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288                            <p class="sec-sub-title">
289                              Multi-dimensional Google shopping labelizer
290                            </p>
291                            <h1 class="sec-title">
292                             Increase the performance of Google Shopping campaigns thanks to AI segmentation 
293                            </h1>
294                            <p class="sec-desc">
295                                Dividing the product into consistent Google PLA campaigns, for which Google <b>can easily predict</b> ROAS, brings up to <b>25% increase</b> in overall campaign performance.
296                            </p>
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320    <!-- Section How Karsa Works -->
321    <section class="how-karsa-works-sec">
322        <div class="container">
323            <div class="row">
324                <div class="col-12">
325                    <div class="sec-content ">
326                        <h2 class="sec-title text-center">
327                            Why better product clustering delivers better Google PLA campaign results
328                        </h2>
329                        <p class="sec-desc text-center">
330                            If products are inconsistently placed in campaigns Google cannot optimally predict what conversion value to expect. 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>
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434                <div class="col-12 col-md-6">
435
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440        </div>
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449                        <h2 class="sec-title">
450                            Karsa labelizer, when campaigns work
451                        </h2>
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453                                <a href="/freetrial" class="theme-btn">Free trial</a>
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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', {
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476    },
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488        align: 'left'
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526    //////////////////////////////
527    series: [{
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534            name: 'Cost',
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536        },
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540        },
541        {
542            name: 'Conversions',
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549        {
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552        }, 
553        {
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556        },
557		////////////////////
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561        },
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669        },
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672            value: 7.5
673        }]
674    }, 
675	/////////////////////////////////////////
676	{
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680            value: 12.1
681        },
682		{
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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
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700            value: 24.5
701        },
702        {
703            name: 'Kenya',
704            value: 21.5
705        },
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708            value: 24.5
709        },
710        {
711            name: 'Tunisia',
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716            value: 20.2
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718        {
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720            value: 62.7
721        },
722        {
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725        },
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728            value: 72.6
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730        {
731            name: 'Ethiopia',
732            value: 21.1
733        },
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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	//////////////////////////////
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749        name: 'Cluster3',
750        data: [{
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752            value: 393.2
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755            name: 'New Zealand',
756            value: 32.4
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760            value: 4.7
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772        {
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782            value: 20.1
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787        },
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811        },
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828        {
829            name: 'Peru',
830            value: 61.6
831        },
832        {
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851            value: 15.8
852        },
853        {
854            name: 'Georgia',
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857        {
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861        {
862            name: 'Kyrgyzstan',
863            value: 10.3
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866            name: 'Afghanistan',
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871            value: 37.4
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873        {
874            name: 'Mongolia',
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889        {
890            name: 'Jordan',
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896        },
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898            name: 'Azerbaijan',
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902            name: 'Singapore',
903            value: 53.4
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906            name: 'Hong Kong',
907            value: 32.4
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914            name: 'DPR Korea',
915            value: 54.4
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918            name: 'Israel',
919            value: 61.8
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921        {
922            name: 'Turkmenistan',
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925        {
926            name: 'Oman',
927            value: 91.6
928        },
929        {
930            name: 'Qatar',
931            value: 102.6
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934            name: 'Philippines',
935            value: 155.4
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937        {
938            name: 'Kuwait',
939            value: 110.1
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941        {
942            name: 'Uzbekistan',
943            value: 132.4
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945        {
946            name: 'Iraq',
947            value: 193.8
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949        {
950            name: 'Pakistan',
951            value: 199.3
952        },
953        {
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957        {
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963            value: 277.5
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993        {
994            name: 'ROAS',
995            value: 2693.0
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1002            name: 'Number of orders',
1003            value: 686.4
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1005        {
1006            name: 'Conv. value',
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1012    <!-- Chart Column -->
1013    
1014
1015
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1035    xAxis: {
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1043        column: {
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1057        name: 'Cost',
1058        data: [113, 122, 95],
1059        stack: 'North America'
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1065
1066
1067     });
1068 </script>
1068
1069
1070    <!-- ChartScatter -->
1071    
1072 
1073 
1073<script>
1074        // Detekce mobilního zařízení
1075        const isMobile = window.matchMedia('(max-width: 768px)').matches;
1076        
1077        Highcharts.chart('container2', {
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1079                type: 'scatter',
1080                zoomType: 'xy',
1081                height: isMobile ? 450 : 600,  // Podmíněná výška podle zařízení
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1180                    type: 'scatter'
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