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After splitting into consistent campaigns, the budget and ROAS target can be better managed. You focus on <b>performing products and reduce losses</b> on non-performing products. 331 </p> 332 </div> 333 </div> 334 <div class="col-12"> 335 <div class="karsa-steps"> 336 <div class="karsa-step step1"> 337 <p class="step-count">1</p> 338 <h4>Cluster Designer </h4> 339 <p> 340 It easily helps to set criteria for creating a Google shopping campaign structure according to several parameters. ML algorithm searches for the <b>optimal number</b> of campaigns for your e-store. 341 </p> 342 </div> 343 <div class="karsa-step step2"> 344 <p class="step-count">2</p> 345 <h4>Analyses and reports</h4> 346 <p> 347 It is possible to compare segmentation proposals, analyze the product distribution and choose the one that best suits your project. 348 </p> 349 </div> 350 <div class="karsa-step step3"> 351 <p class="step-count">3</p> 352 <h4>Operation automation </h4> 353 <p> 354 Regular automatic download of all product data from Google Ads with history, export custom label to GMC, <b>product movement</b> between campaigns. 355 </p> 356 </div> 357 </div> 358 </div> 359 </div> 360 </div> 361 </section> 362 <!-- End Section How Karsa Works --> 363 364 <!-- Section Image content --> 365 <section class="img-content-sec"> 366 <div class="container"> 367 <div class="row align-items-center"> 368 <div class="col-12 col-md-6"> 369 <div class="sec-content"> 370 <p class="sec-sub-title"> 371 Proper segmentation as the basis for success 372 </p> 373 <h2 class="sec-title"> 374 Help the Google to achieve better results 375 </h2> 376 <div class="list-2col"> 377 378 <ul class="list-style-img"> 379 <li> 380 Each campaign is built to <b>meet a minimum number</b> of conversions so that Google can successfully optimize it. 381 </li> 382 <li> 383 Create any number of campaigns or let the <b>ML model select the optimal number</b> for your data structure. Don't be fixed by 4 or 6 predefined campaign divisions. 384 </li> 385 <li> 386 Do not use only one parameter for distribution (ROAS), but more than 2 and even 5 parameters to find the <b>ideal campaign predictability.</b> 387 </li> 388 <li> 389 <b>Evaluate the consistency</b> of campaigns over time and compare it to their performance 390 </li> 391 </ul> 392 </div> 393 </div> 394 </div> 395 <div class="col-12 col-md-6"> 396 <div id="container2" class="karsa-img-wrapper"> 397 398 <!-- 399 <img src="img/ilustrace-analyzy-zaklad-cenotvorby.png"> 400 --> 401 </div> 402 </div> 403 404 </div> 405 <!--<div class="row align-items-center">--> 406 <div class="row align-items-center flex-row-reverse"> 407 408 <div class="col-12 col-md-6"> 409 <div class="sec-content"> 410 <p class="sec-sub-title"> 411 Long-term optimization brings success 412 </p> 413 <h2 class="sec-title"> 414 Production campaign optimization 415 </h2> 416 <div class="list-2col"> 417 <ul class="list-style-img"> 418 <li> 419 Products automatically migrate between campaigns according to their <b>performance.</b> This improves campaign performance. 420 </li> 421 <li> 422 Product moves can be <b>limited</b> so that the campaign doesn't lose too much product or a certain % conversion value. 423 </li> 424 <li> 425 There is a detailed report of products that have been moved or are waiting to be moved. 426 </li> 427 <li> 428 For new products <b>without history</b>, it is possible to derive their potential performance from related products. This means that the products will not be left in a non-performing cluster. 429 </li> 430 </ul> 431 </div> 432 </div> 433 </div> 434 <div class="col-12 col-md-6"> 435 436 <div id="container3" class="karsa-img-wrapper"> 437 </div> 438 </div> 439 </div> 440 </div> 441 </section> 442 <!-- End Section Image Content --> 443 <!-- Section CTA --> 444 <section class="cta-sec"> 445 <div class="container"> 446 <div class="row"> 447 <div class="col-12"> 448 <div class="sec-content"> 449 <h2 class="sec-title"> 450 Karsa labelizer, when campaigns work 451 </h2> 452 <div class="btn-wrapper"> 453 <a href="/freetrial" class="theme-btn">Free trial</a> 454 <a href="/reservation" class="theme-btn theme-btn-alt">Online presentation</a> 455 </div> 456 <p class="trial-text"> 457 <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewB
457ox="0 0 16 16"> 458 <path id="ico-info" d="M87.2-868h1.6v-4.8H87.2Zm.8-6.4a.774.774,0,0,0,.57-.23.774.774,0,0,0,.23-.57.774.774,0,0,0-.23-.57A.774.774,0,0,0,88-876a.774.774,0,0,0-.57.23.774.774,0,0,0-.23.57.774.774,0,0,0,.23.57A.774.774,0,0,0,88-874.4ZM88-864a7.79,7.79,0,0,1-3.12-.63,8.078,8.078,0,0,1-2.54-1.71,8.078,8.078,0,0,1-1.71-2.54A7.79,7.79,0,0,1,80-872a7.79,7.79,0,0,1,.63-3.12,8.079,8.079,0,0,1,1.71-2.54,8.078,8.078,0,0,1,2.54-1.71A7.79,7.79,0,0,1,88-880a7.79,7.79,0,0,1,3.12.63,8.078,8.078,0,0,1,2.54,1.71,8.079,8.079,0,0,1,1.71,2.54A7.79,7.79,0,0,1,96-872a7.79,7.79,0,0,1-.63,3.12,8.078,8.078,0,0,1-1.71,2.54,8.078,8.078,0,0,1-2.54,1.71A7.79,7.79,0,0,1,88-864Zm0-1.6a6.177,6.177,0,0,0,4.54-1.86A6.177,6.177,0,0,0,94.4-872a6.177,6.177,0,0,0-1.86-4.54A6.177,6.177,0,0,0,88-878.4a6.177,6.177,0,0,0-4.54,1.86A6.177,6.177,0,0,0,81.6-872a6.177,6.177,0,0,0,1.86,4.54A6.177,6.177,0,0,0,88-865.6ZM88-872Z" transform="translate(-80 880)" fill="#9599a7"/> 459 </svg> No credit card required 460 </p> 461 </div> 462 </div> 463 </div> 464 </div> 465 </section> 466 <!-- End Section CTA --> 467 468 <!-- Chart bubliny --> 469 470 471
471<script> 472 Highcharts.chart('container', { 473 chart: { 474 type: 'packedbubble', 475 height: '90%' 476 }, 477 exporting: { 478 enabled: false 479 }, 480 legend: { 481 enabled: false 482 }, 483 credits: { 484 enabled: false 485 }, 486 title: { 487 text: '', 488 align: 'left' 489 }, 490 subtitle: { 491 text: '', 492 align: 'left' 493 }, 494 tooltip: { 495 enabled: false 496 }, 497 plotOptions: { 498 packedbubble: { 499 minSize: '20%', 500 maxSize: '100%', 501 zMin: 0, 502 zMax: 1000, 503 layoutAlgorithm: { 504 gravitationalConstant: 0.05, 505 splitSeries: true, 506 seriesInteraction: false, 507 dragBetweenSeries: true, 508 parentNodeLimit: true 509 }, 510 dataLabels: { 511 enabled: true, 512 format: '{point.name}', 513 filter: { 514 property: 'y', 515 operator: '>', 516 value: 250 517 }, 518 style: { 519 color: 'black', 520 textOutline: 'none', 521 fontWeight: 'normal' 522 } 523 } 524 } 525 }, 526 ////////////////////////////// 527 series: [{ 528 name: 'Cluster1', 529 data: [{ 530 name: 'ROAS', 531 value: 673.6 532 }, 533 { 534 name: 'Cost', 535 value: 322.0 536 }, 537 { 538 name: 'Conv. value', 539 value: 322.9 540 }, 541 { 542 name: 'Conversions', 543 value: 254.4 544 }, 545 { 546 name: 'CTR', 547 value: 481.2 548 }, 549 { 550 name: 'CPC', 551 value: 340.6 552 }, 553 { 554 name: 'Clicks', 555 value: 315.5 556 }, 557 //////////////////// 558 { 559 name: 'Croatia', 560 value: 17.2 561 }, 562 { 563 name: 'Belgium', 564 value: 90.4 565 }, 566 { 567 name: 'Czech Republic', 568 value: 111.5 569 }, 570 { 571 name: 'Netherlands', 572 value: 134.7 573 }, 574 { 575 name: 'Ukraine', 576 value: 132.5 577 }, 578 { 579 name: 'Romania', 580 value: 77.3 581 }, 582 { 583 name: 'Greece', 584 value: 56.8 585 }, 586 { 587 name: 'Austria', 588 value: 61.2 589 }, 590 { 591 name: 'Belarus', 592 value: 57.4 593 }, 594 { 595 name: 'Serbia', 596 value: 56.9 597 }, 598 { 599 name: 'Finland', 600 value: 37.3 601 }, 602 { 603 name: 'Bulgaria', 604 value: 50.1 605 }, 606 { 607 name: 'Portugal', 608 value: 41.3 609 }, 610 { 611 name: 'Norway', 612 value: 42.2 613 }, 614 { 615 name: 'Sweden', 616 value: 37.8 617 }, 618 { 619 name: 'Hungary', 620 value: 47.3 621 }, 622 { 623 name: 'Switzerland', 624 value: 36.1 625 }, 626 { 627 name: 'Denmark', 628 value: 29.2 629 }, 630 { 631 name: 'Slovakia', 632 value: 35.2 633 }, 634 { 635 name: 'Ireland', 636 value: 37.8 637 }, 638 { 639 name: 'Croatia', 640 value: 17.2 641 }, 642 { 643 name: 'Estonia', 644 value: 10.8 645 }, 646 { 647 name: 'Slovenia', 648 value: 13.9 649 }, 650 { 651 name: 'Lithuania', 652 value: 13.3 653 }, 654 { 655 name: 'Luxembourg', 656 value: 7.6 657 }, 658 { 659 name: 'Macedonia', 660 value: 8.3 661 }, 662 { 663 name: 'Moldova', 664 value: 8.7 665 }, 666 { 667 name: 'Latvia', 668 value: 6.7 669 }, 670 { 671 name: 'Cyprus', 672 value: 7.5 673 }] 674 }, 675 ///////////////////////////////////////// 676 { 677 name: 'Cluster2', 678 data: [{ 679 name: 'Senegal', 680 value: 12.1 681 }, 682 { 683 name: 'ROAS', 684 value: 266.0 685 }, 686 { 687 name: 'Conv. value', 688 value: 405.0 689 }, 690 { 691 name: 'Cameroon', 692 value: 10.1 693 }, 694 { 695 name: 'Zimbabwe', 696 value: 10.2 697 }, 698 { 699 name: 'Ghana', 700 value: 24.5 701 }, 702 { 703 name: 'Kenya', 704 value: 21.5 705 }, 706 { 707 name: 'Sudan', 708 value: 24.5 709 }, 710 { 711 name: 'Tunisia', 712 value: 35.9 713 }, 714 { 715 name: 'Angola', 716 value: 20.2 717 }, 718 { 719 name: 'Libya', 720 value: 62.7 721 }, 722 { 723 name: 'Ivory Coast', 724 value: 14.5 725 }, 726 { 727 name: 'Morocco', 728 value: 72.6 729 }, 730 { 731 name: 'Ethiopia', 732 value: 21.1 733 }, 734 { 735 name: 'United Republic of Tanzania', 736 value: 17.0 737 }, 738 { 739 name: 'Nigeria', 740 value: 122.8 741 }, 742 { 743 name: 'Algeria', 744 value: 177.1 745 }] 746 }, 747 ////////////////////////////// 748 { 749 name: 'Cluster3', 750 data: [{ 751 name: 'CTR', 752 value: 393.2 753 }, 754 { 755 name: 'New Zealand', 756 value: 32.4 757 }, 758 { 759 name: 'Papua New Guinea', 760 value: 4.7 761 }] 762 }, { 763 name: 'Cluster4', 764 data: [{ 765 name: 'Costa Rica', 766 value: 8.6 767 }, 768 { 769 name: 'Honduras', 770 value: 10.6 771 }, 772 { 773 name: 'Jamaica', 774 value: 6.1 775 }, 776 { 777 name: 'Panama', 778 value: 11.4 779 }, 780 { 781 name: 'Guatemala', 782 value: 20.1 783 }, 784 { 785 name: 'Dominican Republic', 786 value: 23.5 787 }, 788 { 789 name: 'Cuba', 790 value: 24.8 791 }, 792 { 793 name: 'Conv. value', 794 value: 4853.8 795 }, { 796 name: 'ROAS', 797 value: 582.1 798 }, { 799 name: 'Cost',
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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.