B Demand Gen + SDR management",linkedin:"https://www.linkedin.com/in/dahmadi"},{image:"/images/jonas-bauer.png",name:"Jonas Bauer",role:"Engineering",bio:"Branch Principal Engineer, 7+ years dev experience",linkedin:"https://www.linkedin.com/in/jonas-f-bauer"},{image:"/images/mada-seghete.jpg",name:"Mada Seghete",role:"CEO & Marketing",bio:"Branch ($100M+ revenue) founder and CMO, Stanford MBA",linkedin:"https://www.linkedin.com/in/madalina"}],Sce=[{name:"XYZ Ventures",logo:"/images/xyz.svg",url:"https://www.xyz.vc/"},{name:"Emerson Collective",logo:"/images/emerson-collective.svg",url:"https://www.emersoncollective.com/"},{name:"Peterson Ventures",logo:"/images/peterson-ventures.svg",url:"https://www.petersonventures.com/"},{name:"Westbound",logo:"/images/westbound.svg",url:"https://westboundequity.com/"}],Cce=()=>n.jsxs(Qe,{navVariant:"light",children:[n.jsx(Je,{title:"About Upside",description:"Meet the team behind Upside: former operators, marketers, and engineers building better revenue attribution for modern B2B teams.",canonicalUrl:"https://upside.tech/about"}),n.jsx(Hr,{eyebrow:"About",title:"About Us",description:"We're a team of data enthusiasts and former operators on a mission to help revenue leaders make better decisions."}),n.jsx("div",{className:"container",children:n.jsxs("div",{className:"content",children:[n.jsxs("section",{className:"text-image-section",children:[n.jsxs("div",{className:"text-image-section__content-wrapper",children:[n.jsx("div",{className:"badge",children:"Our Story"}),n.jsx("div",{className:"text-image-section__heading-wrapper",children:n.jsx("h3",{className:"heading-m",children:"We built the platform we always wished we had."})}),n.jsx("p",{className:"body-m",children:"We're a team of engineers who spent over a decade in B2B sales and marketing, struggling to make sense of the giant mess that is revenue attribution. After building internal tools to tackle the problem at companies like Branch, Outreach, and MuleSoft, we're now on a mission to bring lasting clarity to every B2B revenue leader out there."})]}),n.jsx("img",{src:"/images/upside-founding-team.avif",loading:"lazy",alt:"",className:"text-image-section__image"})]}),n.jsxs("section",{className:"text-image-section cc-direction-rtl",children:[n.jsxs("div",{className:"text-image-section__content-wrapper",children:[n.jsx("div",{className:"badge",children:"Mission"}),n.jsx("div",{className:"text-image-section__heading-wrapper",children:n.jsx("h3",{className:"heading-m",children:"Better attribution. Smarter decisions. Stronger outcomes."})}),n.jsx("p",{className:"body-m",children:"We believe great attribution is the foundation for smarter, faster go-to-market decisions—knowing what to do next to close a deal, where to invest the next dollar, and where to place your next hire. Our mission is to replace noise with clarity, and help every B2B team act with confidence."})]}),n.jsx("img",{src:"/images/mission-visual.jpg",loading:"lazy",alt:"",srcSet:"/images/mission-visual-p-500.jpg 500w, /images/mission-visual-p-800.jpg 800w, /images/mission-visual-p-1080.jpg 1080w, /images/mission-visual-p-1600.jpg 1600w, /images/mission-visual-p-2000.jpg 2000w, /images/mission-visual.jpg 2136w",sizes:"(max-width: 2136px) 100v

1122w, 2136px",className:"text-image-section__image"})]}),n.jsxs("section",{id:"investors",className:"investors",children:[n.jsx("p",{className:"body-l",children:"Meet our investors"}),n.jsx("div",{className:"investors__list",children:Sce.map(e=>n.jsx("a",{href:e.url,target:"_blank",rel:"noopener noreferrer",className:"investors__item w-inline-block",children:n.jsx("img",{src:e.logo,loading:"lazy",alt:e.name,className:"investors__image"})},e.name))})]}),n.jsxs("section",{id:"team",className:"section",children:[n.jsx("div",{children:n.jsxs("div",{className:"section-header",children:[n.jsxs("div",{className:"section-header__content",children:[n.jsx("div",{className:"badge",children:"Founding Team"}),n.jsx("div",{className:"section-header__heading-wrapper",children:n.jsx("h2",{className:"heading-l",children:"Meet our founders."})})]}),n.jsx("div",{className:"section-header__paragraph-wrapper",children:n.jsx("p",{className:"body-m",children:"We've scaled to $100M+ ARR and felt every data pain along the way. Now we're solving it."})})]})}),n.jsx("div",{className:"w-dyn-list",children:n.jsx("div",{role:"list",className:"collection-list w-dyn-items",children:jce.map(e=>n.jsx("div",{role:"listitem",className:"w-dyn-item",children:n.jsxs("div",{className:"team-member-card",children:[n.jsx("img",{loading:"lazy",src:e.image,alt:e.name,className:"team-member-card__image"}),n.jsxs("div",{className:"team-member-card__content",children:[n.jsx("div",{className:"team-member-card__header",children:n.jsx("h3",{className:"heading-xs",children:e.name})}),n.jsx("p",{className:"body-s",children:e.bio})]}),n.jsx("a",{href:e.linkedin,target:"_blank",rel:"noopener noreferrer",className:"team-member-card__social-link w-inline-block",children:n.jsx("img",{loading:"lazy",src:"/images/linkedin_1.svg",alt:"",className:"team-member-card__social-link-image"})})]})},e.name))})})]})]})})]}),yP="#1b1f1c",Ace="#394142",Tce="#eaeeea",_ce="#fafafa",Mce=()=>n.jsxs(Qe,{navVariant:"light",children:[n.jsx(Je,{title:"AIE Artwork",description:"Submit your details and we'll be in touch about AIE artwork.",canonicalUrl:"https://upside.tech/AIE"}),n.jsx("section",{style:{background:_ce,minHeight:"calc(100vh - 80px)",display:"flex",flexDirection:"column",alignItems:"center",padding:"clamp(8rem, 9vw, 9rem) 1.25rem clamp(2.5rem, 6vw, 4rem)"},children:n.jsxs("div",{style:{width:"100%",maxWidth:"32rem",margin:"auto 0"},children:[n.jsx("h1",{className:"it-title",style:{margin:"0 0 0.5rem",color:yP,textAlign:"center"},children:"AIE Artwork"}),n.jsx("p",{className:"it-lead",style:{margin:"0 auto 2rem",color:Ace,maxWidth:"44ch",textAlign:"center"},children:"Fill out this form to see the results of your contribution—we'll keep you updated."}),n.jsxs("div",{style:{border:`1px solid ${Tce}`,borderRadius:"14px",padding:"clamp(1.25rem, 3vw, 1.75rem)",background:"#fff",boxShadow:"0 1px 3px rgba(0,0,0,0.05)",minWidth:0},children:[n.jsx("div",{className:"it-card-title",style:{marginBottom:"1.125rem",color:yP},children:"Let us know how to get in touch"}),n.jsx(Zw,{type:"aie-artwork"})]})]})})]}),Fj="#2d6a4f",bP="#509a79",Gc="#1b1f1c",hf="#5f6563",Ece="#394142",Hc="#eaeeea",Rce="#fafafa",pf=[{quote:"This is more data than we've ever been able to put together and visualize so quickly. This is amazing that Upside can do this.",name:"Lisa Cole",title:"Chief Marketing & AI Officer, 2X",photo:"/images/mentions/case/lisa-cole.jpg"},{quote:"This is probably the most impressive product demo I've ever seen.",name:"Jason Schleweis",title:"Senior Director, Marketing Technology & Automation, Commvault",photo:"/images/mentions/linkedin/jason-s.png"},{quote:"Their team has been the BEST partners. The platform just keeps getting better — definitely game changing for us.",name:"Wendy Werve",title:"Chief Market Officer, Comply",photo:"/images/mentions/linkedin/wendy-werve.png"},{quote:"Consultative, intelligent, and extremely accurate. Valuable for our company strategy.",name:"Stephen Daniels",title:"VP, GTM & Strategic Operations, Cresta",photo:"/images/mentions/g2/stephen-d.webp"},{quote:"Upside stitched all these data points together — and when I saw that, it created an epiphany for me. I finally understood what was possible.",name:"Matt Grebow",title:"Director of Enterprise Marketing, ServiceTitan",photo:"/images/mentions/case/matt-grebow.png"},{quote:"Upside is doing it in innovative ways we haven't seen other startups do 

1122yet.",name:"Amy Wellersdick",title:"Senior Director of Marketing Strategy & Growth, Syndio",photo:"/images/mentions/case/amy-wellersdick.jpg"},{quote:"Upside provides one of the clearest 360-degree views of our marketing activities and their impact.",name:"Carl Gunlefinger",title:"Senior Marketing Operations Manager, Comply",photo:"/images/mentions/g2/carl-g.jpg"},{quote:"Upside is pretty impressive. It can take over a huge chunk of the time most marketing leaders spend digging through the CRM doing dot-connecting detective work.",name:"Kacie Jenkins",title:"Marketing Leader",photo:"/images/mentions/linkedin/kacie-jenkins.jpg"},{quote:"Mada and the Upside team are amazing!",name:"Felicia Curcuru",title:"Co-founder & CEO, Binti",photo:"/images/mentions/linkedin/felicia-curcuru.jpg"},{quote:"The Upside team is the best!",name:"Tina Staroselsky",title:"VP, Revenue Marketing, Ironclad",photo:"/images/mentions/linkedin/tina-staroselsky.jpg"}];function Nce(){const[e,t]=M.useState(0),[a,i]=M.useState(!1),[r,o]=M.useState(!1),l=a||r,d=M.useRef(!1);M.useEffect(()=>{var m;d.current=typeof window<"u"&&((m=window.matchMedia)==null?void 0:m.call(window,"(prefers-reduced-motion: reduce)").matches)},[]);const u=M.useCallback(()=>{t(m=>(m+1)%pf.length)},[]);M.useEffect(()=>{if(l||d.current)return;const m=Math.min(11e3,Math.max(6500,pf[e].quote.length*55)),f=window.setTimeout(u,m);return()=>window.clearTimeout(f)},[e,l,u]);const h=pf[e];return n.jsxs("figure",{className:"demo-quote","aria-roledescription":"carousel","aria-label":"What customers say about Upside",onMouseEnter:()=>i(!0),onMouseLeave:()=>i(!1),onFocusCapture:()=>o(!0),onBlurCapture:()=>o(!1),children:[n.jsx("div",{className:"demo-quote-stage",children:n.jsxs("div",{className:"demo-quote-slide",children:[n.jsxs("blockquote",{className:"demo-quote-text",children:["“",h.quote,"”"]}),n.jsxs("figcaption",{className:"demo-quote-footer",children:[n.jsx("img",{src:h.photo,alt:"",className:"demo-quote-photo",loading:"lazy",width:44,height:44}),n.jsxs("div",{className:"demo-quote-who",children:[n.jsx("p",{className:"demo-quote-name",children:h.name}),n.jsx("p",{className:"demo-quote-title",children:h.title})]})]})]},e)}),n.jsx("div",{className:"demo-quote-dots",role:"tablist","aria-label":"Choose a testimonial",children:pf.map((m,f)=>n.jsx("button",{type:"button",role:"tab","aria-selected":f===e,"aria-label":`Show testimonial ${f+1} of ${pf.length}`,className:`demo-quote-dot${f===e?" is-active":""}`,onClick:()=>t(f)},m.name+f))})]})}const Oce=["send-time-optimizer","aeo-influence","channel-analysis","expansion-readiness","campaign-follow-up","paid-media-impact"],Pce=Oce.map(e=>Ed.find(t=>t.slug===e)).filter(e=>!!e);function Ice(){return n.jsx("div",{className:"demo-app-tiles",children:Pce.map(e=>n.jsxs("a",{href:xa(e.slug),className:"demo-app-tile",children:[n.jsx("span",{className:"demo-app-thumb",children:n.jsx("img",{src:e.thumbnail,alt:"",loading:"lazy",onError:t=>{t.currentTarget.style.display="none"}})}),n.jsxs("span",{className:"demo-app-info",children:[n.jsx("span",{className:"demo-app-cat",children:e.category}),n.jsx("span",{className:"demo-app-title",children:e.title})]})]},e.slug))})}function Dce(){const e=[...oa,...oa];return n.jsxs("div",{className:"demo-logos","aria-label":"Trusted by leading go-to-market teams",children:[n.jsx("p",{className:"demo-eyebrow",children:"Trusted by"}),n.jsx("div",{className:"demo-logos-marquee",children:n.jsx("div",{className:"demo-logos-track",children:e.map((t,a)=>{const i=a>=oa.length;return n.jsx("img",{src:t.src,alt:i?"":t.name,"aria-hidden":i||void 0,loading:"lazy",className:"demo-logo",style:{height:Math.round(t.height*.7)}},`${t.name}-${a}`)})})})]})}const Wce=()=>{const[e,t]=M.useState(!1);return M.useEffect(()=>{cG()},[]),n.jsxs(n.Fragment,{children:[n.jsx(Je,{title:"Request a Demo",description:"Book a walkthrough with Upside. See how GTM teams connect their revenue data, get AI-powered answers, and make every bet with evidence — not guesswork.",canonicalUrl:"https://upside.tech/request-demo"}),n.jsx("style",{children:`
1123        .demo-fullpage {
1124          min-height: 100vh;
1125          background: ${Rce};
1126          display: flex;
1127          flex-direction: column;
1128          overflow-x: hidden;
1129        }
1130        .demo-header {
1131          display: flex;
1132          align-items: center;
1133          justify-content: space-between;
1134          padding: clamp(1.1rem, 2.5vw, 1.6rem) clamp(1.25rem, 5vw, 3rem);
1135        }
1136        .demo-brand {
1137          display: inline-flex;

1138          height: 26px;
1139          color: ${Gc};
1140        }
1141        .demo-main {
1142          flex: 1;
1143          display: flex;
1144          flex-direction: column;
1145          justify-content: center;
1146          padding: clamp(1.5rem, 4vw, 3rem) clamp(1.25rem, 5vw, 3rem) clamp(2rem, 4vw, 3rem);
1147        }
1148        .demo-shell {
1149          max-width: 1120px;
1150          width: 100%;
1151          margin: 0 auto;
1152          display: grid;
1153          grid-template-columns: minmax(0, 1fr) minmax(0, 560px);
1154          grid-template-areas:
1155            "intro form"
1156            "proof form";
1157          column-gap: clamp(2.5rem, 5vw, 4rem);
1158          row-gap: clamp(1.5rem, 3vw, 2rem);
1159          align-items: start;
1160        }
1161        .demo-intro { grid-area: intro; }
1162        .demo-proof { grid-area: proof; }
1163        .demo-form-col { grid-area: form; }
1164        .demo-headline {
1165          margin: 0 0 1rem;
1166          color: ${Gc};
1167          max-width: 16ch;
1168        }
1169        .demo-lead {
1170          margin: 0;
1171          color: ${Ece};
1172          max-width: 42ch;
1173        }
1174        .demo-quote {
1175          margin: 0;
1176          padding: clamp(1.25rem, 3vw, 1.5rem);
1177          border: 1px solid ${Hc};
1178          border-radius: 14px;
1179          background: #fff;
1180          box-shadow: 0 1px 3px rgba(0, 0, 0, 0.04);
1181        }
1182        /* Fixed-ish stage so the card (and the sticky form beside it) doesn't
1183           jump as quotes of different lengths rotate through. */
1184        .demo-quote-stage {
1185          position: relative;
1186          min-height: 188px;
1187          display: flex;
1188        }
1189        .demo-quote-slide {
1190          display: flex;
1191          flex: 1;
1192          flex-direction: column;
1193          justify-content: center;
1194          animation: demoQuoteFade 480ms ease both;
1195        }
1196        @keyframes demoQuoteFade {
1197          from { opacity: 0; transform: translateY(6px); }
1198          to { opacity: 1; transform: translateY(0); }
1199        }
1200        @media (prefers-reduced-motion: reduce) {
1201          .demo-quote-slide { animation: none; }
1202        }
1203        .demo-quote-text {
1204          /* Reset the global blockquote left-bar + padding (upside.css). */
1205          margin: 0 0 1.25rem;
1206          padding: 0;
1207          border: none;
1208          color: ${Gc};
1209          font-size: clamp(1.05rem, 2vw, 1.15rem);
1210          line-height: 1.5;
1211          text-align: center;
1212        }
1213        .demo-quote-footer {
1214          /* figcaption is centered globally (webflow-components.css) — undo it.
1215             The quote text + attribution group is vertically centered in the
1216             stage via the slide's justify-content. */
1217          display: flex;
1218          align-items: center;
1219          justify-content: center;
1220          text-align: left;
1221          margin-top: 0;
1222          gap: 0.85rem;
1223        }
1224        .demo-quote-photo {
1225          width: 44px;
1226          height: 44px;
1227          border-radius: 999px;
1228          object-fit: cover;
1229          flex-shrink: 0;
1230        }
1231        .demo-quote-who {
1232          min-width: 0;
1233        }
1234        .demo-quote-name {
1235          margin: 0;
1236          color: ${Gc};
1237          font-weight: 600;
1238          font-size: 0.92rem;
1239        }
1240        .demo-quote-title {
1241          margin: 0.15rem 0 0;
1242          color: ${hf};
1243          font-size: 0.82rem;
1244        }
1245        .demo-quote-dots {
1246          display: flex;
1247          justify-content: center;
1248          gap: 0.4rem;
1249          margin-top: 1.35rem;
1250        }
1251        .demo-quote-dot {
1252          width: 6px;
1253          height: 6px;
1254          padding: 0;
1255          border: none;
1256          border-radius: 999px;
1257          background: rgba(45, 106, 79, 0.2);
1258          cursor: pointer;
1259          transition: width 240ms ease, background-color 240ms ease;
1260        }
1261        .demo-quote-dot.is-active {
1262          width: 22px;
1263          background: ${Fj};
1264        }
1265        .demo-quote-dot:hover { background: ${bP}; }
1266        .demo-form-card {
1267          border: 1px solid ${Hc};
1268          border-radius: 16px;
1269          padding: clamp(1.35rem, 3vw, 1.75rem);
1270          background: #fff;
1271          box-shadow: 0 8px 28px rgba(27, 31, 28, 0.06);
1272          min-width: 0;
1273        }
1274        /* Keep HubSpot's injected markup inside the card on narrow screens. */
1275        .demo-form-card form,
1276        .demo-form-card input,
1277        .demo-form-card select,
1278        .demo-form-card textarea {
1279          max-width: 100%;
1280          box-sizing: border-box;
1281        }
1282        /* Post-submit thank-you state: a big, centered card holding the
1283           confirmation message and a gallery of explorable miniapps. */
1284        .demo-thanks-shell {
1285          width: 100%;
1286          max-width: 960px;
1287          margin: 0 auto;
1288          padding: clamp(2rem, 5vw, 3.25rem) clamp(1.5rem, 4vw, 3rem) clamp(2rem, 4vw, 2.75rem);
1289          background: #fff;
1290          border: 1px solid ${Hc};
1291          border-radius: 20px;
1292          box-shadow: 0 12px 36px rgba(27, 31, 28, 0.07);
1293          text-align: center;
1294          animation: demoQuoteFade 480ms ease both;
1295        }
1296        .demo-thanks-mark {
1297          width: 60px;
1298          height: 60px;
1299          margin: 0 auto 1.25rem;
1300          border-radius: 999px;
1301          background: rgba(45, 106, 79, 0.12);
1302          color: ${Fj};
1303          display: inline-flex;
1304          align-items: center;
1305          justify-content: center;
1306          font-size: 1.7rem;
1307          font-weight: 700;
1308        }
1309        .demo-thanks-title {
1310          margin: 0 0 0.65rem;
1311          color: ${Gc};
1312          font-size: clamp(1.6rem, 3vw, 2.1rem);
1313        }
1314        .demo-thanks-sub {
1315          margin: 0 auto 0.5rem;
1316          max-width: 52ch;
1317          color: ${hf};
1318          font-size: 1rem;
1319          line-height: 1.55;
1320        }
1321        .demo-thanks-link {
1322          display: inline-block;
1323          margin-top: 0.5rem;
1324          color: ${Fj};
1325          font-weight: 600;
1326          text-decoration: none;
1327        }
1328        .demo-thanks-link:hover { text-decoration: underline; }
1329
1330        /* Shared small uppercase eyebrow (e.g. "Trusted by"). */
1331        .demo-eyebrow {
1332          margin: 0 0 1rem;
1333          color: ${hf};
1334          font-size: 0.72rem;
1335          font-weight: 600;
1336          letter-spacing: 0.08em;
1337          text-transform: uppercase;
1338        }
1339
1340        /* Miniapp tiles on the thank-you state. */
1341        .demo-app-tiles {
1342          display: grid;
1343          grid-template-columns: repeat(3, 1fr);
1344          gap: 1rem;
1345          margin: 2rem 0 0.5rem;
1346          text-align: left;
1347        }
1348        .demo-app-tile {
1349          display: flex;
1350          flex-direction: column;
1351          border: 1px solid ${Hc};
1352          border-radius: 14px;
1353          overflow: hidden;
1354          background: #fff;
1355          text-decoration: none;
1356          transition: border-color 160ms ease, box-shadow 160ms ease, transform 160ms ease;
1357        }
1358        .demo-app-tile:hover {
1359          border-color: rgba(45, 106, 79, 0.4);
1360          box-shadow: 0 14px 34px rgba(27, 31, 28, 0.12);
1361          transform: translateY(-3px);
1362        }
1363        .demo-app-thumb {
1364          display: block;
1365          aspect-ratio: 16 / 10;
1366          background: #eef2f5;
1367          overflow: hidden;
1368          border-bottom: 1px solid ${Hc};
1369        }
1370        .demo-app-thumb img {
1371          width: 100%;
1372          height: 100%;
1373          object-fit: cover;
1374          display: block;
1375        }
1376        .demo-app-info {
1377          display: flex;
1378          flex-direction: column;
1379          gap: 0.3rem;
1380          padding: 0.85rem 0.95rem 1rem;
1381        }
1382        .demo-app-cat {
1383          color: ${bP};
1384          font-size: 0.68rem;
1385          font-weight: 600;
1386          letter-spacing: 0.04em;
1387          text-transform: uppercase;
1388        }
1389        .demo-app-title {
1390          color: ${Gc};
1391          font-weight: 600;
1392          font-size: 0.92rem;
1393          line-height: 1.3;
1394        }
1395
1396        /* "Trusted by" logo marquee — slow right-to-left loop. Sits under the
1397           quote carousel in the left copy column. */
1398        .demo-logos {
1399          width: 100%;
1400          margin-top: clamp(1.5rem, 3vw, 2rem);
1401          padding-top: clamp(1.25rem, 2.5vw, 1.75rem);
1402          border-top: 1px solid ${Hc};
1403        }
1404        .demo-logos-marquee {
1405          position: relative;
1406          overflow: hidden;
1407          mask-image: linear-gradient(90deg, transparent 0%, black 6%, black 94%, transparent 100%);
1408          -webkit-mask-image: linear-gradient(90deg, transparent 0%, black 6%, black 94%, transparent 100%);
1409        }
1410        .demo-logos-track {
1411          display: flex;
1412          width: max-content;
1413          align-items: center;
1414          gap: clamp(2.5rem, 5vw, 4rem);
1415          padding-right: clamp(2.5rem, 5vw, 4rem);
1416          animation: demoLogoScroll 45s linear infinite;
1417          will-change: transform;
1418        }
1419        @keyframes demoLogoScroll {
1420          from { transform: translateX(0); }
1421          to { transform: translateX(-50%); }
1422        }
1423        @media (prefers-reduced-motion: reduce) {
1424          .demo-logos-track { animation: none; flex-wrap: wrap; }
1425        }
1426        .demo-logo {
1427          width: auto;
1428          object-fit: contain;
1429          filter: brightness(0);
1430          opacity: 0.45;
1431          flex-shrink: 0;
1432        }
1433
1434        /* Minimal legal footer — kept since this page omits the global footer. */
1435        .demo-footer {
1436          display: flex;
1437          align-items: center;
1438          justify-content: space-between;
1439          flex-wrap: wrap;
1440          gap: 0.75rem 1.25rem;
1441          padding: clamp(1.1rem, 2.5vw, 1.5rem) clamp(1.25rem, 5vw, 3rem);
1442          border-top: 1px solid ${Hc};
1443        }
1444        .demo-footer-copy {
1445          color: ${hf};
1446          font-size: 0.8rem;
1447        }
1448        .demo-footer-links {
1449          display: flex;
1450          flex-wrap: wrap;
1451          gap: 1.25rem;
1452        }
1453        .demo-footer-links a {
1454          color: ${hf};
1455          font-size: 0.8rem;
1456          text-decoration: none;
1457          transition: color 160ms ease;
1458        }
1459        .demo-footer-links a:hover { color: ${Gc}; }
1460        @media (max-width: 900px) {
1461          /* Stack to one column and flow intro → form → proof so the form
1462             stays near the top, right under the headline, on phones. */
1463          .demo-main { justify-content: flex-start; }
1464          .demo-shell {
1465            /* minmax(0, 1fr) — not bare 1fr — so the HubSpot form's min-content
1466               width can't blow the column past the viewport on phones. */
1467            grid-template-columns: minmax(0, 1fr);
1468            grid-template-areas:
1469              "intro"
1470              "form"
1471              "proof";
1472            row-gap: clamp(1.5rem, 5vw, 2rem);
1473          }
1474          .demo-headline { max-width: none; }
1475          .demo-lead { max-width: none; }
1476          .demo-app-tiles { grid-template-columns: repeat(2, 1fr); }
1477          /* On phones, stack the logos above the quote carousel. */
1478          .demo-proof {
1479            display: flex;
1480            flex-direction: column;
1481            gap: clamp(1.25rem, 4vw, 1.75rem);
1482          }
1483          .demo-logos {
1484            order: -1;
1485            margin-top: 0;
1486            padding-top: 0;
1487            border-top: none;
1488          }
1489        }
1490        @media (max-width: 480px) {
1491          .demo-header { padding: 0.95rem 1.1rem; }
1492          .demo-form-card { padding: 1.15rem; }
1493          .demo-app-tiles { grid-template-columns: 1fr; }
1494          .demo-footer {
1495            justify-content: center;
1496            text-align: center;
1497            gap: 0.45rem 1rem;
1498          }
1499          .demo-footer-links { gap: 0.9rem; }
1500        }
1501      `}),n.jsxs("div",{className:"demo-fullpage",children:[n.jsx("header",{className:"demo-header",children:n.jsx(J,{to:"/",className:"demo-brand","aria-label":"Upside home",children:n.jsx(au,{})})}),n.jsx("main",{className:"demo-main",children:e?n.jsxs("section",{className:"demo-thanks-shell",children:[n.jsx("div",{className:"demo-thanks-mark","aria-hidden":"true",children:"✓"}),n.jsx("h2",{className:"demo-thanks-title",children:"Thanks, you're all set."}),n.jsx("p",{className:"demo-thanks-sub",children:"We're excited to meet you. In the meantime, explore the kinds of interactive apps you can build with Upside:"}),n.jsx(Ice,{}),n.jsx(J,{to:xa(),className:"demo-thanks-link",children:"Explore all miniapps →"})]}):n.jsxs("div",{className:"demo-shell",children:[n.jsxs("div",{className:"demo-intro",children:[n.jsx("h1",{className:"it-title demo-headline",children:"See Upside in action."}),n.jsx("p",{className:"it-lead demo-lead",children:"Book a 30-minute walkthrough to see how it works."})]}),n.jsx("div",{className:"demo-form-col",children:n.jsx("div",{className:"demo-form-card",children:n.jsx(Zw,{type:"demo",onSubmitted:()=>t(!0)})})}),n.jsxs("div",{className:"demo-proof",children:[n.jsx(Nce,{}),n.jsx(Dce,{})]})]})}),n.jsxs("footer",{className:"demo-footer",children:[n.jsxs("span",{className:"demo-footer-copy",children:["© ",new Date().getFullYear()," Upside"]}),n.jsxs("nav",{className:"demo-footer-links","aria-label":"Legal",children:[n.jsx(J,{to:"/legal/privacy-policy",children:"Privacy Policy"}),n.jsx(J,{to:"/legal/terms-of-service",children:"Terms of Service"}),n.jsx(J,{to:"/security",children:"Security"})]})]})]})]})},Pi="#2d6a4f",Wv="#509a79",oo="#1b1f1c",so="#5f6563",Tl="#eaeeea",zce="#fafafa",Gj="#c0392b",Lce=`https://api.hsforms.com/submissions/v3/integration/submit/${H0}/${MM.tshirt.formId}`,Uce=`https://api.hsforms.com/submissions/v3/integration/submit/${H0}/${MM.demo.formId}`,vP=[{value:"dark-funnel",name:"“I am the dark funnel.”",note:"Charcoal",image:"/images/tshirts/dark-funnel.png"},{value:"utm-source",name:"“?utm_source=this_tee”",note:"Forest green",image:"/images/tshirts/utm-source.png"},{value:"data-driven",name:"“That wasn’t very data driven of you.”",note:"Sage green",image:"/images/tshirts/data-driven.png"}],Bce=["XS","S","M","L","XL","2XL"],qce="upside",$ce="inbound_router";function Fce(e){const t=window.ChiliPiper;t!=null&&t.submit&&t.submit(qce,$ce,{map:!0,lead:{FirstName:e.firstname,LastName:e.lastname,Email:e.email,Company:e.company}})}const Gce={tshirt_design:"",tshirt_size:"",firstname:"",lastname:"",email:"",ph

1501one:"",shipping_address:""},Hce={company:"",message:""},Yce=/^[^\s@]+@[^\s@]+\.[^\s@]+$/,Vce=()=>{const[e,t]=M.useState(!0),[a,i]=M.useState(Gce),[r,o]=M.useState(Hce),[l,d]=M.useState({}),[u,h]=M.useState("idle"),[m,f]=M.useState(!1),[y,b]=M.useState(null);M.useEffect(()=>{if(!y)return;const C=_=>{_.key==="Escape"&&b(null)};return window.addEventListener("keydown",C),()=>window.removeEventListener("keydown",C)},[y]);const x=C=>d(_=>_[C]?{..._,[C]:void 0}:_),k=(C,_)=>{i(E=>({...E,[C]:_})),x(C)},v=(C,_)=>{o(E=>({...E,[C]:_})),x(C)},j=()=>{const C={};a.tshirt_design||(C.tshirt_design="Pick a design."),a.tshirt_size||(C.tshirt_size="Pick a size."),a.firstname.trim()||(C.firstname="Tell us your first name."),a.lastname.trim()||(C.lastname="Tell us your last name."),a.email.trim()?Yce.test(a.email.trim())||(C.email="That doesn't look like a valid email."):C.email="Add an email so we can confirm your order.";const _=a.phone.replace(/\D/g,"");return a.phone.trim()?(_.length<7||_.length>20)&&(C.phone="That doesn't look like a valid phone number."):C.phone="We need a number to reach you about delivery.",a.shipping_address.trim()||(C.shipping_address="Where should we ship it?"),e&&(r.company.trim()||(C.company="What company are you with?")),C},T=async C=>{var W;C.preventDefault();const _=j();if(d(_),Object.keys(_).length>0){(W=document.getElementById("tso-order"))==null||W.scrollIntoView({behavior:"smooth",block:"start"});return}const E=e,R={pageUri:window.location.href,pageName:"Upside T-shirt Order"};h("submitting");try{const z=await fetch(Lce,{method:"POST",headers:{"Content-Type":"application/json"},body:JSON.stringify({fields:Object.keys(a).filter(H=>a[H].trim()!=="").map(H=>({name:H,value:a[H].trim()})),context:R})});if(!z.ok)throw new Error(`HubSpot t-shirt form responded ${z.status}`);if(E){const H=[{name:"email",value:a.email.trim()},{name:"firstname",value:a.firstname.trim()},{name:"lastname",value:a.lastname.trim()},{name:"company",value:r.company.trim()}];r.message.trim()&&H.push({name:"message",value:r.message.trim()});const $=await fetch(Uce,{method:"POST",headers:{"Content-Type":"application/json"},body:JSON.stringify({fields:H,context:R})});if(!$.ok)throw new Error(`HubSpot demo form responded ${$.status}`);Fce({firstname:a.firstname.trim(),lastname:a.lastname.trim(),email:a.email.trim(),company:r.company.trim()})}f(E),h("success"),window.scrollTo({top:0,behavior:"smooth"})}catch{h("error")}},A=u==="submitting";return n.jsxs(n.Fragment,{children:[n.jsx(Je,{title:"Order your Upside T-shirt",description:"Pick a design, choose your size, and tell us where to ship it. Optionally book a walkthrough of Upside while you're here.",ogImage:"/og/tshirt-order.png",canonicalUrl:"https://upside.tech/tshirt-order"}),n.jsx("style",{children:`
1502        .tso-page { min-height: 100vh; background: ${zce}; display: flex; flex-direction: column; overflow-x: hidden; }
1503        .tso-header { display: flex; align-items: center; padding: clamp(1.1rem,2.5vw,1.6rem) clamp(1.25rem,5vw,3rem); }
1504        .tso-brand { display: inline-flex; height: 26px; color: ${oo}; }
1505        .tso-main { flex: 1; padding: clamp(0.5rem,2vw,1rem) clamp(1.25rem,5vw,3rem) clamp(2rem,4vw,3rem); }
1506        .tso-shell { max-width: 760px; margin: 0 auto; width: 100%; }
1507        .tso-h1 { margin: 0 0 .6rem; color: ${oo}; }
1508        .tso-lead { margin: 0 0 2rem; color: ${so}; max-width: 52ch; }
1509
1510        .tso-card { background: #fff; border: 1px solid ${Tl}; border-radius: 16px; padding: clamp(1.35rem,3vw,1.9rem); box-shadow: 0 8px 28px rgba(27,31,28,.06); margin-bottom: 1.5rem; }
1511        .tso-row-head { display: flex; align-items: center; margin-bottom: .35rem; }
1512        .tso-num { display: inline-flex; align-items: center; justify-content: center; width: 26px; height: 26px; border-radius: 999px; background: rgba(45,106,79,.12); color: ${Pi}; font-weight: 700; font-size: .85rem; margin-right: .55rem; flex-shrink: 0; }
1513        .tso-section-title { margin: 0; color: ${oo}; font-size: 1.15rem; font-weight: 700; }
1514        .tso-section-sub { margin: .1rem 0 1.1rem; color: ${so}; font-size: .9rem; }
1515
1516        /* Tee lineup: click any tee to blow it up in the lightbox */
1517        .tso-lineup { display: grid; grid-template-columns: 1fr; gap: 1.1rem; }
1518        .tso-tee { border: 1px solid ${Tl}; border-radius: 14px; overflow: hidden; background: #fff; }
1519        .tso-tee-img { display: block; width: 100%; margin: 0; padding: 0; border: 0; border-bottom: 1px solid ${Tl}; aspect-ratio: 8 / 5; background: #eef1ee; cursor: zoom-in; position: relative; overflow: hidden; font: inherit; color: inherit; }
1520        /* Crop the square mockup to an 8:5 band anchored at the shirts' shoulders
1521           (object-position 30% down): trims the dead top whitespace while keeping
1522           both shirts intact — front slogan + back logo fully in frame. */
1523        .tso-tee-img img { width: 100%; height: 100%; object-fit: cover; object-position: center 30%; display: block; transform: scale(1); transition: transform .2s ease; }
1524        .tso-tee-img:hover img, .tso-tee-img:focus-visible img { transform: scale(1.03); }
1525        .tso-tee-img:focus-visible { outline: 2px solid ${Wv}; outline-offset: -2px; }
1526        .tso-tee-zoom { position: absolute; top: .5rem; right: .5rem; display: inline-flex; align-items: center; justify-content: center; width: 28px; height: 28px; border-radius: 999px; background: rgba(27,31,28,.55); color: #fff; opacity: 0; transition: opacity .16s ease; }
1527        .tso-tee-img:hover .tso-tee-zoom, .tso-tee-img:focus-visible .tso-tee-zoom { opacity: 1; }
1528        .tso-tee-cap { display: flex; align-items: flex-start; gap: .55rem; padding: .8rem 1rem 1rem; }
1529
1530        /* Lightbox: expanded tee view */
1531        .tso-lightbox { position: fixed; inset: 0; z-index: 1000; display: flex; align-items: center; justify-content: center; padding: clamp(1rem,4vw,3rem); background: rgba(27,31,28,.72); animation: tsoFade .18s ease both; }
1532        @keyframes tsoFade { from { opacity: 0; } to { opacity: 1; } }
1533        /* Cap width off the viewport height minus the caption/padding so the
1534           square image + caption never exceed short/landscape viewports. */
1535        .tso-lightbox-inner { position: relative; background: #fff; border-radius: 16px; overflow: hidden; max-width: min(560px, calc(88vh - 96px)); width: 100%; box-shadow: 0 24px 60px rgba(27,31,28,.35); animation: tsoZoomIn .2s ease both; }
1536        @keyframes tsoZoomIn { from { opacity: 0; transform: scale(.96); } to { opacity: 1; transform: scale(1); } }
1537        @media (prefers-reduced-motion: reduce) { .tso-lightbox, .tso-lightbox-inner { animation: none; } }
1538        .tso-lightbox-img { width: 100%; aspect-ratio: 1 / 1; object-fit: contain; background: #eef1ee; display: block; }
1539        .tso-lightbox-cap { display: flex; align-items: center; gap: .6rem; padding: .9rem 1.15rem 1.1rem; border-top: 1px solid ${Tl}; }
1540        .tso-lightbox-cap .tso-tee-name { font-size: 1rem; }
1541        .tso-lightbox-close { position: absolute; top: .6rem; right: .6rem; appearance: none; border: 0; cursor: pointer; width: 34px; height: 34px; border-radius: 999px; background: rgba(27,31,28,.55); color: #fff; font-size: 1.15rem; line-height: 1; display: inline-flex; align-items: center; justify-content: center; transition: background .16s ease; }
1542        .tso-lightbox-close:hover { background: rgba(27,31,28,.8); }
1543        .tso-lightbox-close:focus-visible { outline: 2px solid #fff; outline-offset: 2px; }
1544        .tso-tee-badge { display: inline-flex; align-items: center; justify-content: center; width: 22px; height: 22px; border-radius: 999px; background: ${Pi}; color: #fff; font-weight: 700; font-size: .78rem; flex-shrink: 0; }
1545        .tso-tee-text { display: flex; flex-direction: column; gap: .15rem; min-width: 0; }
1546        .tso-tee-name { font-weight: 700; color: ${oo}; font-size: .95rem; }
1547        .tso-tee-note { color: ${so}; font-size: .8rem; }
1548
1549        /* Custom order form */
1550        .tso-field { margin-bottom: 1.15rem; }
1551        .tso-field:last-child { margin-bottom: 0; }
1552        .tso-label { display: block; color: ${oo}; font-weight: 600; font-size: .9rem; margin-bottom: .45rem; }

1553        .tso-optional { color: ${so}; font-weight: 400; }
1554        .tso-input, .tso-select, .tso-textarea { width: 100%; box-sizing: border-box; padding: .7rem .85rem; border: 1px solid ${Tl}; border-radius: 10px; font-family: inherit; font-size: .95rem; color: ${oo}; background: #fff; transition: border-color .14s ease, box-shadow .14s ease; }
1555        .tso-input:focus, .tso-select:focus, .tso-textarea:focus { outline: none; border-color: ${Pi}; box-shadow: 0 0 0 3px rgba(45,106,79,.12); }
1556        .tso-textarea { min-height: 92px; resize: vertical; }
1557        .tso-select { appearance: none; -webkit-appearance: none; background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='12' height='8' viewBox='0 0 12 8'%3E%3Cpath fill='%235f6563' d='M1 1l5 5 5-5'/%3E%3C/svg%3E"); background-repeat: no-repeat; background-position: right .9rem center; padding-right: 2.2rem; }
1558        .tso-two-col { display: grid; grid-template-columns: 1fr; gap: 1.15rem; }
1559        .tso-two-col .tso-field { margin-bottom: 0; }
1560        .tso-invalid, .tso-invalid:focus { border-color: ${Gj}; box-shadow: 0 0 0 3px rgba(192,57,43,.1); }
1561        .tso-error { color: ${Gj}; font-size: .8rem; margin: .4rem 0 0; }
1562
1563        /* Design radio cards */
1564        .tso-picks { display: grid; grid-template-columns: 1fr; gap: .6rem; }
1565        .tso-pick { display: flex; align-items: center; gap: .7rem; border: 1px solid ${Tl}; border-radius: 12px; padding: .8rem .95rem; cursor: pointer; transition: border-color .14s ease, background .14s ease; }
1566        .tso-pick:hover { border-color: ${Wv}; }
1567        .tso-pick.is-selected { border-color: ${Pi}; background: rgba(45,106,79,.05); }
1568        .tso-pick input { appearance: none; -webkit-appearance: none; width: 18px; height: 18px; flex-shrink: 0; border: 1.5px solid ${Pi}; border-radius: 999px; margin: 0; position: relative; cursor: pointer; }
1569        .tso-pick input:checked { background: ${Pi}; box-shadow: inset 0 0 0 3px #fff; }
1570        .tso-pick input:focus-visible { outline: 2px solid ${Wv}; outline-offset: 2px; }
1571        .tso-pick-badge { display: inline-flex; align-items: center; justify-content: center; width: 22px; height: 22px; border-radius: 999px; background: rgba(45,106,79,.12); color: ${Pi}; font-weight: 700; font-size: .78rem; flex-shrink: 0; }
1572        .tso-pick-text { display: flex; flex-direction: column; gap: .1rem; min-width: 0; }
1573        .tso-pick-name { font-weight: 600; color: ${oo}; font-size: .92rem; }
1574        .tso-pick-note { color: ${so}; font-size: .78rem; }
1575
1576        /* Demo opt-in toggle + revealed demo-request fields */
1577        .tso-demo-toggle { display: flex; align-items: flex-start; gap: .7rem; cursor: pointer; user-select: none; }
1578        .tso-demo-check { appearance: none; -webkit-appearance: none; width: 20px; height: 20px; margin: 0; flex-shrink: 0; border: 1.5px solid ${Pi}; border-radius: 6px; background: #fff; cursor: pointer; position: relative; transition: background .14s ease, border-color .14s ease; }
1579        .tso-demo-check:checked { background: ${Pi}; }
1580        .tso-demo-check:checked::after { content: ""; position: absolute; left: 6px; top: 2px; width: 5px; height: 10px; border: solid #fff; border-width: 0 2px 2px 0; transform: rotate(45deg); }
1581        .tso-demo-check:focus-visible { outline: 2px solid ${Wv}; outline-offset: 2px; }
1582        .tso-demo-toggle-text { display: flex; flex-direction: column; gap: .15rem; }
1583        .tso-demo-toggle-title { font-weight: 700; color: ${oo}; font-size: .98rem; line-height: 1.3; }
1584        .tso-demo-toggle-hint { color: ${so}; font-size: .84rem; }
1585        .tso-demo-panel { margin-top: 1.25rem; border-top: 1px solid ${Tl}; padding-top: 1.25rem; animation: tsoReveal .28s ease both; }
1586        @keyframes tsoReveal { from { opacity: 0; transform: translateY(-4px); } to { opacity: 1; transform: translateY(0); } }
1587        @media (prefers-reduced-motion: reduce) { .tso-demo-panel { animation: none; } }
1588
1589        /* Bottom submit */
1590        .tso-submit-wrap { text-align: center; }
1591        .tso-submit { appearance: none; border: 0; cursor: pointer; background: ${Pi}; color: #fff; font-family: inherit; font-weight: 700; font-size: 1rem; padding: .95rem 2.4rem; border-radius: 999px; transition: background .16s ease, opacity .16s ease; }
1592        .tso-submit:hover { background: #245740; }
1593        .tso-submit:disabled { opacity: .6; cursor: default; }
1594        .tso-submit-error { color: ${Gj}; font-size: .85rem; margin: .9rem 0 0; }
1595        .tso-footnote { text-align: center; color: ${so}; font-size: .78rem; margin-top: 1rem; }
1596        .tso-footnote a { color: ${Pi}; }
1597
1598        /* Success state */
1599        .tso-success { text-align: center; padding: clamp(2rem,5vw,3.25rem) clamp(1.35rem,3vw,1.9rem); }
1600        .tso-success-mark { display: inline-flex; align-items: center; justify-content: center; width: 56px; height: 56px; border-radius: 999px; background: rgba(45,106,79,.12); margin-bottom: 1.1rem; }
1601        .tso-success-mark::after { content: ""; width: 14px;

1601 height: 26px; border: solid ${Pi}; border-width: 0 3px 3px 0; transform: rotate(45deg) translate(-1px,-2px); }
1602        .tso-success h2 { margin: 0 0 .5rem; color: ${oo}; font-size: 1.35rem; }
1603        .tso-success p { margin: 0 auto; color: ${so}; max-width: 44ch; }
1604
1605        .tso-footer { display: flex; align-items: center; justify-content: space-between; flex-wrap: wrap; gap: .75rem 1.25rem; padding: clamp(1.1rem,2.5vw,1.5rem) clamp(1.25rem,5vw,3rem); border-top: 1px solid ${Tl}; }
1606        .tso-footer-copy { color: ${so}; font-size: .8rem; }
1607        .tso-footer-links { display: flex; flex-wrap: wrap; gap: 1.25rem; }
1608        .tso-footer-links a { color: ${so}; font-size: .8rem; text-decoration: none; transition: color .16s ease; }
1609        .tso-footer-links a:hover { color: ${oo}; }
1610
1611        @media (min-width: 620px) {
1612          .tso-lineup { grid-template-columns: repeat(3, 1fr); }
1613          .tso-two-col { grid-template-columns: 1fr 1fr; }
1614        }
1615        /* On roomy screens let the tee lineup break out wider than the 760px
1616           form column so the shirts render big (the form stays comfortable). */
1617        @media (min-width: 1080px) {
1618          .tso-card-lineup { width: 980px; margin-left: -110px; }
1619        }
1620        @media (max-width: 560px) {
1621          .tso-footer { justify-content: center; text-align: center; }
1622        }
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1628<p>

1628Most B2B attribution systems give too much credit to the hand-raise.</p>
1629<p>Forms, demo requests, and inbound messages are clean, time-stamped events. The activities that created buyer preference often are not: an article forwarded internally, prior product experience, years of trust, or the arrival of a real buying window.</p>
1630<p>When attribution starts with structured touchpoints, it rewards what the system can observe rather than what changed the buyer's mind.</p>
1631<p>We saw this when we ran <a href="/platform/pipedash">Pipedash</a> across three recent closed-won deals from our own pipeline. In every deal, the clearest hand-raise received <strong>0% credit</strong>:</p>
1632<ul><li>A demo request form</li><li>A text saying "we need Upside"</li><li>A buyer proactively reconnecting</li></ul>
1633<p>These events still mattered. They were valuable buying signals. But they captured demand that earlier activities had created.</p>
1634<p>That distinction changes the action. If a form gets credit for demand created by content, the team optimizes the form. If the content gets credit, the team can invest in what actually created buyer readiness.</p>
1635<h2>Why attribution over-credits visible events</h2>
1636<p>Traditional models distribute credit among the events they can see. The weakness is not the arithmetic. It is the evidence set.</p>
1637<p>Much of the B2B journey lives outside campaign fields, inside calls, emails, product history, internal forwarding, peer conversations, and relationships that carry across companies.</p>
1638<p>A useful analysis distinguishes among the <strong>source</strong> that set the journey in motion, the <strong>influences</strong> that built trust or conviction, and the <strong>response</strong> that revealed the buyer was ready. A response should not receive explanatory credit simply because it has the cleanest timestamp.</p>
1639<h2>How we analyzed our own deals</h2>
1640<p>Upside reconstructs the account journey across CRM, marketing, email, calendar, and call data. Pipedash uses that timeline to evaluate what worked and why.</p>
1641<p>We built a small miniapp inside Upside using our live Pipedash results. It removed deal values while preserving the percentage allocation, timeline, and evidence behind each decision.</p>
1642<p>Pipedash separates <strong>tracked</strong> activities already present in GTM systems, <strong>extracted</strong> evidence found in calls and emails, and <strong>inferred</strong> influences that mattered but could not be tied honestly to one event. Every deal adds up to 100%, without manufacturing precision the evidence cannot support.</p>
1643<h2>Deal one: the form recorded demand; it did not create it</h2>
1644<p>A piece of thought leadership received <strong>34%</strong> of the credit. Internal forwarding received <strong>17%</strong>, newsletter distribution <strong>10%</strong>, and an offline peer recommendation <strong>2%</strong>.</p>
1645<p>The demo request form received <strong>0%</strong>.</p>
1646<p>A conventional report would likely classify this as an inbound conversion and make the form the hero. The reconstructed journey showed that content created interest, distribution extended its reach, and internal and peer validation built conviction.</p>
1647<p>The form told the team the buyer was ready. It did not explain why.</p>
1648<figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/bandwidth-form-zero-highlight-redacted.png" width="auto" height="auto" alt="Attribution analysis where the web form is labeled as a hand-raise response with 0% credit, while earlier sources and inferred influences receive the allocation." loading="lazy" /></div><figcaption>The web form is labeled as a hand-raise response with 0% credit. Earlier sources and influences receive the allocation.</figcaption></figure>
1649<h2>Deal two: a new opportunity was not a new journey</h2>
1650<p>Internal championing received <strong>47%</strong>, the pre-existing relationship <strong>28%</strong>, and the discovery call <strong>13%</strong>.</p>
1651<p>The buyer then sent a text saying "we need Upside." It received <strong>0%</strong>.</p>
1652<p>The message was a clear hand-raise, but the conviction came from prior product experience and years of trust. That history moved with the champion into a new role.</p>
1653<p>The CRM saw a new opportunity. The buyer did not experience a new journey.</p>
1654<figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/springbrook-text-zero-highlight-redacted.png" width="auto" height="auto" alt="Attribution analysis where a text to Mada is labeled as a hand-raise response with 0% credit, while internal 

1654championing, a pre-existing relationship, and inferred influences receive credit." loading="lazy" /></div><figcaption>The text to Mada is labeled as a hand-raise response with 0% credit. Internal championing, a pre-existing relationship, and inferred influences receive credit.</figcaption></figure>
1655<h2>Deal three: uncertainty was part of the answer</h2>
1656<p>Prior product experience received <strong>22%</strong>, relationship cultivation <strong>10%</strong>, and a LinkedIn post that brought Upside back to mind <strong>5%</strong>.</p>
1657<p>The buyer's proactive reconnection received <strong>0%</strong>. The largest category was <strong>63% inferred</strong>.</p>
1658<p>That inferred share included timing, organizational readiness, and influence the evidence could not resolve precisely. Forcing it into the visible activities would have produced a cleaner answer, but a less honest one.</p>
1659<p>An inferred pool is a boundary on the claim. It shows where specific evidence ends and uncertainty begins.</p>
1660<figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/exa-hand-raise-zero-highlight-redacted.png" width="auto" height="auto" alt="Attribution analysis where a reconnection is labeled as a hand-raise response with 0% credit, while founder relationships and inferred influences account for the allocation." loading="lazy" /></div><figcaption>The reconnection is labeled as a hand-raise response with 0% credit. Founder relationships and inferred influences account for the allocation.</figcaption></figure>
1661<h2>What this changes</h2>
1662<p>Attribution should separate demand capture from demand creation, include unstructured and cross-cycle evidence, and preserve uncertainty instead of assigning every point of credit with false confidence.</p>
1663<p>For us, the results support investing in thought leadership worth forwarding, the people and channels that help ideas travel, better tools for internal champions, and long-term product trust.</p>
1664<p>Three deals show a pattern, not a statistical conclusion. Attribution is also not incrementality. Attribution reconstructs an observed journey; incrementality estimates what would have happened without a program, usually through experiments or a credible counterfactual.</p>
1665<p>Pipedash gives teams a more complete and defensible account of what happened in complex B2B deals. It does not turn post-hoc evidence into a causal experiment.</p>
1666<p>The wrong question is:</p>
1667<p><strong>Which tracked activity sourced this deal?</strong></p>
1668<p>The better question is:</p>
1669<p><strong>What evidence explains why the buyer became ready, and which motions should we repeat?</strong></p>
1670<p>If you want to see what Pipedash finds in your own closed deals, <a href="/request-demo">book a demo</a>.</p>`,"how-we-built-pipedash":'<p id="">Everyone has a love/hate relationship with attribution. Mostly hate. I was the CMO of Branch and I built a multi-touch model there that was directionally useful, not truly right. It over-credited what was easy to measure (late-stage clicks, form fills and email) and under-credited what actually created the deal (content, referrals, events, the dark funnel). For the past two years at Upside we&rsquo;ve been building the <a href="/platform/data-foundation">data layer</a> to fix that, and a few months ago Alex Bauer (Branch early employee, now my co-founder at Upside) built something on top of it that I think is truly new: an attribution engine that runs a panel of independent LLM analysts on every deal, argues with itself, and produces a zero-sum credit allocation where every dollar is accounted for.</p><p id="">This post is the technical overview of how it works. The part that most marketers will be surprised by, but eventually agree with: <strong>demo request forms (and all form fills and customer actions) get exactly 0% credit.</strong> I&rsquo;ll get to why.</p><hr><h2 id="">Why B2B attribution was never actually solved</h2><p id="">One thing I have learned while working on <a href="/use-cases/revenue-attribution">attribution</a> at Upside and at Branch is that there is no attribution model that can be applied to every problem. The tagline &ldquo;all models are wrong, but some are useful&rdquo; applies to attribution models, and most of what this article explores is the concept of <strong>applying a zero-sum credit allocation to the activities that led to sourcing a complex B2B deal.</strong></p><p id="">The market currently agrees that there is no accurate model to solve this and instead uses time-based heuristics: first-touch, last-touch, U-shape, W

1670-shape. These are just weighting curves over timestamps. The first or last touchpoint might not matter at all, but the curve doesn&rsquo;t know that, because the curve doesn&rsquo;t read the emails.</p><p id="">Our thesis is that finding a more accurate B2B attribution model failed for two reasons, and neither of them is &ldquo;attribution is conceptually impossible&rdquo;:</p><ul role="list"><li><strong>The data was never complete enough.</strong> A huge fraction of what creates pipeline (a coffee meeting, a peer referral, someone seeing your billboard) never becomes a CRM record.</li><li><strong>B2B has the worst possible shape for statistical modeling.</strong> Thousands of touchpoints per deal, and a limited number of deals. You cannot fit a credible causal model on 40 deals with 2,000 touchpoints each; there is not enough signal to get a statistically accurate causality model. Some companies like Paramark are tackling this problem, but they only do it for actions that have significant volume and distinct touchpoints, like ads, not the whole deal.</li></ul><h2 id="">Prerequisite: 24 months of unglamorous data work</h2><p id="">The first version of an attribution model was a more complex weighted model &mdash; give weights to all the touchpoints involved based on rules: emails take less effort and should get less weight than webinars.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 47.0rem"><div><img src="/images/blog/pipedash-weighted-model.png" width="auto" height="auto" alt="A spreadsheet of the early rules-based weighted model, with touchpoints classified across five levels (for example Sales/SDR/Partnerships &rarr; Meeting &rarr; In Person) and each assigned a hand-tuned weighting, from 45 for an in-person meeting down to 0.0001 for an outbound email." loading="lazy"/></div></figure><p id="">To know if this kind of model was right we decided to manually look at hundreds of complex customer deals ($1M+) and understand what actually drove them. We found two things:</p><ul role="list"><li>There were many touchpoints that were mostly irrelevant &mdash; many marketing and sales emails, sometimes other marketing touchpoints that never led to any real engagement.</li><li>The real story was sometimes found inside unstructured data: email forwards that were never logged, SFDC call notes, call transcripts. But finding it felt like forensic digging &mdash; sometimes two of us would look at the same deal and only one would find a buried relevant touchpoint. Someone was referred by an existing customer or a partner and mentioned it on a call, but they showed up as a direct website visit. Someone <a href="/use-cases/event-field-marketing">attended an event</a> and asked for a demo after but never registered, so the demo form was getting all the credit.</li></ul><p id="">In most cases the real story of the deal was completely different from what an attribution model showed, because any model would <em>only look at structured data</em>.</p><p id="">So before any of the interesting agent stuff, we spent about two years building the boring part: pulling CRM, email, calendar, call transcripts, and web activity into one place, then healing it. Identity resolution (the same person with two emails across two systems becomes one person), buying group detection, activity deduplication across systems, and extracting touchpoints that only exist inside unstructured text. The output is an account-level timeline that is the cleanest, most complete version of &ldquo;what happened&rdquo; we can reconstruct.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 47.0rem"><div><img src="/images/blog/pipedash-account-explorer.png" width="auto" height="auto" alt="Account Explorer: the audit console view of a deal timeline, showing every touchpoint reconstructed across CRM, email, calendar, and call transcripts." loading="lazy"/></div></figure><p id="">That gets you a great <a href="/platform/dashboard">audit console</a> but does not tell you what <em>worked</em>. Some of our customers&rsquo; deals run 18+ months with thousands and even tens of thousands of touchpoints. Knowing everything that happened just makes the credit question harder.</p><h2 id="">The core idea: attribution as evidence reasoning, not curve fitting</h2><p id="">Alex&rsquo;s insight was that we already had an <a href="/platform/mcp">intelligence layer that agents use to query this data</a>, and now we could use AI to replicate the human logic we did in the early days. So instead of fitting a model, what if we made AI agents do what a smart human analyst does: read the whole deal, reason about causality, and defend a credit allocation with citations?</p><p id="">That required building a real domain theory first, because &ldquo;just ask the LLM&rdquo; produces vibes, not attribution. The theory lives in two prompt documents every agent reads: a commander&rsquo;s intent (what we&rsquo;re building, for whom, and the governing principles) and structural rules (the mechanics). Our main concepts:</p><h3 id="">Bids, responses, and cobids</h3><p id="">Every discrete action on a deal timeline is one of three things:</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 47.0rem"><div><img src="/images/blog/pipedash-bid-response-cobid.png" width="auto" height="auto" alt="Diagram of the three types of actions on a deal timeline: a bid from the selling org to the buyer, a response from the buyer back to the selling org, and a cobid from a third party to the buyer." loading="lazy"/></div></figure><ul role="list"><li><strong>Bid</strong>: selling org &rarr; buyer. Someone on your team instantiated it. Sent the email, organized the meeting, wrote the blog post, hosted the event.</li><li><strong>Response</strong>: buyer &rarr; selling org. A form fill, an email reply, meeting attendance.</li><li><strong>Cobid</strong>: third party &rarr; buyer, about you. A peer referral, an advisor intro, a press mention, a search ranking, a ChatGPT recommendation. You didn&rsquo;t orchestrate it (if you did, it&rsquo;s a bid).</li></ul><p id="">The rule that everything else is based on, which can be quite controversial: <strong>credit belongs to whoever instantiated the thing, not to the buyer&rsquo;s observable reaction to it.</strong> Responses always carry 0% credit. They are evidence that something worked, not the thing that worked.</p><p id="">This is why the demo request gets zero. From the buyer&rsquo;s perspective, the demo request form is the annoying thing they have to get through to talk to someone about buying. It did n

1670othing to convince them. If anything it was friction. Instead, everything that led them to be convinced enough to fill it out is what deserves the credit. Most attribution models we&rsquo;ve seen, including the one I built at Branch, hand form fills 20% of the deal. Then leadership reads the report and concludes the growth strategy is &ldquo;more forms.&rdquo;</p><p id="">And if we can&rsquo;t see anything before the demo request? What happened after doesn&rsquo;t get more credit &mdash; instead the <strong>pool</strong> of circumstantial influence gets the credit, more on that below.</p><p id="">The 0% rule is also a structural constraint, not just philosophy: in our classification hierarchy, bids roll up through the selling org branch (team &rarr; person &rarr; channel &rarr; campaign &rarr; engagement) and cobids through the third-party branch (entity type &rarr; actor). A response with non-zero credit belongs to neither branch and shows up as &ldquo;Unassigned&rdquo; in every rollup. Making responses 0% keeps the whole reporting tree zero-sum and aggregatable.</p><h3 id="">Direct, extracted, inferred</h3><p id="">Touchpoints come in three evidence classes:</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 47.0rem"><div><img src="/images/blog/pipedash-direct-extracted-inferred.png" width="auto" height="auto" alt="Diagram of the three evidence classes for influences: direct (a CRM record exists), extracted (citable evidence in other records), and inferred (existence strongly implied by context)." loading="lazy"/></div></figure><ul role="list"><li><strong>Direct</strong>: a CRM record exists. Deterministic.</li><li><strong>Extracted</strong>: no CRM record, but citable evidence in other records. &ldquo;Great meeting you for coffee yesterday&rdquo; in an email thread means a meeting happened that nobody logged. A customer saying &ldquo;I&rsquo;ve been seeing your billboards&rdquo; on a call means the billboard mattered. These get held to the same quality bar as tracked touchpoints; they simply weren&rsquo;t tracked.</li><li><strong>Inferred</strong>: no direct evidence, but existence strongly implied by context. A cold inbound from a C-level exec implies brand awareness got built somewhere.</li></ul><h3 id="">The pool: uncertainty as a first-class output</h3><p id="">Direct and extracted touchpoints each get a specific credit percentage. The remainder goes into a <strong>pool</strong> of circumstantial influence, broken into named, explicitly probabilistic subdivisions (organizational readiness, internal champion relay, brand awareness, and so on). The pool is not a hedge, but a specific claim about invisible influence, and its size is computed from signal coverage:</p><ul role="list"><li>If most tracked activity is inbound with few outbound actions, the outbound bids must be happening somewhere invisible. Bigger pool.</li><li>A 6-month gap with zero activity followed by immediate re-engagement means something sustained the relationship through the gap. Bigger pool.</li><li>25 touchpoints over 14 months with a coherent story from first engagement to hand-raise. Small pool.</li></ul><p id="">A deal with a 10% pool has great CRM coverage. A deal with a 65% pool is telling you that most of your pipeline creation is invisible to your systems, which is itself one of the most useful things a CMO can learn.</p><h2 id="">The architecture: jury, judge, and two skeptics</h2><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 47.0rem"><div><img src="/images/blog/pipedash-pipeline-architecture.png" width="auto" height="auto" alt="The full Pipedash pipeline: an orchestrator dispatches three independent analyst agents, whose outputs go to a consensus judge, then through enrichment and canonicalization agents to produce the final report." loading="lazy"/></div></figure><p id="">A run starts with &ldquo;run attribution on the Acme deal.&rdquo; An orchestrator dispatches <strong>three independent analyst agents</strong>. Each is an Opus-tier model with the same CRM query tools and the same instructions, running up to an hour. They are not assigned different angles on purpose. The point is that three equally capable analysts looking at the same messy deal will not reach identical conclusions, same as three skilled humans wouldn&rsquo;t. Each one&rsquo;s primary obligation is traceability: every claim needs citations (record IDs, transcript quotes, email references) a downstream agent can verify.</p><p id="">Their outputs go to a <strong>consensus judge</strong>. The judge weighs the reasoning, not the votes. A year ago Alex and I were doing this manually, disagreeing about deals and arguing it out, and the judge behaves the way 

1670we did: where the runs agree, follow. Where they disagree, evaluate <em>why</em>. A minority run that noticed evidence the others missed can override the majority, but overriding requires explicit reasoning. We deliberately rejected majority vote and median-of-three, because the entire value of the system is contextual reasoning, and mechanical aggregation throws that away. When the spread is too wide to adjudicate, the judge escalates: 3 analysts &rarr; 5 &rarr; 7.</p><p id="">(Why start at three? Cost. Each analyst is an expensive model running up to an hour. On a straightforward deal, five analysts agree the same way three do, just slower and pricier.)</p><p id="">Then two more agents that exist mostly to be skeptical:</p><ul role="list"><li><strong>Enrichment</strong> attaches full CRM data to every touchpoint in the consensus. Its instruction is to over-query rather than under-query. It also holds veto power: if the consensus contains a citation to a record that doesn&rsquo;t exist, it rejects the whole deal back to re-analysis. This is our hallucination firewall.</li><li><strong>Canonicalization</strong> maps every touchpoint onto a per-customer classification tree and influence taxonomy, so results aggregate across deals. Its bar for every dimension value is what we call the rollup test: &ldquo;would a GTM leader reading a report grouped by this dimension find this value useful and truthful?&rdquo; It has the same veto power.</li></ul><p id="">One prompt-design principle runs through all of them, straight from the structural rules doc: <strong>exhaust before escaping.</strong> Every agent has fallbacks (a classification path can stop at team level, a source can be &ldquo;unknown&rdquo;) but using one requires documenting what you tried first. A short path means you looked everywhere and truly could not resolve deeper, not that it was hard and you moved on. Agent time is cheap; a misleading value in a CMO&rsquo;s report is not. Relatedly, we made classification paths variable-depth with explicit null gaps, because &ldquo;we know the team and channel but not the person&rdquo; is a more trustworthy statement than a fabricated person name, and downstream consumers can see exactly where knowledge stops.</p><h2 id="">What comes out the other end</h2><p id="">Per deal, two outputs: a zero-sum multi-touch allocation across all identified influences, and an opportunity source (the system distinguishes the <em>hand-raise</em>, the buyer&rsquo;s decision moment, from the <em>source</em>, the upstream event whose removal would have broken the causal chain). Here&rsquo;s a real (anonymized) deal:</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 47.0rem"><div><img src="/images/blog/pipedash-deal-timeline.png" width="auto" height="auto" alt="An anonymized deal timeline with credit amounts assigned to each touchpoint, including the RFP email flagged as the hand-raise with $0 credit." loading="lazy"/></div></figure><p id="">Note the RFP email flagged HAND-RAISE with $0 credit, while the AE&rsquo;s relationship maintenance during dormancy, the SDR&rsquo;s 12-month outbound sequence, and the webinar carry the dollars. And every touchpoint, including the $0 ones, carries a reasoning trace exposed in the UI, so when someone asks &ldquo;why did the chatbot get nothing but the chatbot <em>campaign</em> get credit,&rdquo; the answer is right there.</p><p id="">The causal chain reads like a <a href="/use-cases/win-loss-intelligence">deal post-mortem</a> written by someone with perfect memory: persistent SDR outreach &rarr; webinar attendance &rarr; post-webinar follow-up &rarr; discovery and demos &rarr; budget dormancy &rarr; relationship maintenance &rarr; buyer re-initiates RFP. Then everything <a href="/use-cases/campaign-intelligence">rolls up across the portfolio</a> by team, channel, campaign, and salesperson, which is where it starts looking like a normal attribution dashboard, except the numbers underneath are built from evidence instead of curve positions.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 47.0rem"><div><img src="/images/blog/pipedash-channel-rollup.png" width="auto" height="auto" alt="Portfolio-level rollup of attributed revenue by channel across all analyzed deal

1670s." loading="lazy"/></div></figure><p id="">For the times when leadership does want the ONE thing that got the deal, our model also allows you to see the one activity that got this deal moving, and run aggregate reporting using that model as well.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 47.0rem"><div><img src="/images/blog/pipedash-causal-chain.png" width="auto" height="auto" alt="Single-touch opportunity source example, showing the redacted causal chain from the upstream sourcing event through to the buyer&rsquo;s hand-raise." loading="lazy"/></div></figure><p id="">The interesting part is that this approach finds sources that were thought impossible to detect: &ldquo;there is no way your model can tell this deal came through an alumni network.&rdquo; It could. Someone had mentioned it on a call, the analyst extracted it, and it survived the judge.</p><h2 id="">This is not the only model</h2><p id="">While we believe this approach outputs the most accurate results in zero-sum B2B attribution, we also know it has its limitations. This is one way to approach attribution powered by a clean and unified data layer, but the possibilities in <a href="/platform/miniapps">what you can build to measure</a> are endless. Examples of other types of measurement our customers built on top of Upside data can be found in our <a href="/resources/miniapp-examples">miniapp library</a>.</p><h2 id="">Limitations</h2><ul role="list"><li><strong>It&rsquo;s expensive and slow.</strong> Three to seven Opus-tier agents at up to an hour each, per deal. This works because B2B deal counts are small (the same fact that killed statistical models makes agent reasoning affordable). If you have 50,000 small deals a quarter, this architecture is not for you.</li><li><strong>No immediate signals.</strong> This is the type of model you run once the deal is closed to help you understand where to invest, not something that gives you short-term signal on whether a campaign or tactic is working. Metrics like influence or number of opportunities created post engagement are better measurements for that type of attribution.</li><li><strong>It needs a clean data layer.</strong> Two years of identity resolution and extraction work is doing a lot of lifting here. Pointed at a raw CRM export, the analyst might struggle to make sense of duplicate identities, accounts, and the rest.</li><li><strong>The full reasoning is extensive.</strong> Each touchpoint&rsquo;s trace is exposed in the UI, but the complete analysis runs many pages and nobody consuming a marketing report wants that. We&rsquo;re still figuring out the right way to surface it.</li></ul><p id="">We packaged the whole orchestration layer as a product called <a href="/platform/pipedash">Pipedash</a>, and the underlying <a href="/platform/mcp">data layer is queryable</a> if you&rsquo;d rather build your own version of this on top. Alex and I did a <a href="https://youtu.be/6PbHptEcQ-c?si=N-1kjyZcsa-ZLs-p">full webinar walkthrough</a> with live examples if you want the video version. And yes, if you submit a demo request after reading this, the form itself will get 0% credit in our system. This blog post will get it instead.</p>',"agentic-gtm-is-here-your-data-isnt-ready-for-it":`<p id="">For as long as there’s been B2B, hiring more humans has been the best way to scale go-to-market. <strong>That age is over</strong>. People still matter (the exceptional ones more than ever), but headcount need no longer be the default scaling lever: the teams pulling ahead today are deploying agents, building internal tools, and running analyses to answer questions that would seem miraculous to someone visiting from even as recently as 2023.</p><p id=""><strong>This is the new age of agentic GTM,</strong> and the winners are leaning in far beyond <em>‘give everyone Microsoft Copilot seats and call it a day’. </em></p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 47.0rem"><div><img src="/images/blog/agentic-gtm-1-the-quest-begins.png" width="auto" height="auto" alt="Comic — “The Quest Begins.” A GTM team sets off down the path to AI-ready revenue intelligence, dwarfed by a dragon-topped mountain of messy GTM data labeled Salesforce, HubSpot, Gong, spreadsheets, product logs, and misc.csv." loading="lazy"/></div></figure><p id="">You probably know all of that, and you've heard the next part too, from about a dozen vendors in the last quarter: <strong>your data isn’t ready for this.</strong> It’s the detritus of a tech stack built for humans working with records by hand. The agents, internal tools, and analyst-quality reports that help you scale in this new age can be afternoon projects now…but not until you have a clean, AI-native data layer to power them.</p><p id=""><strong>AI is only as good as the data under it, and B2B data is a swamp.</strong> We've had dirty CRMs for thirty years, but people are astonishingly good at mentally repairing the mess. AI isn’t. It can't build that context graph on its own. For the age of agentic GTM, you need a governed layer that reconstructs what actually happened and makes it usable by humans, agents, and apps.</p><hr><h2 id="">GTM circa 2026: valedictorian interns, new heroes, and a tech stack of the walking dead</h2><p id="">Our team has talked to hundreds of go-to-market leaders over the past two years. <strong>The future of agentic GTM is here; it’s just not evenly distributed yet.</strong> If you’re already living in the new world, nothing in this section will be news and you should probably ski

1670p it. But if your organization is just getting started on its AI-fueled journey of self-transformation, here is a quick state-of-the-industry for agentic GTM in mid-2026:</p><ul role="list"><li><strong>AI is ‘valedictorian intern on demand’.</strong> It’s book-smart as hell, a little too self-confident, and still lacking real-world experience. But it’s also able to do an absurd amount of impressive work when you point it in the right direction.</li><li><strong>We’re witnessing the rapid emergence of the first truly new GTM role in many years.</strong> Whether you call them <a href="/gtme">GTM Engineers</a> (that job title has come a long way since Clay coined it as a synonym for 'outbound automation manager'), Chief AI Intern Wranglers, or simply <em>‘the people who know how to turn data into intelligence and ship tools that everyone else can use’</em>, they’re force-multipliers for their teams. <strong>They are the heroes of this new age, </strong>and nearly every org we talk to is either hiring this role or trying to grow it internally.</li><li><strong>The traditional GTM tech stack is decomposing into its elemental primitives.</strong> LinkedIn influencers would have us all believe that we’re in the middle of the SaaSpocalypse and legacy products are just waiting around to die. This is overblown, of course, but many purpose-built tools are no longer necessary when Claude can do the same things for you over an MCP, or a GTME can spin up a better-fit internal replacement in a weekend.</li></ul><h2 id="">If B2B GTM has a Rosetta Stone, attribution is probably it</h2><p id="">At Upside, we discovered the importance of a GTM data layer while trying to do something else: in early 2024, our team set out to help B2B go-to-market orgs make better decisions by building the best multi-touch attribution model in the world. We eventually succeeded (it's called <a href="/platform/pipedash">Pipedash</a>, and it’s quite good), but that’s the headline for a different article.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 42.0rem"><div><img src="/images/blog/agentic-gtm-2-attribution-rosetta-stone.png" width="auto" height="auto" alt="Comic — “Attribution is a Rosetta Stone.” Archaeologists brush off a stone tablet that translates tangled GTM signals — same person, buying group, touchpoint order, campaign names, the 2017 ABM layer, CRM history — into clean, joined entities." loading="lazy"/></div></figure><p id="">Later on, we realized what we’d <em>actually</em> built.<strong> </strong>The quest to fix attribution had forced us to reckon with the encyclopedia of all the ways that B2B GTM data can get screwed up: figuring out which contacts are really the same person, reconstructing who was actually in each buying group, putting scattered touchpoints back in the right order, untangling campaign naming conventions that are an archaeological dig through who was in charge back when ABM became fashionable in 2017.</p><p id="">We'd thought of it all as plumbing for an attribution model, but this created exactly what our customers were missing for all their other GTME projects: <strong>an AI-native GTM data layer.</strong> We just had to turn on the lights and open it up to all the places GTMEs were already working.</p><h2 id="">Established teams need to defeat the three bosses of agentic GTM…with a handicap</h2><p id="">Today, Upside works primarily with <a href="/customers">large, established B2B companies</a>. We help them transform their go-to-market data swamp into something AI can use. We find they are drowning in raw data, but typically still feel like they have no answers. In fact, they often can’t even get access to many of the new tools that AI-native startups consider critical, because the compliance team is busy working through how to update their evaluation rubrics for the age of AI.</p><p id="">The good news is these coins flip over to reveal strengths on the other side: years (sometimes decades) of history worth mining, plus the playbooks, the operational discipline, and the customer relationships that startups don’t even realize they’re missing. But if you’re part of a more established organization that is learning to play the new agentic GTM game, you have some work ahead of you.</p><p id="">Here are the three big bosses we see these teams struggle with most often, and the tactics to beat each one. Look carefully, and you’ll notice that all three tie back to the same missing layer: governed GTM context that agents, apps, and humans can use safely and effectively.</p><h3 id=""><strong class="it-heading-accent">Boss 1:</strong> The best AI tools aren't allowed anywhere near your data</h3><p id="">After nearly 50 years, the spreadsheet finally has a competitor: general-purpose AI harnesses (Claude, Codex, Cursor) are the new 'default business tool'.</p><p id="">But not everyone has access yet, because what works at a hot startup (‘everyone gets a license, connect it to whatever you want’) is a non-starter in a Fortune 500 company, where pointing an agent at production data is how you accidentally create an insider-trading problem instead of a productivity gain.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 40.2rem"><div><img src="/images/blog/agentic-gtm-3-boss-1-ai-governance-ogre.png" width="auto" height="auto" alt="Comic — “Boss #1: AI Governance Ogre.” An AI Governance Committee ogre guards customer data, PII, and secrets, blocking agents from production data." loading="lazy"/></div></figure><p id="">So when the IT team hands people a locked-down, enterprise-approved derivative, the gap between the real thing and the sanctioned version is the difference between ‘game-changing’ and ‘frustrating, unusable trash’.</p><p id=""><strong>The fix: </strong>don't accept the ‘pick one’ framing of frontier AI vs. governance, because you can have both. Build the data layer that lets state-of-the-art AI operate safely. If that means a dataset sandboxed inside a safe-by-default shell to enforce security and permissions, so be it. Datasets scoped by role, permissioned hosting for generated apps. Focus on making the <em>environment</em> secure, and if you absolutely cannot get approval for a best-in-class generic AI harness like Claude, Codex, or Cursor, make sure you stay as close to that layer as you can instead of taking the easy, bundled-with-your-office-suite way out.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 49.9rem"><div><img src="/images/blog/agentic-gtm-4-defeating-the-governance-ogre.png" width="auto" height="auto" alt="Comic — “Defeating the AI Governance Ogre.” The boss falls to boring-sounding tools: a safe-by-default shell, permissioning, and governance — policies, audit logs, access reviews, separation of duties, insider-trading prevention." loading="lazy"/></div></figure><h3 id=""><strong class="it-heading-accent">Boss 2:</strong>

1670 The quality of your 'boring data infrastructure' actually matters a lot</h3><p id="">You can point your AI agent directly at Salesforce today. It'll even dig up some interesting trivia. Unfortunately, a typical enterprise CRM instance is somewhere between a geological record and a garbage dump — much like crude oil, the value is in there but it’s not something you should put directly into your car.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 41.0rem"><div><img src="/images/blog/agentic-gtm-5-boss-2-data-quality-hydra.png" width="auto" height="auto" alt="Comic — “Boss #2: Data Quality Hydra.” A many-headed hydra of lead, contact, account, opportunity, workspace, user, buying committee, deal, and pipeline, with one company logged three ways as Acme, Acme Inc., and Acme?." loading="lazy"/></div></figure><p id="">Let's say you're trying to run a deal analysis. You’re going to need signals scattered across Salesforce, HubSpot, Gong, and your ad platforms, all overlapping, half-logged, none of it joined. If you — a knowledgeable-on-GTM human — open all those sources in different browser tabs, it'll take you a couple hours to piece through the unstructured mess, untangle all the overlap, and develop a thesis about what is actually going on in that single account. If you give that same task to a skilled data analyst (but one without your GTM knowledge), they'll probably be quicker at the mechanics but more likely to miss the forest for the trees about what it all means.</p><p id="">Claude is that data analyst wearing rocket boots. Yes, you can connect it to all of those sources directly (via MCP, or even an intermediate warehouse like Snowflake or BigQuery), but that very much does not take care of the mess. Copilot it through the process carefully and you can do something great once; you'll start from scratch next time, and so will everyone else on your team.</p><p id=""><strong>The fix: </strong>materialize the data fabric first, so your agents can understand it intuitively and aren’t reinventing how everything connects in each new session.</p><ul role="list"><li><strong>At minimum:</strong> document an ontology of how your sources relate and make sure your agent knows about it.</li><li><strong>Better:</strong> create a joined data lake on a shared schema.</li><li><strong>Best:</strong> treat every upstream system as a source of signal (not a source of truth), and reconstruct a knowledge graph of what actually happened.</li></ul><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 46.5rem"><div><img src="/images/blog/agentic-gtm-6-three-levels-of-weapons.png" width="auto" height="auto" alt="Comic — “Beating Boss #2: Three Levels of Weapons.” At minimum, an ontology of sources; better, a joined data lake on a shared schema; best, a knowledge graph of what actually happened." loading="lazy"/></div></figure><h3 id=""><strong class="it-heading-accent">Boss 3:</strong> Nobody trusts an answer they can't trace</h3><p id="">AI is non-deterministic. Just like with people, you get different answers if you ask the same question twice. But with AI, add in that you're hiring a fresh intern straight off the street for each new thread, and that means they come with only what they learned in school — they haven't gone through onboarding for your company yet.</p><p id="">Worse, the quality of AI results still depends on the skill of the human operator in non-obvious ways. Did Jane, the event manager, know to batch fifty deals across subagents so the run didn't get lazy halfway through? To check meeting transcripts for event attendance, not just the Salesforce campaign? That ‘pipeline’ here means deals past Qualification?</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 50.0rem"><div><img src="/images/blog/agentic-gtm-7-boss-3-trust-sphynx.png" width="auto" height="auto" alt="Comic — “Boss #3: Trust Sphynx.” A sphinx labeled DATA TRUST asks “Why should anyone believe the answer?”, guarding the door to answers people will actually believe." loading="lazy"/></div></figure><p id="">A small, AI-native team improvises around that. A large, more established one can't afford multiple answers to the same question if they differ in definitionally critical ways.</p><p id=""><strong>The fix: </strong>architect the system so that individual operator skill can’t become a failure. This means encoding the scaffolding and context engineering that your best operators use to get great results, and then applying it automatically when Jane the event manager asks a basic-sounding question about campaign performance.</p><ul role="list"><li><strong>Bad: </strong>trust the AI to figure it out. Models get better all the time, plan mode and the ask-user-question tool help a lot, and there have been multiple step-function improvements in intelligence over the past year. But remember the overconfident intern; this is where the bill comes due.</li><li><strong>Better:</strong> invest in solid documentation about how the data is structured, with a context layer of ‘anchor assets’ that you keep up to date as onboarding material for each new agent session (skills and agent-specific memory are this category in a trench coat).</li><li><strong>Best:</strong> give the agents live, just-in-time coaching (this is how Upside’s <a href="/platform/radiant">Radiant</a> active context layer works). Train a 'librarian agent' that they can go to for advice before each query. Set the librarian up with context on how your data works (including shared memories about org-specific nuance), and design a continual learning loop that extracts new entries w

1670orth recalling from day-to-day usage. This way, wins compound team wide and no one is starting from scratch.</li></ul><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 37.5rem"><div><img src="/images/blog/agentic-gtm-8-bad-good-better-librarian-agent.png" width="auto" height="auto" alt="Comic — “Bad, Good, Better.” Bad: tell the AI to figure it out. Good: a shelf of documentation and anchor assets. Better: a librarian agent that briefs each new agent on how the company’s data works, so wins compound team-wide." loading="lazy"/></div></figure><p id=""><strong>Bonus:</strong> a few tactics and tools that we’ve found help with overall trustworthiness:</p><ul role="list"><li>Make sure you have non-AI audit and traceability tooling, so users can ‘go to ground’ in the data layer when needed. Give them an old-fashioned tabular dashboard with filters and a visual timeline explorer, where they can investigate by hand. For new users, these things can also help with the cold start problem of an empty chat window.</li><li>When you use AI to generate deep-researched outputs, do it once and store them for queryable reference in future. This is both more consistent and cheaper than running the research on demand every time someone needs the answer.</li><li>For more subjective, high-complexity questions, use jury-and-judge workflows (for example: a panel of three analyst agents independently run the same investigation, and then a separate evaluator is responsible for verifying their findings and normalizing the final answer).</li></ul><h2 id="">What happens when you win</h2><p id="">If you want to work agentically and are already operating beyond the scale of ‘vibecoders in a Blue Bottle’, <strong>you need a GTM data layer.</strong> This is the single greatest accelerator your team will find, and you’ll see a Cambrian explosion of creativity once you have it: the person you had filed under ‘average’ starts shipping brilliant things nobody thought to ask for, and everyone gets more data-driven because the data is finally on tap (for examples of what we mean, <a href="/resources/miniapp-examples">check out this gallery of miniapps</a> inspired by things we’ve seen actual Upside customers create).</p><p id="">But be careful before you sign up to rebuild the boring infrastructure yourself. This layer does not come out of the box with Claude, and unless you happen to have a spare top-tier engineering team just hanging around, an in-house solution quickly becomes a trap — a constant hope that ‘just one more fix’ will finish the project and finally make things stable. <strong>Because it won't.</strong> <a href="https://openai.com/index/inside-our-in-house-data-agent/">OpenAI</a> and <a href="https://claude.com/blog/how-anthropic-enables-self-service-data-analytics-with-claude">Anthropic</a> have both published long write-ups of the un-glamorous, non-model work they had to do before their own internal data agents became usable. You wouldn’t build all of Excel just to use a spreadsheet, and your company probably shouldn’t run its own go-to-market context graph either.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 60.7rem"><div><img src="/images/blog/agentic-gtm-9-one-weapon-dragon-on-gold.png" width="auto" height="auto" alt="Comic — turning the tools into one weapon (observability, lineage, testing, guardrails, monitoring, policies forged into a single source of truth), and discovering the dragon was just sitting on a hoard of clean data, trusted metrics, complete history, context, and growth gold." loading="lazy"/></div></figure><p id="">That's why Upside exists. We believe AI makes it possible to ask better questions, run deeper analyses, and build internal tools faster. But that only works if GTM engineers have a clean, normalized data layer that covers every GTM team. We extract the raw signals from everywhere they exist across your systems and transform them into a knowledge graph, the single best source of what actually happened in your accounts. Then we make that data available in all the places and ways you need: a powerful MCP, analytics dashboards for your broader team, and hosted interactive miniapps with live data, all in a way that won't get you in trouble with your compliance team. <strong>We keep the boring part boring so that your team can do the exciting stuff.</strong></p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" style="max-width: 38.9rem"><div><img src="/images/blog/agentic-gtm-10-to-the-promised-land.png" width="auto" height="auto" alt="Comic — “To the Promised Land.” With trusted data, the GTM team reaches a Cambrian explosion of growth and creativity: miniapps built by GTM teams, Customer 360, a deal desk copilot, an account planning agent, a pipeline forecaster, win/loss insights, a content generator, and a churn risk detector." loading="lazy"/></div></figure><p id="">We’ve been building in the GTM space for a long time, and this is the most fun we've ever had. If you’re already duct-taping this together by hand, you're ahead of the curve, and you also don't have to do it alone. The old GTM stack was built for humans updating records. The next one will be built for humans, agents, and apps reasoning over context.</p><p id="">Upside is the AI-native data layer for GTM engineers. <a href="/platform">Come build the age of agentic GTM with us!</a></p>`,"how-to-build-the-ultimate-win-back-sales-kit":`<p id="">Every sales team has a pile of closed-lost deals they treat as dead in the water. It's unfortunate, but it's true. The usual next step is a generic "just checking in, here's what's new" email, written reluctantly or fired off by an automated workflow, hoping someone bites. But that's the same recycled outreach that lost the deal in the first place. If a prospect passed once, why would the second attempt land any better?</p><p id="">So we tested an alternative: custom win-back kits for 100 closed-lost accounts that saw a demo once and passed. The result was outstanding. Turns out teams are far more willing to re-engage when the follow-up isn't mass produced. Who would have thought! Deals aren't dead, and treating them that way only does more damage. The follow-ups are what's dead. Here's how y

1670ou can turn them around too.</p><h2 id="">Building it</h2><p id="">So how do you actually put one of these together? The recipe is simpler than it sounds. Tell the agent what you want, point it at your data, and let it work. Here's the sequence we followed.</p><p id="">Prior to building, do some thought work to identify the sections you want in every account's kit. The strongest kits we've seen from customers include:</p><ul role="list"><li><em>Why you lost the deal.</em> Sometimes this is well documented, but often it has to be inferred from account history. AI is already capable of this when you give it the data in a structured format. Upside helps with the unstructured, parsing and ingesting CRM notes, call transcripts, and email threads to give you a full understanding of the deal history.</li><li><em>What the customer cared about.</em> Pull this from a summary of all previous opportunity notes and calls, including transcript summaries.</li><li><em>Talking points and objection handling.</em> Map each contact's role to their business concerns.</li><li><em>A draft email sequence.</em> Write one for every company and contact.</li><li><em>A targeted approach plan by person.</em> Decide the specific play that earns the next meeting with each contact.</li></ul><p id="">The best plans get personal. One contact collected wine, so the play was a gift box to earn the meeting. Another account's budget planning cycle was about to open, making the timing finally right. A third had a pain point the product now solves outright. Each is a different door in, and the kit tells the rep which one to use.</p><p id="">Now here are the steps to create it and scale it with AI:</p><h3 id="">1. Pull the external signals (Tool: Exa)</h3><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/win-back-recent-company-news.png" width="auto" height="auto" alt="Recent Company News panel surfacing partnership, recognition, and growth signals for a closed-lost account, with an insight summary" loading="lazy"/></div></figure><p id="">Getting up to date with customer and industry news is essential for generating an updated, bespoke approach.</p><ul role="list"><li><em>Leverage an external signal engine to surface what's going on at an account in real time.</em> We suggest Exa: its strength lies in niche, specific discovery and it's built for AI-native and semantic querying.</li><li><em>Look for funding, hiring, leadership shuffles, expansion, or new initiatives.</em> Find anything that hints a closed-lost account's situation has changed since they passed. This puts you in a great position to find new angles in with their team.</li></ul><h3 id="">2. Contextualize all signals (Tool: Upside)</h3><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/win-back-account-context.png" width="auto" height="auto" alt="Account context card showing tech stack, business context, why they lost, what's changed since then, and key internal notes" loading="lazy"/></div></figure><p id="">An external signal means nothing without the history sitting behind it: what the account evaluated, who was in the room, and why they walked.<

1670/p><ul role="list"><li><em>Unify and deduplicate your touchpoints for internal sources.</em> The agent needs one clean account story instead of three systems stitched together that all disagree with each other. This is the part Upside handles for you.</li><li><em>Connect data from external signals to cleaned internal data.</em> The goal is to have every external signal land against the real context of the deal instead of floating on its own.</li></ul><h3 id="">3. Answer the question that lost the deal (Tool: Claude x Upside MCP)</h3><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/win-back-why-they-lost.png" width="auto" height="auto" alt="Why They Lost and What's Changed Since Then summary for a closed-lost account" loading="lazy"/></div></figure><p id="">A kit that can say "you passed because of X, and here's exactly what we fixed" is worth more than a hundred generic feature lists. That's the aim here.</p><ul role="list"><li><em>For each account, use AI to pin down why you actually lost.</em> Find the objection, the gap, the dealbreaker. These results determine the perspective with which you can approach the buying group.</li><li><em>Answer the question head-on: have you solved it since?</em> Take the enhancements you've shipped since they last looked and tie each one back to that account's specific interests for the highest chance of re-engagement.</li></ul><h3 id="">4. Arm the rep (Tool: Upside)</h3><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/win-back-rep-enablement.png" width="auto" height="auto" alt="Recommended talking points and recommended case studies and content tailored to a closed-lost account" loading="lazy"/></div></figure><p id="">Build the part the rep actually uses. The more details you include, the easier it will be for the rep to act upon the information and close deals.</p><ul role="list"><li><em>Map out the important contacts and what each one cares about.</em> Pull from both first-party conversations and third-party research. Knowing who to engage is just as important as knowing how to engage; the right person can open a new door for you.</li><li><em>Turn the contact list into talking points and objection handling.</em> Map each person's role to their business concerns. By anticipating personalized concerns, reps come to the meeting one step ahead.</li><li><em>Draft an email sequence for every company and contact.</em> Trace each one back to something real that account said or a signal you just surfaced — personalization is the key here.</li></ul><h3 id="">5. Scale and filter (Tool: Upside)</h3><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/win-back-account-overview.png" width="auto" height="auto" alt="Roofing Win-Back Account Overview: a filterable table of closed-lost accounts with owner, signal strength, and summary and strategy" loading="lazy"/></div></figure><p id="">Run the whole thing across your list.</p><ul role="list"><li><em>Build a single filterable view where every account shows its owner, signal strength, and recommended strategy.</em> This is what turns a hundred kits into something a rep can actually work, sorting the full list down to the ten worth a call this morning, instead of staring at a wall of accounts and not knowing where to start. Upside's MCP can help build this view with live data.</li></ul><h2 id="">What we didn't expect</h2><p id="">The agent went well beyond our initial brief, and two surprises stood out. Pointed at our positioning docs, it started generating mini battle cards tailored to each account's specific competitors. Pointed at the case study library, it pulled the exact stats and testimonials that mapped to each account's concerns, without ever being told which to use.</p><p id="">This is a pattern we keep seeing: once the data foundation is in place (which Upside handles for you), the agent finds angles a human would never have time to chase. The kit got better than the person who specced it.</p><p id=""><strong>Try it yourself</strong> — drop this into an agent connected to the Upside MCP:</p><pre class="copy-prompt" style="background:#f5f5f7;color:#1d1d1f;padding:1.25rem 1.5rem;border-radius:10px;font-size:0.9rem;line-height:1.6;overflow-x:auto;margin:1.5rem 0;border:1px solid #d2d2d7;white-space:pre-wrap;word-wrap:break-word;"><code style="font-family:inherit;font-size:inherit;color:inherit;background:none;">Build me a Win-Back Sales Kit for my closed-lost accounts that reached a demo before passing. For each account, generate: a re-engagement assessment with a signal-strength score and recommended strategy; an account narrative covering what they evaluated, why they passed, and recent news that makes now worth a second look; a contact map with what each decision-maker cares about and the objections they raised; the product enhancements shipped since they last engaged, mapped to those specific objections; the case studies and proof points that match their concerns; and a draft email sequence per contact. Then give me a filterable view across all accounts with owner, signal strength, and strategy.</code></pre><h2 id="">The result</h2><p id="">With Upside, going from idea to draft took a day. Implementing feedback took another. Two days, start to finish, for a custom system covering a hundred accounts, each with their own narratives, contacts, and history.</p><p id="">The reason closed-lost lists go stale is not about strategy; everyone knows that a personalized approach beats a generic one. It was the time, effort, and money going into each view. A hundred accounts, analyzed properly, takes weeks of manual work nobody has the bandwidth to do. Lists sit untouched or they get a lazy blast that goes unnoticed.</p><p id="">When dossiers are rebuilt with AI and context, the story changes. Reps actually start re-engaging and selling, walking into calls already knowing what the account saw last time, why they passed, what's changed, and which proof points to lead with. The prep that used to define the ceiling of how many accounts you could reopen doesn't serve as a constraint anymore, and closed-lost deals start winning.</p>`,"how-to-build-an-email-send-time-optimizer-using-the-upside-mcp":`<p>Marketers have all heard the traditional advice for sending outbound emails: stick to Tuesday mornings, avoid Fridays, and don't even think about weekends. It <em>sounds</em>

1670 reasonable, but most of this guidance is <strong>just recycled best practice</strong> rather than insight tailored to your specific audience. GTM leaders don't need more generic benchmarks; they need to understand the rhythms of the people they are actually trying to reach.</p><p>In practice, when you look across SDR, AE, and executive outbound and marketing emails, the data is pretty messy. To address the ambiguity of outbound timing, we built the Send Time Optimizer, using the power of the Upside MCP to reconstruct complex email chains and unify engagement signals.</p><h2>Here's how you can do it too</h2><h3>1. Consolidate</h3><p>Gather all of your interactions into a single, unified view. Make sure all contacts are deduplicated and people are attributed to the correct identity. Upside users automatically have this handled for them.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/send-time-optimizer-touchpoints.png" width="auto" height="auto" alt="Upside Touchpoints view showing thousands of unified email and meeting touchpoints across executive, marketing, and SDR activity" loading="lazy"></div></figure><h3>2. Filter</h3><p>Differentiate between direct outbounding and broader marketing initiatives (such as email blasts), as each generates distinct behavioral signals. This can be done by filtering in a dashboard view or by querying your data directly with an AI agent such as Claude or Cursor.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/send-time-optimizer-touchpoints-filtered.png" width="auto" height="auto" alt="Touchpoints view filtered to direct email only, separating personal outbound from bulk marketing sends" loading="lazy"></div></figure><h3>3. Clean, audit, and normalize</h3><p>Audit the timestamps and responses attached to all emails for accuracy, so they reflect true outbound activity rather than bulk emails. Critically, add logic that addresses the friction of conflicting timezones within threads. In Upside this is already handled for you, and a quick prompt lets you toggle between timezones so you can optimize for yourself <em>and</em> the receiver in the final product (along with anyone else who has enterprise-level permissions to the dashboard you build).</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/send-time-optimizer-timezone-toggle.png" width="auto" height="auto" alt="Timezone toggle switching the Send Time Optimizer between CT, ET, and PT" loading="lazy"></div></figure><h3>4. Contextualize</h3><p>Contextualize response rates with total volume in mind, so you are not misled by outliers in low-traffic windows. Even better if you can switch between a few options. We want as much information as possible here.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/send-time-optimizer-volume-filter.png" width="auto" height="auto" alt="Minimum weekly volume filter that sets low-traffic time slots aside" loading="lazy"></div></figure><h3>5. Visualize</h3><p>Combine all of this by leveraging an AI agent to build a visual interface that maps engagement signals like opens and clicks against timing. Here's the example we built using the Upside MCP:</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/send-time-optimizer-heatmap.png" width="auto" height="auto" alt="Send Time Optimizer dashboard with KPI cards and a response-rate heatmap by day and hour, highlighting Tuesday at 5:00 am as the best window" loading="lazy"></div></figure><p><strong>Try it yourself.</strong> Copy this prompt and drop it into an AI agent connected to the Upside MCP:</p><pre class="copy-prompt" style="background:#f5f5f7;color:#1d1d1f;padding:1.25rem 1.5rem;border-radius:10px;font-size:0.9rem;line-height:1.6;overflow-x:auto;margin:1.5rem 0;border:1px solid #d2d2d7;white-space:pre-wrap;word-wrap:break-word;"><code style="font-family:inherit;font-size:inherit;color:inherit;background:none;">Build me a Send Time Optimizer dashboard. Pull all direct email touchpoints from the last 12 months, excluding bulk marketing sends. Normalize timestamps to the recipient&#x27;s timezone. Create a heatmap showing response rates by day of week and hour of day, with a minimum volume filter to exclude low-traffic windows. Include KPI cards for overall response rate, best send window, and worst send window. Add a timezone toggle so I can switch between sender and recipient perspectives. Also include a monthly trend chart comparing response rate against email volume, a day-of-week breakdown, and a ranked list of the top best and worst send windows.</code></pre><h3>Bonus</h3><p>Build out additional table and graph views that visualize the data broken down further.</p><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/send-time-optimizer-monthly-trend.png" width="auto" height="auto" alt="Monthly trend chart comparing response rate against email volume over time" loading="lazy"></div></figure><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/send-time-optimizer-day-of-week.png" width="auto" height="auto" alt="Day-of-week response rate summary across business hours" loading="lazy"></div></figure><figure class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div><img src="/images/blog/send-time-optimizer-best-worst-windows.png" width="auto" height="auto" alt="Top eight best and worst send windows ranked by response rate" loading="lazy"></div></figure><h2>Common pitfalls</h2><p>These are where standard analysis tends to get tripped up. A reliable dashboard will account for these inconsistencies.</p><ul><li>Not identifying hidden engagement and activity, which is frequently masked as simple CRM entries without context.</li><li>Not managing time variances by directing the system to normalize data based on the recipient's specific geographic location.</li><li>Not adding a volume filter that sets low-traffic time slots aside, so a handful of off-hours email sends don't distort the recommendations as a whole.</li></ul><h2>The result</h2><p>When implemented correctly, the Send Time Optimizer lets teams prioritize their most important outreach during high-performance windows while shifting lower-priority tasks to "dead zones." Having this level of detail transforms send timing from a game of chance into a pillar of the outbound effort.</p><p>Developing this app revealed some unconventional patterns:</p><ul><li>The optimal send window was Tuesday at 5:00 am.</li><li>The least effective window was Tuesday at 9:00 am.</li><li>The overall response rate reached 27%.</li></ul><p>We could not have surfaced these insights without building this custom dashboard using Upside.</p><h2>How Upside makes this effortless</h2><p>The magic of the Upside platform lies in the data foundation. We take on the work of cleaning, normalizing, and connecting your fragmented go-to-market data so you walk away with clear advantages:</p><ul><li>Teams spend time asking questions instead of wrestling with spreadsheets.</li><li>Natural language prompts allow for instant generation of insights and dashboards.</li><li>Relevant filters and logic are applied automatically based on your descriptions.</li></ul><p>Upside represents a fundamental shift away from static reports toward a dynamic environment where insights are generated in real time.</p><p>By removing the chasm between a business question and a data-backed answer, we ensure that your outbound strategy keeps evolving alongside your audience's behavior. Your most ambitious ideas for custom applications and data tools are no longer destined to be stuck in a technical backlog; instead, they are a prompt away from becoming reality. This accessibility empowers everyone from executive leadership to frontline contributors to make data-driven decisions that compound revenue and build stronger connections with customers.</p>`,"best-marketing-attribution-tools-software-b2b":`<p id="">You've installed the marketing attribution tools. You've connected the integrations. And yet, when the CFO asks which campaigns actually drove revenue last quarter, the room goes quiet.</p><p id="">You're not alone. According to Gartner's 2025 survey of 174 senior marketing leaders, only 30% of CMOs feel confident in their attribution data. That's a striking number for an industry that spends billions on measurement te

1670chnology every year.</p><p id="">Here's the uncomfortable truth most attribution tool roundups won't tell you: the problem isn't which tool you pick. The problem is that every tool on the market is modeling incomplete data. Dreamdata's 2025 B2B Benchmarks Report found the average B2B customer journey involves 76 touchpoints across 211 days. But most CRM systems capture a fraction of those interactions. Your attribution tool can only score what it can see.</p><p id="">So before we get into the best marketing attribution software for B2B teams in 2026, we need to talk about what actually makes attribution work (and why most tools fail before they start).</p><h2 id="">Why Most Marketing Attribution Tools Fail Before They Start</h2><p id="">Every attribution tool on the market solves the same problem: assign credit to marketing touchpoints. First-touch, last-touch, multi-touch, algorithmic. The models differ. But they all share one assumption.</p><p id=""><strong id="">They assume the data is there.</strong></p><p id="">It isn't. Research from multiple sources estimates that most brands are making attribution decisions on data that is missing 50-70% of actual touchpoints. When 97% of B2B website traffic is anonymous (6sense/Unbounce 2023) and buying committees average 13 internal stakeholders plus 9 external participants (Forrester 2025), the interactions that matter most are often the ones nobody logged.</p><p id="">Think about it: the casual intro over coffee that led to a champion referral. The Slack DM from a peer recommending your product. The three internal meetings where your brand came up but no one tagged a campaign. These are phantom touchpoints, and they're invisible to every traditional attribution tool.</p><p id="">67% of marketers cite data silos as their top attribution challenge (Gitnux 2026). But the issue runs deeper than silos. Even with perfectly connected systems, you're still missing the interactions that happen outside tracked channels.</p><p id="">As marketing strategist Kathleen Schaub put it: <em id="">"Customer journeys look less like a linear funnel and more like a child's scribble. Markets are complex systems, not predictable machines attribution tries to mold them into."</em></p><p id="">So what's the fix? You have two options. Accept that attribution will always be partial and use it directionally. Or invest in tools that recover missing signals before trying to model them.</p><p id=""><strong id="">Forensic attribution</strong> takes the second path. Instead of starting with a model, it starts with data recovery: pulling signals from emails, calendars, meeting transcripts, and web sessions to reconstruct what actually happened. Then it interprets what mattered. It's a fundamentally different starting point, and it's why we built Upside.</p><h2 id="">What to Look for in Marketing Attribution Software</h2><p id="">Not every team needs the same thing. But there are five criteria that separate tools that actually work from tools that generate pretty dashboards nobody trusts.</p><h3 id="">Data completeness and signal recovery</h3><p id="">Does the tool just model what's in your CRM, or does it actively recover missing touchpoints? If it only works with data you've already captured, you're building attribution on a foundation with gaps. Look for tools that connect to email, calendar, meeting transcripts, and web analytics, not just your marketing automation platform.</p><h3 id="">Buying group support</h3><p id="">B2B deals involve committees, not individuals. Forrester's 2025 data shows buying groups now average 13 internal stakeholders. If your attribution tool tracks individual lead journeys instead of account-level buying group activity, it's solving the wrong problem. Ask whether the tool can reconstruct who was actually involved in a deal, including people who never filled out a form.</p><h3 id="">CRM integration depth</h3><p id="">Surface-level Salesforce or HubSpot integrations won't cut it. You need bi-directional sync at the opportunity level, stage history access, and the ability to match touchpoints to specific deals. The best tools pull from CRM, MAP, ad platforms, and communication channels simultaneously.</p><h3 id="">Model flexibility (or model irrelevance)</h3><p id="">Some teams want to compare first-touch vs. multi-touch vs. time-decay. Others have moved past the model debate entirely and want evidence-based deal narratives instead of credit scores. Know which camp y

1670ou're in before evaluating.</p><h3 id="">Time to value</h3><p id="">If setup takes 8 weeks and requires a dedicated analytics engineer, factor that into your decision. Some tools deliver insights within days. Others require months of configuration before they're useful.</p><h2 id="">The Best Marketing Attribution Tools for B2B Teams in 2026</h2><p id="">Here are the tools worth evaluating, organized by what they do best. We've included tools across different price points and approaches, because the right choice depends on your sales motion, data maturity, and team size.</p><h3 id="">1. Upside</h3><p id=""><strong id="">Best for:</strong> B2B teams that want attribution built on complete data, not partial CRM records</p><p id="">Upside is a forensic revenue intelligence platform. Where traditional marketing attribution tools assign credit based on whatever data happens to be in your CRM, Upside starts by recovering the signals that are missing.</p><p id="">The platform connects to Salesforce, email systems, calendars, meeting transcripts (via Gong, Fireflies, or Fathom), and web analytics. It then runs a process called data healing: deduplicating records, resolving identities across systems, and reconstructing the most likely correct version of events. No manual data cleanup required from your team.</p><p id="">Once the data foundation is solid, Upside uses agentic analysis (AI agents that reason across evidence) to generate deal stories: chronological narratives of what actually happened in each deal, with citations from real emails, meetings, and web sessions. Instead of seeing "LinkedIn gets 23% credit," you see the actual sequence of events that influenced the outcome.</p><p id="">Upside also automatically reconstructs buying groups by detecting all deal stakeholders from meeting attendees, email threads, and transcripts. No reliance on manually maintained CRM contact roles.</p><p id="">Founded by Mada Seghete and Dan Ahmadi, who built the attribution model at Branch that scaled the company to $100M+ revenue and a $4B valuation through complex enterprise deals. This is lived experience, not theory.</p><p id=""><strong id="">Strengths:</strong> Signal recovery, buying group reconstruction, deal narratives with evidence, data healing, account-level attribution<br><strong id="">Limitations:</strong> Best suited for B2B teams with Salesforce or HubSpot; not designed for B2C or ecommerce<br><strong id="">Pricing:</strong> Custom, based on data volume</p><h3 id="">2. HockeyStack</h3><p id=""><strong id="">Best for:</strong> B2B teams wanting a broad GTM analytics platform with attribution as one component</p><p id="">HockeyStack positions itself as a GTM intelligence platform that combines attribution with broader analytics, intent data, and AI-powered insights. It pulls data from CRM, ad platforms, website, and product usage to build a unified view.</p><p id="">The platform's AI agents can surface pipeline insights, campaign performance, and lift analysis. It's a wide platform that tries to be the single analytics layer for revenue teams.</p><p id=""><strong id="">Strengths:</strong> Breadth of GTM data, AI agents for analysis, unified marketing and sales visibility<br><strong id="">Limitations:</strong> Breadth comes at the cost of attribution depth; the platform doesn't recover signals from emails, calendars, or meeting transcripts. Still relies on data that's already in connected systems. Pricing is enterprise-level (starting around $1,400/month per G2 reviews).<br><strong id="">Pricing:</strong> Custom, enterprise-focused</p><h3 id="">3. Dreamdata</h3><p id=""><strong id="">Best for:</strong> Mid-market B2B teams wanting CRM-native buyer journey visualization</p><p id="">Dreamdata maps marketing touches to accounts, opportunities, and closed-won revenue using your Salesforce or HubSpot data. Its strength is buyer journey visualization at the account level, showing how multiple stakeholders interacted across channels before a deal closed.</p><p id=""><strong id="">Strengths:</strong> Clean account-level journey visualization, solid CRM integration, good for teams with standardized sales processes<br><strong id="">Limitations:</strong> Setup takes 4-8 weeks. Attribution models are rule-based with limited flexibility. Struggles with complex ABM motions. Doesn't recover signals outside of connected marketing and CRM platforms.<br><strong id="">Pricing:</strong> Free tier available; Team at $999/month; Business at $2,499/month; Enterprise custom</p><h3 id="">4. CaliberMind</h3><p id=""><strong id="">Best for:</strong> Enterprise B2B teams with complex, fragmented data environments</p><p id="">CaliberMind is a GTM intelligence platform built on an enterprise-grade marketing data warehouse. It handles custom Salesforce objects, unique workflows, and complex business logic that break other tools. Multi-touch attribution, ABM insights, account scoring, and funnel analysis in one platform.</p><p id=""><strong id="">Strengths:</strong> Handles enterprise data complexity, flexible data modeling, strong strategic consulting support<br><strong id="">Limitations:</strong> Requires significant implementation investment. Best for large orgs with dedicated marketing ops. Not self-serve.<br><strong id="">Pricing:</strong> Custom, enterprise</p><h3 id="">5. SegmentStream</h3><p id=""><strong id="">Best for:</strong>

1670 Paid media teams spending $50K+/month who need attribution tied to budget optimization</p><p id="">SegmentStream combines ML-powered multi-touch attribution with incrementality testing, marketing mix modeling, and predictive lead scoring. It's specifically built for teams that want attribution outputs to directly inform spend decisions.</p><p id=""><strong id="">Strengths:</strong> Attribution connected to budget optimization, incrementality testing, predictive scoring<br><strong id="">Limitations:</strong> Focused on paid media channels. Less suited for teams where organic, events, or sales-assisted touchpoints drive most pipeline. Requires meaningful ad spend to be useful.<br><strong id="">Pricing:</strong> Custom</p><h3 id="">6. Ruler Analytics</h3><p id=""><strong id="">Best for:</strong> Inbound-heavy B2B companies with significant phone and form-based lead flow</p><p id="">Ruler Analytics specializes in call tracking, form attribution, and offline conversion tracking. It connects phone calls and form fills back to the marketing source and pushes revenue data into your CRM.</p><p id=""><strong id="">Strengths:</strong> Call tracking and offline attribution, strong for industries where phone calls drive business, good CRM revenue linking<br><strong id="">Limitations:</strong> Focused on inbound lead attribution. Less useful for complex enterprise sales cycles with buying committees and long timelines.<br><strong id="">Pricing:</strong> Small business at ~$179/month; scales up to enterprise</p><h3 id="">7. Fibbler</h3><p id=""><strong id="">Best for:</strong> Teams focused specifically on LinkedIn and Google Ads revenue attribution</p><p id="">Fibbler is a focused tool that connects LinkedIn and Google Ads performance directly to CRM revenue. It's narrow by design: rather than trying to be a full attribution platform, it solves the specific problem of proving ROI on LinkedIn and paid search spend.</p><p id=""><strong id="">Strengths:</strong> Fast setup (connects to HubSpot and Salesforce in minutes), affordable, impression capping for cost control<br><strong id="">Limitations:</strong> Only tracks digital paid channels. No offline, organic, or event attribution. Google Ads is a paid add-on.<br><strong id="">Pricing:</strong> Growth plan at $89/month; Google Ads add-on at $59/month</p><h3 id="">8. Attribution (AttributionApp)</h3><p id=""><strong id="">Best for:</strong> Growing B2B and SaaS teams taking their first step into multi-touch attribution</p><p id="">Attribution uses automated multi-touch modeling with custom data science algorithms. It provides full-funnel visibility from first touch to close, with features for ABM campaign measurement and ROAS by vend

1670or.</p><p id=""><strong id="">Strengths:</strong> Easy setup (G2 awards for fastest implementation), good entry point for teams new to attribution, ABM-specific reporting<br><strong id="">Limitations:</strong> Lighter than enterprise solutions. May not handle the data complexity of large, multi-product organizations.<br><strong id="">Pricing:</strong> Starting at $399/month</p><h3 id="">9. Google Analytics 4 (GA4)</h3><p id=""><strong id="">Best for:</strong> Early-stage teams that need free, basic attribution before investing in a dedicated tool</p><p id="">GA4 provides data-driven attribution out of the box, analyzing conversion paths to assign credit based on statistical impact. It's free, widely adopted, and integrates with the Google ecosystem.</p><p id=""><strong id="">Strengths:</strong> Free, powerful for web and paid channel analytics, data-driven attribution model included<br><strong id="">Limitations:</strong> Not built for B2B. No account-level attribution, no buying group tracking, no CRM integration by default. Requires significant customization for B2B use cases. Companies implementing multi-touch attribution see 37% more accurate ROI measurement vs. GA4's default models (HubSpot Attribution Report).<br><strong id="">Pricing:</strong> Free</p><h2 id="">Multi-Touch Attribution vs. Marketing Mix Modeling vs. Forensic Attribution</h2><p id="">Most "best attribution tools" articles compare features and pricing. But the more important question is which approach to measurement your team actually needs.</p><p id=""><strong id="">Multi-touch attribution (MTA)</strong> tracks individual touchpoints and assigns credit to each one. First-touch, last-touch, linear, time-decay, U-shaped, W-shaped, data-driven. These are all MTA variants. MTA works well when you have complete journey data and your buyer interactions happen in tracked digital channels. It breaks down when buying groups involve 13+ stakeholders (Forrester 2025), sales cycles span 211 days (Dreamdata 2025), and most interactions happen offline or in untracked channels.</p><p id=""><strong id="">Marketing mix modeling (MMM)</strong> takes a top-down statistical approach, analyzing how aggregate marketing spend correlates with business outcomes over time. It doesn't track individuals. According to eMarketer's November 2025 data, 46.9% of U.S. marketers plan to invest more in MMM over the next year. MMM is useful for budget allocation decisions at the channel level, but it can't tell you what happened in a specific deal.</p><p id=""><strong id="">Forensic attribution</strong> starts from a different premise entirely. Rather than choosing a credit-assignment model, it first reconstructs reality by recovering missing signals across all available evidence sources (emails, meeting transcripts, calendars, web sessions, CRM data). Then it uses AI to interpret what influenced outcomes, generating deal stories instead of attribution scores.</p><p id="">The difference matters. MTA and MMM both accept incomplete data as a given and try to model around it. Forensic attribution treats data completeness as the prerequisite. When your B2B deals involve dozens of stakeholders and hundreds of touchpoints over months, the model you use matters less than whether you have the data to feed it.</p><p id="">As Tonya Walker, a fractional CMO and marketing advisor, wrote in MarTech: <em id="">"Every attribution model is built on partial data. Attribution will always matter, but mature organizations understand its role. They don't ask 'What does the model say?' They ask 'What are we responsible for and what are we doing about it?'"</em></p><p id="">For a deeper look at how Upside approaches this problem, read our <a href="/blog/two-radical-new-solutions-to-b2b-revenue-attribution" id="">manifesto on two radical new solutions to B2B revenue attribution</a>.</p><h2 id="">How to Choose the Right Marketing Attribution Tool for Your Business</h2><p id="">The right tool depends on your sales motion, not your marketing stack.</p><p id=""><strong id="">If you sell to enterprise accounts with 6+ month sales cycles and buying committees:</strong> You need account-level attribution with buying group reconstruction and evidence from emails, meetings, and transcripts. Tools that only track digital touchpoints will miss most of what actually influenced the deal. Look at Upside for forensic attribution, CaliberMind for enterprise data complexity, or Dreamdata for CRM-native journey visualization.</p><p id=""><strong id="">If you sell to mid-market with 2-4 month cycles and inbound-heavy motion:</strong> Multi-touch attribution connected to your CRM will serve you well. HockeyStack provides broad GTM analytics. Attribution (AttributionApp) is a strong, affordable entry point. If phone calls drive significant pipeline, Ruler Analytics solves that specific problem.</p><p id=""><strong id="">If you're a paid-media-heavy team spending $50K+ per month:</strong> You need attribution tied directly to budget optimization. SegmentStream connects attribution to spend decisions. Fibbler is excellent for LinkedIn and Google Ads ROI specifically.</p><p id=""><strong id="">If you're early-stage and budget-constrained:</strong> Start with GA4 for basic web attribution. Supplement with manual CRM tagging and self-reported attribution ("How did you hear about us?") on forms. As your pipeline grows, graduate to a B2B-specific tool.</p><p id="">Whatever you choose, remember this: 91% of marketers say attribution is important to their success, but only 31% are confident in their current models (MarketingLTB 2025). The confidence gap isn't a tool problem. It's a data problem. Start with the completeness of your data, then pick the model.</p><h2 id="">The Future of Marketing Attribution: From Scores to Stories</h2><p id="">The attribution industry is going through a fundamental shift. For two decades, the question has been "which channel gets credit?" The next decade will be about "what actually happened?"</p><p id="">Three forces are driving this change.</p><p id=""><strong id="">First, buying groups are getting larger and harder to track.</strong> Forrester's 2026 data shows that when a purchase includes AI features, the buying group doubles in size. More stakeholders means more interactions, more untracked channels, and more phantom touchpoints that traditional tools miss.</p><p id=""><strong id="">Second, AI is enabling a new kind of analysis.</strong> Instead of applying rules to incomplete data, AI agents can now reason across evidence from multiple sources (CRM records, emails, transcripts, calendars) and reconstruct what likely happened. This is agentic analysis, and it's the foundation of forensic attribution.</p><p id=""><strong id="">Third, executives are losing patience with attribution theater.</strong> Gartner's research shows revenue growth remains the top priority for 46% of CMOs in 2026, but budgets have flatlined at 7.7% of revenue, down from 11% pre-pandemic. When every dollar is scrutinized, attribution that produces scores nobody trusts isn't useful. Attribution that tells evidence-backed stories about what influenced revenue is.</p><p id="">The marketing attribution tools market is projected to reach $10.2 billion by 2032 (Gitnux 202

16706). But the winners won't be the tools with the fanciest dashboards. They'll be the ones that solve the data completeness problem first and deliver narratives that revenue teams actually believe.</p><p id="">Mike Harty of The B2B Stack captured it well: <em id="">"Perfect attribution will never exist in B2B. Because buyers are human, journeys are collective, intent forms invisibly, behaviour fragments, and privacy continues evolving. The future of attribution is not certainty. It's signal intelligence."</em></p><p id="">We agree. And we'd add: the future is forensic.</p>`,"4-ways-upside-cleans-your-data-out-of-the-box":`<h2 id="">‍<strong id="">Why clean data comes first</strong></h2><p id="">B2B attribution isn’t just about models or math, it's about cause and effect. One of the first insights we had as we started on the journey to tackle attribution: you can’t understand what worked if you don’t understand what happened.</p><p id="">That’s the core problem with attribution today. Buyer journeys in B2B stretch over months or years, with hundreds of touchpoints and dozens of stakeholders. But the systems we rely on to track them. CRMs, marketing automation software, email, calendars only capture fragments of that journey. They’re messy, duplicated, incomplete, and siloed.</p><p id="">‍</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/upside-data-cleaning-clean-data-comes-first-1.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">If you feed that “Swiss cheese data” into any attribution model, you get Swiss cheese results, or as some say, garbage in, garbage out. That’s why most teams end up abandoning complex attribution models and falling back to naive last-touch reporting. It’s not that they believe last-touch to be accurate; it’s just <em id="">easier</em> than pretending broken data can support sophistication.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/upside-data-cleaning-clean-data-comes-first-2.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">Upside approaches it differently. We assume your systems are <em id="">signals</em>, not unquestionable truth. Then we reconstruct the actual story of the deal using a knowledge graph designed to handle complex relationships. Out of the box, here are four ways we clean and enhance your GTM data so you can finally trust it.</p><h2 id=""><strong id="">1. Persona resolution</strong></h2><p id="">Buyers appear across systems under different guises, from a Gmail address on an event signup, a Salesforce lead without a job title that is the same person appearing on a corporate email on an opportunity.</p><p id="">Upside automatically merges these into one unified identity. That means both one complete record of a person’s journey across systems AND&nbsp;events and campaigns get the credit they deserve. <br></p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/upside-data-cleaning-1-persona-resolution.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h2 id=""><strong id="">2. Buying group detection</strong></h2><p id="">Every deal has a buying group, but CRMs rarely capture everyone involved. AEs sometimes only add the minimum number of contacts their leaders require, leaving out key influencers.</p><p id="">Upside automatically reconstructs the <em id="">real</em> buying group by scanning emails, calendar invites, and meeting transcripts. We don’t rely on what was manually logged. Instead, we spider out the connections so you see the actual group of stakeholders who shaped the decision.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/upside-data-cleaning-2-buying-group-detection.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h2 id=""><strong id="">3. Touchpoint direction detection</strong></h2><p id="">Not every touchpoint means the same thing, for example <em id="">sending an email isn’t the same as clicking on it.</em> Upside automatically distinguishes between:</p><p id=""><strong id="">Engagements</strong> → what the customer actually did (clicked, replied, attended).<br></p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/upside-data-cleaning-3-touchpoint-direction-detection-1.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id=""><strong id="">Attempts (“bids”)</strong> → what your team tried to do (sent, invited, called).<br></p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/upside-data-cleaning-3-touchpoint-direction-detection-2.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">This lets you separate effort from impact and see when true momentum measured as engagements over time shifted on a deal and what drove that shift.</p><h2 id=""><strong id="">4. Email chain reconstruction</strong></h2><p id="">Email is where deals live, but it can also be where attribution breaks down. Execs don’t track their emails or log their intros, some reps track, others don’t and sometimes forwarded threads disappear into the ether and the first thing you find tracked on an opp is a meeting. Upside reconstructs email chains, including forwarded ones and trunks them into singular touchpoints appearing at the appropriate time in a journey. If a CEO forwards an intro, we pull it into the record. If an AE joins a forwarded thread, we stitch it back into the journey. That way, you can see the full conversation and how it interacted with other deal activities.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/upside-data-cleaning-4-email-chain-reconstruction.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h3 id=""><strong id="">Why this matters</strong></h3><p id=""><em id="">If you don’t have a good understanding of what happened, there’s no way you can have a good understanding of what worked. </em>That’s why Upside starts with cleaning and structuring data. Persona resolution, buying group detection, touchpoint direction, and email chain deconstruction aren’t just conveniences, they are the foundation for attribution you can trust. But clean data isn’t the end goal. It’s the beginning of attribution that actually helps you make better decisions. With Upside we think about is as a pyramid:</p><ol id=""><li id=""><strong id="">What happened</strong> → Reconstruct the deal story.<br></li><li id=""><strong id="">What worked</strong> → Analyze which signals truly mattered.<br></li><li id=""><strong id="">What to do next</strong> → Forecast, allocate, and decide with confidence.<br></li></ol>`,"8-b2b-attribution-assassins-and-how-to-beat-them-with-agentic-analysis":'<h1 id=""><strong id="">Intro: “Is it just me…?&nbsp;</strong></h1><p id="">If you’ve ever stared at an immaculate-looking funnel report and thought <em id="">“this feels wrong”</em>, you’re not alone. Modern marketers bend over backwards to label every click, form-fill, webinar, and handshake – yet the story in the CRM still reads like a half-finished novel. It’s not because you’re bad at tagging. It’s because humans are…well, human. They ghost meetings, swap tickets, clear cookies, and generally refuse to behave like tidy rows in a spreadsheet.</p><p id="">At Upside, we’ve spent the last year watching attribution break in the wild. The good news: the chaos is patterned. We studied eight of the most common culprits that pop up again and again so you don’t have to. Recognizing them is the first step; using an agentic, forensic model to <em id="">reason</em> around them is the real fix. Let’s meet the offenders.</p><h2 id="">1. The Invite Swapper</h2><blockquote id="">"<em id="">I can’t make it – sending my colleague instead.</em>"</blockquote><p id="">Your VIP Sporting Event looks like a no-show in Mark’s record, even though Jenna from his team actually showed up, got engaged, and later became the champion. Because nobody backfilled the attendee list (did we ever get her email address?), your event ROI just evaporated.</p><p id=""><strong id="">What it breaks:</strong> Event → Opp attribution<br></p><p id=""><strong id="">Agentic Save:</strong> An agent can link the AE’s follow up email thread to Mark’s original event registration and stitch the story back together.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/attribution-saboteurs-1-invite-swapper.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">‍</p><h2 id=""><strong id="">2. The Zero-Click User</strong></h2><blockquote id=""><em id="">Looks admiringly at your ads, but never clicks one</em>.</blockquote><p id=""><strong id="">What it breaks:</strong> Ad → Opp attribution<br></p><p id=""><strong id="">Agentic save:</strong> An agent can detect mentions of ad influence and links in emails. Call transcript and SFDC notes, revealing that ads did actually make a difference in the deal.&nbsp;</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/attribution-saboteurs-2-zero-click-user.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">‍</p><h2 id=""><strong id="">3. The Contactless Closer</strong></h2><blockquote id=""><em id="">“I sell to people, not opportunity contact roles in Salesforce.”</em></blockquote><p id="">The AE logs the opp but has precisely zero opportunity contact roles listed. They have a great sense of who they’re selling to, but your attribution engine thinks you’re selling a deal to thin air.&nbsp;</p><p id=""><strong id="">What it breaks:</strong> No attribution due to no contacts</p><p id=""><strong id="">Agentic Save:</strong> Entity resolution fills the gap, matching calendar invites, email threads, and meeting transcripts to detect the entire buying group, even if they don’t exist in y

1670our CRM as records.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/attribution-saboteurs-3-contactless-closer.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">‍</p><h2 id=""><strong id="">4. The Badge-Scanner Avoider</strong></h2><blockquote id=""><em id="">They hover at your booth or after your talk, death-gripping the lanyard.</em></blockquote><p id="">Great vibes, no scan. By day three of the show, your reps can’t even remember the face, let alone the name or company.</p><p id=""><strong id="">What it breaks:</strong> Conference/tradeshow → Opp attribution<br></p><p id=""><strong id="">Agentic Save:</strong> Follow-up emails and meeting transcripts can triangulate who was actually there and influenced, even if there’s no record of it.&nbsp;</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/attribution-saboteurs-4-badge-scanner-avoider.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">‍</p><h2 id=""><strong id="">5. The Serial Job Swapper</strong></h2><blockquote id=""><em id="">Same human, brand-new email address.</em></blockquote><p id="">They loved you at their last company and brought you into every new role. Unfortunately, your CRM treats <em id="">[email protected]</em> and <em id="">[email protected]</em> as two completely different people.</p><p id=""><strong id="">What it breaks:</strong> Visibility into what’s truly inbound vs. champion-led</p><p id=""><strong id="">Agentic Save:</strong> Identity resolution ties the two records together and flags “repeat champion” for your nurture team. Even better if they talk about their previous role on a call, leaving fingerprints of the association in transcripts.<br>&nbsp;</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/attribution-saboteurs-5-serial-job-swapper.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">‍</p><h2 id=""><strong id="">6. The Invisible Referrer</strong></h2><blockquote id=""><em id="">Not in the meeting, and not in the CRM. But they’re the reason the meeting exists.</em></blockquote><p id="">They make a quiet connection, then step aside. The buyer engages, the conversation shifts hands, and momentum builds – all without the system ever knowing who opened the door.</p><p id=""><strong id="">What it breaks:</strong> Referral influence and top-of-funnel clarity.</p><p id=""><strong id="">Agentic Save:</strong> Agents crawl the email threads to detect referral-driven introductions that trigger downstream engagement.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/attribution-saboteurs-6-invisible-referrer.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">‍</p><h2 id=""><strong id="">7. The Double Tapper</strong></h2><blockquote id=""><em id="">The first touch gets noticed. The second gets the reply.</em></blockquote><p id="">Webinar in the inbox. Dinner invite in the calendar. Now you’ve got their attention. The Double Tapper doesn’t wait for one channel to work – they stack the signals. <strong id="">One warms, one converts.</strong>&nbsp;</p><p id=""><strong id="">What it breaks:</strong> True source of engagement from multiple touchpoints.</p><p id="">‍<strong id="">Agentic Save:</strong> An agent recognizes when sequential touches, like emails and calendar invites, converge from different sources to create a compound influence message that single-channel attribution misses.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/attribution-saboteurs-7-double-tapper.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">‍</p><h2 id=""><strong id="">8. The Loop-In Lead</strong></h2><blockquote id=""><em id="">Same partner, new door. They make the quiet intro, and suddenly there’s a meeting on the books. No outbound, no form fill, just trust doing the work.</em></blockquote><p id="">

1670The CRM logs it as inbound. The referral field stays blank. The partner’s role? <strong id="">Erased from the record. </strong>The deal moves, revenue lands, but the signal gets lost. No credit, no visibility, no way to repeat what worked.</p><p id=""><strong id="">What it breaks: </strong>Partner influence and repeatability.</p><p id=""><strong id="">Agentic Save:</strong> Though attribution fields fall silent, an agent surfaces partner-triggered momentum – catching trusted referrals before they disappear into inbound noise.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/attribution-saboteurs-8-loop-lead.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">‍</p><h2 id=""><strong id="">So…now what?</strong></h2><p id="">If any (or all) of these sound painfully familiar, it’s not your tracking that’s broken. It’s the model. Legacy attribution models were built for linear, single-device journeys and perfect CRM hygiene – things that simply don’t exist anymore.</p><p id="">Upside’s agentic model considers <strong id="">context</strong> alongside isolated events. It asks, <em id="">“What actually happened between these two cataloged touchpoints?”</em> and actively gathers evidence from calendars, emails, events, and web sessions to fill in the blanks; just like a great RevOps analyst would, only faster and at scale.</p>',"9-metrics-modern-b2b-teams-use-to-understand-the-real-impact-of-their-campaigns-content-and-channels":`<h1 id="">Enterprise deals don’t behave the way attribution models pretend they do.</h1><p id="">And yet, most B2B teams still try to answer high-stakes questions like: “<em id="">Did this campaign work? What moved this deal? Which channels deserve more budget?</em>” Using systems built under the premise that a single person, a single form fill, and a neatly trackable touchpoint “caused” an opportunity.</p><p id="">That premise doesn’t really work.</p><p id="">In modern ABM cycles, a single deal can involve an extensive network of stakeholders, scattered across functions, geographies, and touchpoint surfaces. You may see dozens of interactions, many invisible to CRM, happening across emails, meetings, forwarded threads, social channels, and unstructured buyer behavior.</p><p id="">We’ve all lived through this chaos. And all Upside customers’ raw data shows the same pattern long before we clean it: duplicates, missing timestamps, disjointed personas, off-CRM conversations, and signals buried in meeting notes, call transcripts or email chains. Traditional attribution tries to compress that complexity into a zero-sum game. If marketing gains credit, sales must lose it, or vice versa, driving arguments over credit and a lack of collaboration.</p><p id="">But enterprise buying cycles are <strong id="">not</strong> zero-sum. When done right, they are <strong id="">collaborative, cumulative, </strong>and<strong id=""> deeply multi-threaded.</strong></p><p id="">At Upside, we aim to replace this <strong id="">illusion of precision</strong> with metrics designed for how deals actually happen using <a href="/blog/what-is-forensic-attribution">forensic attribution</a>. Below are nine metrics modern teams use when they want to understand the <em id="">full</em>, multi-dimensional impact of their GTM efforts across the entire account journey: before, during, and beyond the opportunity.</p><h1 id=""><strong id="">Why Identifying the Real Buying Group Matters More Than Ever</strong></h1><p id="">You cannot measure influence accurately if you don’t know who the real buyers are. Most CRMs list a single “Primary Contact” roleplayer or a sparse handful of names, if any, and few organizations focus on enforcing CRM contact role cleanliness — AEs would rather do what they do best, sell the product vs doing <em id="">“data entry</em>”. But enterprise deals are shaped by a constellation of actors: the economic buyer, the technical screener, the blocker, the champion, the end user, the skeptic, the change-management owner, and others who pop in and out of the process.</p><p id="">Failing to consider activity from the whole buying group can often lead to:</p><h3 id=""><strong id="">1. Wrong conclusions about what worked</strong></h3><p id="">If only the champion is tracked, an entire tail of influence generated by campaigns among engineers, finance, operations, or procurement remains invisible.</p><h3 id=""><strong id="">2. Misallocated budget</strong></h3><p id="">When your reporting dashboards miss the real impact on opportunities, teams end up under-investing in the very channels activating their influencers, technical reviewers, or even deal sponsors.</p><h3 id=""><strong id="">3. Broken GTM alignment</strong></h3><p id="">Marketing, SDR, and Sales end up optimizing for different people, sometimes not the ones who actually drive the deal forward.</p><p id="">To measure what matters, you need to detect the <strong id="">actual</strong> buying group - <em id="">who participated, who engaged, who influenced, and when</em>

1670.</p><p id="">This is where Upside’s approach is fundamentally different.</p><h1 id=""><strong id="">How Upside Detects Activation, Influence, and Buying Groups Using AI</strong></h1><p id="">Upside starts from a simple but radical principle:</p><p id=""><strong id="">You can’t attribute what you can’t see.</strong></p><p id="">To reconstruct how deals really moved, Upside ingests signals across the entire GTM stack CRM, marketing automation, email inboxes, calendars, meeting transcripts, website activity, content interactions, and more. Then, we use a combination of identity resolution, email-chain reconstruction, and AI-powered detection logic to unify everything into a clean, objective timeline.</p><h3 id=""><strong id="">1. Complete Identity Resolution</strong></h3><p id="">People show up under multiple emails, mismatched CRM records, and forwarded threads. Upside merges them into a single identity, so you don’t treat five duplicates as five people.</p><h3 id=""><strong id="">2. Buying Group Reconstruction</strong></h3><p id="">We identify <strong id="">relevant</strong> stakeholders: those who attended relevant meetings or were involved in relevant email threads. We only count participants with engagements as relevant, meaning someone added to a thread who never replied is not considered an active buying group participant.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/b2b-event-attribution-4-buying-groups-incomplete.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h3 id=""><strong id="">3. Directional Touchpoint Detection</strong></h3><p id="">Upside distinguishes <strong id="">attempts</strong> (emails sent, invites, outbound calls) from <strong id="">engagements</strong> (replies, acceptances, meetings held, content consumed). That distinction is essential for measuring influence: only campaigns and emails that drive engagement are counted.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/b2b-impact-metrics-3-directional-touchpoint-detection.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h3 id=""><strong id="">4. AI Detection of Activation Moments</strong></h3><p id="">Upside’s AI agents scan the unified timeline to determine when an account or opportunity moved from “quiet” to “active.” Those moments are summarized into “Milestone Analyses” helping teams understand what really drives their pipeline.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/b2b-impact-metrics-4-ai-detection-activation-1.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h1 id=""><strong id="">The 9 Metrics That Reflect How Enterprise Deals Really Happen</strong></h1><p id="">Below are the nine metrics that replace outdated attribution models with a more accurate, more actionable understanding of influence. In Upside, these metrics can be applied on campaign reports, channel reports, web reports, or even rep reports, and can be filtered by account and opportunity fields or by date.&nbsp;</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/b2b-impact-metrics-4-ai-detection-activation-2.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h2 id=""><strong id="">1. Deal Contacts Engaged</strong></h2><p id=""><strong id="">Who from the true buying group actually engaged?</strong></p><p id="">In enterprise deals, engaging the <em id="">right</em> people matters far more than generating raw volume. This metric reveals whether your efforts reached the actual decision-makers, influencers, blockers, and champions.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/b2b-impact-metrics-1-deal-contacts-engaged.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h2 id=""><strong id="">2. AI-Detected Opportunities Activated</strong></h2><p id="">Which opportunities moved into “active mode” as a result of your touchpoints?</p><p id="">Activation rarely looks like a clean form fill. Upside detects the real inflection point, the moment meaningful engagement begins, providing one of the most accurate “what actually worked” signals in modern GTM.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/b2b-impact-metrics-2-ai-detected-opportunities.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h2 id=""><strong id="">3. Opportunities Influenced</strong></h2><p id="">Which in-cycle opportunities experienced momentum or new engagement because of your touchpoints?</p><p id="">Upside counts influence someone's engagement in the healed buying group. This influence metric is superior to other, more generalized influence metrics because:</p><ul id=""><li id="">It only looks at the buying group, but an enhanced version to what most other systems can report on</li><li id="">It only looks at engagements - meaning non-active campaign memberships (i.e. invites, emails sent, etc) don’t count as influence.</li></ul><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/b2b-impact-metrics-3-opportunities-influenced.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h2 id=""><strong id="">4. Opportunities Created Post-Engagement</strong></h2><p id="">Which new deals opened within a window after someone engaged?</p><p id="">This metric shows how many new deals were created after one of the buying group members engaged with the website, channel, or campaign. This is a great metric to help you understand what is actually helping to create new pipeline vs just influencing it.&nbsp;</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/b2b-impact-metrics-4-opportunities-created-post.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h2 id=""><strong id="">5 and 6. Opportunities Influenced In-Cycle and Progressed Opportunities</strong></h2><p id="">Which touchpoints meaningfully affected or helped progress active deals?</p><p id="">These metrics complement Opportunities Created Post engagement by showing how many deals were influenced in flight. While some teams are heavily focused on pipeline, marketing, partnerships, or exec touches can have a significant impact on deal acceleration. These metrics help you understand what is actually progressing pipeline and what you need to keep investing in to help move deals forward.&nbsp;</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/b2b-impact-metrics-5-6-opportunities-influenced.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h2 id=""><strong id="">7 and 8. Accounts Activated and Accounts Reactivated</strong></h2><p id="">Which accounts showed meaningful first engagement because of your effort?</p><p id="">This metric shows which efforts drive the top of funnel awareness and get the first engagement on an account or the first engagement after a long period of inactivity.&nbsp;</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/b2b-impact-metrics-7-8-accounts-activated.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h2 id=""><strong id="">9. Accounts Engaged Outside the Opportunity Cycle</strong></h2><p id="">Selling into large accounts takes many months and sometimes many years. This metric shows all the early influences current systems might miss entirely. Some of the most essential GTM impact happens months before an opportunity exists.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/b2b-impact-metrics-9-accounts-engaged-outside.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h1 id=""><strong id="">The Future of Measurement Is Forensic, Not Zero-Sum</strong></h1><p id="">The old playbook asked: <strong id="">“Which single touchpoint deserves credit?”</strong></p><p id="">The new playbook asks: <strong id="">“How did this deal actually happen, and where did we create real impact — together?” </strong>Reconstructing the full account story using clean, unified data unlocks the ability to answer questions with confidence, building&nbsp; the foundation for answering the only question that truly matters:</p><p id=""><strong id="">What should we do next to win more deals?</strong></p>`,"how-ai-is-changing-marketing-attribution":'<blockquote>The following is a guest post by <a href="https://www.linkedin.com/in/cariludietric

1670h/">Carilu Dietrich</a>, and was originally published on <a href="https://www.carilu.com/p/how-ai-is-changing-marketing-attribution">https://www.carilu.com/p/how-ai-is-changing-marketing-attribution</a>.<br><br><em>Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.</em></blockquote><p><em>‍</em><br>Have you ever spent hours (or days) reconstructing micro details of marketing, sales, and partner activity behind a big win to show marketing’s <em>real</em> impact at a board meeting?</p><p>AI is fundamentally changing what’s possible: pulling signals from calls, emails, documents, chats, and CRM data, and analyzing momentum across all of it, to understand what actually moves deals forward.</p><p>For years, marketers did the best they could with incomplete data and rigid, incomplete measurement frameworks. First-, last-touch and multi-touch attribution weren’t wrong so much as necessary shortcuts, compressed versions of reality in a world where most meaningful signals and full data simply weren’t accessible.</p><p>That’s what’s changed. AI is set to massively improve attribution by reconstructing the <em>full story</em> of a deal and letting patterns of influence emerge across people, time, and activity - instead of relying on proxy weighting.</p><p>I’ve been watching <a href="https://www.linkedin.com/in/madalina/"><strong>Mada Seghete</strong></a><strong> and </strong><a href="https://www.linkedin.com/in/alexdbauer/"><strong>Alex Bauer</strong></a><strong>, co-founders of </strong><a href="/"><strong>Upside</strong></a> closely over the past year. What first caught my attention wasn’t just the breadth of signals they were pulling together, but how they visualized them, creating the automated version of the board slides I used to build by hand, with many touches, many buyers, over long periods of time, finally telling a complete story.</p><p>After my <a href="https://www.carilu.com/p/brand-vs-demand-isnt-a-budget-fight"><em>Brand vs. Demand</em> <em>is a Measurement Crisis</em></a> post showed that many marketers want to invest more in brand but feel constrained by attribution limitations, I asked Mada and Alex if they would share how they’re approaching marketing attribution in an AI-first world. Below she covers the following with screenshots that will have you drooling : )</p><ul><li><strong>Why Traditional Models Fail</strong></li><li><strong>What’s Actually Changed with AI<br>1. Unstructured context is now capturable<br>2. Analysis that was too expensive is now feasible<br>3. We moved from data scarcity to data abundance</strong></li><li><strong>A Different Approach: Questions Instead of Models</strong></li><li><strong>The Prerequisite: Data Quality</strong></li><li><strong>What This Looks Like in Practice</strong></li><li><strong>How This Applies at Different Stages of Growth</strong></li></ul><h2><strong>I. The Attribution Problem Everyone Knows</strong></h2><p>Marketers hate attribution because it has been fundamentally inaccurate. It drives endless debates and turf wars over credit. It over-credits what’s easy to measure: late-stage clicks, form fills, “sourced” fields, and under-credits what’s hard to track: brand awareness, events, partner influence, the dark funnel, internal forwarding chains.</p><p>The problem isnt’t effort it is access. Marketers can only measure a fraction of the signals shaping deals, so attribution models are often necessary shortcuts rather than sources of truth.</p><p>Plus the data they DO have is bad: <a href="https://www.adverity.com/state-of-play-research-data-quality-2025">Adverity’s 2025 research</a> found nearly half (45%) of the data marketers use to make decisions is incomplete, inaccurate, or out-of-date. And 43% of CMOs believe less than half of their marketing data can be trusted.</p><h2><strong>II. Why Traditional Models Fail</strong></h2><p>Deals are too complex for any single model.</p><p>My experience: I spent a full day analyzing 10,000 touchpoints for one customer’s $3M deal. The activation moment? A BD partner convinced an executive to take a look at the tool. The exec forwarded a note to a manager, who forwarded it to the client team. This was in addition to hundreds of marketing and sales interactions that built awareness.</p><p>The insight: <strong>No rule-based system would catch this.</strong> The internal team didn’t even know it happened. And no human would invest this time for every deal.</p><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!QgVs!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F18d54360-9edb-4bd2-9286-3d2813feacdc_960x540.png"><div><img src="/images/blog/ai-marketing-attribution-ii-traditional-models-fail-1.jpeg" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p>Even sophisticated multi-touch approaches struggle because:</p><ul><li><strong>Tracking is incomplete and noisy</strong> - identity gaps, cross-device behavior, missing interactions</li></ul><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!R_PQ!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8c30f613-81b1-45c5-a568-b3f54dfcd1bf_1600x600.png"><div><img src="/images/blog/ai-marketing-attribution-v-prerequisite-data-quality-1.jpeg" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p><em>Image: Example of the type of persona data found in different systems</em></p><ul><li><strong>Correlation is often mistaken for causation</strong></li><li><strong>Journeys get oversimplified</strong> -everyone wants to know “but what is the one thing that really brought this deal?”</li><li><strong>External factors distort observed paths</strong> - pricing, competition, timing</li><li><strong>True incrementality still requires experimentation</strong></li></ul><p>So leaders settle for “good enough” models they don’t trust (because they need something for Finance), while actually running on instinct.</p><h2><strong>III. What’s Actually Changed with AI</strong></h2><p>Three fundamental shifts:</p><h3><strong>1. Unstructured context is now capturable</strong></h3><p>Conversation intelligence extracts signals from calls, emails, and meetings. The “why” behind a demo request often lives in buyer language, not UTM parameters.</p><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!YAMR!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7d318a26-7602-4c6b-a815-7a758fbcd843_1600x1374.png"><div><img src="/images/blog/ai-marketing-attribution-1-unstructured-context-now.jpeg" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p><em>Image: Example of how an LLM can extract signals from unstructured data.</em></p><h3><strong>2. Analysis that was too expensive is now feasible</strong></h3><p>Questions like “what sequence of touches typically precedes a closed-won enterprise deal?” have always been theoretically answerable. The problem was the work required: manually reconstructing deal timelines, normalizing across systems to recover signals buried inside unstructured data, and accounting for duplicate or missing touchpoints.</p><p>For most teams, the insight wasn’t worth the effort. But thanks to AI, that math has changed.</p><h3><strong>3. We moved from data scarcity to data abundance</strong></h3><p>The context exists across calls, emails, CRM, marketing automation, web behavior, and chat. The hard part is stitching it into a coherent, trustworthy account story.</p><h3><strong>The key reframe:</strong></h3><p>Static rules like “credit goes to sales unless there was a marketing event within 7 days” aren’t stupid, they have been necessary compromises. When the cost of answering more nuanced questions exceeded their value, compression was rational.</p><p>But that’s no longer the case.</p><h2><strong>IV. A Different Approach: Questions Instead of Models</strong></h2><p><strong>The AI paradigm shift:</strong> AI has enabled teams to stop forcing deals into attribution models and start answering specific questions about each deal. Then aggregate those answers into insights and recommendations.

1670</p><p>Attribution has always been greyscale—forcing nuanced reality into a few buckets. What’s now possible is answering “what worked” questions in full color, deal by deal, then aggregating patterns.</p><h3><strong>The questions you can now answer:</strong></h3><p><strong>What actually activated this deal?</strong> Example: “The inflection point was a partner referral to the CFO, followed by an internal forward chain we wouldn’t have seen in CRM.”</p><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!NgZy!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb5c84453-4c93-492c-bc08-0037165d4ca0_1600x793.png"><div><img src="/images/blog/ai-marketing-attribution-questions-can-now-answer-1.png" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p><em>Image: Example how AI can make connections between structured and unstructured data to understand the deal story.</em></p><p><strong>What role did each channel play at each stage?</strong> Example: “Paid search drove initial awareness, but the webinar attendance correlated with deals moving from Stage 2 to Stage 3.”</p><p>One of our customers found that a third of their revenue in 2025 came from 3rd party referrals that did not show up in any other attribution models.</p><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!xkjw!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe95faa58-3d61-43c3-96e2-6689dc10d99f_1600x983.png"><div><img src="/images/blog/ai-marketing-attribution-questions-can-now-answer-2.png" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p><em>Image: Example of results from Upside deep research agents looking for specific channel influence on deals .</em></p><p><strong>What sequences lead to new opportunities?</strong> Example: “Deals that start with content engagement + SDR outreach convert at 2x the rate of cold outbound alone.”</p><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!fJsJ!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fffbbc058-35b9-4a9e-96d7-4b314c697e0e_1600x491.png"><div><img src="/images/blog/ai-marketing-attribution-questions-can-now-answer-3.png" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p><em>Image: Example of GTM motions detected by Upside AI Deep Research agents analyzing a set of deals.</em></p><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!SQpu!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4cd44710-8663-4955-b3d5-db8c436a66e9_1600x431.png"><div><img src="/images/blog/ai-marketing-attribution-questions-can-now-answer-4.png" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p><em>Image: Example of GTM channel progression detected by Upside AI Deep Research agents analyzing a set of deals.</em></p><p><strong>Who are the real buying personas, and which ones do AEs consistently miss?</strong> Example: “Technical evaluators appear in 80% of closed-won deals but are added as contact roles in only 30%.”</p><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!c4yv!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc213099e-2b58-4e19-8c10-b205f757f6b5_1600x626.png"><div><img src="/images/blog/ai-marketing-attribution-questions-can-now-answer-5.png" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p><em>Image: Example of persona presence detected in buying groups (beyond contact roles in Salesforce) by Upside and impact on win rates.</em></p><h2><strong>V. The Prerequisite: Data Quality</strong></h2><p><strong>The critical caveat:</strong> AI is only as good as its data.</p><p>Wrong campaign dates, missing statuses, incomplete records. Without careful guidance, mess like this causes AI to reach wrong conclusions just as it would a human. Garbage in, garbage out.</p><p>In my experience, the prerequisite for AI-powered deal analysis is a data layer that:</p><ul><li>Resolves identities across systems</li><li>Reconstructs timelines from fragmented touchpoints</li><li>De-duplicates and heals messy GTM data</li></ul><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!R_PQ!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8c30f613-81b1-45c5-a568-b3f54dfcd1bf_1600x600.png"><div><img src="/images/blog/ai-marketing-attribution-v-prerequisite-data-quality-1.jpeg" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p><em>Image: Example of the type of persona data found in different systems needed to be merged into one identity.</em></p><p>That’s what my team has been building for the past two years. But the principle applies regardless of tooling: if your underlying data is scattered and incomplete, you’ll just get bad answers more quickly. Even AI can’t perform miracles.</p><p><strong>AI also needs a way to be checked AND check itself.</strong> While AI is getting better, it still makes mistakes and needs a way to be debugged. Any analytics and data tool using AI needs a debug console—a way that a human can check the results.</p><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!By15!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc0538082-6c0a-43b5-af38-ae2e93ad1486_1600x789.png"><div><img src="/images/blog/ai-marketing-attribution-v-prerequisite-data-quality-2.png" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p><em>Image: Visual representation of a deal with filters and citations for AI insights.</em></p><h2><strong>VI. What This Looks Like in Practice</strong></h2><p><strong>Custom, smart insights for every org: </strong>Traditional rules-based models often get it wrong. But you can ask AI to understand what type of influence a campaign actually had on an opportunity, and whether it was the first meaningful interaction with the buying group. AI does this much better than rules-based systems because it won’t get bogged down by edge cases, like someone visiting the website during an event before the attendee list was uploaded, which would make direct traffic look like the first touch.</p><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!iXUP!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F19c31f72-6e71-4c21-9fe0-8d21a9caa29c_1600x1014.png"><div><img src="/images/blog/ai-marketing-attribution-vi-what-looks-like-1.jpeg" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p><em>Image: Events influence on deals sorted by type of touch in the deal cycle defined by customer.</em></p><p><strong>Faster reporting + hidden signals:</strong> <a href="/case-studies/dscout-driving-confident-growth-with-upside">Another reduced reporting time from 2-3 hours to minutes</a> and identified a 5% pipeline increase from in-person hosted events. This signal was invisible in their previous attribution model.</p><p><strong>Measuring things that were impossible:</strong> AEO influence on pipelines is incredibly hard to measure because people don’t click on links. But in many cases prospects mention how they heard about you on calls or in forms. By combining citation traffic, call mentions, and email threads, Assembled tracked actual AEO influence on the pipeline.</p><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!BH06!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F31f54fa5-df9

16700-4843-876a-cab7204bb541_1600x634.png"><div><img src="/images/blog/ai-marketing-attribution-vi-what-looks-like-2.png" alt="" width="auto" height="auto" loading="auto"></div></a></figure><figure class="w-richtext-figure-type-image " data-rt-type="image" data-rt-align=""><a target="_blank" href="https://substackcdn.com/image/fetch/$s_!2AQ3!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F54cbb478-781d-48c1-ab8d-e423c60109f7_1600x845.png"><div><img src="/images/blog/ai-marketing-attribution-vi-what-looks-like-3.png" alt="" width="auto" height="auto" loading="auto"></div></a></figure><p><em>Image: AEO analysis for Assembled team.</em></p><h2><strong>VII. How This Applies at Different Stages of Growth</strong></h2><p>This approach requires enough deal volume to see patterns.</p><p><strong>If you’re running a large enough business</strong> (meaningful spend across multiple channels, significant deal volume, consistent GTM investment) then asking “what actually happened in our deals?” is one of the most important questions you can answer.</p><p><strong>But if you’re earlier-stage, the reality is different.</strong> Volumes are lower. Noise is higher. You might do forensic deal analysis and see patterns that don’t generalize yet. That doesn’t mean the approach doesn’t matter. It means how you use it has to evolve with you.</p><p><strong>For smaller teams, the goal isn’t statistical significance—it’s building directional confidence.</strong> You’re looking for patterns: Do deals that involve events close faster? Do certain personas correlate with larger deals? Even without perfect data, that framing matters.</p><p>The mistake smaller companies make isn’t that they can’t do this perfectly. It’s that they don’t do it at all. Or they force everything into last-touch attribution and conclude that nothing except bottom-funnel works.</p><h2><strong>VIII. Conclusion: A New Question</strong></h2><p>The wrong question has always been: <em>“Which attribution model should we use?”</em></p><p>The better question is: <em>“Do we actually know what happened in our deals?”</em></p><p>Attribution doesn’t have to be the starting point anymore. When you begin with reconstructing deal reality—who was involved, what signals mattered, how influence accumulated over time—the “what worked” answers emerge naturally from there.</p><p>And once you can defend those answers to Finance with real evidence, rather than simplified models or gut instinct, the budget conversations change entirely.</p><p>‍</p>',"how-to-build-a-visual-customer-journey-map-with-existing-gtm-data":'<h2 id="">Why is it important to understand the customer journey?</h2><p id="">Every closed deal has a story, but most companies only have clear visibility into the final chapter. There are clear dates for when the deal began and when it was signed, but everything else is often a blur. This is especially true of longer deal cycles in the B2B space, where there are often multiple stakeholders involved throughout the process.&nbsp;</p><p id="">But being able to tell the story of a deal is crucial to understanding what worked, so that your GTM team knows what to repeat to bring more customers in. The questions that often come up around customer journeys include: How did the prospect first hear about you? What sparked their interest enough to take a meeting? Who inside their organization drove the conversation forward? Without answers to these questions, you’re left guessing about what truly influences your pipeline. Or, even worse, relying on a handful of fields in your CRM that attempt to bucket a unique journey into a single source.&nbsp;</p><p id="">Customer journey research fills in that gap. It’s about reconstructing the sequence of meaningful interactions that move a buyer from awareness to action, so you can understand not just <em id="">what</em> happened, but <em id="">why</em> it happened. When done well, it can reveal the real decision-makers, pivotal touchpoints, and unseen forces that shaped the deal into what it was. Once you have clear visibility into this, your team can double down on activities that actually work, and stop wasting time, energy, and resources on those that don’t.&nbsp;</p><p id="">In this post, we’ll break down exactly how to build a complete, data-driven picture of your customer journey, from gathering and cleansing your data to mapping every influential activity, so you can accurately depict the story of any deal.&nbsp;</p><blockquote id=""><em id="">So how do you build one?!</em></blockquote><h2 id="">Make an inventory of your data</h2><p id="">Here’s an organized inventory of where you can usually find the core data for mapping your customer journey, along with the types of details to expect from each source:</p><p id=""><strong id="">CRM</strong></p><ul id=""><li id="">Deal lifecycle: opportunity stage history, open/closed dates, outcomes</li><li id="">Sales engagement data: logged emails, meeting invites, call notes</li><li id="">Accounts &amp; people: companies, leads, and contacts with associated context added by sales</li><li id="">Campaigns: event, webinar, and content touchpoints</li><li id="">Custom objects: any unique company-specific data models (e.g., product usage, contracts)</li></ul><p id=""><strong id="">Marketing Automation</strong></p><ul id=""><li id="">Web activity tied to identifiable individuals (page visits, form fills)</li><li id="">Marketing email activity (sends, opens, clicks)</li><li id="">Campaign participation records (may overlap with CRM campaigns)</li></ul><p id=""><strong id="">Meeting Recording Platforms</strong></p><ul id=""><li id="">Meeting metadata: date, time, duration, attendee lists</li><li id="">Conversation transcripts or summaries</li></ul><p id=""><strong id="">Other Systems &amp; Signals</strong></p><ul id=""><li id="">Any platform that logs customer or prospect actions (e.g., chat tools, community platforms, product analytics)</li><li id="">Actions can be behavioral (downloads, logins) or engagement-based (likes, shares, feedback)</li></ul><h2 id="">Bring all the data to one place</h2><p id="">Once you’ve identified the data points you want to use, consolidate them in a single location. Many companies opt for a data warehouse to make blending and querying easier, but you can start as simply as exporting the relevant records and creating one spreadsheet per account. The goal is to line up activities from different platforms so you can see the full sequence. For example, a prospect might click a LinkedIn ad to visit your website, register for a webinar, and later receive an email from sales. You only see the real story when web traffic, campaign activity, and email history are shown together in chronological order.</p><h2 id="">Annotate and cleanse the data</h2><p id="">Once all your data is in one place, you’ll likely spot inconsistencies, missing fields, or unclear actions that make it harder to tell a clean story. This is where annotation and cleansing come in. At a minimum, every activity should include:</p><ul id=""><li id=""><strong id="">Timestamp</strong>: The exact date and time the activity happened (date alone works if time isn’t available). This lets you accurately place events in sequence.<br><br></li><li id=""><strong id="">Action clarity</strong>: “Event” is not that useful. Did the prospect register for the event, or attend live? Make sure you’re capturing what actually happened, and identifying significant engagement.<br><br></li><li id=""><strong id="">Actor</strong>: Who performed the action? Was it the prospect or your internal team? Sending an email to a prospect is very different than receiving an email from one. <br><br></li><li id=""><strong id="">Context</strong>: How did the action happen? If someone visited a webpage, did they arrive via search, a direct link, a referral, or an email campaign? If they clicked an email link, which email did they engage with?</li></ul><p id="">These details transform raw activity logs into a narrative-ready dataset, albeit one that is likely to be getting unwieldy! But we’ll fix that next…</p><h2 id="">Filter down to people and periods that matter</h2><p id="">To tell a clear, actionable story, you can’t include every action from everyone tied to an account as the real signal will disappear in the noise. Focus only on <strong id="">significant</strong> actions taken by members of the <a href="/glossary/buying-group" id=""><strong id=""><em id="">buying group</em></strong></a>. Identifying that group can be done in a few ways, in order of accuracy:</p><ol id=""><li id=""><strong id="">Build your own buying group</strong>: Use context and activity patterns to assemble it yourself. Typically, this includes anyone who interacts directly with your sales reps, plus others who consistently appear in email threads or meetings. Usually the decision-maker is clear in any deal, and you’ll want to find the people that surround

1670/influence them. <br><br></li><li id=""><strong id="">Trust the CRM data</strong>: Use the contacts your sales reps have linked to opportunities as the buying group. This is the easiest method, but it’s often incomplete as busy reps often miss updating the CRM to add all the accurate buying group members. <br><br></li><li id=""><strong id="">Include everyone in the account</strong>: This is rarely worth it. You’ll drown in excess activity and end up reporting on people who had no real influence on the deal.</li></ol><p id="">With all activities neatly organized by date, you can sort them chronologically and zoom in on only the period relevant to your story. The right window depends on the question you’re trying to answer. Common examples include:</p><ul id=""><li id=""><strong id="">What activated this deal?</strong> From the earliest recorded activity up to the moment a meeting was accepted.<br><br></li><li id=""><strong id="">How did we win this deal?</strong> From the first meeting through contract signature.<br><br></li><li id=""><strong id="">How did we earn this upsell, cross-sell, or expansion?</strong> From the prior contract signature to the subsequent one.<br><br></li><li id=""><strong id="">How did each buying group member first hear about us?</strong> Look for the earliest engagement from each person.<br><br></li></ul><p id="">For this guide, we’ll use <em id="">“What activated this deal?”</em>—the most common scenario when answering, <em id="">“What should we invest more in to bring in more deals?”</em></p><h2 id="">Pick a moment, and look backwards</h2><p id="">Begin at the date/time of the first accepted meeting. Who accepted the calendar invite? Who showed up to the meeting? Those people should be members of your buying group.</p><p id="">Then ask: how did they get there? Usually, there’s an activity immediately preceding the meeting that provides a clue. It could be a sales rep outbounding the prospect, or a call record marked as “answered / meeting booked.” It could also be a marketing interaction that led to sales follow-up.</p><p id="">With true inbound deals, you might see a meeting accepted without ANY rep involvement either through a web chat or a meeting booking link on your site.</p><p id="">Keep going backwards: what made them inclined to take the meeting? Did they interact with your team in the past? Did they consume content that made them aware of your product or service? Maybe you met months ago at a tradeshow and they remembered your brand.</p><p id="">Meeting transcripts can be a goldmine here, as the “how we got here” story they share often surfaces in the first few minutes.</p><p id="">Your end goal is to identify <strong id="">the handful of activities that clearly influenced the journey for members of your buying group</strong>. Include older activities if they were impactful, but lean toward excluding activity that resulted in long gaps of inactivity or low momentum.&nbsp;</p><p id="">‍</p><h2 id="">Assemble the customer journey map</h2><p id="">A meaningful customer journey map for a particular customer includes:</p><ul id=""><li id="">The key stakeholders involved</li><li id="">The touchpoints that made a significant influence on the customer</li><li id="">A chronological timeline</li><li id="">An indicator for aggregate engagement across all members of the buying group. Often this is a line chart overlay that helps viewers understand when velocity picked up/dropped off.&nbsp;</li></ul><h2 id="">Examples</h2><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/customer-journey-map-examples-1.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">This customer journey map shows the five main members of the buying group and their engagement over time, leading up to the sales opportunity in question. It highlights the key events that built awareness among the buying group, as well as the connections between certain members. By mapping interactions chronologically, you can see not just who was involved, but how each person’s path into the deal unfolded.</p><p id="">There’s a clear moment when four out of five members attended the same demo meeting, represented by the horizontal line. Moments like this often signal a tipping point when interest shifts from individual curiosity to a collective evaluation. From here, the conversation typically accelerates, decision-making becomes more coordinated, and momentum builds toward a deal.</p><p id="">‍</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/customer-journey-map-examples-2.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">Here’s another example, this time in a horizontal format. It shows multiple attempts to close the deal before the main opportunity finally took shape in 2025. A map like this makes it clear how long a company has been working to engage an account before a meaningful opportunity emerges.</p><p id="">As is often the case with Account-Based Marketing (ABM) motions, those early touchpoints help build awareness, establish familiarity with your brand, and keep you top of mind so that when the right trigger appears, you’re already part of the conversation. Tracking these interactions over time highlights how persistence and multi-channel engagement can turn long-term pursuits into eventual wins.</p><h2 id="">Account Story tools can help</h2><p id=""><a href="/blog/what-is-forensic-attribution" id="">Forensic attribution platforms</a> make this kind of deep journey analysis possible and Upside was built for exactly this. We pull every relevant touchpoint from your CRM, marketing automation, sales engagement, calendars, and meeting recordings into a single, unified dataset. Then we heal and enrich that data, stitching together the full account view so you can filter out the noise, zoom in on pivotal interactions, and see the real story behind a deal.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/customer-journey-map-account-story-tools-can.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">

1670With Upside’s Account Explorer, you can scan thousands of tracked activities and instantly isolate the moments that actually mattered. If you’d like to learn more about how this works, check out this <a href="/#video" id="">3-minute video</a>!</p><p id="">When you can see the full customer journey, you stop making decisions based on hunches and start operating with precision. Every touchpoint, from a single web visit click to a critical meeting, becomes part of a bigger picture that shows what truly moves deals forward. By connecting the dots across your GTM data, you give your team the power to focus on what works, cut out what doesn’t, and confidently repeat success.</p><p id="">The <strong id="">story of a deal is already hiding in your data</strong>, it just needs to be <strong id="">uncovered </strong>and<strong id=""> told</strong>. With a clear, visual customer journey map, you can share that story in a way that drives alignment, sharpens strategy, and fuels growth.</p>',"how-to-finally-measure-the-impact-of-b2b-events":'<p id="">Events are one of the most powerful marketing tactics in B2B. They bring people together, build trust, and create genuine relationships that no digital ad or automated sequence can replicate. In the age of AI, that human connection has become even more valuable. Buyers want to see faces, shake hands, and make buying decisions based on advice from people they know and trust.</p><p id="">And yet, when CFOs start reviewing performance and ROI, events are almost always the first to be cut. Not because they don’t work - but because their true impact is notoriously hard to prove.</p><h3 id=""><strong id="">Why B2B Events Are So Hard to Measure</strong></h3><p id="">Chatting with a marketing operations customer the other day, I heard a common story - he used to feel dread when the events team would come and ask for reports on their performance. Here are the top four reasons we consistently see events measurement being so hard in enterprise GTM teams:</p><h3 id=""><strong id="">1. Tracking is unreliable.</strong></h3><p id="">Events are messy by nature. People forget to register, attend under a colleague’s name, or drop in because they saw a LinkedIn post that morning. That means there’s often no clean record of their attendance, and the CRM never knows they were there. By the time your sales team follows up, that person’s engagement looks like it came out of nowhere, making it nearly impossible to connect the dots between your event and their interest.</p><h3 id=""><strong id="">2. B2B buying journeys are complicated.</strong></h3><p id="">Enterprise deals are rarely linear, with multiple stakeholders, alternative paths, and long time-horizons. One conversation at a dinner might spark awareness, another touchpoint weeks later might deepen interest, and the actual decision could happen months after that. The human side of influence (who met whom, what sparked trust, how an internal champion formed) is almost impossible to quantify through standard campaign tracking. Attendance data alone can’t tell whether the event actually mattered.</p><h3 id=""><strong id="">3. Dates don’t line up.</strong></h3><p id="">Because so much event follow-up is manual, campaign dates often appear <em id="">after</em> the opportunity is already created in the CRM. Data entry from Marketing could be delayed, and by then, the deal may already be underway. In an attribution model, it now looks like the opportunity came first—flipping cause and effect and erasing the real influence your event had on pipeline creation.</p><h3 id=""><strong id="">4. Buying groups are incomplete.</strong></h3><p id="">Incomplete contact roles are the silent killer of event measurement. In most enterprise deals, influence rarely comes from the people directly tied to the opportunity. It’s often someone adjacent — an influencer who attends your dinner, then goes back to their company and gets the team to request a demo. If that person isn’t part of the buying group in your CRM, their impact disappears completely. You lose the thread between the human connection and the business outcome.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-normal" data-rt-type="image" data-rt-align="normal"><div id=""><img src="/images/blog/b2b-event-attribution-4-buying-groups-incomplete.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h2 id=""><strong id="">Measuring What Was Once Unmeasurable</strong></h2><p id="">While at Branch, I built one of the largest mobile growth communities in the world. We even ran our own conference, Branchout. It was incredible — packed rooms, powerful relationships, and deep brand awareness. But when Alex, my old co-founder and CEO, asked, “Was it worth it?” I didn’t have the data to prove it. That frustration stayed with me.</p><p id="">Now, with Upside, we can finally see those moments clearly. We can connect the dots between a dinner conversation and a deal closed months later, between a hallway intro and a reactivated account. Here is how 

1670we do it.</p><h3 id=""><strong id="">Healing the buying group.</strong></h3><p id="">Our AI automatically detects and merges fragmented identities across systems, uncovering all the people connected to a deal, even if they weren’t tagged in Salesforce. It builds a “healed” buying group, so when an influencer from an event triggers engagement from their teammates, that connection is captured and attributed.</p><h3 id=""><strong id="">Surfacing hidden mentions.</strong></h3><p id="">We also detect event mentions in Salesforce notes, emails, and call transcripts, the types of unstructured data that traditionally were impossible to analyze at an aggregate level. When an AE writes, “Met them at Dreamforce,” or a prospect references your dinner in a follow-up email, Upside extracts that signal and ties it directly to the opportunity. Suddenly, what was invisible becomes measurable evidence of influence.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-normal" data-rt-type="image" data-rt-align="normal"><div id=""><img src="/images/blog/b2b-event-attribution-surfacing-hidden-mentions.jpeg" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h3 id=""><strong id="">AI-powered deep research.</strong></h3><p id="">Our Deep Research engine goes a step further, analyzing every touchpoint, call, transcript or piece of unorganized data in a deal to understand how much an event contributed to awareness, activation, or deal progression. For example, when we worked with one customer, we helped them trace how a single sponsored conference reactivated dormant accounts and accelerated in-cycle opportunities, even though none of those signals existed in their CRM before.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-normal" data-rt-type="image" data-rt-align="normal"><div id=""><img src="/images/blog/b2b-event-attribution-ai-powered-deep-research.jpeg" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h3 id=""><strong id="">Measuring what actually matters.</strong></h3><p id="">Instead of stopping at “leads sourced” or “pipeline influenced,” Upside reports on a richer, more nuanced set of metrics that helps you compare and understand the influence of events on a more nuanced level. Our event impact report includes:</p><ul id=""><li id=""><strong id="">Deal contacts engaged:</strong> attendees who were part of a buying group for any opportunity.</li><li id=""><strong id="">AI Detected Opportunities Activated</strong>:opportunities where AI analysis identified the event’s touchpoints as influential in opportunity activation.</li><li id=""><strong id="">Opportunities influenced:</strong> deals where a buying group member engaged at the event.</li><li id=""><strong id="">Opportunities created post-engagement:</strong> new deals opened after the event, within a set time window.</li><li id=""><strong id="">Opportunities influenced in-cycle:</strong> active deals that progressed after event engagement.</li><li id=""><strong id="">Progressed opportunities:</strong> deals that moved forward in pipeline following event touchpoints.</li><li id=""><strong id="">Accounts activated:</strong> previously untouched accounts that engaged because of the event.</li><li id=""><strong id="">Accounts reactivated:</strong> dormant accounts that reengaged after months of inactivity.</li><li id=""><strong id="">Accounts engaged outside the opportunity cycle:</strong> engagement that happened before or between opportunities — the kind of early influence most systems miss entirely.</li></ul><figure id="" class="w-richtext-figure-type-image w-richtext-align-normal" data-rt-type="image" data-rt-align="normal"><div id=""><img src="/images/blog/b2b-event-attribution-measuring-what-actually-matters.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><h3 id=""><strong id="">Finding the GTM gaps.</strong></h3><p id="">Bringing this more holistic view to events Upside enables teams to understand what works, what doesn’t, and why, so they can invest their time and resources confidently in what actually makes a difference.</p>',"how-to-identify-which-web-content-is-driving-net-new-pipeline-upsells-and-renewals":'<h3 id=""><strong id="">The “Why”</strong></h3><p id="">Every marketing team tracks web analytics — page views, bounce rates, conversions. But few can answer the question that truly matters:<strong id=""> Which parts of our website actually drive revenue growth, and which types of revenue are they influencing?</strong></p><p id="">You might know that your pricing page has high engagement or that your blog drives organic traffic, but can you distinguish whether those visits are fueling <strong id="">net-new pipeline</strong>, <strong id="">renewals</strong>, or <strong id="">upsells</strong>?</p><p id="">If the answer is no, you’re not alone. Most attribution systems were built for simpler GTM motions: when one buyer filled out a form, and one channel got the credit. Today, enterprise deals involve <strong id="">dozens of people, hundreds of touchpoints</strong>, and systems that rarely agree on what happened or when. The result? Analytics that track clicks, not outcomes.</p><p id="">The only way to truly understand what drives revenue is to reconstruct the true sequence of buyer interactions across systems <em id="">and</em> across teams to connect those patterns back to opportunity types. That’s what Upside calls <a href="/blog/what-is-forensic-attribution" id=""><strong id="">Forensic Revenue Intelligence</strong></a>: rebuilding a clean, trustworthy picture of what happened so you can finally see what worked.</p><p id="">Here’s how you can approximate that process, and why companies now use Upside to automate it at scale.</p><h3 id=""><strong id="">1. Take Inventory of Your Data</strong></h3><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center" data-rt-max-width=""><div id=""><img src="/images/blog/web-content-attribution-1-take-inventory-data.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">Before tracing which web pages move which deals, you need to know what data you have and where it lives. Most of it already exists across your GTM stack, just in a scattered and messy pattern.</p><p id="">First, start with a digital scavenger hunt:</p><p id=""><strong id="">CRM Data</strong></p><ul id=""><li id="">Opportunities: type (new, renewal, expansion), stage history, value, owner.</li><li id="">Accounts &amp; contacts: complete buying groups and relationships between people and deals.</li></ul><p id=""><strong id="">Web Analytics / Marketing Automation</strong></p><ul id=""><li id="">Page views and session data.</li><li id="">Form fills, demo requests, gated content.</li><li id="">UTM tags and referral sources.</li></ul><p id=""><strong id="">Product &amp; Customer Systems</strong></p><ul id=""><li id="">Login activity, feature engagement, support tickets (all early renewal or expansion signals).</li></ul><p id=""><strong id="">Other Sources</strong></p><ul id=""><li id="">Calendar invites, meeting notes, email metadata, or call summaries.<br></li></ul><p id="">Your goal isn’t sophistication yet; it’s visibility. Take the time to really understand what’s there before you begin architecting something new.</p><h3 id=""><strong id="">2. Bring the Fragmented Data Together</strong></h3><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/web-content-attribution-2-bring-fragmented-data.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">Once you’ve located your data, align it in a single unified view. If you have a data warehouse, query it there; if not, even a spreadsheet can work as a first pass.</p><p id="">Build a <strong id="">chronological record</strong> of all page activity tied to known accounts. Include:</p><ul id=""><li id="">Account name</li><li id="">Contact (if known)</li><li id="">Page URL or title</li><li id="">Page type (blog, docs, pricing, etc.)</li><li id="">Timestamp</li><li id="">Associated account</li><li id="">Associated opportunity (if applicable)</li></ul><p id="">Patterns will start to emerge. Certain blogs appear before the new pipeline opens, while pricing or product update pages precede renewals. This is the first glimpse of a “storyline” that will tell you how web behavior unfolds before revenue outcomes.</p><h3 id=""><strong id="">3. Annotate and Clean Your Data</strong></h3><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" style="max-width:60%" data-rt-type="image" data-rt-align="center" data-rt-max-width="60%"><div id=""><img src="/images/blog/web-content-attribution-3-annotate-clean-data.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">Raw logs alone won’t get you far. For each page activity, make sure you clean up the data so each touchpoint has</p><ul id=""><li id="">Timestamp: When the visit happened (e.g. 2025-10-12 14:32).</li><li id="">Individual: Who visited (new prospect, customer, or internal user).</li><li id="">Context: How they got there (organic search, email click, paid ad).</li><li id="">Intent: Based on page type. Awareness (blog), evaluation (product page), conversion (pricing).</li></ul><p id="">Cleaning and classifying data is the difference between noise and signal. It’s also the step that breaks most teams because GTM data is notoriously <em id="">inconsistent</em>, <em id="">duplicated</em>, and <em id="">out of sync </em>across systems .</p><p id="">This is why Upside built, and is powered by, a <strong id="">knowledge graph</strong> that automatically reconstructs missing or conflicting signals: deduplicating contacts, aligning timestamps, and recovering lost touchpoints from unstructured data.</p><h3 id=""><strong id="">4. Segment by Opportunity Type</strong></h3><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" style="max-width:60%" data-rt-type="image" data-rt-align="center" data-rt-max-width="60%"><div id=""><img src="/images/blog/web-content-attribution-4-segment-opportunity-type.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">Now, it’s time to connect those page visits to <em id="">deal outcomes</em>. To do that you can pull CRM opportunity and categorize types into three key buckets:</p><ol id=""><li id="">Net-new pipeline: new logos, POCs or first-time deals.</li><li id="">Upsells/expansions: increased ARR or new product lines within existing accounts.</li><li id="">Renewals: continuation of existing contracts.</li></ol><p id="">Match each web visit to an account and determine with which opportunity it aligns. You might use fields like <em id="">Opportunity Created Date</em>, <em id="">Close Date</em>, and <em id="">Account ID</em> to make the connections.</p><p id="">When done well, this segmentation will give you three mini datasets, each representing a distinct motion in your revenue cycle.</p><h3 id=""><strong id="">5. Calculate metrics that help you understand web traffic influence on deals</strong></h3><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" style="max-width:40%" data-rt-type="image" data-rt-align="center" data-rt-max-width="40%"><div id=""><img src="/images/blog/web-content-attribution-5-calculate-metrics-help.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">‍</p><h5 id="">Some of the metrics you should calculate if you have all the data clean and unified in one place include:<br></h5><p id=""><strong id="">Accounts engaged</strong>: Count of accounts where any person associated with that account viewed this page.</p><p id=""><strong id="">People engaged</strong>: Count of people who viewed this page.</p><p id=""><strong id="">Deal contacts engaged</strong>: Count of people who viewed this page and have been in the buying group for any opportunity.&nbsp;</p><p id=""><strong id="">Opportunities influenced</strong>: Count and total amount of opportunities where the web page influenced a member of the buying group.</p><p id=""><strong id="">Won opportunities influenced</strong>: Count and total amount of won opportunities where a member of the buying group was influenced by the web page.</p><p id=""><strong id="">Opportunities created post-engagement</strong>: Count and total amount of opportunities that were created in a specific time window after a member of the buying group viewed the page.</p><p id=""><strong id="">Opportunities influenced in-cycle</strong>: Count and total amount of opportunities influenced by this web page during their active cycle.</p><p id=""><strong id="">Progressed opportunities</strong>: Count and total amount of opportunities that progressed after page view from at least one buying group member.</p><p id=""><strong id="">Accounts activated</strong>: Count of accounts that had no engagement at any time before a person associated with that account viewed this web page.</p><p id=""><strong id="">Accounts reactivated</strong>: Count of accounts that had no engagement for 90 days prior to a person associated with that account viewing this web page.</p><p id=""><strong id="">Accounts engaged outside opportunity cycle</strong>: Count of accounts where all page views happened while no matching opportunity was open.</p><p id="">By comparing and contrasting different webpages on the metrics above and filtering by opportunity type, you can start seeing insights and trends. For example:</p><ul id=""><li id="">Net-new deals might be created after views of specific blog pages, comparison pages, or pricing.</li><li id="">Upsells might be tied to product updates or feature announcements.</li><li id="">Successful renewals could be tied to specific educational content.</li></ul><h3 id=""><strong id="">The Hard Part (and Why It Breaks)</strong></h3><p id="">Doing this manually can be hard, which is why most teams just look at visits and sessions instead. An in-house analysis requires:</p><ul id=""><li id="">Clean buying groups; if the person doing research on the web is missing from the buying group, it’s hard to know what made a difference.</li><li id="">Identity resolution to unify visits across systems and merge identities of people (the visit from a home computer entering their personal email on a form is the same as the person who has been outbounded by an SDR on their work email).</li><li id="">Timestamp alignment to reconstruct event order (the campaign upload date is not always the correct date).</li></ul><p id="">That’s why even the most sophisticated RevOps teams struggle to maintain this over time. Every new tool, campaign, or tracking change can break the analysis. You end up with what Upside calls <strong id="">dashboard whiplash</strong>: impressive charts that no one trusts.</p><h3 id=""><strong id="">The Alternative: Forensic Revenue Intelligence with Upside</strong></h3><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img src="/images/blog/web-content-attribution-alternative-forensic-revenue-intelligence.png" width="auto" height="auto" alt="" loading="auto" id=""></div></figure><p id="">Upside automates everything above, from buying group detection, to people identity resolution, to tying webpage visits to opportunities and accounts. Our AI agents pull data from your CRM, web analytics, marketing automation, and sales systems — then <strong id="">reconstruct a clean, forensic-level timeline</strong> of buyer engagement. The result? Surfacing the <strong id="">patterns that actually drive pipeline, renewals, and upsells</strong>, without spreadsheets, SQL scripts, or guesswork.</p><h3 id=""><strong id="">The Bottom Line</strong></h3><p id="">Your website already tells a story — one of discovery, evaluation, and loyalty. The challenge is that most of that story lives hidden across fragmented systems.</p><p id="">When you can see the complete, causal web-to-revenue journey, you stop guessing what works. You can double down on content that truly drives growth, and stop o

1670ptimizing for clicks that don’t.</p><p id="">Upside makes that visibility automatic, so your team can spend less time cleaning data and more time driving revenue.</p>',"how-to-leverage-utm-tagging-for-better-b2b-attribution":`<p>If your analytics don’t line up with what’s actually happening in your pipeline, start here.<br/>Because the truth is, most attribution problems don’t start with models or dashboards.<br/>They start with messy and inconsistent UTM tags.</p><p>Let’s fix that!</p><h2><strong>So what are UTM Tags?</strong></h2><p>UTM stands for <em>Urchin Tracking Module</em> (Google bought a company called Urchin back in 2005).<br/>They’re the tiny URL parameters you tack onto the end of a link so analytics tools know where a click came from.</p><p>For example, you’ll find several of these in the url below:</p><blockquote>https://upside.tech/?utm_source=linkedin&amp;utm_medium=paid_social&amp;utm_campaign=demo_push&amp;utm_content=carousel1</blockquote><p>When someone clicks that link, Google Analytics (or any analytics platform) reads each of those bits and says, “This visit originated from LinkedIn, through a paid social ad, for the Demo Push campaign, using the Carousel 1 creative.”</p><p>Simple, but also fragile.</p><h2><strong>What Each Parameter Means</strong></h2><p>Here’s the quick cheat sheet:</p><div class="w-embed"><table>
1671  <thead>
1672    <tr>
1673      <th>Parameter</th>
1674      <th>What It Means</th>
1675      <th>Example</th>
1676    </tr>
1677  </thead>
1678  <tbody>
1679    <tr>
1680      <td><strong>utm_source</strong></td>
1681      <td>The platform or vendor sending the traffic</td>
1682      <td>linkedin, hubspot, google</td>
1683    </tr>
1684    <tr>
1685      <td><strong>utm_medium</strong></td>
1686      <td>The marketing channel type</td>
1687      <td>email, paid_social, cpc, event</td>
1688    </tr>
1689    <tr>
1690      <td><strong>utm_campaign</strong></td>
1691      <td>The initiative or offer</td>
1692      <td>demo_push, summer_launch, competitive_takeout</td>
1693    </tr>
1694    <tr>
1695      <td><strong>utm_term</strong></td>
1696      <td>(Optional) Keyword or audience</td>
1697      <td>revops, enterprise_accounts</td>
1698    </tr>
1699    <tr>
1700      <td><strong>utm_content</strong></td>
1701      <td>(Optional) The creative variant</td>
1702      <td>carousel1, subjectline_b</td>
1703    </tr>
1704  </tbody>
1705</table></div><p>Note: Some analytics systems treat the first three (source / medium / campaign) as required for proper attribution.</p><p>Consistency matters here… “LinkedIn” is not the same as “linkedin”. Capitalization, spacing, or varying underscores can fragment your reporting.</p><h2><strong>How to Make Sure You Use Them Correctly</strong></h2><h3>How to use UTM tags correctly</h3><p><strong>1. Define a standard set</strong></p><p>Create one authoritative list of approved values and share it with the broader marketing team to use as a resource. Think of it like a brand style guide for your data.</p><p>Decide upfront how to name platforms (e.g. linkedin not LI), how to label mediums (paid_social not social_paid), and how to format campaigns (product_launch_q1). Picklists are your friend here!</p><p>Periodically review the actual values to ensure everyone is using standard values.</p><p><strong>2. Use a generator</strong></p><p>A common best-practice: build a shared spreadsheet or internal tool where each UTM field is selected from a dropdown/picklist. Then a simple \`CONCATENATE\` formula produces the full tagged URL.</p><p>Benefits: fewer typos, consistent naming, easier grouping in reports.</p><p>Pro tip- if you’re excel-savvy, add some IF’s to make sure that a link will NOT be generated until all four inputs have been provided: the destination, source, medium, and campaign. </p><p><strong>3. Automate when possible</strong></p><p>If you run ads, enable auto-tagging features where available (for example in Google Ads). This reduces manual work and improves consistency.</p><p>But! make sure your manual tagging conventions <em>do not</em> conflict with platform-generated tags.</p><h2><strong>Getting Google Analytics to Read Them Correctly</strong></h2><p>GA4 assigns traffic into “channel” buckets based on source/medium values and internal rules. If a combination doesn’t match known rules, it may land in the <strong>Unassigned</strong> bucket.</p><p>In GA4 you can create <strong>custom channel groups</strong> (via Admin → Data Settings → Channel Groups) to map your custom fields into broader buckets.</p><p>Example:<br/>If you use utm_medium=paid_social, tell GA4 that “paid_social” belongs under the “Paid Social” channel group. Otherwise, all your hard-earned ad clicks will, sadly, sit in an “Unassigned” channel bucket. For reference, here’s <a href="https://support.google.com/analytics/answer/13051316?hl=en#zippy=%2Cin-this-article">Google’s help documentation</a> on how to set this up in your instance. </p><h2><strong>Turning UTM Values Into Actual Reporting Data</strong></h2><p>Once you’ve tagged everything consistently, what do you <em>do</em> with those values?</p><h3>Option 1: Capture UTMs in Your Marketing Automation System</h3><p>Capture UTM parameters when someone submits a form by writing them into custom fields on lead/contact records.<

1705/p><p><strong>Pros:</strong></p><ul role="list"><li>Easy to set up, either powered by triggers/workflows or hidden fields on your forms that read from url parameters or cookie values. <br/><br/></li><li>Easily tie that data into existing reporting including lead, contact, and opportunity reports. <br/><br/></li></ul><p><strong>Cons:</strong></p><ul role="list"><li>Usually you are only able to capture only first or last touch, not the full touch-history.<br/><br/></li><li>The lead/contact record holding the UTM may <em>not</em> be the same one that sales chooses to convert, leaving it up to chance. <br/><br/></li><li>Without write-protection logic, values can be overwritten, making it hard to audit the data for quality and accuracy.</li></ul><h3>Option 2: Proactive touchpoint tracking</h3><p>There are platforms such as Marketo Measure (formerly Bizible) that will log every web visit or click into a persistent model (visit‐level or session‐level data). This way, you can capture all UTM parameters associated with every touchpoint.</p><p><strong>Pros:</strong></p><ul role="list"><li>Individual records for every tagged click<br/><br/></li><li>Better data for multi-touch attribution<br/><br/></li></ul><p><strong>Cons:</strong></p><ul role="list"><li>Requires setup of scripts/pixels<br/><br/></li><li>Retrofitting older data may not be feasible</li></ul><p>‍</p><figure class="w-richtext-align-center w-richtext-figure-type-image"><div><img src="/images/blog/utm-tagging-option-2-proactive-touchpoint.png" loading="lazy" alt=""/></div></figure><h3>Option 3: Full-journey tracking (recommended)</h3><p>If your organization is using a marketing automation platform such as Marketo or HubSpot, you’re already logging web visit data, and you can leverage all that data for attribution. Forensic Attribution Systems like Upside can extract the existing data and build reporting off of the historical values. </p><p><strong>Pros:</strong></p><ul role="list"><li>More accurate influence reporting on pipeline and revenue<br/><br/></li><li>Leverages historical data without needing to recapture visits<br/><br/></li></ul><p><strong>Cons:</strong></p><ul role="list"><li>Data completeness depends on use of UTM parameters historically</li></ul><p>‍</p><p>The result: clean, trustworthy attribution without spreadsheets, overwriting, or manual stitching. In the screenshot below, you can see how Upside can tie UTM-tagged traffic to sales opportunities based on engagement from members of the buying group. As new channels get released (such as ChatGPT citations tagging their traffic as utm_source=chatgpt.com, you can set up rules that retroactively classify the traffic into a custom-tracked channel. </p><figure class="w-richtext-align-center w-richtext-figure-type-image"><div><img src="/images/blog/utm-tagging-option-3-full-journey.png" loading="lazy" alt=""/></div></figure><h2><strong>Final words</strong></h2><p>Strong UTM conventions improve every downstream report and transform disordered data into clarity. If you’re investing in driving web traffic to your website, it’s best practice to be using them consistently.</p><p>They’re the first step toward understanding not just <em>what</em> got clicked, but what actually drove revenue.</p><p>When you’re ready to tie your existing UTM values into pipeline performance, we can show you how Upside does full-journey influence attribution leveraging the UTMs you already have!</p><p>‍</p>`,"the-quest-for-b2b-revenue-attribution-and-intelligence":`<p id=""><em>We've posted an update to this journey, <a href="/blog/two-radical-new-solutions-to-b2b-revenue-attribution">One Year In: Two Radical New Solutions to B2B Revenue Attribution</a>.</em></p><p id="">Once upon a time, “growth at any cost” was an acceptable strategy for B2B companies. Money was cheap, so teams could drive new revenue simply by spending their way to success.</p><p id="">Those days are gone. <strong>Now, it is the age of efficient growth,</strong> and B2B go-to-market leaders need to know what is working so they don’t waste their limited resources.</p><p id="">We believe that the foundation of efficient growth is trustworthy, reliable data. This foundation is transformational for a go-to-market organization, because it enables data-driven decisions and answers questions like “where should I spend an extra $100k next quarter?” or “how can I increase sales pipeline without a bigger budget?” across every dollar spent and every person hired. It’s a transformation we have experienced first-hand: at our previous company, <a href="https://www.branch.io/" id="">Branch</a>, the holistic attribution model we built in-house was such a game-changer that it quickly became the cornerstone for most of our key decisions and optimizations while scaling to over $100m in revenue.</p><p id="">It was only later, when industry peers began to tell us that the insights we were getting sounded better than anything else they’d ever heard of, that we began to realize the model we built at Branch might be something special. <strong id="">With Upside, our mission is to help go-to-market leaders make better decisions by bringing the same game-changing, data-driven perspective to every B2B team in the world.</strong></p><p id="">In the article below, we share many of the best practices we wish we had known when we first started wrestling with these problems, and describe some of the biggest measurement traps we often see teams falling into, including:</p><ol id=""><li id="">Believing that the best way to measure is by identifying a single “source” for each deal.</li><li id="">Relying on the sequence of events to allocate credit across many touchpoints.</li><li id="">Looking at the contributions of each go-to-market team separately.</li><li id="">Using attribution data as a weapon, rather than a feedback loop.</li></ol><p id="">Whether you’ve been struggling to solve your company’s revenue measurement challenge for years, or you have a solution that is already working well, or even if attribution is just something you have strong opinions about, we are passionate about this topic and we would love to hear from you.<strong id=""> </strong><a href="/" id=""><strong id="">Please drop us a note</strong></a><strong id="">!</strong></p><h2 id="">Why is B2B revenue measurement such a tangled mess?</h2><p id="">Branch, the company where we first took on these challenges together, was founded in 2014. The product was deep linking and attribution for mobile apps, and the target customers were large consumer brands. Eight years later, we had scaled the company to over $100 million in revenue (and a valuation of $4 billion), primarily through large, complex deal

1705s with long cycles and multiple buyers.</p><p id="">But partway through that growth curve, <strong id="">we began to miss our quarterly sales pipeline targets.</strong> During a post-mortem meeting to investigate why, the conversation went something like this:</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-b2b-revenue-measurement-such.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">You might be smiling ruefully in commiseration, perhaps remembering similar discussions of your own (and if you’re thinking that we could have solved this pipeline duplication problem by tracking opportunity source, we’d tried that already — we’ll explain why we believe it isn’t the answer in the next section).</p><p id="">As we began trying to understand what had caused us to miss our targets, we discovered many layers of complexity that make measurement challenging. These layers are present in almost every B2B sales process, but they compile together to become increasingly misleading in large enterprise deals with multiple decision makers.</p><h3 id="">Complexity layer 1: there is never just one source for a deal</h3><p id="">One of the most widely-adopted concepts in B2B marketing is the Demand Waterfall, from SiriusDecisions. It has become so deeply embedded across the industry that most readers can likely recite these stages in their sleep:<br><br></p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-complexity-layer-1-there-1.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">Today, almost every system for B2B pipeline measurement is designed to identify<strong id=""> </strong>the one single action that drove each lead to raise their hand (submit a demo request, take a call, etc.). That single action is then reported as “the source of the deal.”</p><h6 id=""><em id="">The idea that any single touchpoint, with any single person, had somehow sourced the entire deal was obviously wrong.</em></h6><p id="">If only it were so easy.</p><p id="">When we talk to go-to-market leaders, they know that their one-source attribution is not correct, but often their hunch is that it’s close enough. The idea of parsing through the mess of everything else that they know helps both source and close a deal is daunting, and it’s hard to find a frame of reference to decide whether it would even be worth the effort. </p><p id="">We’ve found that these one-source models become especially problematic as deals grow bigger and more complicated. At Branch, by the time an opportunity turned into qualified pipeline, we usually saw 5-8 people engaging with us, reading content, attending webinars, replying to outbound emails, receiving a referral from a partner, and so on. Some of these activities clearly mattered more than others,<strong id=""> but the idea that any single touchpoint, with any single person, had somehow sourced the entire deal was obviously wrong.</strong></p><h6 id=""><em id="">It was all of the engagement together that eventually led to results.</em></h6><p id="">To illustrate the issue with one-source attribution, here is a simplistic example showing touchpoints from the early stages of a deal with just three people involved:</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-complexity-layer-1-there-2.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">In this scenario, here’s what is going on behind the scenes to generate these touchpoints:</p><ol id=""><li id="">An SDR is outbounding multiple people.</li><li id="">The decision maker eventually replies to one of those emails, and is invited to attend a dinner. Afterwards, she asks her analyst to research the solution.</li><li id="">The analyst has been ignoring the SDR, but now he reads some content and attends a webinar. </li><li id="">The analyst asks an engineer on another team to take a look too, and they agree a demo would be helpful.</li><li id="">All three people attend the demo, during which the decision maker expresses interest. This results in an opportunity getting created.</li></ol><p id="">What’s the correct way to attribute this deal?</p><ul id=""><li id="">A typical lead-source model would probably give credit to the webinar, because that was what the analyst did right before submitting the demo request.</li><li id="">The SDR team would argue that this is a successful outbound deal, because the SDR sent emails before anything else.</li></ul><p id="">We believe neither of these answers is valid, because with all the data laid out, it’s clear that no single touchpoint got this deal moving — in fact, <strong id="">it was all of the engagement together that eventually led to results.</strong></p><p id="">

1705If you hope to get useful insights about complex deals like this one, you need an attribution model that is sophisticated enough to look beyond one-source reporting.</p><h3 id="">Complexity layer 2: time matters, but touchpoint sequence (usually) doesn’t</h3><p id="">Every go-to-market team can benefit from attribution, but experienced marketers are usually the ones who have spent the most time thinking about it. We hear CMOs mention trying first-touch, last-touch, U-shape, W-shape, J-shape, reverse J-shape, full-path…and they keep going.</p><p id="">The problem with this alphabet soup of models? None of them are designed to acknowledge that some types of touchpoint inherently have more impact on a deal than others. Instead, position-based models like these try to figure out which touchpoints deserve more credit <strong id="">solely based on the sequence of events.</strong></p><h6 id=""><em id="">Demo requests reflect the intent built up over all the previous touchpoints, but they aren’t a meaningful touchpoint of their own.</em></h6><p id="">To illustrate this, let’s look at the touchpoint activity from the early stages of another deal. We’ll use a single person this time, just to keep the example a little simpler:</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-complexity-layer-2-time.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">In this scenario, here’s what is happening to generate these touchpoints:</p><ol id=""><li id="">The lead gets a cold email and initially replies “not interested” or “let’s talk later.”</li><li id="">But then they attend a conference talk about the product, during which they learn more about the value it could bring them.</li><li id="">Next, they do some preliminary research on the website, and submit a demo request.</li><li id="">After a short demo meeting with the sales team, they do some additional research on the website, and then email the salesperson to say they would like to move forward.</li></ol><p id="">How would this activity typically be attributed?</p><ul id=""><li id="">A first-touch model would credit the outbound emails, even though the most they accomplished was some brand awareness.</li><li id="">Last-touch would credit the demo request form (or maybe either the Google search or the product page, if the system is slightly more sophisticated).</li></ul><p id="">But in reality, the conference talk was where this person spent the most time learning about the solution…shouldn’t that show up somewhere? And why should we give credit to the demo request at all? Demo requests reflect the intent built up over all the previous touchpoints, but they aren’t a meaningful touchpoint of their own!</p><p id="">Our opinion: time matters, because something that happened a year ago is less relevant than what happened yesterday. But all of the touchpoints in this example helped move the deal forward, and <strong id="">the sequence of events indicates very little about which ones were more incremental.</strong> </p><p id="">This is why advanced teams have moved beyond position-based multi-touch, into impact-weighted attribution and econometric models. Embracing a non-chronological approach also comes with two wonderful side benefits:</p><ol id=""><li id="">You no longer need to worry about debugging misattribution caused by minor errors in how your systems captured the exact order of events.</li><li id="">“Dark funnel” activities (podcasts, Slack communities, word-of-mouth) are far less disruptive to your measurement. This is because you still see the relative contributions of all the other things that also moved the deal forward, and you can more easily incorporate the answers to self-reported attribution questions like “how did you hear about us?” that don’t come with an exact timestamp.</li></ol><h3 id="">Complexity layer 3: deals are always influenced by more than one team</h3><p id="">When teams are siloed and don’t share a common language of data that helps them work together, there is often a lot of double counting, political finger-pointing, or both. <strong id="">It’s hard to know what worked, and where to invest.</strong></p><p id="">Let’s use a simplified example involving just marketing and sales: historically, B2B attribution has been built on the assumption that there is one buyer going through the journey, and this buyer follows a 

1705stage-based waterfall model. That’s why marketing organizations measure everything by counting leads — which is a metric they can fully control — but it’s also why there is typically a strong divide between marketing and sales:</p><ul id=""><li id=""><strong id="">Marketing “captures the leads” and sends the qualified ones to sales.</strong> The failure mode for this is sales always asking for more leads, but then complaining that the leads they’re getting are not good.</li><li id=""><strong id="">Sales “works the leads” to generate opportunities.</strong> The failure mode for this is marketing thinking that sales simply doesn’t follow through on all the excellent leads they’ve been sent.</li></ul><p id="">The end result is a lot of blame shifted around when pipeline is not up to par.</p><p id="">But in today’s world, almost every large deal is brought together through the efforts of both marketing and sales, at minimum, and often also with the assistance of teams such as partnerships and customer support. And what’s missing from this picture is a holistic understanding of how all of the teams involved are contributing to the thing that really matters: <strong id="">revenue.</strong> </p><p id="">For example, sales and marketing might see a deal like this, in separate silos:</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-complexity-layer-3-deals-1.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">But the full story, as experienced by the customer, is more like this:</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-complexity-layer-3-deals-2.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">At Branch, one of our top sources of new deals was VIP dinners that the marketing team organized. But the people at those dinners were identified and invited by our sales teams. There was simply no way to attribute the resulting opportunities to only sales or marketing — the dinner was important, but so was the 1:1 invitation. <strong id="">Any measurement system that tries to evaluate just a single team in a silo is doomed to produce misleading data from the start.</strong></p><h3 id="">Complexity layer 4: attribution is just one piece of the puzzle</h3><p id="">Finally, one of the biggest issues with most attribution systems today isn’t about the data itself; it’s with how the data is used. Time and time again, we have seen examples of attribution being used to say “look what a good job I did!” instead of “what should I do next?” or “what changes should I make to improve?”</p><p id="">In other words, attribution is being misused as a weapon by leaders to assert their worth, rather than as data to inform a feedback loop that helps the business succeed.</p><p id="">Better attribution models and insights can go a long way, but attribution data must be viewed through the lens of what it can do to drive revenue, ROI, and efficiency metrics like the company’s <a href="https://www.wallstreetprep.com/knowledge/saas-magic-number/" id="">magic number</a>.</p><h2 id="">What does it take to untangle the mess of B2B revenue measurement?</h2><p id=""><a href="https://en.wikipedia.org/wiki/All_models_are_wrong" id="">All models are wrong, but some are useful</a>. This gets at one of the fundamental challenges for any measurement solution: the real world has so many variables that it’s impossible to identify true cause and effect. </p><h6 id=""><em id="">Customers don’t remember everything that influenced them either, so there is huge value to measuring the recipe that led to a successful outcome.</em></h6><p id="">Some B2B thought leaders have begun talking about why attribution has become a fool’s errand, due to the growth of unattributable “dark funnel” activity (podcasts, Slack communities, social media interactions, and so on). In their view, the real solution is to give up on attribution models and instead simply ask customers “

1705how did you hear about us?”</p><p id=""><strong id="">We don’t agree that attribution is dead. </strong>Data from asking customers (also known as self-reported attribution) is absolutely something that should be incorporated into a model, but it shouldn’t be the only source of data because it’s incomplete; <strong id="">customers don’t remember everything that influenced them either, </strong>so there is huge value to measuring the recipe that led to a successful outcome.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-what-does-take-untangle.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">In other words, imperfect data is far better than none at all, and every step up the attribution maturity curve will improve the answers to questions like “where should I spend an extra $100k next quarter?” or “how can I increase sales pipeline without a bigger budget?”. This requires balancing two objectives that are often in opposition to each other:</p><ol id=""><li id=""><strong id="">Include as many inputs as possible. </strong>For an attribution model to generate meaningful results, it must capture a high percentage of the raw touchpoints across every team and every channel.</li><li id=""><strong id="">Produce simple outputs. </strong>For a measurement system to have a lasting impact, it must not be a black box or a massive, arcane dashboard that only a few specially-trained analysts can make sense of. </li></ol><p id="">After chipping away at the problem of B2B revenue measurement for many years, here are some of the best practices we’ve learned along the way:</p><ol id=""><li id="">Consider everyone involved in a deal, not just the individual leads.</li><li id="">Bring together all customer touchpoints, from every go-to-market team.</li><li id="">Start from an objective baseline, like “minutes of engagement,” to compare different types of touchpoints.</li><li id="">Apply a time-decay to give less weight to touchpoints that happened long before the conversion.</li><li id="">Use personas as a multiplier to allocate more weight to decision makers.</li><li id="">Look at attribution data as a diagnostic, not a target.</li><li id="">Be OK with using different models to answer different questions.</li><li id="">Don't stop at attribution, because ROl is much better.</li><li id="">Make data actionable for everyone on the team.</li></ol><p id="">In the section below, we’ll explore each of these areas in more detail.</p><h3 id="">Consider everyone involved in a deal, not just the individual leads</h3><p id="">When your customers’ buying committees contain multiple people, you won’t get a clear picture of what makes your deals progress by running attribution on the activity done by just one of them. You need to consider the touchpoints of everyone involved.</p><p id="">At the level of practical implementation, there are two ways we’ve seen teams do to this, each with pros and cons:</p><ol id=""><li id=""><strong id="">Opportunity contact roles. </strong>In theory, contact role relationships set in the CRM should be the simplest approach to figure out who is important on a deal. But this often fails in practice, because it has an unavoidable dependency on AEs adding contact roles for everyone they interact with. We’ve found maintaining that level of CRM hygiene to be a losing battle. </li><li id=""><strong id="">Account-level attribution.</strong> Unlike contact roles, contact-to-account mapping is often automated and quite reliable. Using the activity of every contact mapped to an account solves the CRM hygiene challenge, but comes with the risk of too much noise, especially when accounts are so big that there are multiple business units with distinct deal cycles active at the same time. And in CRMs that maintain a distinction between “leads” and “contacts” (like Salesforce), there is a risk of missing relevant touchpoints from people who have not yet been converted.</li></ol><p id="">We think both of these methods lead to unreliable data. Instead, we recommend an approach that gives the best of both worlds: use contact roles, and detect additional contact roles automatically (such as whenever new people attend meetings or join email threads about an in-progress deal).</p><h3 id="">Bring together all customer touchpoints, from every go-to-market team</h3><p id="">By breaking down the data silos between teams like marketing, sales, partnerships, SDRs, and customer success, you can make sure that you are looking at your whole go-to-market presence holistically. After we did this at Branch, we noticed our entire organization working together far more collaboratively — in fact, we eventually did an analysis that showed deal sizes went up when more teams were involved.</p><p id="">Data centralization is essentially just a problem of extract, transform, and load (<a href="https://en.wikipedia.org/wiki/Extract,_transform,_load" target="_blank" id="">ETL</a>). We’ve seen some companies use a dedicated vendor, and others that simply pipe everything back into Salesforce. How you do it is less important, though vendor consolidation is worth considering since this is usually commoditized functionality.</p><h3 id="">Find an objective baseline and use it to create a touchpoint weighting schema</h3><p id="">Bringing data together across teams is just the first step. You still need to DO something with it. That is 

1705where we’ve seen many teams get stuck, because turning raw data into attribution insights requires addressing something that is always contentious: <strong id="">you’ll need to create a common language across these touchpoints so they can be compared.</strong></p><p id="">Some customer interactions clearly do more to move a deal forward than others. The challenge is agreeing which ones, and by how much — without some sort of ground truth, this is a political fight just waiting to happen. With position-based models (first-touch, last-touch, W-shape, and so on) off the table, there are three main options:</p><ol id=""><li id=""><strong id="">Impact-weighted.</strong> This involves defining a relative “size” for each type of touchpoint, and then using those scores as the baseline for allocating credit.</li><li id=""><strong id="">Econometrics. </strong>Incrementality studies and media mix models have been around for decades, and can give a statistical answer about what is driving results. The challenge with econometric techniques is they’re typically slow (weeks or months for each report), and difficult to use for tactical decisions because of how abstract their outputs are.</li><li id=""><strong id="">Data-driven</strong>. So-called “data-driven attribution” models are based on machine learning algorithms. They’re more tactically useful than econometric analyses, but they’re best suited to specific channels with large quantities of similar-looking activities (for example, digital marketing for consumer brands). Data-driven models are also incredibly dark black boxes, and we have yet to meet a leader who fully understands or trusts their output.</li></ol><p id="">For B2B revenue attribution, we believe impact-weighted models are the current state of the art. A simplified example might look something like this:</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-find-objective-baseline-use-1.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">Then, use these weighted scores to generate metrics like “weighted pipeline” and “weighted revenue” based on the full story of the deal across everyone involved:</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-find-objective-baseline-use-2.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id=""><strong id="">Yes, agreeing on the “size” scores for each touchpoint type in an impact-weighted attribution model is hard.</strong> It’s probably the most contentious step in the whole process, because everyone will come with a different opinion. The impact-weighted model we used at Branch was anchored to the average amount of time a prospect spent with each activity, which provided an objective starting point, and we had a cross-functional group of leaders we called “the weighting council” that was tasked with iterating and refining these scores as we learned more and tried new things.</p><p id="">Fortunately, there are several approaches on the horizon to make impact-weighted scores more accurate and less subjective:</p><ol id=""><li id=""><strong id="">Deep assessment using AI.</strong> With the proliferation of LLMs, it will soon be possible to make an even more nuanced assessment of each touchpoint’s impact. For example, scores can be refined based on how someone replied to an email, or how engaged they were in a Zoom meeting or webinar.</li><li id=""><strong id="">Using an econometric model for calibration. </strong>Even if a media mix model is challenging to use for everyday decisions, it can provide a very useful checkpoint for weighting refinements. For example, you might get a recommendation that sounds something like this: “based on a statistical analysis of your deals last quarter, it appears your current weightings schema might be under-estimating the impact of your webinars, but slightly over-crediting conferences.”</li><li id=""><strong id="">Incorporating cross-company benchmarks.</strong> All of this can be made more accurate when models are trained on a larger dataset.</li></ol><h3 id="">Apply a time decay</h3><p id="">Something that just happened yesterday holds more weight in someone’s mind than something that happened months ago, and should therefore receive more attribution credit in an active deal cycle, even if both touchpoints are otherwise equivalent.</p><p id="">In our opinion, this is important but it isn’t worth over-thinking. We recommend starting with a fixed time window prior to opportunity creation (probably in the range of 12-18 months, depending on the average length of your deal cycles), and then applying a linear time decay on top of all the touchpoints within that window.</p><h3 id="">Use personas as a multiplier</h3><p id="">This one is easy: not every customer contact involved in an opportunity has equal influence on the outcome. An executive decision maker has more sway on a deal than a 

1705junior analyst doing research, which means your attribution model should allow different weighting multipliers for each persona on the deal.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-use-personas-multiplier.png" width="auto" height="auto" loading="auto" id=""></div></figure><h3 id="">Look at attribution data as a diagnostic, not a target</h3><p id=""><a href="https://en.wikipedia.org/wiki/Goodhart%27s_law" target="_blank" id="">Goodhart’s law</a> says “when a measure becomes a target, it ceases to be a good measure.” This definitely applies to revenue attribution.</p><p id="">One thing we learned early on: <strong id="">don’t tie individual contributor compensation to cross-team attribution metrics. </strong>This will seem like a seductively logical path, but you must keep in mind that as a leader, you have a strong incentive to do what is best for the business. Cross-functional initiatives (for example, SDRs inviting prospects to marketing-hosted VIP dinners) are often some of the most effective programs you can run, but not everyone on your team has the same level of context that you do.</p><p id="">In other words, take care to avoid creating a dynamic where ICs are hesitant to collaborate with others because their own numbers could go down.</p><h3 id="">Be OK with using different models to answer different questions</h3><p id="">While we said this once already, it’s worth repeating: <a href="https://en.wikipedia.org/wiki/All_models_are_wrong" target="_blank" id="">all models are wrong, but some are useful</a>. In our opinion, a cross-team, impact-weighted multi-touch model is the best option for answering questions like “what do we need to do to get more revenue?” This is a question most boards and investors care about, so it’s a great one to ask. </p><p id="">But sometimes you need answers to more tactical questions like “what is most effective at booking us initial meetings?”. In that situation, a “breakthrough touchpoint report” (an improved version of the infamous “original source” model) might still be your best bet.</p><p id="">Ultimately, there is no single attribution model that can answer every question. You’ll make better decisions if you get comfortable with using the right model for the right task.</p><h3 id="">Don’t stop at attribution, because ROI is much better</h3><p id="">In general, most sales and marketing activities “work” at some level. The question is, do they work well enough to continue investing? The way to answer this question is with an ROI report.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-don-t-stop-attribution.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">At Branch, our ROI calculations included both direct campaign costs and estimated people costs, which involved estimating the amount of time spent to execute each activity.</p><p id="">We found that the more difficult challenge was training everyone on the team to view results through the lens of ROI, rather than raw outcome metrics. This is a cultural change that has to start from the top with go-to-market leadership, and must prevail through both good times and bad. </p><h3 id="">Make data actionable for everyone on the team</h3><p id="">B2B attribution is valuable to help a go-to-market organization understand what worked, but the real power comes from going beyond passive reporting.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/b2b-revenue-attribution-make-data-actionable-everyone.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">If you know what has helped close deals in the past, <strong id="">you can equip every person on the team with insight about the best step to take next for each deal. </strong>For example, once you know the types of people who are positively influenced by webinars, you can automatically create lists for your SDR team to invite to the next one.</p><p id="">The power of generating recommendations based on cross-team insights is that you can make higher-level decisions, and allocate budget and resources even more intelligently to get the best possible outcome. </p><h2 id="">Takeaway: B2B attribution IS achievable, and worth the effort!</h2><p id="">B2B revenue measurement is a giant mess, but <strong id="">we don’t agree with those in the industry who argue that attribution is no longer worth the effort. </strong>With the end of “growth at any cost,” and increasing scrutiny on efficie

1705nt go-to-market motions, game-changing measurement is possible. Using reliable data to get the full picture of your growth engine is one of the best things you can do to scale your business.</p><p id="">Here are just a few of the decisions our holistic attribution model helped us make at Branch:</p><ul id=""><li id=""><strong id="">Replacing conferences with VIP dinners.</strong> Our model supported what the marketing team had suspected for a long time: most of the conferences we were sponsoring were never worth it for us. However, small group dinners with a mixture of prospects and existing customers were hugely successful. And despite appearing expensive on a per-event basis, the ROI analysis convinced our finance team to get on board with scaling that program up.</li><li id=""><strong id="">Cutting paid promotion. </strong>We eliminated almost all spending on paid acquisition, including content syndication. Even though we saw a moderate volume of net new leads through these channels, the cost was extremely high and the leads we captured were low-quality and rarely led to opportunities.</li><li id=""><strong id="">Executive events. </strong>We hosted an all-expenses-paid, multi-day event in Hawaii for customers and prospects. Despite rave reviews from attendees and from our own teams, our data showed that single-day events in local cities were a more cost-effective way to get the same results. And the model also helped the team responsible for these events decide who to invite, without needing to constantly bother sales reps for nominations.</li><li id=""><strong id="">How many SDRs to hire, and which ones were consistently underperforming. </strong>While we warned earlier about using cross-team metrics to determine compensation, they can be extremely helpful as an input when comparing the results of people in equivalent roles. Our model helped us understand when we needed to hire additional SDRs in a given region, and which individuals were not a strong fit for their roles. </li><li id=""><strong id="">Identifying high performing outbound messaging</strong>. By comparing the weighted pipeline per outbound email sent by the SDR team, we were able to surface creative strategies that opened doors at target companies, then subsequently roll them out to the whole team.</li></ul>`,"two-radical-new-solutions-to-b2b-revenue-attribution":'<p id="">A year ago, we set out to help B2B go-to-market teams make better decisions by building <a href="/blog/the-quest-for-b2b-revenue-attribution-and-intelligence" id="">the best multi-touch attribution model in the world</a>.<strong id=""> We failed. </strong>But that turned out to be a good thing.</p><p id="">The reasons why we failed were both surprising and illuminating. Across all of our conversations, for every industry and persona, we keep hearing the same thing:<strong id=""> it doesn’t matter how advanced a team’s attribution solution is today — no one is ever HAPPY with it. </strong>Attribution is simply a necessary evil in order to keep the trains running on time.</p><p id="">Even Upside’s best-in-the-world multi-touch model became just the least-bad option, and we noticed <strong id="">it still wasn’t enough to help go-to-market teams with their most important questions:</strong></p><ul id=""><li id="">“What really happened in our deals?”</li><li id="">“What worked?”</li><li id="">“How much did it cost us to acquire this customer?”</li><li id="">“Where should we spend an extra $100k next quarter?”</li><li id="">“What is not performing, and what can I cut to make space for new initiatives?”</li><li id="">“How can we increase our pipeline without a bigger budget?”</li><li id="">“Which half of our GTM spend is actually driving results?”</li></ul><p id="">These are the questions our customers need to answer for their board meetings, and to close more lucrative deals, but these aren’t the insights that come out of any attribution model that exists today.</p><h6 id=""><em id="">You can’t answer “what worked” if you don’t know what happened. </em></h6><p id="">After working with our design partners for the past year, <strong id="">we know what’s missing: you can’t answer “what worked” if you don’t know what happened. </strong>Relying on better sales rep hygiene, or even more rigorous list import guidelines for marketing managers cannot be the solution. But we’ve discovered that you CAN find game-changing insights that help you make decisions…if you find a way to consider the full story of what happened, as experienced by your customers. </p><h2 id="">The future of attribution doesn’t look like its past</h2><p id="">For the past six months, we’ve been researching two ground-breaking solutions to the problem of B2B measurement:</p><ol id=""><li id=""><strong id="">A radical improvement to data recovery and healing. </strong>Next-generation attribution must be built on next-generation data, because what’s captured in Salesforce or HubSpot simply isn’t good enough for this level of quantitative analysis. And this isn’t something you can do in a spreadsheet or a Looker dashboard either — getting it right involves using the latest in AI and graph theory to extract all the buried signals in your raw data. </li><li id=""><strong id="">Deal story analysis powered by AI agents. </strong>With careful training, a team of AI agents can replicate the investigative rigor of expert data analysts to unlock insights from your data — even when it is poorly-structured chaos that can’t support quantitative analysis — and make astoundingly perceptive qualitative assessments about the nuanced drivers behind conversions. This isn’t just uploading a Salesforce export into ChatGPT and asking questions, because you need the AI to follow the same forensic process that an experienced data analyst would use.</li></ol><p id="">Neither of these approaches would have been technically feasible even a year ago, but state-of-the-art is moving quickly and we’ve already seen outstanding results from both. In this article, we will break down how they work, and why we believe this is the future of B2B revenue attribution and intelligence<strong id="">.</strong></p><p id="">And whether you’ve been struggling to solve your company’s revenue measurement challenge for years, or you have a solution that is already working well, or even if attribution is just something you have strong opinions about, we are passionate about this topic and we would love to hear from you. <a href="/" id=""><strong id="">Please drop us a note</strong></a>!</p><h2 id="">Why attribution fails today: messy data, subjective weightings, and “accurately” measuring the wrong things</h2><p id="">The attribution model we spent most of last year testing was based on an in-house tool we created while at <a href="https://branch.io/" id="">Branch</a>, which had helped us scale that company to over $100 million in revenue (and a valuation of $4 billion), primarily through complex enterprise deals with long cycles and large buying committees.</p><p id="">It was an impact-weighted attribution model, and it worked as designed — that wasn’t the issue. 

1705Viewed through the frame of traditional B2B attribution, our results were more accurate and more sensitive than anything else out there. But if you spend time trying to build a multi-touch attribution model, you’ll eventually run into at least one of these brick walls:</p><h3 id="">Problem 1: Messy data is kryptonite to trustworthy insights</h3><p id=""><strong id="">B2B enterprise data is always a disaster. </strong>Attribution is a garbage-in-garbage-out problem, and teams seem almost perversely proud to tell us how screwed up their data is! </p><ul id=""><li id="">Sales reps and marketing managers log and structure touchpoints in ways that help their workflows, not the way customers actually interact with the company (for example, adding people to campaigns days after an event happened).</li><li id="">People are busy with their day jobs, so even benign requests, such as for AEs to add important people to their deals as contact roles, becomes a distracting and distasteful chore.</li><li id="">Email is the lowest common denominator of any B2B go-to-market motion, which means it contains a goldmine of useful signals. But email tracking is a cemetery of ambition — tools like Gong still struggle to dedupe the same customer reply in three different AE inboxes, or extract quoted email messages buried deep in a forwarded email chain.</li><li id="">Every time team leadership changes, or the company upgrades to a more capable marketing automation platform, or someone is tasked to “clean up the campaign list,” data goes missing and classification guidelines get updated. The result is campaigns logged multiple times in different systems, channel values changing every year, and the same prospect appearing as multiple contacts with different email addresses.</li></ul><p id="">Here’s an example of how easily this messy data problem can lead you astray:</p><p id=""><strong id="">What your systems captured:</strong></p><ol id=""><li id="">Sean M, a prospect, registered for an event but didn’t show up.</li><li id="">The AE sent an email to him and his CEO. </li><li id="">There was an inbound email 15 days later from a new contact on this account.</li></ol><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-problem-1-messy-data-1.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">To an attribution model, this collection of touchpoints looks like a direct inbound contact and a deal with only the sales team involved. </p><p id=""><strong id="">What actually happened:</strong></p><ol id=""><li id="">Sean M and Edith H were invited to a Warriors Game.</li><li id=""><strong id="">Sean M registered for the Warriors game.</strong></li><li id="">Edith H attended the warriors Game.</li><li id="">Edith H had a conversation about CompanyA with Rob P, the COO.</li><li id=""><strong id="">Dylan followed up with Edith H.</strong></li><li id="">Edith forwarded the email to Jennifer D, the right person at Company A.</li><li id=""><strong id="">Jennifer D sent an email to Dylan expressing interest.</strong></li></ol><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-problem-1-messy-data-2.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">In reality, <strong id="">this deal was probably sparked by a VIP event that included an executive assist from the COO</strong>, with outbound follow-up help from the AE. All of the relevant details exist somewhere in the deal story, they’re just buried and not legible to any attribution model we’ve ever seen. This muddling of deal drivers is a problem for every go-to-market team, but the impact is particularly evident in marketing — marketers rarely get the credit they deserve in bringing deals, and the CMO role is the shortest-tenure C-level role out there.</p><h6 id=""><em id="">Marketers rarely get the credit they deserve in bringing deals, and the CMO role is the shortest-tenure 

1705C-level role out there. </em></h6><h3 id="">Problem 2: There is no objective “right answer” to multi-touch attribution weightings</h3><p id="">Our typical customer has somewhere between 2,000 and 10,000 touchpoints per account. Some of these are clearly more important than others, but it’s nearly impossible — or it at least takes many hours — to look at all of them and untangle what helped close the deal and what was just noise along the way. Is an email from sales worth the same if it happens before deal qualification vs. during the negotiation phase? What about a thought-leadership webinar vs. product content? Should these activities always get the same weighting on every deal, or does it depend? </p><p id="">No human could write enough rules to cover all of these scenarios, and there is a significant risk of the “<a href="https://en.wikipedia.org/wiki/Coastline_paradox" id="">infinite shoreline problem</a>” where more touchpoints are created simply by spending more time to define more rules. But more statistically-rigorous approaches, such as a regression analysis, quickly run out of steam <strong id="">if you can’t feed them clean data about what happened</strong>.</p><h3 id="">Problem 3: Attribution makes it too easy to focus on the wrong questions</h3><p id="">When looking for credit and ways to quantify a team’s performance, the concept of “I was first!” is easy to understand. Everybody from an SDR to the CEO gets how that model works. It’s also obviously incomplete, which leads to plenty of “common-sense suggestions” from everyone involved about how to make things better. This is often what leads to an extended session in the B2B attribution hamster wheel,<strong id=""> and no one is ever happy at the end.</strong></p><p id="">Fortunately, we’re now in <strong id="">the age of efficient growth</strong>, which is inspiring go-to-market leaders to think differently. As one CMO put it to us recently: </p><p id=""><em id="">“I feel like attribution — the way we’ve been talking about it for years — is kind of old-fashioned. Remember how obsessed we used to be with marketing-sourced and SDR-sourced? People just aren’t thinking that way anymore. Now it’s all about pipeline, and how do I understand what’s working and what’s not so I can make better decisions.”</em></p><h2 id="">AI can’t work miracles, but an infinite supply of “interns” sure helps</h2><p id="">Last fall, for one of our design partners, we compiled an audit of their most common opportunity activation reasons. We’re highly-experienced go-to-market leaders with data analysis skills, but this project took us weeks and required laboriously poring through thousands of touchpoints by hand.</p><h6 id=""><em id="">Don’t expect to upload an unstructured dataset into ChatGPT and get good results</em></h6><p id="">Today, our team of AI analysts can run an equivalent “milestone analysis” in about five minutes, and it regularly finds buried influences even expert human analysts might miss. This is very exciting, because we see deal stories as another form of attribution, even if not expressed in dollars and cents. An analyst-generated story essentially provides a shortcut to answering some of our customers’ most important questions without getting entirely stymied by the messy data problem.</p><p id="">Our preferred format generates an opinion about what led up to the milestone, and then supports that opinion with specific citations from the deal story to validate that the AI team hasn’t manufactured evidence out of thin air:</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-comprehensive-mission-control-account-3.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">However, <strong id="">don’t expect to upload an unstructured dataset into ChatGPT and get good results.</strong> Much like training a team of interns, you need to give the AI agent team careful instruction about how to interpret the data it’s being fed. Even the differences between a New Business and an Upsell deal are enough to require additional guidance.</p><p id="">A deal story is also notably different from unbounded “chat with your data” AI experiences, which we think are still universally terrible. Even though this is a common feature request, we see people try them once, get underwhelming (or even hallucinated) results, and never try again. This won’t be the case forever — between technology improvements and end users becoming more familiar with how to interact with AI — but in the meantime, “working together with AI to create a shared asset” (such as one of these deal stories, or to configure a dashboard or add a column to an existing report) is a better paradigm.</p><p id="">The capabilities of AI are advancing rapidly, which means today is the worst these analyses will ever be. But even far more advanced LLMs won’t be the panacea, especially for any kind of quantitative analysis. For these use cases, we still need structured, clean data.</p><h2 id="">The road to true next-generation attribution involves next-generation data</h2><p id="">We believe that the only true solution to the B2B attribution problem is through solving the underlying data problem first. If the dataset you start from is wrong — either because it’s missing records or contains duplicates — it doesn’t matter what quantitative attribution model you put it through. <strong id="">The results will be bad.</strong></p><h6 id=""><em id="">The traditional systems of record (like Salesforce or Marketo) aren’t trustworthy.</em></h6><p id="">All B2B attribution models today are descended from an archaic, legacy data foundation based on lead funnels and designed to work primarily on single-buyer deals. It’s the structure that we all know so well from Salesforce: pre-defined objects like accounts/opportunities/leads/contacts/campaigns, and the static relationships between them: </p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-road-true-next-generation-1.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">This foundation has served us all well enough in the past, and the problem is not with what it captures, but with what it misses. The language of Salesforce simply doesn’t have grammar to handle all the embedded signals when a prospect sends you an email like this:</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-road-true-next-generation-2.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">We know this email is almost too good to be true (lightning does strike sometimes!), but the key insight for attribution purposes is that <strong id="">this prospect is a returning customer.</strong> Now, imagine this email arrives right after a website session with a chatbot conversation:</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-road-true-next-generation-3.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">Any attribution tool based on a traditional data model is going to get this wrong every time — in fact, it will probably send you down the road towards a strategic faceplant, because it’ll tell you that <strong id="">“good deals come from chatbots” </strong>when the truth is that the chatbot simply happened to be the most convenient button on the website for someone with pre-existing high intent.</p><p id="">A next-generation attribution model needs access to these hidden and buried signals.</p><h2 id="">How do you build a next-generation data foundation?</h2><p id="">At Upside, our mission to build a better data foundation for next-gen attribution has forced us to make a big mindshift: the traditional systems of record (like Salesforce or Marketo) aren’t trustworthy. The data inside them is an extremely useful source of fuel, but not a source of truth. </p><p id="">For example:</p><ol id=""><li id="">

1705An AE might not add someone to Salesforce as a contact, <strong id="">but will add them to a calendar meeting.</strong> They might not log the coffee meeting or quick call,<strong id=""> but will send a follow up email after.</strong></li><li id="">Marketing teams might not enter the exact dates for a VIP event, <strong id="">but will add people to campaigns and send invitations with the right date inside.</strong></li></ol><p id="">The goal is not to “clean up” the data coming from these sources — it’s much more transformational: to reconstitute the most-likely-correct version of what happened based on all the signals available.</p><p id="">We’ve been working on this for over a year and still don’t have all the answers, <strong id="">but we’re starting to see breakthrough improvements.</strong> Here’s the outline of how we are approaching the problem now:</p><h3 id="">Step 1: Ingest raw data, and create a dense graph of signals</h3><p id="">Signals exist everywhere. If you’re capturing data, it contains signals that could be useful. Systems like Salesforce and Marketo or HubSpot are the places to start, but more is better.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-step-1-ingest-raw-1.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">ETL tools like Fivetran make this process much easier, because they have prebuilt integrations with most main sources. In practice, we’ve found that we can usually get pretty far with just Salesforce and Marketo/HubSpot because mature go-to-market-organizations tend to already treat Salesforce as their de facto data warehouse: the raw data ends up dumped in there somewhere, even if it isn’t addressable for any sort of meaningful analysis.</p><h6 id=""><em id="">We’re extracting signals not only about the touchpoint itself, but also about all of the “phantom touchpoints” referenced within it (for example, the casual mention in an email of “coffee yesterday”).</em></h6><p id="">At this stage in the process, the goal is not to create the final dataset for reporting because there will be a lot of overlap. We’re extracting signals not only about the record itself, but also about all of the “shadow records” referenced within it (for example, the casual mention in an email of “coffee yesterday” — we call these “phantom touchpoints”). Conceptually, it’s a little like letting bread dough rise, and the output is going to be a giant, extremely messy graph with literally billions of individual “nodes”:</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-step-1-ingest-raw-2.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">This is ultimately a structured data problem, not dissimilar to what a search engine like Google does when crawling the web. We’ve been bootstrapping our work with industry standards like <a href="http://schema.org/" id="">Schema.org</a> and <a href="https://www.w3.org/TR/prov-o/" id="">PROV-O</a>, which provide extensive references for how to map real-world activities into structured data. We’ve also been testing ways to use AI to speed up the mapping of data sources into these schemas.

1705</p><h3 id="">Step 2: Use entity resolution to synthesize a canonical version of what happened</h3><p id="">Once you have a giant, semi-structured soup of overlapping signals, it’s time to distill back into a set of canonical records. Entity resolution is a well-established discipline, and we’ve been researching a variety of techniques to understand which ones give the best results with go-to-market data.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-step-2-use-entity.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">The goal is to create a comprehensive dataset, equivalent to traditional objects like people/accounts/campaigns/touchpoints, but with far fewer missing pieces. In Genesis, our data foundation, we sometimes refer to this as “the blood-brain barrier.” Downstream analytics systems (such as the attribution model) no longer need to deal with the chaos of data in source systems like Salesforce or Marketo because they’re receiving a clean feed of deduplicated, healed data. In the example above, Genesis has correctly identified that the “Mada Seghete” who founded Branch is the same “Mada Seghete” who founded Upside, even though there is no direct link between these records.</p><h2 id="">What you can do with this data</h2><h3 id="">Comprehensive “mission control” for your account stories </h3><p id="">People instinctively distrust black boxes, and want the option to drill into the raw data behind any roll-up number or analysis to confirm it is believable. </p><p id="">One of the first customer-accessible tools we built was an “account story viewer” because it was something we needed ourselves on a daily basis (jumping between Salesforce, Marketo, Gong, and half a dozen other tools to debug an account story gets old, fast).</p><p id="">We’ve talked to a number of teams who built a similar tool in-house (often in a platform like Looker), because nothing comprehensive exists on the market for it today. But these still suffer from the flaws of messy source data, and also inherit all the typical challenges of in-house tooling, including unpleasant UX and frustrating load times with larger datasets.</p><p id="">Some of the key features we’ve needed for this include:</p><h4 id="">A “minimap”</h4><p id="">A “minimap” (like in a video game) that gives a clear visual representation of when interesting things are happening in the account. This includes when opportunities were open and progressing, when new people showed up for the first time, and when the account had velocity (calculated by showing customer engagements against the “bids” coming from teams like sales and marketing). The minimap helps to-go-market teams see where interesting things were happening, and jump straight to that moment for further investigation.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-comprehensive-mission-control-account-1.png" width="auto" height="auto" loading="auto" id=""></div></figure><h4 id="">A chronological log of touchpoints</h4><p id="">A chronological log of touchpoints showing who engaged, in which channel, with which go-to-market team, and so on. This supports multiple filter modes, so you can drill in and understand the impact of every campaign or person on a deal. Once you see what happened, you can re-engineer the most effective thing to do on similar accounts going forward.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-comprehensive-mission-control-account-2.png" width="auto" height="auto" loading="auto" id=""></div></figure><h4 id="">Summaries of “milestone moments”</h4><p id="">Summaries of “milestone moments” to make sure that people don’t miss the forest for the trees about what drove progress in the account. These summaries cite their sources, so that it’s easy to jump back into the full deal story for follow-up investigation.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-comprehe

1705nsive-mission-control-account-3.png" width="auto" height="auto" loading="auto" id=""></div></figure><h4 id="">Deep touchpoint forensics</h4><p id="">Deep touchpoint forensics so that people can dig into all the details in one place without having to jump between different platforms. In our platform, this already includes information like sales email content and web session details, and we are expanding it to cover additional data types like call transcripts, marketing email content, and chatbot conversations. This dramatically simplifies the task of finding all the hidden handoffs that happen in a typical enterprise deal cycle.<br><br></p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-comprehensive-mission-control-account-4.png" width="auto" height="auto" loading="auto" id=""></div></figure><h3 id="">Campaign and channel scorecard reporting</h3><p id="">Every B2B go-to-market organization needs to report on KPIs for their investments. This is often an exercise in vanity metrics, because real data is so painfully tedious to pull. But with a comprehensive account story for every prospect, it suddenly becomes much easier to generate meaningful insights for each campaign. For example, the number of opportunities activated or progressed after an interaction with that campaign, combined with the ability to drill down to the deal story and check how that activation happened.</p><p id="">With this level of insight consistently available to everyone on the team, it’s much easier to make good decisions without waiting for a QBR or an overloaded BI analyst to pull one-off data reports.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/two-radical-attribution-solutions-campaign-channel-scorecard-reporting.png" width="auto" height="auto" loading="auto" id=""></div></figure><h3 id="">Customer acquisition cost and deal ROI</h3><p id="">Once you have a comprehensive audit of the touchpoints involved in closing a deal, you can attach costs for all of them and estimate how expensive it was to land each customer. <strong id="">These insights help you make decisions about which industries, geographies, and customer tiers are worth pursuing.</strong> We’ve already started running proof-of-concept experiments for this, and expect to start working on it in the near future.</p><h3 id="">Weighted attribution</h3><p id="">We know that assigning credit for pipeline or revenue dollars to specific activities is still an attractive goal for many teams because it enables zero-sum reporting where everything adds up. We’ve continued to monitor the improvement of our original impact-weighted model as the underlying data quality improves, and we’re also researching other approaches to weighting schemas that were not possible within the limitations of a traditional data foundation.<strong id=""> One promising possibility includes a predictive model that allocates credit to each touchpoint based on how much it increases the likelihood of a deal closing.</strong></p><h3 id="">Next-best-action predictions</h3><p id="">This is one of our most common customer requests: after you know what has helped close deals in the past, you can equip every person on the team with insight about the best step to take next for each deal that is open today. This is also one of the most highly-leveraged modeling exercises, which means it’s extremely important to be confident in the stability and quality of data at lower levels first.</p><h2 id="">Takeaway: B2B attribution CAN be great, and it’s worth the effort!</h2><p id="">A year and a half into this journey, we’re more convinced than ever that game-changing measurement is possible, and that using reliable data to get the full picture of your growth engine is one of the best things you can do to scale your business.</p><p id="">We have helped our design partners uncover what drove their largest deals, given insights to C-level execs to inform shifts in their inbound vs. outbound spend, and shown teams which signals truly mean a deal has a high likelihood to close. </p><p id="">We also have a far clearer picture of what it will take to make meaningful recommendations about what to do next, taking into consideration contributions from the whole go-to-market organization, and why traditional approaches to attribution and prediction are continually doomed to failure.</p><p id="">If you’ve been trying to solve this problem for years, somehow never managed to make progress, and are now looking for a more rigorous approach to your GTM insights, we would love to hear from you. Please <a href="/" id=""><strong id="">reach out to us</strong></a> and we’ll be in touch!</p>',"what-is-forensic-attribution":'<h2 id=""><strong id="">TL;DR</strong></h2><p id=""><strong id="">Forensic attribution</strong> is a new way of doing B2B large opportunity and ABM attribution. Instead of using your existing data and applying models to it, a forensic attribution system pulls every signal from different data sources (your CRM, marketing tools, email tracking or meeting video recording, and so on) and recreates what happened to understand what actually worked.</p><h2 id=""><strong id="">Why do we need a new way of doing attribution?</strong></h2><p id="">I recently posted something on LinkedIn about my struggles as a B2B CMO: showing what works and knowing where to spend my budget. The post went viral because every marketing leader from all over the world faces this problem. And the reason is simple: attribution for B2B is broken and needs a new solution. We are proposing forensic revenue attribution as the answer.</p><h2 id=""><strong id="">Why We Need a New Category of Attribution</strong></h2><p id="">Several years ago, I was CMO and a co-founder at Branch. During a board review, I was asked a question every CMO is asked over and over:</p><p id="">“But which of your initiatives actually created revenue? And what can you cut?”</p><p id="">Despite well-designed slides and a working multi-touch attribution model, I could not provide a clear, evidence-based answer. &nbsp;The systems built to measure GTM efforts are broken and antiquated and built for leads, not for the complexity of B2B large enterprise deals.</p><p id="">That experience, while humbling, set me on a path to create what I believe is a more rigorous approach to attribution—one designed for the complexities of B2B sales and buying behavior.</p><h2 id=""><strong id="">Why Traditional Attribution Models Fall Short</strong></h2><h3 id=""><strong id="">1. Incomplete Data Capture</strong></h3><p id="">CRMs and marketing automation tools capture only a fraction of the buyer journey. Informal interactions—executive outreach, Slack conversations, calendar events, partner emails—often go untracked, leaving attribution models to operate on partial data. The current systems also rely too much on humans and humans are fallible. AEs miss adding people to CRMs, marketing teams upload contact to a campaign days after it happened, meetings are not always logged.</p><h3 id=""><strong id="">2. Inadequate Modeling for Complex Journeys</strong></h3><p id="">First-touch and last-touch attribution oversimplify buyer behavior, especially for large deals with over hundreds or even thousands of touchpoints. Even multi-touch models (e.g., U-shaped, W-shaped, time-decay) focus on weighting algorithms rather than reconstructing what actually occurred. These models are blind to the reality of multi-stakeholder buying groups and non-linear sales cycles.</p><h3 id=""><strong id="">3. Metrics Without Strategic Utility</strong></h3><p id="">Attribution dashboards frequently become internal scorecards rather than strategic tools. Marketing, sales, and finance interpret the same data differently, often undermining trust and alignment. Worse, attribution rarely informs real-time decision-making around spend, headcount, or pipeline risk.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/forensic-attribution-3-metrics-without-strategic.png" width="auto" height="auto" loading="auto" id=""></div></figure><h2 id=""><strong id="">What Is Forensic Attribution?</strong></h2><p id=""><strong id="">Forensic attribution</strong> is a methodology using data signals and reconstructive techniques for B2B revenue attribution. It starts by creating a knowledge graph for a company’s GTM data and uses it to read signals across different systems and tools. The next step is to &nbsp;recover unlogged or fragmented touchpoints and reconstruct the buying account’s journey at a multi-person, multi-channel level. Lastly the model uses all the data and applies <strong id="">contextual analysis</strong> to identify what truly influenced pipeline and revenue outcomes.</p><p id="">In contrast to traditional models that apply weighted models like first touch or last touch to existing incomplete data, forensic attribution builds a complete picture by first understanding what happened, then interpreting what mattered.</p><h2 id=""><strong id="">How It Works: Upside’s Forensic Attribution Framework</strong></h2><h3 id=""><strong id="">1. Ingest raw data and unify it</strong></h3><p id="">While many different vendors claim to unify data, much of that unification is bringing data together in one place without much thought, resulting in duplicate contacts, campaigns, accounts and beyond, and <strong id="">making it even harder to get insights</strong>.</p><p id="">A true forensic model like Upside cleans up, deduplicates, and unifies data at the record level, finding ways to resolve and merge even duplicate contacts with different information — like a contact with different emails or titles.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/forensic-attribution-1-ingest-raw-data-1.png" width="auto" height="auto" loading="auto" id=""></div></figure><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/forensic-attribution-1-ingest-raw-data-2.png" width="auto" height="auto" loading="auto" id=""></div></figure><h3 id=""><strong id="">2. Signal Recovery</strong></h3><p id="">Upside connects to your full GTM stack including CRM, marketing automation, email tracking or video recording. This enables recovery of touchpoints that never made it into your CRM, in most cases being the touchpoints that actually drove deals forward.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/forensic-attribution-2-signal-recovery.png" width="auto" height="auto" loading="auto" id=""></div></figure><h3 id=""><strong id="">3. Genesis™ Person and Actions Knowledge Graph</strong></h3><p id="">Recovered signals are cleaned, deduplicated, and organized into a unified graph that links every buyer persona, timestamp, system, and channel. This structure accommodates role changes, buying group dynamics, and cross-functional collaboration within an opportunity.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/forensic-attribution-3-genesis-person-actions-1.png" width="auto" height="auto" loading="auto" id=""></div></figure><p id="">Most organizations have data scattered across various systems, formats, and schemas. Knowledge graphs provide a powerful way to integrate and harmonize this disparate information into a coherent, queryable whole, creating not just a central physical repository, but a normalized single source of truth in a common level of semantic meaning.<br><br></p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/forensic-attribution-3-genesis-person-actions-2.png" width="auto" height="auto" loading="auto" id=""></div></figure><h3 id=""><strong id="">4. Visual representation of the deal that helps detect when momentum inflections happen</strong></h3><p id="">Each opportunity is reconstructed as a chronological journey, highlighting influence inflection points, internal alignment shifts, deal momentum, or deal deceleration, transforming attribution from a model to a story that you can zoom in at any point and understand what happened - who was involved, how did they engage with sales and marketing and in many cases WHY they did it.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/forensic-attribution-4-visual-representation-deal.png" width="auto" height="auto" loading="auto" id=""></div></figure><h3 id=""><strong id="">5. Dragonsight AI™ Analysis of the momentum shifts and clean data to understand what happened</strong></h3><p id="">Our proprietary AI agent acts like a virtual analyst, evaluating causality, velocity shifts, and spend to generate concise, explainable insights, such as “campaign X drove re-engagement with a stalled buyer group” or “executive outreach accelerated deal velocity post-stall” by looking at the clean, enhanced and unified data.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/forensic-attribution-5-dragonsight-ai-analysis.png" width="auto" height="auto" loading="auto" id=""></div></figure><h3 id=""><strong id="">6. Strategic Reporting</strong></h3><p id="">Upside surfaces campaign ROI, rep influence, channel effectiveness, and momentum decay in decision-ready formats for campaign managers, but also leaders including CMOs, RevOps, and CFOs.</p><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/forensic-attribution-6-strategic-reporting.png" width="auto" height="auto" loading="auto" id=""></div></figure><h2 id=""><strong id="">Why This Matters in 2025</strong></h2><ul id=""><li id=""><strong id="">Go-to-market complexity is growing</strong>: Buyers operate across community, peer referrals, partner ecosystems, and AI-driven sequences, most of which aren’t captured in structured Salesforce or HubSpot fields.

1705<br><br></li><li id=""><strong id="">Buying groups have multiplied</strong>: Enterprise deals involve on average 7+ stakeholders, often spanning functions, roles, and geographies.<br><br></li><li id=""><strong id="">Budgets are under scrutiny</strong>: In an era of efficiency, boards and CFOs expect attribution models that can stand up to executive review and audit and won’t allocate budgets without clear ROI.<br><br></li><li id=""><strong id="">AI content saturation demands evidence-based measurement</strong>: Marketing output has increased tenfold with more content, emails and engagement than ever before, making it even harder to understand what actually works. A forensic model can distinguish noise from real influence.<br><br></li></ul><h2 id=""><strong id="">Outcomes: What You Can Do With Forensic Attribution</strong></h2><p id=""><strong id="">Executive Question</strong></p><p id=""><strong id="">Forensic Attribution Insight</strong></p><p id=""><strong id="">Traditional Models</strong></p><p id=""><strong id="">“What should we double down on?”</strong></p><p id="">Channel and campaign ROI linked to closed-won revenue—not just MQLs.</p><p id="">Each weighted model will give different results, the best channels might not get the right credit depending on how weights are assigned.</p><p id=""><strong id="">“Why is this deal stalling?”</strong></p><p id="">Timeline flags missing champions, executive drop-off, or a cold gap in activity.</p><p id="">No way of showing deal level attribution or engagement.</p><p id=""><strong id="">“Where’s the budget waste?”</strong></p><p id="">Cost-per-influence and momentum analysis highlight non-performing spend.</p><p id="">Impossible to tell, every model shows a different result.</p><p id=""><strong id="">“Can we trust the data?”</strong></p><p id="">Yes. Every insight is backed by system-level evidence, traceable to original interactions.</p><p id="">Most insights are a black box.</p><h2 id=""><strong id="">How can you try out forensic attribution with Upside?</strong></h2><ol id=""><li id=""><strong id="">Connect GTM systems</strong> (OAuth-based; minimal configuration) - 1-2 hours</li><li id=""><strong id="">We ingest and prepare your data</strong> (no manual or cleanup data prep) - overnight</li><li id=""><strong id="">Review and map data</strong> - 1-2 hours kickoff meeting</li><li id=""><strong id="">Access dashboards for Marketing, RevOps, and Finance - </strong>after kick-off</li><li id=""><strong id="">Act on insights</strong>: Reallocate spend, recover stalled deals, prove impact - ongoing</li></ol><figure id="" class="w-richtext-figure-type-image w-richtext-align-center" data-rt-type="image" data-rt-align="center"><div id=""><img alt="" src="/images/blog/forensic-attribution-can-try-out-forensic.png" width="auto" height="auto" loading="auto" id=""></div></figure><h2 id=""><strong id="">Conclusion: From Attribution Guesswork to Evidence-Based Growth</strong></h2><p id="">Forensic attribution is not just a model—it’s an operating framework for GTM leadership in modern B2B organizations. It equips teams with the evidence to make confident decisions, eliminate internal misalignment, and unlock real growth.</p><p id="">If you’re ready to move beyond attribution debates and start acting on what truly works, we’d be happy to walk you through it.</p><p id="">👉 <a href="/" id=""><strong id="">Book a demo</strong></a></p>'};function hde(e){const a=e.replace(/<[^>]+>/g,"").trim().split(/\s+/).length;return Math.max(1,Math.ceil(a/200))}function pde(e){const t=new Date(e);return isNaN(t.getTime())?e:t.toLocaleDateString("en-US",{month:"long",day:"numeric",year:"numeric"})}const mde=()=>{const{slug:e}=Ta(),t=I4.find(m=>m.slug===e),a=e?ude[e]:void 0,i=`https://upside.tech/blog/${e}`,r=M.useRef(null),{items:o,html:l,activeId:d,scrollToHeading:u}=dde(a),h=o.length>0;return M.useEffect(()=>{if(!r.current)return;r.current.querySelectorAll(".copy-prompt").forEach(f=>{if(f.querySelector(".copy-btn"))return;const y=document.createElement("button");

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1705<div role="list" class="case-study-testimonials-list w-dyn-items"><div role="listitem" class="w-dyn-item"><div class="testimonial-small-container"><div class="testimonial-small-content-wrapper"><div class="testimonial-small-image-wrapper"><img loading="lazy" src="/images/ethan-smith.jpeg" alt="" class="testimonial-small-image"/></div><div class="testimonial-small-quote-title-wrapper"><div class="testimonial-small-quote">“We stopped doing events because we didn’t have data saying they were impactful. With Upside, we can see events are actually a massive flywheel for our business.”</div><div class="testimonial-small-logo-title-wrapper"><div class="testimonial-small-logo_wrapper"><img loading="lazy" src="/images/graphite-logo.jpeg" alt=""/></div><div class="vertical-separator cc-display-none-mobile-l"></div><div class="horizontal-separator cc-testimonials-mobile-xl"></div><div class="testimonial-small-details-wrapper"><p class="body-m cc-color-text-primary">Ethan Smith</p><p class="body-m">CEO &amp; Founder</p></div></div></div></div></div></div></div></div></div><div class="horizontal-separator"></div><div class="case-study-main-content-section"><div class="badge">Metrics</div><div class="margin-bottom-24px"><h2 class="case-study-title">Full-Journey Attribution Revealed the Real Channels Driving Graphite&#x27;s 100% Inbound Revenue</h2></div><div class="case-study-metrics_wrapper"><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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1710</svg></div></div><p class="paragraph-4">53% of Deals from Referrals</p><div class="div-block-3"></div><p class="paragraph-5">Over half of deals in the past 2 years came from referrals — almost none of which showed up in standard systems</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
1711<path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
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1715</svg></div></div><p class="paragraph-4">$10M+ Referral Revenue Uncovered</p><div class="div-block-3"></div><p class="paragraph-5">Full-journey attribution traced relationships invisible to traditional CRM tracking</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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1719<path d="M12 15.75V16.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
1720</svg></div></div><p class="paragraph-4">Events Restarted After Data</p><div class="div-block-3"></div><p class="paragraph-5">Graphite had stopped events based on bad attribution data. Upside revealed events were a core growth flywheel</p></div></div></div><div class="case-study-main-content-section"><div class="badge">Highlighted Results</div><div class="case-study-main-content-rich-text w-richtext"><h2>From invisible referrals to a data-driven growth playbook: Upside revealed the hidden engines behind Graphite's revenue.</h2><ul><li><strong>Referrals Quantified for the First Time:</strong> 53% of deals — over $10M — traced to ~20 distinct referrers whose influence was completely invisible in the CRM. Upside looked at emails, call transcripts, and SFDC notes to find every referrer.</li><li><strong>Events Restarted After Data Proved Their Value:</strong> Graphite had stopped events because attribution showed zero impact. Upside revealed events fuel the referral flywheel — the people referring the most deals had attended Graphite events. Events were restarted immediately.</li><li><strong>The "Ethan Channel" Quantified:</strong> Created a dedicated attribution channel for the CEO's thought leadership, confirming his outsized revenue influence while surfacing key-man risk that needs diversification.</li><li><strong>Last-Touch Attribution Debunked:</strong> Traditional models said "send more emails, get more form fills." Upside's full-journey analysis told the opposite story — referrals, events, and thought leadership are the real growth engine.</li><li><strong>8 Lost Deals Recovered:</strong> Identified deals initially closed-lost that came back and closed as won — many through referral relationships that developed over years.</li></ul><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width: 100%;"><img src="/images/graphite-last-touch-vs-multi-touch.png" loading="lazy" alt="" style="width: 100%;" /></figure></div></div><div class="case-study-main-content-section"><div class="badge">

1720Background</div><div class="case-study-main-content-rich-text w-richtext"><h2>A $15M+ Agency Built Entirely on Inbound — With an Unconventional Playbook</h2><p>Graphite has built $15M+ in revenue entirely through inbound channels — with zero outbound sales, no cold email, a marketing team of 4, and a sales team of 2. Neither salesperson had any sales experience, they moved to sales from practitioners roles. Everyone at the company is a practitioner who also happens to do marketing and sales.</p><p>CEO Ethan Smith's approach to growth defies conventional wisdom at every turn. Where the typical marketing playbook says post daily on LinkedIn, run paid ads, produce content hubs and video shorts — Graphite does almost none of these. Instead, they've built a business on a radically different set of channels: original never-been-done-before research from PhDs, authentic bespoke events with no sales pitches, premium bespoke swag given on special occasions only, and leadership that personally writes everything.</p><p>The results speak for themselves. Graphite works with companies like Notion, Webflow, OpenAI, Netflix, Adobe, and Meta. <strong>53% of their deals in the past two years came from referrals.</strong> A single <a href="https://www.youtube.com/watch?v=iT7kq-R3Gjc">Lenny's Podcast episode</a> directly influenced over $1.5M in closed-won revenue. Ethan's LinkedIn posts — written once a month, maximum length, packed with original research — regularly generate 4,000+ likes.</p><p>But Graphite had a measurement problem. Their unconventional channels — thought leadership, referrals, intimate dinners, premium swag — were largely invisible to traditional marketing attribution.</p></div></div><div class="case-study-main-content-section"><div class="badge">The Challenge</div><div class="case-study-main-content-rich-text w-richtext"><h2>When Traditional Attribution Tells the Wrong Story</h2><p>When Graphite first started working with Upside, the initial attribution reports painted a misleading picture. Traditional last-touch and first-touch models showed only three things driving revenue: <strong>web activity, marketing email, and website forms.</strong> A significant number of deals appeared to have no attribution data at all.</p><p>Under a last-touch model, 100% of the credit goes to whatever happened right before conversion — typically an email or a form fill. The recommendation from this data: <strong>send more people to the website, send more emails, get more people to fill forms.</strong> This is the standard marketing playbook — and it would have been exactly wrong for Graphite.</p><p>The real problem was that Graphite's most powerful channels operate outside the boundaries of what traditional attribution can see:</p><ul><li><strong>Referrals</strong> happen through personal relationships built over years — sometimes a decade — and never through a tracked link</li><li><strong>Events</strong> create influence through authentic connection, not campaign URLs. Guests weren't always added to CRM campaigns</li><li><strong>Thought leadership</strong> drives deals when a CMO hears Ethan on Lenny's Podcast and reaches out on LinkedIn — a touchpoint that never hits a UTM parameter</li><li><strong>Premium swag</strong> (custom graphite mechanical pencils from an English manufacturer) generates enthusiastic responses that correlate with referrals — but lives entirely outside digital tracking</li></ul><p>Worse, acting on the incomplete data nearly led to a strategic mistake. <strong>Graphite stopped doing events</strong> because the numbers didn't show they were working. The data wasn't wrong, the attribution model just couldn't see the impact.</p></div></div><div class="case-study-main-content-section"><div class="badge">The Outcome</div><div class="case-study-main-content-rich-text w-richtext"><h2>Full-Journey Attribution Reveals What Really Drives Revenue</h2><p>Upside connected all of Graphite&#x27;s data sources — Salesforce, HubSpot, Apollo, Fathom call transcripts, RB2B anonymous traffic data — into a unified knowledge graph. Rather than relying on simple first-touch or last-touch credit, Upside used AI to extract missing touchpoints from call transcripts and emails, perform intelligent buying group detection, and recover persona identifications that weren&#x27;t properly tracked in the CRM.</p><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width: 100%;"><img src="/images/graphite-data-healing-buying-group.png" loading="lazy" alt="" style="width: 100%;" /></figure><h3>1. Referrals: The $10M+ Hidden Engine</h3><p>Upside&#x27;s analysis revealed that <strong>53% of Graphite&#x27;s deals in the past two years came from referrals — and almost none showed up in standard systems.</strong>

1720 Across 40 closed-won deals, referrals directly influenced over $10 million in revenue. These referrals were invisible in Graphite&#x27;s CRM because they happened through personal conversations, dinners, and professional networks rather than tracked links.</p><p>The data uncovered approximately 20 distinct referrers, each contributing one to three deals. These weren&#x27;t transactional referral program participants — they were people whose relationships with Ethan spanned years, sometimes over a decade, before generating the first deal. As Ethan describes it: &quot;We never ask anyone for a referral. Everything is people coming to us and hearing about us from somewhere other than us reaching out to them.&quot;</p><blockquote>&quot;We had no idea how many people were referring deals to us. Upside looked at all of the data and showed us the full picture.&quot; — Ethan Smith, CEO &amp; Founder, Graphite</blockquote><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width: 100%;"><img src="/images/graphite-referrer-directory.png" loading="lazy" alt="" style="width: 100%;" /></figure><h3>2. Events: From &quot;Not Working&quot; to Core Growth Flywheel</h3><p>This was perhaps the most consequential insight. Events didn&#x27;t show up in first-touch, last-touch, <strong>or even regular multi-touch attribution at all.</strong> Not all attendees were added to CRM campaigns. Traditional models simply could not see their impact. The conclusion seemed clear: events weren&#x27;t driving revenue.</p><p><strong>Graphite stopped doing events based on this data.</strong></p><p>Upside&#x27;s full-journey analysis told a completely different story. By looking through call transcripts and emails, Upside found multiple deals where prospects explicitly mentioned attending a Graphite dinner as a key moment in their decision process. More importantly, when Upside mapped the referral network, a pattern emerged: <strong>the people referring the most deals were often people who had attended Graphite events.</strong> Events weren&#x27;t directly converting attendees into customers — they were fueling the referral engine that drives 53% of Graphite&#x27;s revenue.</p><p>Upside also revealed that <strong>events help Graphite build champions</strong> — people who become long-term advocates. When those champions change jobs, they bring Graphite with them (accounting for 4 additional deals). Events create a compounding flywheel of referrals and champion-building that is invisible to any single-touch attribution model.</p><p>Ethan&#x27;s events are distinctly authentic — he personally cooks everything from farmer&#x27;s market ingredients, makes the cocktails, and hosts in his own garden. There are no salespeople, no pitches. The follow-up email contains a list of attendees, a summary of the conversation, and links to photos — nothing more.</p><p>With this data in hand, Graphite has now <strong>restarted their event program</strong>, armed with a much better understanding of how events create long-term value through the referral and champion flywheel.</p><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width: 100%;"><img src="/images/graphite-event-influenced-deals.png" loading="lazy" alt="" style="width: 100%;" /></figure><h3>3. External Webinars: Closing Deals, Not Just Building Awareness</h3><p>Traditional attribution often treats content and thought leadership as top-of-funnel awareness channels. Upside&#x27;s analysis revealed something dramatically different for Graphite.</p><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width: 100%;"><img src="/images/graphite-top-webinars-podcasts.png" loading="lazy" alt="" style="width: 100%;" /></figure><p>What made these webinars effective is the same philosophy Ethan applies everywhere: every single webinar features unique information he hasn&#x27;t shared before, with live demos and practical takeaways rather than static slides.</p><h3>4. Deal Recovery: Pricing Objections That Resolved Themselves</h3><p>Upside uncovered another hidden pattern: <strong>8 deals that were initially closed-lost later came back and closed as won.</strong> Many of these were originally rejected on pricing. The data showed that as Answer Engine Optimization (AEO) became a hot category alongside traditional SEO, the perceived value of Graphite&#x27;s services increased — changing the pricing equation without Graphite having to do anything differently.</p><h3>5. The CEO Channel: Quantifying What Everyone Suspected</h3><p>One of the most distinctive findings was the creation of an &quot;Ethan Smith&quot; channel in the attribution model. Upside&#x27;s analysis confirmed what Graphite already sensed — Ethan&#x27;

1720s personal thought leadership drives a significant portion of revenue. His LinkedIn posts (infrequent, maximum length, packed with original research) outperform most daily posters. His podcast appearances create immediate pipeline. His personal relationships generate referrals.</p><p>This data validated the importance of Ethan&#x27;s role but also surfaced the <strong>key-man risk</strong> — as Ethan himself acknowledges: &quot;I am the Kim Kardashian of answer engine optimization. But we need more Kardashians.&quot; Upside&#x27;s data is now helping Graphite plan how to diversify thought leadership across the team while maintaining the authenticity that makes it effective.</p></div></div><div class="case-study-main-content-section"><div class="badge">What&#x27;s Next</div><div class="case-study-main-content-rich-text w-richtext"><h2>What Ethan Plans to Do Next</h2><p>Armed with Upside's full-journey attribution data, Graphite is doubling down on the channels that actually drive their business:</p><ul><li><strong>Resuming Events:</strong> With better follow-up processes and co-hosting partnerships, knowing events fuel the referral and champion flywheel</li><li><strong>Expanding the Referral Flywheel:</strong> Proactively (but authentically) engaging their top 20+ referrers — starting with sending premium pencil swag</li><li><strong>Diversifying Thought Leadership:</strong> Developing other team members as content creators and subject matter experts to reduce CEO dependency</li><li><strong>Investing in Premium Webinars:</strong> Continuing to create unique content for every external appearance, with live demos and practical takeaways</li><li><strong>Using Upside's MCP Integration:</strong> Querying attribution data directly through Claude for self-serve insights without bottlenecks</li></ul><blockquote>"Finding hidden touchpoints and fixing the data tells a very different story about what we should focus on. We were able to show Graphite channels that aren't just touching deals, but channels that actually drove meaningful engagement." — Mada Seghete, CEO & Founder, Upside</blockquote></div></div></div>`,"syndio-driving-strategic-gtm-decisions-with-upside":`<div class="case-study-testimonial-section"><div class="w-dyn-list"><div role="list" class="case-study-testimonials-list w-dyn-items"><div role="listitem" class="w-dyn-item"><div class="testimonial-small-container"><div class="testimonial-small-content-wrapper"><div class="testimonial-small-image-wrapper"><img loading="lazy" src="/images/amy-wellersdick-testimonial.webp" alt="" class="testimonial-small-image"/></div><div class="testimonial-small-quote-title-wrapper"><div class="testimonial-small-quote">“Self‑serve campaign performance numbers without MOPS tickets.”</div><div class="testimonial-small-logo-title-wrapper"><div class="testimonial-small-logo_wrapper"><img loading="lazy" src="/images/syndio-logo.svg" alt=""/></div><div class="vertical-separator cc-display-none-mobile-l"></div><div class="horizontal-separator cc-testimonials-mobile-xl"></div><div class="testimonial-small-details-wrapper"><p class="body-m cc-color-text-primary">Amy Wellersdick</p><p class="body-m">Director of Marketing Ops</p></div></div></div></div></div></div></div></div></div><div class="horizontal-separator"></div><div class="case-study-main-content-section"><div class="badge">Metrics</div><div class="margin-bottom-24px"><h2 class="case-study-title">Transformative Insights That Removed Bottlenecks and Reshaped Strategic Decision-Making</h2></div><div class="case-study-metrics_wrapper"><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
1721<path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
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1725</svg></div></div><p class="paragraph-4">100% Elimination of Data Bottlenecks</p><div class="div-block-3"></div><p class="paragraph-5">Program managers shifted from relying on Ops for reports to being fully self-sufficient with data, saving significant manual reporting hours.</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
1726<path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
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1730</svg></div></div><p class="paragraph-4">1 Function Strategically Restructured</p><div class="div-block-3"></div><p class="paragraph-5">Validated the true performance of the ADR team (inbound vs. outbound), leading to a data-backed restructuring of the entire department.</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
1731<path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
1732<path d="M14.25 8.75H11.375C10.4775 8.75 9.75 9.47754 9.75 10.375C9.75 11.2725 10.4775 12 11.375 12H12.625C13.5225 12 14.25 12.7275 14.25 13.625C14.25 14.5225 13.5225 15.25 12.625 15.25H9.75" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
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1735</svg></div></div><p class="paragraph-4">Hidden Revenue Uncovered</p><div class="div-block-3"></div><p class="paragraph-5">Identified critical revenue influence from events and channels (like LinkedIn) that traditional &quot;sourced&quot; models had previously undervalued or missed entirely.</p></div></div></div><div class="case-study-main-content-section"><div class="badge">Highlighted Results</div><div class="case-study-main-content-rich-text w-richtext"><blockquote><strong>Strategic Team Restructuring:</strong> Validated true inbound vs. outbound performance, leading to a data-backed restructuring of the ADR function to optimize efficiency.

1735</blockquote><blockquote><strong>Holistic Event ROI:</strong> Moved beyond &quot;one-touch&quot; attribution to identify events that accelerated in-cycle opportunities, revealing hidden revenue influence and guiding budget allocation for FY26.</blockquote><blockquote><strong>Operational Self-Sufficiency: </strong>Eliminated data bottlenecks by empowering program managers to access their own campaign insights, fostering a culture of self-sufficient, data-driven decision-making.</blockquote></div></div><div class="case-study-main-content-section"><div class="badge">Background</div><div class="case-study-main-content-rich-text w-richtext"><p><strong>GTM Background:</strong> Syndio targets enterprise accounts involving complex, multi-touch sales cycles. The team needed to understand not just what sourced pipeline, but what messaging and channels were resonating with our new ICP. The team had the usual tools: Marketo, Salesforce, and a pile of reports. <strong>What they didn’t have was a trustworthy way to connect all their touchpoints into a coherent picture of how enterprise deals were really created and won.</strong></p></div></div><div class="case-study-main-content-section"><div class="badge">The Challenge</div><div class="case-study-main-content-rich-text w-richtext"><h2>The &quot;One-Touch&quot; Trap and Data Distrust</h2><p>Before Upside, Syndio relied on traditional attribution models (like first-touch or last-touch) within Salesforce and Marketo. While these tools provided data, they failed to capture the complexity of an enterprise sales cycle. &quot;We didn&#x27;t have just one thing that was bringing in an enterprise account,&quot; Amy explains. &quot;They take many different touchpoints.&quot;</p><p>The reliance on a single-touch model created significant blind spots. The team struggled to see the full impact of their efforts—specifically, what was driving account activation, converting them to demos, and nurturing them through the funnel. This lack of visibility led to internal skepticism. When reports were pulled from Salesforce, stakeholders often questioned the data, noting that certain impactful events weren&#x27;t showing up, while others appeared inflated.</p><p>Furthermore, there was a disconnect in understanding the ADR (Account Development Representative) function&#x27;s actual performance. There was a perception that outbound efforts were driving results, but without a unified view of the customer journey, it was difficult to distinguish actual outbound hunting from inbound demand capture.</p></div></div><div class="case-study-main-content-section"><div class="badge">The Outcome</div><div class="case-study-main-content-rich-text w-richtext"><h3>Uncovering the Truth About Outbound</h3><p>One of the most significant impacts of adopting Upside was the clarity it brought to the &quot;Inbound vs. Outbound&quot; debate. Syndio used Upside to analyze the touchpoints of their ADR team. The data revealed a critical insight: much of what was perceived as outbound success was actually the result of ADRs engaging with leads that had already demonstrated intent via marketing channels or events.</p><p>&quot;It helped us validate that what we thought was true was actually true,&quot; Amy notes. &quot;We were just not seeing the impact of the ADR function like we needed to.&quot; This insight empowered leadership to make a difficult but necessary strategic decision: restructuring the ADR function to optimize for efficiency and actual performance. This move, driven by credible data, allowed Syndio to level-set strategically for its next phase of growth.</p><h3>AI-Driven Segmentation &amp; Self-Sufficiency</h3><p>As Syndio pivots to a new solution, they are leveraging Upside’s AI agent to perform complex qualitative analysis on their funnel. The team is now using Upside to view what drives the pipeline on a weekly basis using the milestone analysis. This granularity—previously impossible to track manually—is helping them refine our outreach strategy in real time.</p><p>Culturally, Upside has transformed the marketing team’s relationship with data. Previously, Amy was the bottleneck for all data requests. Now, program managers utilize Upside’s dashboards independently to monitor program success.</p><p>‍</p><h2>What changed for Syndio</h2><p>Across these projects, the real shift wasn’t just prettier charts. It was how Syndio’s teams made decisions.</p><p><strong>

17351. Budget conversations became grounded instead of political.</strong></p><p>When Q4 and FY27 planning rolled around, Amy’s team wasn’t arguing over whose spreadsheet was “more right.” They were walking through Upside dashboards and analyses that tied budget lines directly to inbound vs. outbound performance, CAC by segment, and funnel outcomes.</p><p>That made it far easier to justify big bets—like where to keep investing in events and webinars—and to cut experiments that weren’t paying off, even if they were beloved internally.</p><p><strong>2. Marketing, Sales, and SDRs had a shared view of inbound/outbound.</strong></p><p>The inbound vs. outbound analysis reframed the conversation from “who sourced what” to “how do we work together to activate high-quality opportunities?” For example, the data showed that AEs were leaning on marketing-assisted outbound far more effectively than SDRs, especially in EMEA—fuel for a concrete discussion about training and process, not finger-pointing. </p><p><strong>3. Optimizing Event Spend with Multi-Touch Insight</strong></p><p>Moving into budget planning for the next fiscal year, Syndio needed to decide where to place its &quot;big bets.&quot; Using Upside’s campaign report cards, the team moved beyond simple &quot;sourced&quot; metrics to look at &quot;in-cycle&quot; influence.</p><p>They discovered that certain events, which Salesforce attribution ignored because they didn&#x27;t <em>source</em> the deal, were critical in accelerating existing opportunities. &quot;That brought us a different list of events,&quot; Amy says. By seeing how events influenced revenue and pipeline velocity, Syndio was able to confidently cut underperforming programs (such as specific roundtables in the EU) and double down on the events that actually moved the needle.</p><p><strong>4. The team became more self-sufficient with data.</strong></p><p>Once the core dashboards were live, Amy noticed a behavioral shift: marketers and RevOps leaders were logging into Upside on their own, using the campaign and channel views to evaluate performance and plan their programs—without waiting for a special analysis deck. That freed Amy from being the bottleneck and nudged the whole org toward more data-driven decisions.</p><p>&quot;The biggest shift I saw is that once we launched that... they were way more self-sufficient with data,&quot; Amy concludes. &quot;It saved time on my end, and they became more data-driven in their decision-making.&quot;</p><p><strong>5. Upside became a strategic partner, not just a tool.</strong></p><p>When asked to summarize the value of Upside, Amy didn’t talk about a specific metric. She talked about partnership:</p><p>“The value we find is that we have a strong partner to help us make data-driven decisions. We trust what you’re doing, and you’re doing it in innovative ways we just haven’t seen other startups do yet.” </p><p>That trust matters, especially as Syndio continues to evolve its AI-native product and go-to-market motion. With a shared data foundation, the team can ask harder questions—like how “compliance-mindset” leads behave versus those responding to the new Syndi-led messaging—and expect to get meaningful, timely answers. </p></div></div><div class="case-study-main-content-section w-condition-invisible"><div class="badge w-dyn-bind-empty"></div><div class="case-study-main-content-rich-text w-dyn-bind-empty w-richtext"></div></div></div>`,"dscout-driving-confident-growth-with-upside":`<div class="case-study-testimonial-section"><div class="w-dyn-list"><div role="list" class="case-study-testimonials-list w-dyn-items"><div role="listitem" class="w-dyn-item"><div class="testimonial-small-container"><div class="testimonial-small-content-wrapper"><div class="testimonial-small-image-wrapper"><img loading="lazy" src="/images/kate-johnson.jpeg" alt="" class="testimonial-small-image"/></div><div class="testimonial-small-quote-title-wrapper"><div class="testimonial-small-quote">&quot;One thing that stood out to me was how quickly we were able to get started. It took us 5 months and 100s of hours of our MOPS person&#x27;

1735s time to stand up another measurement provider at a previous company.&quot;</div><div class="testimonial-small-logo-title-wrapper"><div class="testimonial-small-logo_wrapper"><img loading="lazy" src="/images/dscout-logo-dark.svg" alt=""/></div><div class="vertical-separator cc-display-none-mobile-l"></div><div class="horizontal-separator cc-testimonials-mobile-xl"></div><div class="testimonial-small-details-wrapper"><p class="body-m cc-color-text-primary">Kate Johnson</p><p class="body-m">CMO</p></div></div></div></div></div></div><div role="listitem" class="w-dyn-item"><div class="testimonial-small-container"><div class="testimonial-small-content-wrapper"><div class="testimonial-small-image-wrapper"><img loading="lazy" src="/images/charlie-flanagan.jpg" alt="" class="testimonial-small-image"/></div><div class="testimonial-small-quote-title-wrapper"><div class="testimonial-small-quote">&quot;What used to take 2-3 hours I can now do in a few clicks, and it&#x27;s made me a smarter operations person than I&#x27;ve ever been in my career.&quot;</div><div class="testimonial-small-logo-title-wrapper"><div class="testimonial-small-logo_wrapper"><img loading="lazy" src="/images/logo-dscout.svg" alt=""/></div><div class="vertical-separator cc-display-none-mobile-l"></div><div class="horizontal-separator cc-testimonials-mobile-xl"></div><div class="testimonial-small-details-wrapper"><p class="body-m cc-color-text-primary">Charlie Flanagan</p><p class="body-m">Head of GTM Ops</p></div></div></div></div></div></div></div></div></div><div class="horizontal-separator"></div><div class="case-study-main-content-section"><div class="badge">Metrics</div><div class="margin-bottom-24px"><h2 class="case-study-title">Transformative Insights That Removed Bottlenecks and Reshaped Strategic Decision-Making</h2></div><div class="case-study-metrics_wrapper"><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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1740</svg></div></div><p class="paragraph-4">95% Time Saved on Reporting Results</p><div class="div-block-3"></div><p class="paragraph-5">Complex analysis that previously took 2-3 hours of manual spreadsheet work can now be generated in minutes with a few clicks.</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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1742<path d="M14.25 8.75H11.375C10.4775 8.75 9.75 9.47754 9.75 10.375C9.75 11.2725 10.4775 12 11.375 12H12.625C13.5225 12 14.25 12.7275 14.25 13.625C14.25 14.5225 13.5225 15.25 12.625 15.25H9.75" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
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1745</svg></div></div><p class="paragraph-4">5% Increase in Pipeline from In-Person Hosted Events</p><div class="div-block-3"></div><p class="paragraph-5">Based on strategy shifts from Upside, in the first quarter alone.</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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1750</svg></div></div><p class="paragraph-4">100s of Hours Saved on Onboarding the Platform and Pulling Data Reports</p><div class="div-block-3"></div><p class="paragraph-5">Other tools in previous jobs took 100+ hours and 5mo to onboard vs 1-2 hours for Upside and 24hrs from signing to a live dashboard.</p></div></div></div><div class="case-study-main-content-section"><div class="badge">Highlighted Results</div><div class="case-study-main-content-rich-text w-richtext"><h2>From scattered spreadsheets to GTM-wide clarity: quicker reports, sharper insights, better ROI.</h2><ul role="list"><li><strong>Unified GTM Insights:</strong> Built a centralized, enhanced dataset across the entire organization, delivering fast, accurate insights with minimal manual effort.</li><li>Fast time to Insights: Kate Johnson’s experience with other vendors was 5 months and 100s of hours of our MOPS person&#x27;

1750s time to stand up, so Upside’s ability to be functional in less than a day from contract signing was a huge win for the team.</li><li><strong>Time Efficiency Gains:</strong> What used to take hours of spreadsheet work now takes mere minutes – <strong>reporting that once took 2–3 hours can be done in a few clicks</strong>, enabling the team to share insights proactively without adding analyst headcount.</li><li><strong>Culture shift toward data-driven decision-making:</strong> With newfound trust in the data, the organization now expects detailed pipeline metrics in every report, reflecting a <strong>culture shift </strong>across marketing and sales.  <br/><ul role="list"><li>“Our GTM leaders have faith in the accuracy and cleanliness of our data — now they expect a level of insight.”</li></ul></li><li><strong>Strategic Event Optimization:</strong> Revealed that &quot;Co-Lab Connection&quot; happy hours yielded higher ROI and more diverse pipeline than expensive invite-only dinners, prompting a strategic reallocation of budget.</li><li><strong>Sales Enablement via Account Intelligence:</strong> Deployed &quot;Account Report Cards&quot; to give new account directors an instant, comprehensive view of their territory, drastically reducing ramp time and improving account handover.</li><li><strong>Cultural Shift to Data Trust:</strong> Transformed internal skepticism into confidence; leadership now expects and relies on detailed pipeline influence metrics for every major decision.</li></ul></div></div><div class="case-study-main-content-section"><div class="badge">Background</div><div class="case-study-main-content-rich-text w-richtext"><h2>Dscout scaled its multi-touch GTM and ran into a complex attribution puzzle.</h2><p><strong>About Dscout:</strong> Dscout is a leading experience research platform that empowers organizations to conduct flexible, human-centered user research. From diary studies to live interviews, Dscout helps product teams and UX researchers capture in-context insights to build better experiences. Backed by a $70 million Series C, Dscout has rapidly scaled to serve hundreds of innovative enterprise companies.</p><p><strong>The Challenge:</strong> As Dscout sells into the enterprise, their sales cycle is increasingly complex, involving multiple stakeholders and touchpoints. The marketing team runs a sophisticated mix of webinars, digital campaigns, and field events, but lacked a unified way to measure impact.</p><p>&quot;We faced a classic B2B puzzle,&quot; explains Charlie Flanagan. &quot;We had detailed campaign data in Salesforce, but turning that data into a story was incredibly manual.&quot;</p><p>Answering a simple question—like the ROI of a specific field event—required exporting reports to spreadsheets, manually deduplicating rows, and running complex calculations to separate new business from expansion. This process was slow, prone to error, and led to a lack of trust in attribution data across the organization.</p></div></div><div class="case-study-main-content-section"><div class="badge">The Outcome</div><div class="case-study-main-content-rich-text w-richtext"><h3>From Hours to Clicks: Speed &amp; Accuracy</h3><p>Adopting Upside transformed Dscout’s analytics from a manual chore into a strategic advantage. The platform plugged directly into their CRM, automatically unifying touchpoints and deduplicating data.</p><p>&quot;The impact was immediate,&quot; says Charlie. &quot;What used to take me hours of spreadsheet wrangling now takes minutes.&quot; With interactive dashboards, the team can now slice data by region, persona, or campaign type on the fly. This speed didn&#x27;t just save time; it democratized data access. Instead of waiting for analyst reports, stakeholders could see &quot;beautiful tiles&quot; of accurate data, fostering a new level of trust in marketing’s influence.</p><h3>Strategic Pivots: The &quot;Happy Hour&quot; Insight</h3><p>With trusted data, Dscout began testing long-held assumptions. The team compared their exclusive, high-cost executive dinners against larger, more casual &quot;Collab Connection&quot; happy hours.</p><p>Upside’s analysis revealed a clear winner: the inclusive happy hours were driving substantially more pipeline and reaching a more diverse set of prospects than the intimate dinners. Armed with this proof, the marketing team confidently reallocated budget toward these high-ROI formats and prioritized key hubs like San Francisco and New York, where the data showed the strongest traction.</p><p>Upside didn’t just speed up reporting; it enabled <strong>data-driven course corrections</strong> in Dscout’s go-to-market strategy.</p><p><strong>Having these on-demand insights “definitely is a much more helpful way of putting a number to [our strategies]…we can even compare, like, do we have better luck in New York vs. Minneapolis…is it worth going back to Minneapolis next year?”</strong></p><p><em>“This level of granularity was simply not feasible before.”</em></p><h3>Empowering Sales with the Account Report Card</h3><p>Beyond marketing attribution, Dscout utilized Upside’s Insight Engineering offering to empower their sales team during a critical transition. With several new account directors joining and territories shifting, leadership needed a way to get reps familiar with their new books of business immediately.</p><p>Charlie worked with the Upside team to deploy a <strong>Account Report Card</strong> (Account Dossier), a customized unified view that provided sales reps with instant intelligence on their accounts:</p><ul role="list"><li><strong>Engagement History:</strong> Who is engaging, and with what content?</li><li><strong>Buying Signals:</strong> Recent contract discussions and renewal dates.</li><li><strong>Competitive Landscape:</strong> Identification of competitors currently entrenched in the account.</li></ul><p>&quot;If you try to do that through Salesforce reports, it&#x27;s clunky,&quot; Charlie notes. &quot;The dossier showed us everything in one view.&quot;</p><p>This deep research capability also highlighted &quot;false pipeline&quot;

1750—deals that looked active but had low engagement. Analysis showed that Dscout only won ~6% of deals with fewer than three contacts involved. The Account Report Card flagged these single-threaded deals early, allowing managers to coach reps on multi-threading before the opportunity was lost.</p><h3>A Partnership, Not Just a Platform</h3><p>Ultimately, the shift to Upside was cultural. Data is no longer viewed with caution; it is the foundation of planning. &quot;People are spoiled now,&quot; Charlie jokes. &quot;They just expect this level of insight.&quot; By moving from intuition to evidence, Dscout has aligned their sales and marketing teams around a single source of truth, driving confident growth in a complex market.</p></div></div><div class="case-study-main-content-section w-condition-invisible"><div class="badge w-dyn-bind-empty"></div><div class="case-study-main-content-rich-text w-dyn-bind-empty w-richtext"></div></div><div class="case-study-main-content-section w-condition-invisible"><div class="badge w-dyn-bind-empty"></div><div class="case-study-main-content-rich-text w-dyn-bind-empty w-richtext"></div></div></div>`,"assembled-from-attribution-guesswork-to-pipeline-surge":`<div class="case-study-testimonial-section"><div class="w-dyn-list"><div role="list" class="case-study-testimonials-list w-dyn-items"><div role="listitem" class="w-dyn-item"><div class="testimonial-small-container"><div class="testimonial-small-content-wrapper"><div class="testimonial-small-image-wrapper"><img loading="lazy" src="/images/lindsey-marymont.png" alt="Lindsey Marymont" class="testimonial-small-image"/></div><div class="testimonial-small-quote-title-wrapper"><div class="testimonial-small-quote">“I've been in marketing for 10+ years, and I've never been able to get a clear picture of the buyer journey. But with Upside, we are able to see a complete milestone analysis with data pulled from Gong we wouldn't ever be able to see in Salesforce.”</div><div class="testimonial-small-logo-title-wrapper"><div class="testimonial-small-logo_wrapper"><a href="https://www.assembled.com/" target="_blank" rel="noopener noreferrer"><img loading="lazy" src="/images/assembled-logo-black.png" alt="Assembled"/></a></div><div class="vertical-separator cc-display-none-mobile-l"></div><div class="horizontal-separator cc-testimonials-mobile-xl"></div><div class="testimonial-small-details-wrapper"><p class="body-m cc-color-text-primary"><a href="https://www.linkedin.com/in/lindseymarymont/" target="_blank" rel="noopener noreferrer" style="color: inherit; text-decoration: underline;">Lindsey Marymont</a></p><p class="body-m">Head of Demand Generation</p></div></div></div></div></div></div></div></div></div><div class="horizontal-separator"></div><div class="case-study-main-content-section"><div class="badge">Metrics</div><div class="margin-bottom-24px"><h2 class="case-study-title">The Data Hidden in Plain Sight</h2></div><div class="case-study-metrics_wrapper"><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M14.25 8.75H11.375C10.4775 8.75 9.75 9.47754 9.75 10.375C9.75 11.2725 10.4775 12 11.375 12H12.625C13.5225 12 14.25 12.7275 14.25 13.625C14.25 14.5225 13.5225 15.25 12.625 15.25H9.75" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 7.75V8.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 15.75V16.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/></svg></div></div><p class="paragraph-4">30k+ Hidden Contacts Discovered</p><div class="div-block-3"></div><p class="paragraph-5">Upside found over 30,000 people (~14% of total contacts) who joined Assembled's calls and influenced buying decisions but never appeared in Salesforce or HubSpot.</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>

1750<path d="M14.25 8.75H11.375C10.4775 8.75 9.75 9.47754 9.75 10.375C9.75 11.2725 10.4775 12 11.375 12H12.625C13.5225 12 14.25 12.7275 14.25 13.625C14.25 14.5225 13.5225 15.25 12.625 15.25H9.75" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 7.75V8.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 15.75V16.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/></svg></div></div><p class="paragraph-4">37k+ Hidden Touchpoints Uncovered</p><div class="div-block-3"></div><p class="paragraph-5">Upside revealed over 37,000 interactions from Gong recordings, demos, discovery calls, and check-ins that were never logged as activities in any CRM.</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M14.25 8.75H11.375C10.4775 8.75 9.75 9.47754 9.75 10.375C9.75 11.2725 10.4775 12 11.375 12H12.625C13.5225 12 14.25 12.7275 14.25 13.625C14.25 14.5225 13.5225 15.25 12.625 15.25H9.75" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 7.75V8.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 15.75V16.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/></svg></div></div><p class="paragraph-4">2x More Accurate Pipeline Data via MCP</p><div class="div-block-3"></div><p class="paragraph-5">When Assembled connected Upside's MCP to their AI tool (Dust), pipeline queries returned roughly twice the completeness and accuracy compared to querying Salesforce directly.</p></div></div></div><div class="case-study-main-content-section"><div class="badge">Highlighted Results</div><div class="case-study-main-content-rich-text w-richtext"><h2>Upside gave Assembled data-driven clarity that reshaped channel strategy, redirected budget, and surfaced a hidden growth engine.</h2><ul role="list"><li><strong>True Event Impact Uncovered:</strong> Upside confirmed that events drove ~20x more pipeline per opportunity than other sources such as paid advertising. Lindsey used this data to repurpose ad strategies as amplification and invest heavily in events — a decision she couldn't have justified without it.</li><li><strong>AEO Win Rate Lift of +12.6 Percentage Points:</strong> Buyers who discovered Assembled through AI engines like ChatGPT and Claude closed at a measurably higher rate. After months of investing in AEO efforts, Upside was the only tool that could surface this signal and validate this investment.</li><li><strong>Full Buyer Journey Visible for the First Time:</strong> Milestone analysis pulled Gong call data, meeting touchpoints, and email threads into a single view, replacing hours of manual attribution done in spreadsheets.</li><li><strong>Executive Use Case:</strong> The exec team reviews pipeline weekly and relies on Upside data to reveal opportunity analysis and deal sourcing.</li><li><strong>Immediate MCP Adoption Creates Team Alignment:</strong> With over 2,300 MCP calls in just the first week, Assembled's AI workflows are fueling the team to have conversations about what the data is revealing instead of spending time validating the data.</li></ul></div></div><div class="case-study-main-content-section"><div class="badge">Background</div><div class="case-study-main-content-rich-text w-richtext"><h2>A Product Built on Support-First Principles</h2><p>Assembled has been powering the highest caliber support teams since its inception in 2018. Starting with core workforce management, they found a true need for modern solutions to legacy problems — helping teams with scheduling agents, forecasting support ticket volume, and providing real-time adjustments for things like agents calling out sick or surging support requests from a product bug or meme stock virality (that really happened).</p><p>As the first true omni-channel workforce provider, teams running Assembled could manage support across phone, chat, email, and emerging channels like social and text. Later Assembled built solutions for outsourced supp

1750ort teams, providing solutions like Vendor Management so customers could manage in-house support as well as productivity and volume of outsourced call centers.</p><p>Today, those innovative support-first principles have led to the only solution on the market that helps CX leaders manage human agents, AI agents, and outsourced BPOs to provide the best support possible. For many teams this means a small support team of 10 is able to do what would require 50 or more agents previously.</p><p>With a GTM team spanning 9 SDRs, 7 marketers, and 13 AEs across enterprise and mid-market, Assembled is at the forefront of the AI revolution. They compete against native AI-agent players on one side and legacy workforce management incumbents on the other. They're the only solution that combines both.</p></div></div><div class="case-study-main-content-section"><div class="badge">The Challenge</div><div class="case-study-main-content-rich-text w-richtext"><h2>Assembled's Attribution Draws Misleading Conclusions</h2><p>As Assembled transitioned from a strictly software business to a multi-product AI offering, they needed a way to understand how to land and grow customers for their emerging tech. Many potential customers were quick to buy into the latest AI support provider but didn't launch formal evaluations. Or equally common, teams were evaluating AI for support and were finding options in unconventional ways. Assembled needed to understand the new way of buying and stay top of mind for CX leaders.</p><blockquote>"The challenge we were trying to solve was essentially annual planning, getting a good sense of where our pipeline was coming from, where to double down as we were thinking about budget allocation for the year ahead."</blockquote><p>The team's tech stack included 6sense, Salesforce, HubSpot, Gong, and Default among others, but none of these tools talked to each other meaningfully. When a big deal came in, figuring out how it started meant manually piecing together call transcripts, Gong recordings, Salesforce fields, and email threads.</p><blockquote>"I had to piece together Gong calls, transcripts, Salesforce … and most of it just lived in the rep's head. I was constantly asking, 'hey, how did this come to us?' as we were uncovering buying patterns for a new product."</blockquote><p>Their first-touch attribution model, run through Salesforce, had fundamental limitations: it only captured the first marketing touch, couldn't connect Gong call data to deals, and left entire channels like events and AI discovery unmeasured.</p><h3>Finding the Right Partner</h3><p>Before choosing Upside, Assembled also evaluated other solutions such as HockeyStack.</p><blockquote>"HockeyStack had the visualization but not the data. They weren't willing to work with us throughout the evaluation, they just did demos. Upside did a full pilot with our actual data. And the setup was made so easy for us. Data integrity was the most important thing, and we needed to validate that above everything else."</blockquote></div></div><div class="case-study-main-content-section"><div class="badge">The Outcome</div><div class="case-study-main-content-rich-text w-richtext"><h2>Multi-Touch Attribution That Actually Works with Imperfect Data</h2><p>After careful evaluation, Assembled chose Upside.</p><blockquote>"The fact that Upside could work with imperfect data was the selling point. We knew our data wasn't clean. Getting all the Gong transcripts and everything else into a single milestone view — that was something I've never seen before."</blockquote><p>With Upside, Assembled can now see the complete influence picture across 16 distinct channels and 4.9 million touchpoints, with no manual exports and no battles with reps to log activities.</p><blockquote>"Instead of nagging reps to log things in Salesforce and connect their email, Upside just did it for us."</blockquote><h3><strong>Aha Moment #1: The Buyer Journey, Finally Visible</strong></h3><p>The milestone analysis was the feature that changed everything for Lindsey.</p><blockquote>“I've been in marketing for 10+ years, and I've never been able to get a clear picture of the buyer journey. I've spent entire quarters trying to export data and build it in spreadsheets, but all you get is campaign data. With Upside, we got a milestone analysis that pulled in Gong data and surfaced things that weren't in Salesforce.”</blockquote><h3><strong>Aha Moment #2: Activation After the Event</strong></h3><p>This was Lindsey's sleeper favorite — a metric that exists nowhere else.</p><blockquote>"It is possible to use datestamps to see when leads came in vs. when they converted, but it's not easy. Especially if you use Salesforce campaigns as a parking lot for list imports — what actually makes leads convert isn't reflected in the data. Being able to see the progression after a campaign touchpoint was a big deal, especially for events and other offline activity sources. That timestamp is nearly impossible to get in a Salesforce report. And this metric doesn't exist anywhere else."</blockquote><h3><strong>Aha Moment #3: Uncovering the Events Truth</strong></h3><p>The data revealed something the team had suspected but couldn't prove. Events, despite influencing fewer raw opportunities than email or meetings, drove the highest pipeline of any channel.</p><blockquote>"I had the data to make the case: double down on events. We were meeting all the right people and able to engage them live with a demo and discovery of their specific use cases. These conversations lasted 

1750well beyond the event and often led to intros with buyers. With Upside we could see the impact these conversations were having on our bottom line and made the case to invest more in events and even hire a second events marketer."</blockquote><h2>Discovering a Hidden Channel: AI Engine Optimization (AEO)</h2><p>One of the most novel findings: buyers were using AI chatbots to discover and evaluate Assembled but it was extremely hard to quantify. With Upside, they proved the value. A study across 521 opportunities, combining Gong call recordings, UTM citation traffic, and Salesforce notes revealed an AEO attribution goldmine:</p><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width:100%;margin:2rem 0;"><div style="background:#F5F7FA;border-radius:14px;padding:24px;text-align:center;"><p style="font-weight:600;font-size:14px;margin-bottom:16px;color:#1b1f1c;">AEO Discovery Method</p><svg viewBox="0 0 560 300" width="100%" style="max-width:520px;display:block;margin:0 auto;" xmlns="http://www.w3.org/2000/svg"><circle cx="195" cy="150" r="130" fill="#2d6a4f" fill-opacity="0.1" stroke="#2d6a4f" stroke-width="1.5"/><circle cx="375" cy="150" r="100" fill="#347BD1" fill-opacity="0.1" stroke="#347BD1" stroke-width="1.5"/><text x="185" y="115" text-anchor="middle" font-size="13" font-weight="600" fill="#2d6a4f">Call Mentions</text><text x="185" y="155" text-anchor="middle" font-size="42" font-weight="800" fill="#1b1f1c">78%</text><text x="185" y="195" text-anchor="middle" font-size="11" fill="#5f6563">Gong recordings with</text><text x="185" y="210" text-anchor="middle" font-size="11" fill="#5f6563">AI tool mentions</text><text x="385" y="120" text-anchor="middle" font-size="13" font-weight="600" fill="#347BD1">Citation Traffic</text><text x="385" y="158" text-anchor="middle" font-size="34" font-weight="800" fill="#1b1f1c">22%</text><text x="385" y="192" text-anchor="middle" font-size="11" fill="#5f6563">LLM UTM / referral</text><text x="295" y="148" text-anchor="middle" font-size="20" font-weight="700" fill="#1b1f1c">0%</text><text x="295" y="166" text-anchor="middle" font-size="10" fill="#5f6563">overlap</text></svg><p style="font-size:12px;color:#5f6563;margin-top:12px;">No overlap between discovery methods — call mentions and citation traffic were entirely separate paths.</p></div></figure><p>The win rate told the real story: AEO-influenced opportunities closed at a <strong>12.6 percentage point higher rate</strong> than non-AEO opportunities. Buyers who used AI to discover Assembled were more qualified and more likely to buy.</p><blockquote>"Honestly, I found you by going on ChatGPT. I was like, 'what's the best WFM platform?' And then you guys came up and that's how I foun

1750d you guys." — Assembled prospect</blockquote><blockquote>"As AI-driven discovery started to pick up, Upside helped us see it on the back end — being able to actually track that was really exciting."</blockquote><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width:100%;margin:2rem 0;"><div style="background:#F5F7FA;border-radius:14px;padding:24px;text-align:center;"><p style="font-weight:600;font-size:14px;margin-bottom:4px;color:#1b1f1c;">What Buyers Were Searching For Using AI Tools</p><p style="font-size:11px;color:#5f6563;margin-bottom:16px;">% of 22 AEO-influenced opportunities · Buyers often searched for multiple themes</p><svg viewBox="0 0 520 220" width="100%" style="max-width:500px;" xmlns="http://www.w3.org/2000/svg"><text x="150" y="28" text-anchor="end" font-size="12" font-weight="500" fill="#1b1f1c">WFM Tool Discovery</text><rect x="158" y="14" width="296" height="22" rx="4" fill="#2d6a4f" fill-opacity="0.8"/><text x="462" y="29" text-anchor="start" font-size="11" font-weight="700" fill="#1b1f1c">77%</text><text x="498" y="29" text-anchor="start" font-size="10" fill="#b0b5ad">(17)</text><text x="150" y="68" text-anchor="end" font-size="12" font-weight="500" fill="#1b1f1c">Vendor Comparison</text><rect x="158" y="54" width="192" height="22" rx="4" fill="#2d6a4f" fill-opacity="0.65"/><text x="358" y="69" text-anchor="start" font-size="11" font-weight="700" fill="#1b1f1c">50%</text><text x="390" y="69" text-anchor="start" font-size="10" fill="#b0b5ad">(11)</text><text x="150" y="108" text-anchor="end" font-size="12" font-weight="500" fill="#1b1f1c">Integration Research</text><rect x="158" y="94" width="104" height="22" rx="4" fill="#2d6a4f" fill-opacity="0.5"/><text x="270" y="109" text-anchor="start" font-size="11" font-weight="700" fill="#1b1f1c">27%</text><text x="302" y="109" text-anchor="start" font-size="10" fill="#b0b5ad">(6)</text><text x="150" y="148" text-anchor="end" font-size="12" font-weight="500" fill="#1b1f1c">Capability Matching</text><rect x="158" y="134" width="88" height="22" rx="4" fill="#2d6a4f" fill-opacity="0.35"/><text x="254" y="149" text-anchor="start" font-size="11" font-weight="700" fill="#1b1f1c">23%</text><text x="286" y="149" text-anchor="start" font-size="10" fill="#b0b5ad">(5)</text><text x="150" y="188" text-anchor="end" font-size="12" font-weight="500" fill="#1b1f1c">Pricing Research</text><rect x="158" y="174" width="35" height="22" rx="4" fill="#2d6a4f" fill-opacity="0.22"/><text x="201" y="189" text-anchor="start" font-size="11" font-weight="700" fill="#1b1f1c">9%</text><text x="225" y="189" text-anchor="start" font-size="10" fill="#b0b5ad">(2)</text></svg></div></figure><h2>From Dashboard to AI-Native Workflow: The MCP Story</h2><p>Before Upside, Assembled's internal AI tool (Dust) queried Salesforce and HubSpot directly. When they connected the Upside MCP, the impact was immediate: pipeline queries returned roughly 2x the accuracy and completeness compared to Salesforce. In the first week alone, Dust made over 2,300 MCP calls, used by the CEO, marketing team, and ops.</p><blockquote>"Things that used to take me hours to pull, I'm getting them in Upside in minutes. Instead of having to pick what questions to ask based on how long it will take to get a verifiable answer, with a quick MCP search, I'm uncovering issues I didn't even know existed."</blockquote><p>Today, the executive team runs every major opportunity through Upside each week, checking pipeline trends, deal sources, and channel influence before key decisions.</p><blockquote>"The executive team uses Upside every week. We look at pipeline week-over-week, find the major opportunities on everyone's radar, run them through Upside, and figure out how they came to us and what's happening with them."</blockquote></div></div><div class="case-study-main-content-section"><div class="badge">What's Next</div><div class="case-study-main-content-rich-text w-richtext"><h2>A Partnership That Keeps Delivering</h2><p>With clear data validating their channel bets and a weekly executive rhythm built around Upside, Assembled 

1750is now expanding how they use the platform:</p><ul role="list"><li><strong>Deeper event investment optimization</strong> informed by milestone data and post-event activation rates</li><li><strong>Broader MCP adoption</strong> across more of the marketing and ops teams</li><li><strong>Salesforce automation via connected MCPs</strong> to reduce manual data entry</li><li><strong>AEO content investment</strong> guided by the win rate data Upside surfaces</li></ul><blockquote>"The partnership matters as much as the product. It wasn't 'the trial's over, bye.' You're still here with us. Hosting events, fostering networking, and genuinely caring about our business — that's hugely important to who you do business with."</blockquote></div></div></div>`,"comply-from-unfiltered-salesforce-data-to-marketing-intelligence":'<div class="case-study-testimonial-section"><div class="w-dyn-list"><div role="list" class="case-study-testimonials-list w-dyn-items"><div role="listitem" class="w-dyn-item"><div class="testimonial-small-container"><div class="testimonial-small-content-wrapper"><div class="testimonial-small-image-wrapper"><img loading="lazy" src="/images/wendy-werve.png" alt="Wendy Werve" class="testimonial-small-image"/></div><div class="testimonial-small-quote-title-wrapper"><div class="testimonial-small-quote">“You don’t even know how far ahead of the curve we are in marketing with what we’re doing using Upside.”</div><div class="testimonial-small-logo-title-wrapper"><div class="testimonial-small-logo_wrapper"><img loading="lazy" src="/images/comply-logo.png" alt="Comply"/></div><div class="vertical-separator cc-display-none-mobile-l"></div><div class="horizontal-separator cc-testimonials-mobile-xl"></div><div class="testimonial-small-details-wrapper"><p class="body-m cc-color-text-primary"><a href="https://www.linkedin.com/in/wendy-werve/" target="_blank" rel="noopener noreferrer" style="color: inherit; text-decoration: underline;">Wendy Werve</a></p><p class="body-m">CMO, Comply</p></div></div></div></div></div></div><div role="listitem" class="w-dyn-item"><div class="testimonial-small-container"><div class="testimonial-small-content-wrapper"><div class="testimonial-small-image-wrapper"><img loading="lazy" src="/images/carl-gunlefinger.png" alt="Carl Gunlefinger" class="testimonial-small-image"/></div><div class="testimonial-small-quote-title-wrapper"><div class="testimonial-small-quote">“I haven’t seen a single case where I followed the data trail and found something wrong. I’ve stopped checking.”</div><div class="testimonial-small-logo-title-wrapper"><div class="testimonial-small-logo_wrapper"><img loading="lazy" src="/images/comply-logo.png" alt="Comply"/></div><div class="vertical-separator cc-display-none-mobile-l"></div><div class="horizontal-separator cc-testimonials-mobile-xl"></div><div class="testimonial-small-details-wrapper"><p class="body-m cc-color-text-primary"><a href="https://www.linkedin.com/in/carlgunlefinger/" target="_blank" rel="noopener noreferrer" style="color: inherit; text-decoration: underline;">Carl Gunlefinger</a></p><p class="body-m">Senior Marketing Operations Manager</p></div></div></div></div></div></div></div></div></div><div class="horizontal-separator"></div><div class="case-study-main-content-section"><div class="badge">Metrics</div><div class="margin-bottom-24px"><h2 class="case-study-title">The Data Hidden in Plain Sight</h2></div><div class="case-study-metrics_wrapper"><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M14.25 8.75H11.375C10.4775 8.75 9.75 9.47754 9.75 10.375C9.75 11.2725 10.4775 12 11.375 12H12.625C13.5225 12 14.25 12.7275 14.25 13.625C14.25 14.5225 13.5225 15.25 12.625 15.25H9.75" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>

1750<path d="M12 7.75V8.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 15.75V16.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/></svg></div></div><p class="paragraph-4">27.5M+ Touchpoints Unified Across 20+ Channels</p><div class="div-block-3"></div><p class="paragraph-5">Unified emails, calls, web, meetings, webinars, events, and downloads across 356K+ contacts and 72K+ accounts.</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M14.25 8.75H11.375C10.4775 8.75 9.75 9.47754 9.75 10.375C9.75 11.2725 10.4775 12 11.375 12H12.625C13.5225 12 14.25 12.7275 14.25 13.625C14.25 14.5225 13.5225 15.25 12.625 15.25H9.75" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 7.75V8.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 15.75V16.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/></svg></div></div><p class="paragraph-4">18 Active Users Across Marketing, Sales Ops &amp; Product Marketing</p><div class="div-block-3"></div><p class="paragraph-5">Self-serve analytics adopted by digital marketers, product marketers, sales ops, and leadership within weeks.</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M14.25 8.75H11.375C10.4775 8.75 9.75 9.47754 9.75 10.375C9.75 11.2725 10.4775 12 11.375 12H12.625C13.5225 12 14.25 12.7275 14.25 13.625C14.25 14.5225 13.5225 15.25 12.625 15.25H9.75" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 7.75V8.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 15.75V16.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/></svg></div></div><p class="paragraph-4">3,592 AI Milestone Analyses on Live Opportunities</p><div class="div-block-3"></div><p class="paragraph-5">Automatic attribution insights on every open and recently won opportunity, impossible to generate manually.</p></div></div></div><div class="case-study-main-content-section"><div class="badge">Highlighted Results</div><div class="case-study-main-content-rich-text w-richtext"><h2>Upside replaced guesswork with self-serve, data-driven confidence.</h2><ul role="list"><li><strong>AI Adoption and Custom Miniapps via MCP:</strong> Analytics dashboards built in minutes instead of weeks.</li><li><strong>Content Strategy Transformed:</strong> 11,500+ campaigns sortable by downstream engagement in seconds.</li><li><strong>Event Investment Validated:</strong> Doubling down on events, backed by pipeline-influenced data.</li><li><strong>Digital Spend Optimized:</strong> Paid channels evaluated on actual pipeline, not intuition.</li><li><strong>Self-Serve Reporting:</strong> 18 people across GTM look up any campaign in seconds, no custom reports.</li><li><strong>Salesforce Migration De-risked:</strong> New instance stood up in days, caught gaps the migration missed.</li></ul></div></div><div class="case-study-main-content-section"><div class="badge">The CMO Perspective</div><div class="case-study-main-content-rich-text w-richtext"><h2>Why &ldquo;Pinball Marketing&rdquo; Demands Better Data</h2><p>Comply CMO Wendy Werve wants to kill the funnel. Her philosophy is &ldquo;pinball marketing&rdquo; — a signal-driven, AI-first model where marketers are in charge of the flippers, not the ball.</p><blockquote>“A sales rep will say, ‘the deal came in this way.’ We go in and say, ‘actually, they pinballed their way through.’ No two journeys are exactly the same. It’s like a snowflake.”<span class="quote-attribution">Wendy Werve · CMO, Comply</span></blockquote><p>Pinball marketing demands real-time visibility into every touchpoint. That mandate landed on Senior Marketing Operations Manager Carl Gunlefinger.</p></div></div><div class="case-study-main-content-section"><div class="badge">The Challenge</div><div class="case-study-main-content-rich-text w-richtext"><h2>No Standardized Way to Measure What Was Working</h2><p>When Carl joined in October 2024, he walked into two overlapping problems. Marketing had no standardized way to measure campaign performance. And Comply was mid-Salesforce re-implementation — a migration that left 60%+ of 30,000+ historical opportunities without contact roles.</p><blockquote>“There wasn’t any standardization to say, ‘I had a piece of content and here’s how it performed downstream.’”<span class="quote-attribution">Carl Gunlefinger</span></blockquote><p>

1750Carl needed a system that could work with imperfect data and still tell the CMO which campaigns to fund. Comply had evaluated other vendors. Upside was the one that didn’t flinch.</p></div></div><div class="case-study-main-content-section"><div class="badge">The Outcome</div><div class="case-study-main-content-rich-text w-richtext"><h2>Trusted Data Model That Powers Insights and AI Workflows</h2><p>Within days, Upside stood up Comply’s new Salesforce instance, mapped the migrated historical data, and caught attribution gaps the migration had missed.</p><blockquote>“You guys were the easiest vendor to work with and the quickest to debug problems. Upside ended up being a critical tool in figuring out where things had gone not quite as intended during the migration.”<span class="quote-attribution">Carl Gunlefinger</span></blockquote><h3><strong>Aha Moment #1:</strong> Instantly Seeing What Content Matters</h3><p>Once Upside was live, the first thing that clicked was speed. Across 11,500+ campaigns, what used to require custom Salesforce reports became a 30-second task.</p><blockquote>“All I had to do was go into a view, select a filter, sort by engagements, and I can see at the top: this is what people care about right now.”<span class="quote-attribution">Carl Gunlefinger</span></blockquote><p>For a team creating content across six sales segments, that instant read rewrote how the content calendar gets built.</p><h3><strong>Aha Moment #2:</strong> Seeing the Full Multi-Touch Picture</h3><p>Speed was just the entry point. Once the team started digging into accounts and opportunities, the real story showed up: Comply’s deals don’t close from one touchpoint. In Upside’s account explorer, the scale became obvious — and the whole team got hooked.</p><blockquote>“We are like kids in a candy shop digging into accounts and opportunities.”<span class="quote-attribution"><a href="https://www.linkedin.com/in/wendy-werve/" target="_blank" rel="noopener noreferrer" style="color: inherit; text-decoration: underline;">Wendy Werve</a> · CMO, Comply</span></blockquote><p>For Carl, the implication was bigger than curiosity:</p><blockquote>“There are some opportunities that just have hundreds and hundreds of touchpoints. Being able to quickly see the map of that has been huge.”<span class="quote-attribution">Carl Gunlefinger</span></blockquote><h3><strong>Aha Moment #3:</strong> Data You Can Actually Trust</h3><p>Scale is only useful if you can trust what you’re seeing. Carl used to verify everything, cross-checking IDs and timestamps. Not anymore.</p><blockquote>“I’m not doing that following-the-trail thing anymore. I just know you guys have this all built right.”<span class="quote-attribution">Carl Gunlefinger</span></blockquote><p>That trust freed the team to chase surprising patterns — like content downloads, which the org had long undervalued but, in Upside, showed a remarkably high conversion rate to pipeline.</p><blockquote>“The organization hadn’t really considered content downloads as solid for moving things into an opportunity stage. In Upside, they actually have a very high conversion rate.”<span class="quote-attribution">Wendy Werve · CMO, Comply</span></blockquote><h3><strong>Aha Moment #4:</strong> Complex AI-Powered Analysis via the MCP</h3><p>If trusted data was the foundation, the MCP was the unlock.</p><blockquote>“Honestly, the first time I found a huge use case for AI for me. A tool that has the data organized, and I can query it thinking the way I think.”<span class="quote-attribution">Carl Gunlefinger</span></blockquote><p>In a recent GTM Engineers webinar, Carl showcased three miniapps built in minutes via the MCP:</p><ul role="list"><li><strong>Paid Search Impact Dashboard</strong> — 6 tabs, keyword-level pipeline attribution, built in 30 minutes.</li><li><strong>Webinar Performance</strong> — 126 webinars, 25,623 registrations, 1,723 opps influenced, 813 won, with Salesforce attendance statuses auto-mapped.</li><li><strong>Single-Deal Replication Playbook</strong> — 390+ touchpoints on one deal stitched into a repeatable playbook.</li></ul><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width:100%;margin:1.75rem 0;"><div><img loading="lazy" src="/images/comply-paid-search-keywords.png" alt="Carl’s Paid Search Impact miniapp — keyword-level pipeline attribution with influenced pipeline, won deals, views, and average days to opportunity" style="width:100%;height:auto;border-radius:12px;"/></div><figcaption style="font-size:12px;color:#5f6563;margin-top:8px;text-align:center;">

1750Miniapp 1 — Paid Search Impact: every keyword tied to influenced pipeline, won deals, and time-to-opportunity.</figcaption></figure><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width:100%;margin:1.75rem 0;"><div><img loading="lazy" src="/images/comply-webinar-performance.png" alt="Carl’s Webinar Performance Dashboard — 126 webinars, 25,623 registrations, 1,723 opps influenced, 813 won" style="width:100%;height:auto;border-radius:12px;"/></div><figcaption style="font-size:12px;color:#5f6563;margin-top:8px;text-align:center;">Miniapp 2 — Webinar Performance: 126 webinars, 25,623 registrations, 1,723 opps influenced, 813 won. Salesforce attendance statuses auto-mapped by the MCP.</figcaption></figure><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width:100%;margin:1.75rem 0;"><div><img loading="lazy" src="/images/comply-single-deal-playbook.png" alt="Carl’s Single-Deal Replication Playbook — 390+ touchpoints across 5 channels on one deal stitched into a repeatable timeline" style="width:100%;height:auto;border-radius:12px;"/></div><figcaption style="font-size:12px;color:#5f6563;margin-top:8px;text-align:center;">Miniapp 3 — Single-Deal Replication Playbook: 390+ touchpoints on one deal, turned into a replicable sales playbook.</figcaption></figure><blockquote>“Literally from idea to preview publication, probably 30 minutes.”<span class="quote-attribution">Carl Gunlefinger</span></blockquote><p>Next up: a 36-dimension pipeline roll-forward report for Comply’s CFO. The appetite across the company is outpacing one person.</p><h3>From One User to 18: The Self-Serve Moment</h3><p>What started as Carl’s tool now serves 18 people across Sales Ops, Product Marketing, Digital Marketing, and Leadership — all self-serve, zero custom reports. Adoption was accelerated by a team-wide commitment to pipeline as the only scoreboard that mattered.</p><blockquote>“Our team is 100% KPI’d on pipeline. MQLs are a participation trophy. It didn’t make it into pipeline? It didn’t matter.”<span class="quote-attribution">Wendy Werve · CMO, Comply</span></blockquote><p>That focus pairs with Wendy’s AI-first operating philosophy — one she was pushing long before most peers:</p><blockquote>“AI today can do the ‘ING’ of marketing. Get in and be the chief market officer — understand the signals, be first to respond.”<span class="quote-attribution">Wendy Werve · CMO, Comply</span></blockquote><h3>Decisions Driven by Data</h3><p>The real impact isn’t dashboards — it’s decisions. Events, content, and paid channels are now funded (or cut) based on actual pipeline.</p><blockquote>“The biggest thing we’ve decided is where to invest in content and which events to attend. Upside is how we justify it.”<span class="quote-attribution">Carl Gunlefinger</span></blockquote></div></div><div class="case-study-main-content-section"><div class="badge">What’s Next</div><div class="case-study-main-content-rich-text w-richtext"><h2>A Partnership That Keeps Expanding</h2><p>Carl is already extending Upside’s MCP into tools for sales and finance — AI-generated sales toolkits, win-back kits, and the CFO pipeline report. Wendy sees that expansion as vindication of an early bet:</p><blockquote>“The smartest decisions I have ever made is partnering with folks early on. Where you’ve gone is leaps and bounds beyond what I could have even articulated.”<span class="quote-attribution">Wendy Werve · CMO, Comply</span></blockquote><p>Carl puts the recommendation more plainly:</p><blockquote>“Would I recommend Upside? Yes. Simple gives you a lot, and simple is not easy. Plus, you guys are great partners to work with.”<span class="quote-attribution">Carl Gunlefinger · Senior Marketing Operations Manager, Comply</span></blockquote></div></div><div class="case-study-main-content-section"><div class="badge">Featured In</div><div class="case-study-main-content-rich-text w-richtext"><ul role="list"><li><strong>Blog:</strong> <a href="https://aigtm.engineer/p/how-three-marketing-leaders-are-becoming" target="_blank" rel="noopener noreferrer">“How Three Marketing Leaders Are Becoming GTM Engineers With AI”</a> (aigtm.engineer) — Carl’s miniapps featured alongside ServiceTitan and Dscout.</li></ul></div></div></div>',"cresta-from-attribution-to-ai-deep-research":'<div class="case-study-testimonial-section"><div class="w-dyn-list">

1750<div role="list" class="case-study-testimonials-list w-dyn-items"><div role="listitem" class="w-dyn-item"><div class="testimonial-small-container"><div class="testimonial-small-content-wrapper"><div class="testimonial-small-image-wrapper"><img loading="lazy" src="/images/stephen-daniels.jpg" alt="" class="testimonial-small-image"/></div><div class="testimonial-small-quote-title-wrapper"><div class="testimonial-small-quote">“Real life is messy. Enterprise cycles are messy. Upside accurately reflects that messy reality, which I think is rare and was impossible before.”</div><div class="testimonial-small-logo-title-wrapper"><div class="testimonial-small-logo_wrapper"><img loading="lazy" src="/images/cresta-black.svg" alt=""/></div><div class="vertical-separator cc-display-none-mobile-l"></div><div class="horizontal-separator cc-testimonials-mobile-xl"></div><div class="testimonial-small-details-wrapper"><p class="body-m cc-color-text-primary"><a href="https://www.linkedin.com/in/stephenhdaniels" target="_blank" rel="noopener noreferrer" style="color: inherit; text-decoration: underline;">Stephen Daniels</a></p><p class="body-m">VP, GTM &amp; Strategic Operations</p></div></div></div></div></div></div><div role="listitem" class="w-dyn-item"><div class="testimonial-small-container"><div class="testimonial-small-content-wrapper"><div class="testimonial-small-image-wrapper"><img loading="lazy" src="/images/ted-ranney.jpg" alt="" class="testimonial-small-image"/></div><div class="testimonial-small-quote-title-wrapper"><div class="testimonial-small-quote">“Just going to Upside and saying, ‘can you help me analyze what’s making implementations work better or worse?’ has been a massive improvement for my mental health.”</div><div class="testimonial-small-logo-title-wrapper"><div class="testimonial-small-logo_wrapper"><img loading="lazy" src="/images/cresta-black.svg" alt=""/></div><div class="vertical-separator cc-display-none-mobile-l"></div><div class="horizontal-separator cc-testimonials-mobile-xl"></div><div class="testimonial-small-details-wrapper"><p class="body-m cc-color-text-primary"><a href="https://www.linkedin.com/in/ted-ranney-2053352a" target="_blank" rel="noopener noreferrer" style="color: inherit; text-decoration: underline;">Ted Ranney</a></p><p class="body-m">Head of GTM Analytics</p></div></div></div></div></div></div></div></div></div><div class="horizontal-separator"></div><div class="case-study-main-content-section"><div class="badge">Metrics</div><div class="margin-bottom-24px"><h2 class="case-study-title">Deep Research Replaced Weeks of Manual Analysis with Insight at Every QBR</h2></div><div class="case-study-metrics_wrapper"><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M14.25 8.75H11.375C10.4775 8.75 9.75 9.47754 9.75 10.375C9.75 11.2725 10.4775 12 11.375 12H12.625C13.5225 12 14.25 12.7275 14.25 13.625C14.25 14.5225 13.5225 15.25 12.625 15.25H9.75" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 7.75V8.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 15.75V16.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/></svg></div></div><p class="paragraph-4">Weeks of Gong Analysis → Under a Day</p><div class="div-block-3"></div><p class="paragraph-5">Deep research that previously took contractors or staff weeks of Gong-transcript review now completes in under a day, freeing a small GTM ops team to act on insights instead of gathering them.</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>

1750<path d="M14.25 8.75H11.375C10.4775 8.75 9.75 9.47754 9.75 10.375C9.75 11.2725 10.4775 12 11.375 12H12.625C13.5225 12 14.25 12.7275 14.25 13.625C14.25 14.5225 13.5225 15.25 12.625 15.25H9.75" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 7.75V8.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 15.75V16.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/></svg></div></div><p class="paragraph-4">Every Deal Scored for Messaging Adherence</p><div class="div-block-3"></div><p class="paragraph-5">Upside scores every closed deal against PMM-defined messaging, fueling AE coaching, win-rate analysis, and a feedback loop back to product marketing.</p></div><div class="case-study-metrics-card"><div class="case-study-metrics-card-icon_wrapper"><div class="w-embed"><svg display="block" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M19.25 12C19.25 16.0041 16.0041 19.25 12 19.25C7.99594 19.25 4.75 16.0041 4.75 12C4.75 7.99594 7.99594 4.75 12 4.75C16.0041 4.75 19.25 7.99594 19.25 12Z" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M14.25 8.75H11.375C10.4775 8.75 9.75 9.47754 9.75 10.375C9.75 11.2725 10.4775 12 11.375 12H12.625C13.5225 12 14.25 12.7275 14.25 13.625C14.25 14.5225 13.5225 15.25 12.625 15.25H9.75" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 7.75V8.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/><path d="M12 15.75V16.25" stroke="#509A79" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/></svg></div></div><p class="paragraph-4">Persona-Level Insight Reshaped Churn Strategy</p><div class="div-block-3"></div><p class="paragraph-5">Sentiment analysis showed champions almost universally loved Cresta even on churned deals. The gap was with economic buyers, and the finding drove a leadership-level push to engage CFOs.</p></div></div></div><div class="horizontal-separator"></div><div class="case-study-main-content-section"><div class="badge">Highlighted Results</div><div class="case-study-main-content-rich-text w-richtext"><h2>Upside replaced manual transcript work with strategic, AI-driven insight.</h2><ul role="list"><li><strong>From Weeks of Transcript Review to Same-Day Insight:</strong> Deep transcript analysis that once consumed weeks of an ops hire’s time now runs in under a day.</li><li><strong>Deal Reality, Reconstructed in Natural Language:</strong> Milestone analysis turns messy multi-month enterprise cycles into plain-language deal stories that capture SDR follow-up, conference touches, and buying-committee dynamics that were previously invisible.</li><li><strong>Messaging Validation, Coaching, and PMM Feedback in One Pass:</strong> Every deal is scored for adherence to PMM messaging, surfacing AEs to coach, validating which messages convert, and signaling when the playbook itself needs to change.</li><li><strong>Champion vs. Economic-Buyer Sentiment, Quantified:</strong> Persona-level churn analysis exposed an invisible economic-buyer gap that became a leadership-level priority.</li><li><strong>The Spine of Every QBR:</strong> Upside deep research is now integral to Cresta’s Quarterly Business Reviews, replacing pivot-table guesswork with narratives that explain root causes.</li></ul></div></div><div class="case-study-main-content-section"><div class="badge">Background</div><div class="case-study-main-content-rich-text w-richtext"><h2>The Ops Bench Behind Cresta’s Enterprise Deals</h2><p><em>How a two-person operations and analytics team turned messy enterprise deal data into the strategic insight behind every Cresta QBR.</em></p><p>Cresta is one of Upside’s earliest design partners, and the relationship has evolved alongside both companies from the start. Cresta builds the unified platform for human and AI agents in customer experience, serving Fortune 500 contact center operators including Cox Communications, United Airlines, Intuit, and CVS.</p><p>Behind the scenes, Stephen Daniels (VP, GTM &amp; Strategic Operations) and Ted Ranney (Head of GTM Analytics) lead a small, high-leverage operations and analytics team that supports some of the messiest, longest, most multi-stakeholder enterprise cycles in B2B SaaS. Stephen brought Upside in as one of its earliest design partners, drawing on five years he’d spent at a mobile-app attribution company before joining Cresta.</p><blockquote>“We’re very early customers of Upside, so the problem set’s changed over time. Originally it was solving more of an attribution problem, trying to solve what was actually driving deals.”<span class="quote-attribution">

1750Stephen Daniels</span></blockquote></div></div><div class="case-study-main-content-section"><div class="badge">The Challenge</div><div class="case-study-main-content-rich-text w-richtext"><h2>Pivot Tables Don’t Explain Why Deals Win or Lose</h2><p>The deeper bottleneck for Cresta’s GTM team wasn’t access to data. It was the depth pivot tables couldn’t reach: a two-to-three-person ops team can’t manually mine call transcripts for the insights leadership actually wants, and structured reports don’t explain <em>why</em> deals move the way they do.</p><blockquote>“I can make a million pivot tables. I can see the number of days in S2 to S3 is improving slightly. Great. Why is that happening? But a lot of the time it requires another deeper level of understanding.”<span class="quote-attribution">Ted Ranney</span></blockquote><p>Enterprise cycles compound the problem. Cresta’s deals are long, with multiple buying committees, complex sourcing, and lots of start-stops. Manually reading call after call to understand what was making implementations move faster or slower wasn’t sustainable.</p></div></div><div class="case-study-main-content-section"><div class="badge">Finding the Right Partner</div><div class="case-study-main-content-rich-text w-richtext"><h2>Not Just an AI Platform, but a Data Therapist</h2><p>As one of Upside’s first design partners, Cresta has helped shape the platform since the early days. The partnership runs on call transcripts from Gong, structured deal data, and constant back-and-forth between the Cresta ops bench and the Upside team.</p><blockquote>“Working with Upside’s not just working with a technology company. It’s like working with a therapist, a data consultant, a friend, and then many rungs down the list, working with an AI company. They’ve been some of the best partners I’ve worked with.”<span class="quote-attribution">Stephen Daniels</span></blockquote></div></div><div class="case-study-main-content-section"><div class="badge">The Outcome</div><div class="case-study-main-content-rich-text w-richtext"><h2>Deep Research as the Engine of Every QBR</h2><p>Cresta now uses Upside as the analytical engine behind its Quarterly Business Reviews and most consequential GTM decisions. Four use cases stand out: milestone analysis, messaging adherence, implementation sentiment, and churn drivers. Each replaces a manual workflow with a durable source of insight.</p><h3>1. Milestone Analysis: Messy Enterprise Cycles, Told as a Story</h3><p>The first feature Cresta worked with Upside on, and still one of the most heavily used. Milestone analysis takes a deal’s full activity history (calls, emails, conferences, SDR touches, internal notes) and reconstructs the deal as a plain-language narrative.</p><blockquote>“An SDR called this person. They hung up, they called two times later, they went to a conference, they stopped by the booth. To get a very concise story in natural language of how that thing happened was the killer thing for Upside.”<span class="quote-attribution">Stephen Daniels</span></blockquote><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width:100%;margin:1.75rem 0;"><div><img loading="lazy" src="/images/account-touchpoint-timeline.png" alt="Upside account timeline reconstructs a deal narrative from emails, calls, conferences, and CRM activity" style="width:100%;height:auto;border-radius:12px;"/></div><figcaption style="font-size:12px;color:#5f6563;margin-top:8px;text-align:center;">Upside reconstructs enterprise deal narratives from scattered activity (emails, conferences, internal notes, call transcripts) into the kind of plain-language milestone story Stephen describes above.</figcaption></figure><h3>2. Messaging Adherence: Which AEs Followed the Playbook, and Did It Win?</h3><p>Ted worked with Cresta’s product marketing team to codify the specific messages AEs should be using, then asked Upside to analyze every closed deal for adherence, at the level of “this person followed it to extreme accuracy” versus “this person didn’t follow it at all.”</p><p>The output drives three different decisions:</p><ul role="list"><li><strong>Validate the messaging:</strong> Does following the PMM playbook actually correlate with higher win rates?</li><li><strong>Coach the AEs:</strong> Which reps consistently aren’t using messages that convert, and which behaviors should anchor 1:1 coaching?</li><li><strong>Update the messaging:</strong> If adherence has no effect on win rate, the playbook itself needs to be rewritten by PMM.</li></ul><blockquote>“Upside being able to go into every single deal that’s in there and say, yes, this person followed it to an extreme accuracy, or this person didn’t follow it at all. There’s so many decisions you can make out of that.”<span class="quote-attribution">Ted Ranney</span></blockquote><h3>3. Implementation Sentiment: From Hours of Replay to Automatic Analysis</h3><p>When Ted joined Cresta, his first assignment was to figure out what made customer implementations move faster or slower.</p><blockquote>“I remember my first two or three weeks on the job, they wanted me to analyze what was making certain deployments move faster or slower. Watching Gong call after Gong call, I don’t know how many hours I spent. It was a heck of a way to start, and that was painful. Being able to move from that to just going to Upside and saying, ‘can you help me analyze what’s making implementations work better or worse?’ was a massive improvement for my mental health.”<span class="quote-attribution">Ted Ranney</span></blockquote><p>Now implementation sentiment, slowdown signals, and at-risk deployments surface automatically. The analytics team can act on findings instead of spending hours producing them.</p><h3>4. Churn Drivers: Champion Love, Economic-Buyer Gap</h3><p>Cresta’s most consequential Upside-driven insight came out of a QBR-cycle churn anal

1750ysis. Like most teams, Cresta had been relying on churn-reason picklists filled in by the AE and CSM. The data was useful but shallow.</p><p>Stephen asked Upside to look deeper. Across churned and downgraded deals, what did different personas actually think of Cresta? The analysis split into two layers: how champions perceived the product and team, and how economic buyers (the people signing the check) viewed it.</p><p>The finding was stark. Champions almost universally loved Cresta, even on deals that ultimately churned. The real gap was at the economic-buyer level: CFOs and equivalents who didn’t see the ROI the way daily users did.</p><figure class="w-richtext-figure-type-image w-richtext-align-fullwidth" style="max-width:65%;margin:1.75rem auto;"><div><img loading="lazy" src="/images/persona-resolution.png" alt="Upside persona resolution — buying-group roles identified across champions and economic buyers" style="width:100%;height:auto;border-radius:12px;"/></div><figcaption style="font-size:12px;color:#5f6563;margin-top:8px;text-align:center;">Persona resolution maps which roles touched a deal and how each persona engages with the product. It’s the same foundation behind Cresta’s champion-vs-economic-buyer churn finding.</figcaption></figure><blockquote>“It’s hard to interpret for someone like a CFO who may not understand the ROI as well as the actual users of the product do. A big takeaway is how do we connect to those financial leaders who think about ROI in a slightly different way?”<span class="quote-attribution">Stephen Daniels</span></blockquote><p>That insight became a leadership-level assignment for Customer Success and Sales: build a deliberate motion for engaging and educating economic buyers throughout the deal cycle and customer lifecycle.</p></div></div><div class="case-study-main-content-section"><div class="badge">What’s Next</div><div class="case-study-main-content-rich-text w-richtext"><h2>From Quarterly Analysis to Always-On Workflows</h2><p>Cresta is doubling down on the deep research motion. Upside now anchors its Quarterly Business Reviews and most consequential GTM decisions, from messaging reviews to churn analysis to deployment insights. Mini-apps are extending those analyses further, putting always-on deal-level intelligence in the hands of more of the team rather than the analytics function alone.</p><blockquote>“We owe a lot to how we think about leveraging AI in our data analysis and understanding the business. I don’t think we’d be where we are today or have the expertise we have today without the partnership.”<span class="quote-attribution">Stephen Daniels</span></blockquote></div></div>'},yde={"graphite-uncovering-real-channels-behind-inbound-growth":'<div class="case-study-logo_wrapper"><img src="/images/graphite-logo.jpeg" loading="lazy" alt=""/></div><div class="case-study-info-content_wrapper"><div class="case-study-info-content-section"><p class="case-study-info-title">About</p><p class="case-study-info-content">Graphite is a research-driven digital growth agency specializing in SEO and Answer Engine Optimization (AEO). They do original research to inform strategy and help build growth programs for leading technology companies like Notion, Webflow, Rippling, Masterclass, OpenAI, Netflix, Adobe, and Meta.</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Industry</p><p class="case-study-info-content">Digital Marketing &amp; Growth Agency</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Company size</p><p class="case-study-info-content">200+ employees</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Headquarters</p><p class="case-study-info-content">San Francisco, California</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Founded</p><p class="case-study-info-content">2015</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Specialties</p><p class="case-study-info-content">Search Engine Optimization, Answer Engine Optimization, Content Strategy, Original Research, Growth Marketing</p></div></div>',"syndio-driving-strategic-gtm-decisions-with-upside":`<div class="case-study-logo_wrapper"><img src="/images/logo-syndio.png" loading="lazy" alt=""/></div><div class="case-study-info-content_wrapper"><div class="case-study-info-content-section"><p class="case-study-info-title">About</p><p class="case-study-info-content">Syndio is the industry pioneer in Decision Intelligence for Pay. Built on nearly a decade of proprietary compensation insights, Syndio helps global organizations make smarter pay decisions that win talent while protecting internal equity and controlling costs. Using Syndi, the company&#x27;s first AI-native solution, teams get precise, instant pay recommendations embedded in their workflows. More than 350 leading companies rely on Syndio&#x27;s solutions to guide compensation decisions for over 10 million employees across 100 countries.</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Industry</p><p class="case-study-info-content">Software Development</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Company size</p><p class="case-study-info-content">51-200 employees
1751155 associated members</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Headquarters</p><p class="case-study-info-content">Seattle, Washington</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Founded</p><p class="case-study-info-content">2016</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Specialties</p><p class="case-study-info-content">HR Analytics, Pay Analytics, Equity Tech, Pay Equity, Compensation Software, AI for HR, and Decision Intelligence for Pay</p></div></div>`,"dscout-driving-confident-growth-with-upside":`<div class="case-study-logo_wrapper"><img src="/images/logo-dscout.svg" loading="lazy" alt=""/></div><div class="case-study-info-content_wrapper"><div class="case-study-info-content-section"><p class="case-study-info-title">About</p><p class="case-study-info-content">Dscout is a flexible Experience Research Platform for capturing in-context insights from high-quality participants. Leading brands use Dscout to test ideas, iterate quickly, collaborate, and build confidently.</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Industry</p><p class="case-study-info-content">Software Development</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Company size</p><p class="case-study-info-content">201-500 employees
1752278 associated members</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Headquarters</p><p class="case-study-info-content">Chicago, IL</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Founded</p><p class="case-study-info-content">2011</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Specialties</p><p class="case-study-info-content">mobile research, in-context research, qualitative research, remote research, experience research, usability testing, diary studies, participant management, participant recruiting, live interviews, and AI analysis</p></div></div>`,"assembled-from-attribution-guesswork-to-pipeline-surge":'<div class="case-study-logo_wrapper"><img src="/images/assembled-logo-black.png" loading="lazy" alt="Assembled"/></div><div class="case-study-info-content_wrapper"><div class="case-study-info-content-section"><p class="case-study-info-title">About</p><p class="case-study-info-content">

1752Assembled is the AI platform for customer support providing autonomous AI agents and AI-powered workforce management built for modern customer support teams. Founded in 2018, Assembled is the support automation for teams at Autodesk, Canva, Carta, Clear and Hubspot and is the only platform combining the power of AI agents with human workforce management.</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Industry</p><p class="case-study-info-content">Software Development / Customer Support Operations</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Company size</p><p class="case-study-info-content">51–200 employees</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Headquarters</p><p class="case-study-info-content">San Francisco, California</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Founded</p><p class="case-study-info-content">2018</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">GTM Team</p><p class="case-study-info-content">9 SDRs, 7 marketers, 13 AEs across enterprise and mid-market</p></div></div>',"comply-from-unfiltered-salesforce-data-to-marketing-intelligence":'<div class="case-study-logo_wrapper"><img src="/images/comply-logo.png" loading="lazy" alt="Comply"/></div><div class="case-study-info-content_wrapper"><div class="case-study-info-content-section"><p class="case-study-info-title">About</p><p class="case-study-info-content">Comply is a leading AI and SaaS provider of compliance technology and consulting for financial services, serving 5,000+ firms globally across SEC, FINRA, and FCA compliance. Built through strategic acquisitions, Comply unifies multiple compliance technologies under one brand.</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Industry</p><p class="case-study-info-content">Financial Services / RegTech</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Company size</p><p class="case-study-info-content">~350 employees</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Headquarters</p><p class="case-study-info-content">New York, NY</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">GTM Team</p><p class="case-study-info-content">14 marketers supporting 6 sales teams</p></div></div>',"cresta-from-attribution-to-ai-deep-research":'<div class="case-study-logo_wrapper"><img src="/images/cresta-black.svg" loading="lazy" alt="Cresta"/></div><div class="case-study-info-content_wrapper"><div class="case-study-info-content-section"><p class="case-study-info-title">About</p><p class="case-study-info-content">Cresta is the unified platform for human agents and AI agents, transforming customer experience for Fortune 500 contact centers. Customers include Cox Communications, United Airlines, Intuit, and CVS.</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Industry</p><p class="case-study-info-content">Customer Experience / AI for Contact Centers</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Company size</p><p class="case-study-info-content">500+ employees</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Headquarters</p><p class="case-study-info-content">San Francisco, California</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Founded</p><p class="case-study-info-content">2017</p></div><div class="case-study-info-content-section"><p class="case-study-info-title">Specialties</p><p class="case-study-info-content">AI Agents, Agent Assist, Conversation Intelligence, Quality Management Automation, Contact Center AI</p></div></div>'},bde=`
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1913currentColor","aria-hidden":"true",children:n.jsx("path",{d:"M14 13.5h2.5l1-4H14v-2c0-1.03 0-2 2-2h1.5V2.14c-.326-.043-1.557-.14-2.857-.14C11.928 2 10 3.657 10 6.7v2.8H7v4h3V22h4v-8.5Z"})})}),n.jsx("a",{href:`https://twitter.com/intent/tweet?url=${b}&text=${x}`,target:"_blank",rel:"noopener noreferrer","aria-label":"Share on X",children:n.jsx("svg",{width:"18",height:"18",viewBox:"0 0 24 24",fill:"currentColor","aria-hidden":"true",children:n.jsx("path",{d:"M18.244 2.25h3.308l-7.227 8.26 8.502 11.24H16.17l-5.214-6.817L4.99 21.75H1.68l7.73-8.835L1.254 2.25H8.08l4.713 6.231 5.45-6.231Zm-1.161 17.52h1.833L7.084 4.126H5.117L17.083 19.77Z"})})}),n.jsx("a",{href:`https://www.linkedin.com/sharing/share-offsite/?url=${b}`,target:"_blank",rel:"noopener noreferrer","aria-label":"Share on LinkedIn",children:n.jsx("svg",{width:"18",height:"18",viewBox:"0 0 24 24",fill:"currentColor","aria-hidden":"true",children:n.jsx("path",{d:"M20.45 20.45h-3.56v-5.57c0-1.33-.02-3.04-1.85-3.04-1.85 0-2.13 1.44-2.13 2.94v5.67H9.35V9h3.42v1.56h.05c.48-.9 1.64-1.85 3.37-1.85 3.6 0 4.27 2.37 4.27 5.46v6.28ZM5.34 7.43a2.06 2.06 0 1 1 0-4.13 2.06 2.06 0 0 1 0 4.13ZM7.12 20.45H3.55V9h3.57v11.45ZM22.22 0H1.77C.79 0 0 .77 0 1.73v20.54C0 23.22.79 24 1.77 24h20.45c.98 0 1.78-.78 1.78-1.73V1.73C24 .77 23.2 0 22.22 0Z"})})})]})]})]})})}),y.length>0&&n.jsxs("section",{className:"csd-related",children:[n.jsx("p",{className:"csd-related-eyebrow",children:"Case Studies"}),n.jsxs("div",{className:"csd-related-head",children:[n.jsx("h2",{children:"Explore more customer success stories."}),n.jsx("p",{children:"Explore more stories from GTM teams using Upside to recover hidden context, answer revenue questions faster, and make more confident strategic decisions."})]}),n.jsx("div",{className:"csd-related-grid",children:y.map(k=>n.jsxs(J,{to:`/case-studies/${k.slug}`,className:"csd-card",children:[n.jsx("img",{className:"csd-card-img",src:k.activeBgImage??k.featuredImage,alt:k.title.split(":")[0].trim(),loading:"lazy",style:{objectPosition:k.activeBgPosition??"center"}}),n.jsx("span",{className:"csd-card-tag",children:k.theme}),n.jsx("span",{className:"csd-card-title",children:k.title}),n.jsxs("span",{className:"csd-card-more",children:["Read more ",n.jsx(vy,{size:15})]})]},k.slug))})]}),n.jsxs("section",{className:"csd-cta",children:[n.jsx("span",{className:"csd-cta-mark",children:n.jsx(L0,{size:22})}),n.jsx("h2",{children:"Build your GTM on data you can trust."}),n.jsx("p",{children:"Book a demo and see what your team can build, automate, and finally answer once your GTM data is something you trust."}),n.jsxs("div",{className:"csd-cta-actions",children:[n.jsx(J,{to:Wt,className:"cta w-button",children:"Request demo"}),n.jsx(J,{to:"/platform",className:"cta cc-secondary w-button",children:"Explore the platform →"})]})]})]}),n.jsx(fde,{src:(i==null?void 0:i.src)??null,alt:(i==null?void 0:i.alt)??"",onClose:()=>r(null)})]})},xde=()=>n.jsx("section",{id:"hero",style:{position:"relative",marginTop:"-65px",background:Pe.bgSoft,borderBottom:`1px solid ${Pe.border}`,overflow:"hidden"},children:n.jsxs("div",{style:{maxWidth:840,margin:"0 auto",padding:"148px 40px 84px",textAlign:"center"},children:[n.jsx("div",{style:{fontSize:"12px",fontWeight:500,letterSpacing:"0.14em",textTransform:"uppercase",color:Pe.greenSoft},children:"Customers"}),n.jsx("h1",{style:{margin:"20px auto 0",fontSize:"clamp(34px, 6vw, 62px)",fontWeight:600,letterSpacing:"-0.03em",lineHeight:1.04,color:Pe.text,maxWidth:"760px",textWrap:"balance"},children:"How modern GTM teams turn messy data into revenue."}),n.jsx("p",{style:{fontSize:"19px",lineHeight:1.6,color:Pe.muted,margin:"24px auto 0",maxWidth:"600px",textWrap:"pretty"},children:"Across every industry, every motion, and every messy data stack."})]})});function RG(e){const t=e.split("-")[0].toLowerCase();return oa.find(a=>a.name.toLowerCase()===t)}function wde(e){const t=RG(e);return t!=null&&t.src.endsWith(".svg")?t.src:void 0}const S0="#509A79",kde="#3f8266",jde="rgba(255,255,255,0.65)",C0=320,Sde=460,A0=16,Cde=420,Ade=5,CP="vtc-styles",AP=`
1914.vtc-row {
1915  display: flex;
1916  gap: ${A0}px;
1917  overflow-x: auto;
1918  overflow-y: hidden;
1919  scroll-snap-type: x mandatory;
1920  scroll-behavior: smooth;
1921  scrollbar-width: none;
1922  padding: 8px 4px 24px;
1923  margin: 0 -4px;
1924}
1925.vtc-row::-webkit-scrollbar { display: none; }
1926
1927.vtc-grid {
1928  display: grid;
1929  grid-template-columns: repeat(var(--vtc-grid-cols, 3), minmax(0, ${Cde}px));
1930  gap: ${A0}px;
1931  justify-content: center;
1932  padding: 8px 4px 24px;
1933}
1934
1935.vtc-hero-wrap {
1936  display: flex;
1937  justify-content: center;
1938  padding: 8px 4px 24px;
1939}
1940
1941.vtc-card {
1942  position: relative;
1943  flex: 0 0 ${C0}px;
1944  height: ${Sde}px;
1945  border-radius: 18px;
1946  overflow: hidden;
1947  scroll-snap-align: start;
1948  background: #161b1c;
1949  border: 0;
1950  padding: 0;
1951  cursor: pointer;

1952  color: #fff;
1953  text-align: left;
1954  font: inherit;
1955  isolation: isolate;
1956  transition: transform 220ms cubic-bezier(0.22,1,0.36,1);
1957}
1958/* In grid mode the card fills its grid track instead of a fixed flex basis. */
1959.vtc-grid .vtc-card { flex: 1 1 auto; width: 100%; }
1960.vtc-card:hover { transform: translateY(-2px); }
1961.vtc-card:focus-visible {
1962  outline: 2px solid ${S0};
1963  outline-offset: 3px;
1964}
1965
1966.vtc-card__bg {
1967  position: absolute;
1968  inset: 0;
1969  background-size: cover;
1970  background-position: center top;
1971  z-index: 0;
1972  transition: transform 600ms cubic-bezier(0.22,1,0.36,1), filter 220ms ease;
1973}
1974.vtc-card:hover .vtc-card__bg {
1975  transform: scale(1.04);
1976  filter: brightness(0.78);
1977}
1978
1979.vtc-card__veil {
1980  position: absolute;
1981  inset: 0;
1982  background: linear-gradient(
1983    180deg,
1984    rgba(12,30,32,0.05) 0%,
1985    rgba(12,30,32,0.05) 40%,
1986    rgba(12,30,32,0.55) 70%,
1987    rgba(12,30,32,0.92) 100%
1988  );
1989  z-index: 1;
1990  pointer-events: none;
1991}
1992
1993.vtc-card__logo {
1994  position: absolute;
1995  top: 18px;
1996  left: 18px;
1997  height: 24px;
1998  max-width: 130px;
1999  z-index: 3;
2000  opacity: 0.95;
2001  object-fit: contain;
2002}
2003/* For case studies whose only logo is a dark-on-white raster (e.g. Graphite's
2004 * JPEG), fall back to brightness(0) invert(1). For SVGs that already ship
2005 * with fill="white", we don't filter at all. */
2006.vtc-card__logo[data-needs-invert="true"] {
2007  filter: brightness(0) invert(1);
2008}
2009
2010.vtc-card__body {
2011  position: absolute;
2012  left: 0;
2013  right: 0;
2014  bottom: 0;
2015  padding: 22px;
2016  z-index: 3;
2017  display: flex;
2018  flex-direction: column;
2019  gap: 10px;
2020}
2021
2022.vtc-card__quote {
2023  margin: 0;
2024  font-size: 1.02rem;
2025  line-height: 1.35;
2026  font-weight: 500;
2027  color: #fff;
2028  display: -webkit-box;
2029  -webkit-line-clamp: 3;
2030  -webkit-box-orient: vertical;
2031  overflow: hidden;
2032}
2033
2034.vtc-card__person {
2035  display: flex;
2036  flex-direction: column;
2037  gap: 2px;
2038}
2039
2040.vtc-card__person-name {
2041  font-size: 0.92rem;
2042  font-weight: 600;
2043  color: #fff;
2044}
2045
2046.vtc-card__person-title {
2047  font-size: 0.8rem;
2048  color: ${jde};
2049}
2050
2051.vtc-card__play {
2052  position: absolute;
2053  inset: 0;
2054  display: flex;
2055  align-items: center;
2056  justify-content: center;
2057  z-index: 4;
2058  opacity: 0;
2059  transition: opacity 220ms ease;
2060  pointer-events: none;
2061}
2062.vtc-card:hover .vtc-card__play,
2063.vtc-card:focus-visible .vtc-card__play {
2064  opacity: 1;
2065}
2066
2067.vtc-card__play-pill {
2068  display: inline-flex;
2069  align-items: center;
2070  gap: 10px;
2071  background: ${S0};
2072  color: #fff;
2073  padding: 12px 22px;
2074  border-radius: 999px;
2075  font-size: 0.85rem;
2076  font-weight: 700;
2077  letter-spacing: 0.12em;
2078  text-transform: uppercase;
2079  box-shadow: 0 10px 30px rgba(0,0,0,0.45);
2080  transform: scale(0.94);
2081  transition: transform 220ms cubic-bezier(0.22,1,0.36,1), background 160ms ease;
2082}
2083.vtc-card:hover .vtc-card__play-pill { transform: scale(1); background: ${kde}; }
2084
2085/* Hero variant: one flagship card split into image + dedicated quote panel.
2086 * Used when we only have a single video so the section doesn't read as a
2087 * lonely card adrift in a wide frame. */
2088.vtc-card--hero {
2089  flex: 0 0 auto;
2090  width: min(960px, 100%);
2091  height: 480px;
2092  display: grid;
2093  grid-template-columns: 1.15fr 1fr;
2094}
2095.vtc-card--hero .vtc-card__media {
2096  position: relative;
2097  overflow: hidden;
2098}
2099.vtc-card--hero .vtc-card__bg,
2100.vtc-card--hero .vtc-card__veil,
2101.vtc-card--hero .vtc-card__logo,
2102.vtc-card--hero .vtc-card__play {
2103  /* The hero's image-side overlays live inside .vtc-card__media, not the
2104   * whole card, so they stay confined to the left column. */
2105}
2106.vtc-card--hero .vtc-card__veil {
2107  background: linear-gradient(
2108    180deg,
2109    rgba(12,30,32,0.0) 0%,
2110    rgba(12,30,32,0.0) 55%,
2111    rgba(12,30,32,0.55) 100%
2112  );
2113}
2114.vtc-card--hero .vtc-card__body {
2115  position: relative;
2116  inset: auto;
2117  padding: 36px 36px 32px;
2118  background: #161b1c;
2119  justify-content: center;
2120  gap: 18px;
2121}
2122.vtc-card--hero .vtc-card__quote {
2123  font-size: 1.25rem;
2124  line-height: 1.45;
2125  -webkit-line-clamp: 6;
2126}
2127.vtc-card--hero .vtc-card__person-name { font-size: 1rem; }
2128.vtc-card--hero .vtc-card__person-title { font-size: 0.86rem; }
2129
2130.vtc-arrow {
2131  display: inline-flex;
2132  align-items: center;
2133  justify-content: center;
2134  width: 42px;
2135  height: 42px;
2136  border-radius: 999px;
2137  background: #ffffff;
2138  border: 1px solid rgba(27,31,28,0.12);
2139  color: #1b1f1c;
2140  cursor: pointer;
2141  transition: background 160ms ease, border-color 160ms ease, transform 160ms ease, opacity 160ms ease;
2142  box-shadow: 0 1px 2px rgba(27,31,28,0.04);
2143}
2144.vtc-arrow:hover { background: #fafafa; border-color: rgba(27,31,28,0.22); }
2145.vtc-arrow:disabled { opacity: 0.4; cursor: default; }
2146.vtc-arrow:focus-visible { outline: 2px solid ${S0}; outline-offset: 3px; }
2147
2148@media (max-width: 720px) {
2149  .vtc-arrows { display: none !important; }

2150  /* On mobile, every layout collapses to a single horizontally-scrollable column. */
2151  .vtc-grid {
2152    display: flex;
2153    gap: ${A0}px;
2154    overflow-x: auto;
2155    scroll-snap-type: x mandatory;
2156    scroll-behavior: smooth;
2157    scrollbar-width: none;
2158  }
2159  .vtc-grid::-webkit-scrollbar { display: none; }
2160  .vtc-grid .vtc-card {
2161    flex: 0 0 ${C0}px;
2162    width: ${C0}px;
2163    scroll-snap-align: start;
2164  }
2165  .vtc-card--hero {
2166    grid-template-columns: 1fr;
2167    height: auto;
2168    width: 100%;
2169  }
2170  .vtc-card--hero .vtc-card__media { height: 280px; }
2171}
2172`;function Tde(){if(typeof document>"u")return;let e=document.getElementById(CP);e||(e=document.createElement("style"),e.id=CP,document.head.appendChild(e)),e.textContent!==AP&&(e.textContent=AP)}const _de=()=>n.jsx("svg",{width:"18",height:"18",viewBox:"0 0 24 24",fill:"none","aria-hidden":!0,children:n.jsx("path",{d:"M15 6L9 12L15 18",stroke:"currentColor",strokeWidth:"2",strokeLinecap:"round",strokeLinejoin:"round"})}),Mde=()=>n.jsx("svg",{width:"18",height:"18",viewBox:"0 0 24 24",fill:"none","aria-hidden":!0,children:n.jsx("path",{d:"M9 6L15 12L9 18",stroke:"currentColor",strokeWidth:"2",strokeLinecap:"round",strokeLinejoin:"round"})}),Xj=({caseStudy:e,onClick:t,variant:a="standard"})=>{const i=e.videoPresenter,r=(i==null?void 0:i.name)??e.featuredName,o=(i==null?void 0:i.title)??e.featuredTitle,l=(i==null?void 0:i.image)??e.featuredImage,d=(i==null?void 0:i.quoteId)??e.quoteId,u=d?DM[d]:void 0,h=RG(e.slug),m=wde(e.slug),f=m??e.companyLogo,y=h?Math.max(24,h.height):24,b=18-Math.max(0,(y-24)/2),x=n.jsx("div",{className:"vtc-card__bg",style:{backgroundImage:`url("${l}")`}}),k=n.jsx("div",{className:"vtc-card__veil"}),v=n.jsx("img",{src:f,alt:"",loading:"lazy",className:"vtc-card__logo","data-needs-invert":m?void 0:"true",style:{height:y,top:b}}),j=n.jsx("div",{className:"vtc-card__play",children:n.jsxs("span",{className:"vtc-card__play-pill",children:[n.jsx("svg",{width:"12",height:"12",viewBox:"0 0 12 12",fill:"none","aria-hidden":!0,children:n.jsx("path",{d:"M2.5 1.5L10 6L2.5 10.5V1.5Z",fill:"currentColor"})}),"Play"]})});return a==="hero"?n.jsxs("button",{type:"button",className:"vtc-card vtc-card--hero",onClick:t,"aria-label":`Play video: ${e.videoTitle??e.title}`,children:[n.jsxs("div",{className:"vtc-card__media",children:[x,k,v,j]}),n.jsxs("div",{className:"vtc-card__body",children:[u&&n.jsxs("p",{className:"vtc-card__quote",children:["“",u.quote,"”"]}),n.jsxs("div",{className:"vtc-card__person",children:[n.jsx("span",{className:"vtc-card__person-name",children:r}),n.jsx("span",{className:"vtc-card__person-title",children:o})]})]})]}):n.jsxs("button",{type:"button",className:"vtc-card",onClick:t,"aria-label":`Play video: ${e.videoTitle??e.title}`,children:[x,k,v,j,n.jsxs("div",{className:"vtc-card__body",children:[u&&n.jsxs("p",{className:"vtc-card__quote",children:["“",u.quote,"”"]}),n.jsxs("div",{className:"vtc-card__person",children:[n.jsx("span",{className:"vtc-card__person-name",children:r}),n.jsx("span",{className:"vtc-card__person-title",children:o})]})]})]})},Ede=({items:e,onOpen:t})=>{const a=M.useRef(null),[i,r]=M.useState(!1),[o,l]=M.useState(!1),d=e.length===1?"hero":e.length<Ade?"grid":"carousel";if(M.useEffect(()=>{Tde()},[]),M.useEffect(()=>{if(d!=="carousel")return;const m=a.current;if(!m)return;const f=()=>{r(m.scrollLeft>4),l(m.scrollLeft+m.clientWidth<m.scrollWidth-4)};f(),m.addEventListener("scroll",f,{passive:!0});const y=new ResizeObserver(f);return y.observe(m),()=>{m.removeEventListener("scroll",f),y.disconnect()}},[e.length,d]),e.length===0)return null;const u=m=>{const f=a.current;f&&f.scrollBy({left:m*(C0+A0),behavior:"smooth"})},h=d==="carousel";return n.jsx("section",{style:{padding:"4rem 1.5rem 4rem"},children:n.jsxs("div",{style:{maxWidth:1240,margin:"0 auto"},children:[n.jsxs("div",{style:{display:"flex",alignItems:"flex-end",justifyContent:"space-between",marginBottom:"2.25rem",gap:16},children:[n.jsxs("header",{style:{maxWidth:760},children:[n.jsx("p",{style:{margin:"0 0 0.75rem",fontSize:"0.78rem",fontWeight:700,letterSpacing:"0.14em",textTransform:"uppercase",color:S0},children:"Watch"}),n.jsx("h2",{style:{margin:0,color:"#1b1f1c",fontSize:"clamp(1.8rem, 3vw, 2.6rem)",fontWeight:600,letterSpacing:"-0.02em",lineHeight:1.15},children:"Hear it straight from the teams."}),n.jsx("p",{style:{margin:"0.75rem 0 0",color:"#5f6563",fontSize:"1.05rem",lineHeight:1.45},children:"Short videos from customers on how they leverage Upside."})]}),h&&n.jsxs("div",{className:"vtc-arrows",style:{display:"flex",gap:8},children:[n.jsx("button",{type:"button",className:"vtc-arrow",onClick:()=>u(-1),disabled:!i,"aria-label":"Scroll testimonials left",children:n.jsx(_de,{})}),n.jsx("button",{type:"button",className:"vtc-arrow",onClick:()=>u(1),disabled:!o,"aria-label":"Scroll testimonials right",children:n.jsx(Mde,{})})]})]}),d==="hero"&&n.jsx("div",{className:"vtc-hero-wrap",children:n.jsx(Xj,{caseStudy:e[0],onClick:()=>t(0),variant:"hero"})}),d==="grid"&&n.jsx("div",{className:"vtc-grid",style:{"--vtc-grid-cols":e.length},children:e.map((m,f)=>n.jsx(Xj,{caseStudy:m,onClick:()=>t(f)},m.slug))}

2172),d==="carousel"&&n.jsx("div",{className:"vtc-row",ref:a,children:e.map((m,f)=>n.jsx(Xj,{caseStudy:m,onClick:()=>t(f)},m.slug))})]})})};function Rde(e){var a;const t=e.split("-")[0].toLowerCase();return(a=oa.find(i=>i.name.toLowerCase()===t&&i.src.endsWith(".svg")))==null?void 0:a.src}const zv="#509A79",Nde="rgba(80,154,121,0.55)",Ode="rgba(80,154,121,0.18)",Pde="rgba(255,255,255,0.62)",TP="vlb-styles",Ide=`
2173.vlb-overlay {
2174  position: fixed;
2175  inset: 0;
2176  z-index: 10000;
2177  display: flex;
2178  align-items: center;
2179  justify-content: center;
2180  background: rgba(6, 12, 13, 0.93);
2181  backdrop-filter: blur(6px);
2182  -webkit-backdrop-filter: blur(6px);
2183  animation: vlb-fade-in 200ms ease-out;
2184  padding: 28px 24px;
2185}
2186
2187@keyframes vlb-fade-in {
2188  from { opacity: 0; }
2189  to { opacity: 1; }
2190}
2191@keyframes vlb-content-in {
2192  from { opacity: 0; transform: scale(0.985) translateY(8px); }
2193  to { opacity: 1; transform: scale(1) translateY(0); }
2194}
2195
2196.vlb-content {
2197  width: 100%;
2198  max-width: 1180px;
2199  display: grid;
2200  grid-template-columns: minmax(0, 1.85fr) minmax(0, 1fr);
2201  gap: 18px;
2202  position: relative;
2203  animation: vlb-content-in 240ms cubic-bezier(0.22,1,0.36,1);
2204}
2205
2206.vlb-close {
2207  position: absolute;
2208  top: -42px;
2209  right: 0;
2210  background: rgba(255,255,255,0.08);
2211  border: 1px solid rgba(255,255,255,0.18);
2212  color: #fff;
2213  width: 36px;
2214  height: 36px;
2215  border-radius: 999px;
2216  font-size: 18px;
2217  line-height: 1;
2218  cursor: pointer;
2219  display: inline-flex;
2220  align-items: center;
2221  justify-content: center;
2222  transition: background 160ms ease, border-color 160ms ease;
2223}
2224.vlb-close:hover { background: rgba(255,255,255,0.16); border-color: rgba(255,255,255,0.32); }
2225.vlb-close:focus-visible { outline: 2px solid ${zv}; outline-offset: 3px; }
2226
2227.vlb-title {
2228  position: absolute;
2229  top: -42px;
2230  left: 0;
2231  display: flex;
2232  align-items: center;
2233  gap: 12px;
2234  color: rgba(255,255,255,0.85);
2235  font-size: 0.92rem;
2236  font-weight: 500;
2237  max-width: calc(100% - 60px);
2238  min-width: 0;
2239}
2240.vlb-title__logo {
2241  height: 22px;
2242  max-width: 110px;
2243  opacity: 0.85;
2244  object-fit: contain;
2245  flex: 0 0 auto;
2246}
2247.vlb-title__logo[data-needs-invert="true"] { filter: brightness(0) invert(1); }
2248.vlb-title__text {
2249  white-space: nowrap;
2250  overflow: hidden;
2251  text-overflow: ellipsis;
2252  min-width: 0;
2253}
2254
2255.vlb-player {
2256  position: relative;
2257  aspect-ratio: 16 / 9;
2258  background: #000;
2259  border-radius: 14px;
2260  overflow: hidden;
2261}
2262.vlb-player iframe { position: absolute; inset: 0; width: 100%; height: 100%; border: 0; }
2263
2264.vlb-rail {
2265  background: rgba(255,255,255,0.04);
2266  border: 1px solid rgba(255,255,255,0.10);
2267  border-radius: 14px;
2268  display: flex;
2269  flex-direction: column;
2270  min-height: 0;
2271}
2272.vlb-rail__header {
2273  display: flex;
2274  align-items: center;
2275  justify-content: space-between;
2276  padding: 16px 18px 8px;
2277  font-size: 0.72rem;
2278  font-weight: 700;
2279  letter-spacing: 0.14em;
2280  text-transform: uppercase;
2281  color: rgba(255,255,255,0.55);
2282}
2283.vlb-rail__list {
2284  display: flex;
2285  flex-direction: column;
2286  padding: 6px 8px 10px;
2287  gap: 4px;
2288  overflow-y: auto;
2289  flex: 1;
2290}
2291
2292.vlb-moment {
2293  display: flex;
2294  gap: 12px;
2295  align-items: flex-start;
2296  width: 100%;
2297  text-align: left;
2298  background: transparent;
2299  border: 0;
2300  border-left: 2px solid transparent;
2301  padding: 11px 12px;
2302  border-radius: 10px;
2303  cursor: pointer;
2304  font: inherit;
2305  color: #fff;
2306  transition: background 160ms ease, border-color 160ms ease;
2307}
2308.vlb-moment:hover {
2309  background: rgba(255,255,255,0.07);
2310}
2311.vlb-moment:focus-visible { outline: 2px solid ${zv}; outline-offset: 2px; }
2312.vlb-moment[data-active="true"] {
2313  background: ${Ode};
2314  border-left-color: ${Nde};
2315}
2316.vlb-moment[data-active="true"] .vlb-moment__time {
2317  background: ${zv};
2318  color: #fff;
2319}
2320.vlb-moment__time {
2321  flex: 0 0 auto;
2322  background: rgba(255,255,255,0.08);
2323  color: #fff;
2324  font-variant-numeric: tabular-nums;
2325  font-size: 0.74rem;
2326  font-weight: 700;
2327  padding: 4px 8px;
2328  border-radius: 6px;

2329  margin-top: 1px;
2330  transition: background 160ms ease, color 160ms ease;
2331}
2332.vlb-moment__label {
2333  font-size: 0.92rem;
2334  line-height: 1.4;
2335}
2336
2337.vlb-footer {
2338  grid-column: 1 / -1;
2339  display: flex;
2340  align-items: center;
2341  justify-content: flex-end;
2342  gap: 12px;
2343  margin-top: 6px;
2344  color: ${Pde};
2345  font-size: 0.85rem;
2346}
2347.vlb-footer__more {
2348  display: inline-flex;
2349  align-items: center;
2350  gap: 8px;
2351  background: transparent;
2352  border: 1px solid rgba(255,255,255,0.18);
2353  color: #fff;
2354  padding: 8px 14px;
2355  border-radius: 999px;
2356  font: inherit;
2357  font-weight: 600;
2358  cursor: pointer;
2359  transition: background 160ms ease, border-color 160ms ease;
2360}
2361.vlb-footer__more:hover { background: rgba(255,255,255,0.08); border-color: rgba(255,255,255,0.32); }
2362.vlb-footer__more:focus-visible { outline: 2px solid ${zv}; outline-offset: 3px; }
2363
2364@media (max-width: 900px) {
2365  .vlb-content {
2366    grid-template-columns: 1fr;
2367    max-width: 720px;
2368  }
2369  .vlb-rail { max-height: 320px; }
2370  .vlb-title { font-size: 0.84rem; }
2371}
2372`;function Dde(){if(typeof document>"u"||document.getElementById(TP))return;const e=document.createElement("style");e.id=TP,e.textContent=Ide,document.head.appendChild(e)}function Wde(e){const t=Math.floor(e/60),a=Math.floor(e%60);return`${t}:${a.toString().padStart(2,"0")}`}function Qj(e,t,a=[]){!e||!e.contentWindow||e.contentWindow.postMessage(JSON.stringify({event:"command",func:t,args:a}),"*")}const zde=({items:e,index:t,onIndexChange:a,onClose:i})=>{const r=M.useRef(null),o=M.useRef(null),[l,d]=M.useState(0),[u,h]=M.useState(!1),m=e[t];if(M.useEffect(()=>{Dde(),h(!0)},[]),M.useEffect(()=>{const j=document.body.style.overflow;document.body.style.overflow="hidden";const T=A=>{A.key==="Escape"&&i(),A.key==="ArrowRight"&&e.length>1&&a((t+1)%e.length),A.key==="ArrowLeft"&&e.length>1&&a((t-1+e.length)%e.length)};return window.addEventListener("keydown",T),()=>{document.body.style.overflow=j,window.removeEventListener("keydown",T)}},[t,e.length,i,a]),M.useEffect(()=>{if(!(m!=null&&m.youtubeEmbedId))return;d(0);const j=window.setTimeout(()=>{const A=r.current;A!=null&&A.contentWindow&&A.contentWindow.postMessage(JSON.stringify({event:"listening",id:1,channel:"widget"}),"*")},600),T=A=>{var C;if(typeof A.data=="string"&&!(A.origin!=="https://www.youtube.com"&&A.origin!=="https://www.youtube-nocookie.com"))try{const _=JSON.parse(A.data);_.event==="infoDelivery"&&typeof((C=_==null?void 0:_.info)==null?void 0:C.currentTime)=="number"&&d(_.info.currentTime)}catch{}};return window.addEventListener("message",T),o.current=window.setInterval(()=>{Qj(r.current,"getCurrentTime")},800),()=>{window.clearTimeout(j),o.current&&window.clearInterval(o.current),window.removeEventListener("message",T)}},[m==null?void 0:m.youtubeEmbedId]),!u||!m)return null;const f=j=>{Qj(r.current,"seekTo",[j.timestamp,!0]),Qj(r.current,"playVideo")},y=()=>{e.length<=1||a((t+1)%e.length)},b=m.videoSnippets??[];let x=-1;for(let j=0;j<b.length;j+=1)b[j].timestamp<=l+.25&&(x=j);const k=`https://www.youtube.com/embed/${m.youtubeEmbedId}?enablejsapi=1&rel=0&modestbranding=1&playsinline=1&autoplay=1`,v=n.jsx("div",{className:"vlb-overlay",onClick:i,role:"dialog","aria-modal":"true","aria-label":m.videoTitle??m.title,children:n.jsxs("div",{className:"vlb-content",onClick:j=>j.stopPropagation(),children:[n.jsxs("div",{className:"vlb-title",children:[(()=>{const j=Rde(m.slug);return n.jsx("img",{src:j??m.companyLogo,alt:"",className:"vlb-title__logo","data-needs-invert":j?void 0:"true"})})(),n.jsx("span",{className:"vlb-title__text",children:m.videoTitle??m.title})]}),n.jsx("button",{type:"button",className:"vlb-close",onClick:i,"aria-label":"Close video",children:"✕"}),n.jsx("div",{className:"vlb-player",children:n.jsx("iframe",{ref:r,src:k,title:m.videoTitle??m.title,allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowFullScreen:!0},m.youtubeEmbedId)}),n.jsxs("aside",{className:"vlb-rail","aria-label":"Video moments",children:[n.jsxs("div",{className:"vlb-rail__header",children:[n.jsx("span",{children:"Moments"}),n.jsx("span",{style:{color:"rgba(255,255,255,0.4)",fontWeight:600,letterSpacing:0},children:b.length})]}),n.jsx("div",{className:"vlb-rail__list",children:b.map((j,T)=>n.jsxs("button",{type:"button",className:"vlb-moment","data-active":T===x,onClick:()=>f(j),children:[n.jsx("span",{className:"vlb-moment__time",children:Wde(j.timestamp)}),n.jsx("span",{className:"vlb-moment__label",children:j.label})]},`${j.timestamp}-${T}`))})]}),e.length>1&&n.jsxs("div",{className:"vlb-footer",children:[n.jsxs("span",{children:[t+1," / ",e.length]}),n.jsxs("button",{type:"button",className:"vlb-footer__more",onClick:y,children:["More videos",n.jsx("span",{"aria-hidden":!0,

2372children:"→"})]})]})]})});return Fp.createPortal(v,document.body)},Lde="#509A79",_P="cls-styles",MP=`
2373.cls-marquee {
2374  position: relative;
2375  overflow: hidden;
2376  padding: 1.5rem 0;
2377  mask-image: linear-gradient(90deg, transparent 0%, black 6%, black 94%, transparent 100%);
2378  -webkit-mask-image: linear-gradient(90deg, transparent 0%, black 6%, black 94%, transparent 100%);
2379}
2380.cls-track {
2381  display: flex;
2382  width: max-content;
2383  align-items: center;
2384  gap: 4.5rem;
2385  padding-right: 4.5rem;
2386  animation: cls-scroll 38s linear infinite;
2387  will-change: transform;
2388}
2389.cls-marquee:hover .cls-track,
2390.cls-marquee:focus-within .cls-track {
2391  animation-play-state: paused;
2392}
2393@keyframes cls-scroll {
2394  from { transform: translateX(0); }
2395  to { transform: translateX(-50%); }
2396}
2397@media (prefers-reduced-motion: reduce) {
2398  .cls-track { animation: none; }
2399}
2400
2401.cls-item {
2402  display: inline-flex;
2403  flex-direction: column;
2404  align-items: center;
2405  gap: 10px;
2406  flex-shrink: 0;
2407}
2408/* Fixed-height slot so logos with different intrinsic heights (Comply at
2409 * 44px vs the wordmarks at ~22px) don't push their link to a different
2410 * vertical position. With a uniform slot, every "Watch video" / "Read
2411 * case study" sits on the same baseline across the marquee. */
2412.cls-logo-slot {
2413  height: 46px;
2414  display: flex;
2415  align-items: center;
2416  justify-content: center;
2417}
2418.cls-logo {
2419  width: auto;
2420  object-fit: contain;
2421  filter: brightness(0) invert(1);
2422  opacity: 0.85;
2423  transition: opacity 160ms ease;
2424}
2425.cls-item:hover .cls-logo { opacity: 1; }
2426
2427.cls-watch,
2428.cls-readcs {
2429  display: inline-flex;
2430  align-items: center;
2431  gap: 4px;
2432  font-size: 0.72rem;
2433  font-weight: 600;
2434  letter-spacing: 0.04em;
2435  color: rgba(255,255,255,0.75);
2436  background: none;
2437  border: 0;
2438  padding: 0;
2439  cursor: pointer;
2440  text-transform: none;
2441  font-family: inherit;
2442  text-decoration: none;
2443  transition: color 160ms ease;
2444}
2445.cls-watch:hover,
2446.cls-readcs:hover { color: #ffffff; }
2447.cls-watch:focus-visible,
2448.cls-readcs:focus-visible { outline: 2px solid ${Lde}; outline-offset: 3px; border-radius: 2px; }
2449.cls-watch__arrow,
2450.cls-readcs__arrow { display: inline-block; transition: transform 160ms ease; }
2451.cls-watch:hover .cls-watch__arrow,
2452.cls-readcs:hover .cls-readcs__arrow { transform: translateX(2px); }
2453
2454/* Reserve vertical space for the link slots so every item lines up
2455 * regardless of whether it has a video, a case study, both, or neither. */
2456.cls-link-slot { min-height: 14px; display: block; }
2457`;function Ude(){if(typeof document>"u")return;let e=document.getElementById(_P);e||(e=document.createElement("style"),e.id=_P,document.head.appendChild(e)),e.textContent!==MP&&(e.textContent=MP)}const Bde=({videoActions:e={},caseStudyHrefs:t={}})=>{M.useEffect(()=>{Ude()},[]);const a=[...oa,...oa];return n.jsx("section",{style:{padding:"3rem 1.5rem 3.25rem",background:"#2d6a4f"},"aria-label":"Customer logos",children:n.jsxs("div",{style:{maxWidth:1240,margin:"0 auto"},children:[n.jsx("p",{style:{margin:"0 0 1.25rem",fontSize:"0.78rem",fontWeight:700,letterSpacing:"0.14em",textTransform:"uppercase",color:"rgba(255,255,255,0.7)"},children:"Trusted by the best"}),n.jsx("div",{className:"cls-marquee",children:n.jsx("div",{className:"cls-track",children:a.map((i,r)=>{const o=r>=oa.length,l=e[i.name],d=t[i.name];let u;return l?u=n.jsxs("button",{type:"button",className:"cls-watch",onClick:l,"aria-label":`Watch ${i.name} video`,"aria-hidden":o||void 0,tabIndex:o?-1:0,children:["Watch video",n.jsx("span",{className:"cls-watch__arrow","aria-hidden":!0,children:"→"})]}):d?u=n.jsxs(J,{to:d,className:"cls-readcs","aria-label":`Read ${i.name} case study`,"aria-hidden":o||void 0,tabIndex:o?-1:0,children:["Read case study",n.jsx("span",{className:"cls-readcs__arrow","aria-hidden":!0,children:"→"})]}):u=n.jsx("span",{className:"cls-link-slot","aria-hidden":!0}),n.jsxs("div",{className:"cls-item",children:[n.jsx("div",{className:"cls-logo-slot",children:n.jsx("img",{src:i.src,alt:o?"":`${i.name} is an Upside Customer`,"aria-hidden":o||void 0,loading:"lazy",className:"cls-logo",style:{height:i.height}})}),u]},`${i.name}-${r}`)})})})]})})};function Zu(e,t){var a;return((a=oa.find(i=>i.name.toLowerCase()===e))==null?void 0:a.src)??t}const EP="csg-styles",RP=`
2458/* Design-system tokens used by the Ledger grid (from tokens.css). Scoped
2459 * to the section so they can't leak into the rest of the page. */
2460.d1-section {
2461  --signal-300: #8DBBA2;
2462  --signal-500: #509A79;
2463  --signal-600: #3F8265;
2464  --signal-700: #316851;
2465  --ledger-800: #15292C;
2466  --ledger-900: #0C1E20;
2467  /* Site card white — cells read as the same stock as every other card. */
2468  --surface-0: #FFFFFF;
2469  --surface-100: #EDF0F4;
2470  --surface-white: #fafafa;
2471  --fg-1: #0C1E20;
2472  --fg-2: #1E3539;
2473  --fg-3: #3E5C62;
2474  --fg-4: #5A777D;
2475  --border-2: rgba(12, 30, 32, 0.14);
2476  --font-primary: 'Geist', system-ui, sans-serif;
2477  --font-secondary: 'Geist', system-ui, -apple-system, 'Segoe UI', sans-serif;
2478  --font-mono: ui-monospace, 'SF Mono', 'JetBrains Mono', Menlo, monospace;
2479  --radius-pill: 999px;
2480  --ease-standard: cubic-bezier(0.2, 0, 0, 1);
2481  --dur-base: 200ms;
2482  --dur-slow: 320ms;
2483}
2484
2485/* Flat site canvas — the same surface the case-study detail pages put
2486 * their cards on, so the section reads as part of the site rather than
2487 * a separate print artifact. */
2488.d1-section {
2489  background: #fafafa;
2490  padding: 100px 40px 108px;
2491  color: var(--fg-1);
2492  font-family: var(--font-secondary);
2493  position: relative;
2494}
2495.d1-wrap { max-width: 1240px; margin: 0 auto; }
2496
2497/* ---------- Header ---------- */
2498.d1-eyebrow {
2499  display: inline-flex; align-items: center; gap: 11px;
2500  font-size: 12px; font-weight: 600; letter-spacing: 0.16em;
2501  text-transform: uppercase; color: var(--signal-700);
2502  margin: 0 0 22px; white-space: nowrap;
2503}
2504.d1-eyebrow::before {
2505  content: ""; width: 24px; height: 1px; background: var(--signal-500);
2506}
2507.d1-head {
2508  display: grid; grid-template-columns: 1.4fr 1fr; gap: 48px;
2509  align-items: end; margin-bottom: 46px;
2510}
2511.d1-title {
2512  margin: 0; font-family: var(--font-primary); font-weight: 600;
2513  font-size: clamp(30px, 3.4vw, 47px); line-height: 1.06;
2514  letter-spacing: -0.026em; color: var(--ledger-900); text-wrap: balance;
2515}
2516.d1-sub {
2517  margin: 0; font-size: 16.5px; line-height: 1.58; color: var(--fg-3);
2518  padding-bottom: 5px; text-wrap: pretty;
2519}
2520/* ---------- Grid — hairline seams revealed through a 1px gap ---------- */
2521.d1-grid {
2522  display: grid;
2523  grid-template-columns: repeat(3, 1fr);
2524  grid-auto-rows: minmax(200px, auto);
2525  gap: 1px;
2526  background: var(--border-2);
2527  border: 1px solid var(--border-2);
2528  /* Container chrome matches the rest of the site: the global corner
2529   * token (32px desktop, scaling down with the site's breakpoints) and
2530   * the soft ambient card shadow used on light containers. */
2531  border-radius: var(--_corner-radii---l, 32px);
2532  overflow: hidden;
2533  position: relative;
2534  box-shadow: 0 4px 24px #96a3b514;
2535  /* inner radius for the four corner cells: container radius minu