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Choosing the Right Conversion Optimization Objective
2026-02-28 19:40 Diff
https://www.dynamicyield.com/lesson/optimization-objective/
Summarize this articleHere’s what you need to know:
Choose the right conversion optimization objective to avoid wasting resources. Conversions are valuable actions for your business, like clicks or goal completions.
Besides the objective, consider variables like sample size, experiment length, and how prominent the changes are.
Optimize data effectively using the right method. A table in the article helps you choose the right objective based on sample size and experiment length.
For fast results, avoid revenue-based optimization and stick with shorter, simpler conversion cycles like click-based optimization.
Avoid drawing conclusions too quickly. Wait for the optimization process to gather enough data for statistical significance before making changes.
Prioritize tests carefully. Ensure you can send enough traffic and generate conversions to all variations.
Focus on testing noticeable changes that can have a real impact. Don’t waste time testing minor features on low-traffic sites.
Conversion rate optimization can be a very powerful process, but it’s easy to get carried away by the methodology. Despite the best intentions, many conversion optimization initiatives fail to produce valuable results, resulting in misleading and unsubstantiated insights. Fortunately, the biggest blunders can be prevented by working methodically.
A conversion optimization process can fail for a variety of reasons. One of the biggest considerations when running experiments revolves around choosing the right optimization objective. Generally speaking, conversions are measured when a visitor executes actions that are defined as valuable to your business. Conversion can be measured as a click on an element, completion of a goal, or any online action which has a direct impact on revenue. Whether you’re trying to improve display ad clicks using CTR-based optimization, generate more leads using goal-based optimization, or increase sales using revenue-based optimization, choosing the right objective for your optimization initiatives can make a huge difference between their failure or success.
Having a specific, measurable objective in mind, however, is not enough, and that’s the first thing you need to realize before planning a test. Many other variables are involved, such as the type of experiment you’re running, the audience sample size, experiment length, conversion attribution, the number of content variations that are being tested, and how prominent the changes being tested are. It’s not just about measuring the results – it’s about optimizing the data as effectively as possible using the right optimization method.
Below is a table that illustrates this point. Use it to get a better idea of what the optimization objective of your experiments should be, based on sample size and estimated length of the experiment. If you don’t know what the sample size is, use a sample size calculator. Of course, there is no silver bullet, and this table can only serve to guide you towards making the right decision.
How to Choose the Right Optimization Objective:

Low
Sample SizeMedium
Sample Size
High
Sample Size
Short
Experiment
LengthNo OptimizationGoal Completion
OptimizationGoal Completion
OptimizationMedium
Experiment
Length
CTR Based
OptimizationGoal Completion
OptimizationRevenue-based
OptimizationLong
Experiment
Length
CTR Based
OptimizationRevenue-based
OptimizationRevenue-based
Optimization
Time is of great concern, and it’s one of the main factors influencing the decision of choosing the right objective for your experiments. If you’re running an eCommerce site and intending to get fast results for an experiment you’re just about to launch, remember this: Revenue-based optimization requires greater purchase cycles than CTR or goal-based optimization.
With revenue-based optimization, it’s far more complicated to attribute the actual conversion to a single experiment variation. It takes more time for a random visitor exposed to an experiment to complete a purchase than it takes to complete a click on an element. Some customers compare prices, some are just slow buyers.
In fact, many things can happen between the first exposure to a live experiment and the final destination of completing a purchase. So as a general rule of thumb, if you’re looking for fast results, forget about optimizing for revenue and stick with shorter and simpler conversion cycles, such as click-based optimization or goal completion optimization.
Avoid peeking until the completion of the test

Drawing conclusions too quickly is also a major issue. It can be difficult to keep your cool and ignore an (allegedly) obvious winner a few days into a test. While results vary across industries and sample sizes, waiting for the optimization process to accumulate enough data to achieve statistical significance is absolutely crucial. Even if your testing tool forecasts incredible uplift, keep the test running for at least a few weeks or a few purchase cycles before starting to analyze results.
While some experiments reveal major uplifts early in the test process, results can vary dramatically over time, causing many marketers to draw the wrong conclusions and even lose money over time if they implement changes based on the initial results.
Prioritize tests accordingly

Every test is fallible. If you can’t send enough traffic and generate conversions to all variations, your efforts will go unrewarded. Just because you can test a page element, that doesn’t mean you should. In fact, the larger the sample size, the more data becomes available for the system to optimize. Make sure you test noticeable changes that are big enough to have a real impact on the bottom-line business. Without sufficient data (e.g. traffic or conversions), your experiments will never reach the statistical significance minimum of 95% and thus will be bound to fail.
Every test requires considerable resources (budget, time, and people), so stick to the elements you know make a difference, such as messaging, hero banners, and the call-to-action (CTA). Start testing minor features and you’ll find yourself lost in data you can’t use. There’s no point in running short-term optimization initiatives for low-traffic sites. If the sample size is low, the system will struggle to optimize and gain real uplifts. It would take a lot of time and may eventually even fail.
As with any tool or methodology, a single experiment will not revolutionize your business, so don’t expect the test to produce a “Silver Bullet.” However, integrating testing over time and rolling out insights across your website will introduce incremental improvements that can (and do) reach a tipping point. Take a granular approach, choose the right objective, and run your optimization initiatives intelligently and methodically. Plan for small wins, and they’ll add up to a big difference in the long run.
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Optimization Course</h4></div><style>@media only screen and (max-width: 1199px) {
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                                }</style><div class="lesson-content-sidebar-list"><ol><li class=""> <a href="https://www.dynamicyield.com/lesson/introduction-to-ab-testing/">An introduction to A/B testing and optimization</a> <span class="mark-as-read" data-id="97"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/aa-testing-in-experimentation/">A/A testing and decision making in experimentation</a> <span class="mark-as-read" data-id="131419"></span> <span class="has-video"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/statistical-significance/">Why reaching and protecting statistical significance is so important in A/B tests</a> <span class="mark-as-read" data-id="1783"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/traffic-allocation/">Choosing the right traffic allocation in A/B testing</a> <span class="mark-as-read" data-id="115918"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/conversion-attribution-scoping/">Understanding conversion attribution scoping in A/B testing</a> <span class="mark-as-read" data-id="21258"></span> <span class="article-read-mark">Already read</span></li><li class="current"> <a href="https://www.dynamicyield.com/lesson/optimization-objective/">Choosing the right conversion optimization objective</a> <span class="mark-as-read" data-id="7468"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/bayesian-testing/">Frequentist vs. Bayesian approach in A/B testing</a> <span class="mark-as-read" data-id="17150"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/running-effective-bayesian-ab-tests/">Guidelines for running effective Bayesian A/B tests</a> <span class="mark-as-read" data-id="131426"></span> <span class="has-video"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/contextual-bandit-optimization/">Beyond A/B testing: Multi-armed bandit experiments</a> <span class="mark-as-read" data-id="7414"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/client-side-vs-server-side/">Client-side vs server-side A/B testing and personalization</a> <span class="mark-as-read" data-id="130425"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/ab-testing-without-segmentation/">Segmented A/B tests: Avoiding average experiences</a> <span class="mark-as-read" data-id="32157"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/impact-of-ab-testing-on-seo/">The impact of A/B tests and personalization on SEO</a> <span class="mark-as-read" data-id="91"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/no-failed-ab-tests/">There are no failed A/B tests: How to ensure every experiment yields meaningful results</a> <span class="mark-as-read" data-id="135394"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/ab-testing-analysis/">How to analyze and interpret A/B testing results</a> <span class="mark-as-read" data-id="32159"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/kopari-beauty/">Building an experimentation growth plan: A Kopari Beauty case study</a> <span class="mark-as-read" data-id="22970"></span> <span class="article-read-mark">Already read</span></li></ol><div class="view-all-courses-wrap"> <a href="https://www.dynamicyield.com/learning-paths/" class="view-all-courses">Discover more courses<svg xmlns="http://www.w3.org/2000/svg" width="10.928" height="9.501" viewBox="0 0 10.928 9.501" style="&#10;"><path d="M10.929,4.753a.859.859,0,0,0-.19-.491L6.938.224A.835.835,0,0,0,5.892.16.736.736,0,0,0,5.9,1.2L8.577,4.041H.746a.733.733,0,0,0-.734.586.711.711,0,0,0,.7.84H8.577L5.9,8.3a.784.784,0,0,0-.008,1.038.8.8,0,0,0,1.037-.063l3.8-4.039a.652.652,0,0,0,.2-.483Z" transform="translate(-0.001 -0.003)"/></svg></a></div></div></div></div><div class="article-content"><div class="lesson-content-inner"><div class="article-breadcrumbs"> <a href="https://www.dynamicyield.com/learning-paths/" title="Learning Paths">Learning Paths</a> <a href="https://www.dynamicyield.com/course/testing-and-optimization/">A/B Testing &amp; Optimization</a></div><h1 class="article-title">Choosing the right conversion optimization objective</h1><h2 class="article-excerpt">Conversion optimization can be a very powerful process, but it’s easy to get carried away by the methodology. Here's how to choose the right optimization objective.</h2><div class="article-authors lc-flex"><div class="article-author-box lc-col lc-flex"><div class="article-author-image lc-col"><div class="entry_author_image"><img src="https://www.dynamicyield.com/wp-content/authors/gidi-122.jpeg"  alt="Gidi Vigo" width="76" height="76" /></div></div><div class="article-author-title lc-col"><div class="article-author-name"> <a href="https://www.dynamicyield.com/blog/author/gidi/" title="Posts by Gidi Vigo" rel="author">Gidi Vigo</a></div><div class="article-author-title-text"> VP of Product, Dynamic Yield</div></div></div></div><div class="entry-content article-entry-content"><p></p><div id="WYNTK-Sec" class="what-you-need-to-know-sec"><p><img decoding="async" src="/wp-content/uploads/2020/09/star-icon.svg" style="width: 20px; margin-right: 5px; vertical-align: -2px;"><strong class="first">Summarize this article</strong><strong class="hidden">Here&#8217;s what you need to know:</strong></p><ul style="margin-left: 4px; font-size: 16px;"><li style="margin: 10px 0;">Choose the right conversion optimization objective to avoid wasting resources. Conversions are valuable actions for your business, like clicks or goal completions.</li><li style="margin: 10px 0;">Besides the objective, consider variables like sample size, experiment length, and how prominent the changes are.</li><li style="margin: 10px 0;">Optimize data effectively using the right method. A table in the article helps you choose the right objective based on sample size and experiment length.</li><li style="margin: 10px 0;">For fast results, avoid revenue-based optimization and stick with shorter, simpler conversion cycles like click-based optimization.</li><li style="margin: 10px 0;">Avoid drawing conclusions too quickly. Wait for the optimization process to gather enough data for statistical significance before making changes.</li><li style="margin: 10px 0;">Prioritize tests carefully. Ensure you can send enough traffic and generate conversions to all variations.</li><li style="margin: 10px 0;">Focus on testing noticeable changes that can have a real impact. Don&#8217;t waste time testing minor features on low-traffic sites.</li></ul></div><p></p><p><a href="https://www.dynamicyield.com/lesson/cro-plan/">Conversion rate optimization</a> can be a very powerful process, but it’s easy to get carried away by the methodology. Despite the best intentions, many conversion optimization initiatives fail to produce valuable results, resulting in misleading and unsubstantiated insights. Fortunately, the biggest blunders can be prevented by working methodically.</p><p>A conversion optimization process can fail for a variety of reasons. One of the biggest considerations when running experiments revolves around choosing the right optimization objective. Generally speaking, <a href="https://www.dynamicyield.com/glossary/conversion/"> conversions</a> are measured when a visitor executes actions that are defined as valuable to your business. Conversion can be measured as a click on an element, completion of a goal, or any online action which has a direct impact on revenue. Whether you’re trying to improve display ad clicks using CTR-based optimization, generate more leads using goal-based optimization, or increase sales using revenue-based optimization, <a href="https://www.dynamicyield.com/blog/maximum-returns-with-3-ab-testing-rules/">choosing the right objective</a> for your optimization initiatives can make a huge difference between their failure or success.</p><p>Having a specific, measurable objective in mind, however, is not enough, and that’s the first thing you need to realize before planning a test. Many other variables are involved, such as the type of experiment you’re running, the audience sample size, experiment length, <a href="https://www.dynamicyield.com/blog/conversion-attribution-scoping/">conversion attribution</a>, the number of content variations that are being tested, and how prominent the changes being tested are. It’s not just about measuring the results &#8211; it’s about optimizing the data as effectively as possible using the right optimization method.</p><p>Below is a table that illustrates this point. Use it to get a better idea of what the optimization objective of your experiments should be, based on sample size and estimated length of the experiment. If you don’t know what the sample size is, use a <a href="https://marketing.dynamicyield.com/ab-test-duration-calculator/?_ga=2.148207600.699795135.1581350469-1787249608.1541517034">sample size calculator</a>. Of course, there is no silver bullet, and this table can only serve to guide you towards making the right decision.</p><h2 style="text-align: center;">How to Choose the Right Optimization Objective:</h2><div style="overflow-x:scroll"><table class="objective-table"><tbody><tr class="top-objective"><td class="no-border-objective"></td><td style="text-align: center;"><b>Low<br /> Sample Size</b></td><td style="text-align: center;"><b>Medium<br /> <b>Sample Size</b><br /> </b></td><td style="text-align: center;"><b>High<br /> <b>Sample Size</b><br /> </b></td></tr><tr><td style="text-align: center;"><b>Short<br /> <b>Experiment<br /> Length</b></b></td><td class="no-optimization">No Optimization</td><td class="goal-optimization">Goal Completion<br /> Optimization</td><td class="goal-optimization">Goal Completion<br /> Optimization</td></tr><tr><td style="text-align: center;"><b>Medium<br /> <b>Experiment<br /> Length</b><br /> </b></td><td class="ctr-optimization">CTR Based<br /> Optimization</td><td class="goal-optimization">Goal Completion<br /> Optimization</td><td class="revenue-optimization">Revenue-based<br /> Optimization</td></tr><tr><td style="text-align: center;"><b>Long<br /> <b>Experiment<br /> Length</b><br /> </b></td><td class="ctr-optimization">CTR Based<br /> Optimization</td><td class="revenue-optimization">Revenue-based<br /> Optimization</td><td class="revenue-optimization">Revenue-based<br /> Optimization</td></tr></tbody></table></div><style>@media(max-width:680px){
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}</style><p>Time is of great concern, and it’s one of the main factors influencing the decision of choosing the right objective for your experiments. If you’re running an eCommerce site and intending to get fast results for an experiment you’re just about to launch, remember this: Revenue-based optimization requires greater purchase cycles than CTR or goal-based optimization.</p><p>With revenue-based optimization, it’s far more complicated to attribute the actual conversion to a single experiment variation. It takes more time for a random visitor exposed to an experiment to complete a purchase than it takes to complete a click on an element. Some customers compare prices, some are just slow buyers.</p><p>In fact, many things can happen between the first exposure to a live experiment and the final destination of completing a purchase. So as a general rule of thumb, if you’re looking for fast results, forget about optimizing for revenue and stick with shorter and simpler conversion cycles, such as click-based optimization or goal completion optimization.</p><h2>Avoid peeking until the completion of the test</h2><p>Drawing conclusions too quickly is also a major issue. It can be difficult to keep your cool and ignore an (allegedly) obvious winner a few days into a test. While results vary across industries and sample sizes, waiting for the optimization process to accumulate enough data to achieve statistical significance is absolutely crucial. Even if your testing tool forecasts incredible uplift, keep the test running for at least a few weeks or a few purchase cycles before starting to analyze results.</p><p>While some experiments reveal major uplifts early in the test process, results can vary dramatically over time, causing many marketers to draw the wrong conclusions and even lose money over time if they implement changes based on the initial results.</p><h2>Prioritize tests accordingly</h2><p>Every test is fallible. If you can’t send enough traffic and generate conversions to <strong><em>all </em></strong>variations, your efforts will go unrewarded. Just because you <strong><em>can</em></strong> test a page element, that doesn’t mean you should. In fact, the larger the sample size, the more data becomes available for the system to optimize. Make sure you test noticeable changes that are big enough to have a real impact on the bottom-line business. Without sufficient data (e.g. traffic or conversions), your experiments will never reach the <a href="https://www.dynamicyield.com/lesson/statistical-significance/">statistical significance</a> minimum of 95% and thus will be bound to fail.</p><p>Every test requires considerable resources (budget, time, and people), so stick to the elements you know make a difference, such as messaging, hero banners, and the <a href="https://www.dynamicyield.com/glossary/cta/">call-to-action (CTA)</a>. Start testing minor features and you’ll find yourself lost in data you can’t use. There’s no point in running short-term optimization initiatives for low-traffic sites. If the sample size is low, the system will struggle to optimize and gain real uplifts. It would take a lot of time and may eventually even fail.</p><p>As with any tool or methodology, a single experiment will not revolutionize your business, so don’t expect the test to produce a “Silver Bullet.” However, integrating testing over time and rolling out insights across your website will introduce incremental improvements that can (and do) reach a tipping point. Take a granular approach, choose the right objective, and run your optimization initiatives intelligently and methodically. Plan for small wins, and they’ll add up to a big difference in the long run.</p><div class="article-separator"><svg width="54" height="26" viewBox="0 0 54 26" fill="none" xmlns="http://www.w3.org/2000/svg"> <path d="M53.0088 25.6367C52.7884 21.9564 51.8025 18.3625 50.1141 15.0849C48.4256 11.8073 46.0718 8.9181 43.2031 6.60203L43.2031 25.6367L53.0088 25.6367Z" fill="black"/> <path d="M14.4395 3.59038C10.3237 5.69174 6.82743 8.83082 4.29638 12.6973C1.76533 16.5638 0.28711 21.0239 0.0078125 25.6367L14.4395 25.6367L14.4395 3.59038Z" fill="black"/> <path d="M17.6563 2.20809L17.6562 25.6367L39.9829 25.6367L39.9829 4.36701C39.9829 4.36701 30.7303 -2.31342 17.6563 2.20809Z" fill="black"/> </svg></div><div class="article-tags-block"> <strong>Categories:</strong> <a href="https://www.dynamicyield.com/course/testing-and-optimization/">A/B Testing &amp; Optimization</a></div><div class="article-tags-block"> <strong>Tags:</strong> <a href="https://www.dynamicyield.com/tags/personalization-and-testing-frameworks/">Personalization and Testing Frameworks</a></div><div class="article-pagination"><h5>Continue reading</h5><div class="article-pagination-row lc-flex"> <a rel="next" style="background-color: #f4c0c2" class="article-next-link pagination-link" href="https://www.dynamicyield.com/lesson/bayesian-testing/" title="Frequentist vs. Bayesian approach in A/B testing">Frequentist vs. Bayesian approach in A/B testing</a><a rel="prev" style="background-color: #f4c0c2" class="article-previous-link pagination-link" href="https://www.dynamicyield.com/lesson/conversion-attribution-scoping/" title="Understanding conversion attribution scoping in A/B testing">Understanding conversion attribution scoping in A/B testing</a></div></div></div></div></div></article></div><section class="lc-subscription-section"><div class="lc-subscription-form-block lc-subscription-inner"><h4 class="lc-subscription-title">Sign up for the XP&sup2; newsletter</h4><div class="lc-subscription-text">Join thousands of readers from Target, Citi, Spotify, Hulu, Google, Sephora, and other innovative brands who read our bi-weekly XP² newsletter, delivering educational content, research, and insights straight to your inbox</div><form class="mktoForm" data-formId="3876" data-formInstance="sub-inline"></form><div class="lc-privacy-wrapper"><div class="lc-privacy"> You may unsubscribe at any time. 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                    'utm_medium': 'txt_medium__c',
                    'utm_content': 'txt_content__c',
                    'utm_campaign': 'txt_campaign_name__c',
                    'utm_term': 'txt_term__c',
                    'gclid': ['GCLID__c', 'GCLID_c__c']
                };

                for(var i = 0; i < Object.keys(dy_utms).length; i++) {

                    var ls_key   = Object.keys(dy_utms)[i],
                        ls_value = localStorage.getItem(ls_key),
                        form_key = dy_utms[ls_key],
                        form_field;

                    if(ls_value) {

                        if(ls_key === 'gclid') {
                            ls_value = JSON.parse(ls_value).value;
                        }

                        if(typeof(form_key) === 'string') {
                            form_field = form[0].querySelector('input[type="hidden"][name="' + form_key + '"]');

                            if(form_field) {
                                form_field.value = ls_value;
                            }

                        } else {

                            for(var j = 0; j < form_key.length; j++) {
                                form_field = form[0].querySelector('input[type="hidden"][name="' + form_key[j] + '"]');

                                if(form_field) {
                                    form_field.value = ls_value;
                                }
                            }
                        }
                    }
                }

                // IP Address
                fetch('https://www.cloudflare.com/cdn-cgi/trace').then(function(result) {

                    result.text().then(function(values) {

                        values.split("\n").map(function(item) {
                            var pair = item.split('=');

                            if(['ip'].includes(pair[0])) {
                                form[0].querySelector('input[type="hidden"][name="IP__c"]').value = pair[1];
                            }
                        });
                    });
                });

                // GA ID and Referrer
                var dy_utms_cookie = {};

                document.cookie.split(';').forEach(function(el) {
                    var splitCookie = el.split('=');
                    var key = splitCookie[0].trim();
                    var value = splitCookie[1];
                    dy_utms_cookie[key] = value;
                });

                if(dy_utms_cookie["_ga"]) {
                    form[0].querySelector('input[type="hidden"][name="Visitor_ID__c"]').value = dy_utms_cookie["_ga"].substring(6);
                }

                if(dy_utms_cookie["personalize_referrer_string"]) {
                    form[0].querySelector('input[type="hidden"][name="Referral__c"]').value = dy_utms_cookie["personalize_referrer_string"].substring(6);
                }

                // ZoomInfo
                if(localStorage.getItem('ZoomInfoData')) {
                    var zoominfo_data = JSON.parse(localStorage.getItem('ZoomInfoData'));

                    if(zoominfo_data) {
                        form[0].querySelector('input[type="hidden"][name="Alexa_Rank__c"]').value = zoominfo_data.alexaglobalrank;
                        form[0].querySelector('input[type="hidden"][name="Employees__c"]').value = zoominfo_data.employeesrange;
                        form[0].querySelector('input[type="hidden"][name="Company_Revenue_Range__c"]').value = zoominfo_data.estimatedannualrevenue;
                    }
                }

                // Also, dispatch a dataLayer event on success
                _form.onSuccess(function(values, follow_up_url) {
                    window.dataLayer = window.dataLayer || [];

                    if([3739, 4802, 5094, 5110, 4946, 5076, 5116, 5171, 5172, 5173].includes(_form.getId())) {

                        window.dataLayer.push({
                            'event': 'demo_request_complete',
                            'eventCallback': function() {

                                if(follow_up_url) {
                                    window.location.href = follow_up_url;
                                }
                            }
                        });

                        return false;

                    } else if([3876].includes(_form.getId())) {

                        window.dataLayer.push({
                            'event': 'newsletter_signup_complete',
                            'eventCallback': function() {

                                if(follow_up_url) {
                                    window.location.href = follow_up_url;
                                }
                            }
                        });

                        return false;
                    }
                });
            });
        }</script> <script>if(typeof(jQuery) !== 'undefined') {
                $ = jQuery.noConflict();
            }
			function getUrlParameter(sParam) {
    var sPageURL = decodeURIComponent(window.location.search.substring(1)),
        sURLVariables = sPageURL.split('&'),
        sParameterName,
        i;

    for (i = 0; i < sURLVariables.length; i++) {
        sParameterName = sURLVariables[i].split('=');

        if (sParameterName[0] === sParam) {
            return sParameterName[1] === undefined ? true : sParameterName[1];
        }
    }
}
$(window).load(function(){
    var videoID = getUrlParameter('video');
    if(videoID !== undefined){
        if($('#wistia-' + videoID + '-1').length){
    	    var videoName = $('#wistia-' + videoID + '-1').parents('.tab-pane');
    	    videoName = videoName[0].id
        } 
       if($('a[href="#' + videoName +'"]').length){
            $('a[href="#' + videoName +'"]').each(function(){
            $(this).click();
            });
        }
        
        addScript('https://fast.wistia.com/embed/medias/'+ videoID +'.jsonp');
        addScript('https://fast.wistia.com/assets/external/E-v1.js');
        $('body').append('<span class="wistia_embed wistia_async_'+videoID+' popover=true popoverContent=link dy-wistia-popup" style="display:inline"></span>');
    
         window._wq = window._wq || [];
        _wq.push({ id: videoID, onReady: function(video) {
            var video = Wistia.api(videoID);
            video.popover.show(); // open the popover
            video.play(); // play the video
            setTimeout(function(){video.unmute();}, 500);
        }});
    }
});</script> <style>#adBanner {
        background-color: transparent;
        height: 0;
        width: 1px;
    }</style><div id="wrapfabtest"><div id="adBanner"></div></div></body></html>
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