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Choosing the right optimization KPI for your A/B tests
2026-02-28 18:32 Diff
https://www.dynamicyield.com/lesson/maximum-returns-with-3-ab-testing-rules/
Summarize this articleHere’s what you need to know:
Focus on conversions, not unique conversions: Measure all conversions, not just the number of people who converted for the first time.
Average results by users, not sessions: Avoid biasing results in favor of the worst variation by averaging based on users, not sessions.
Choose a primary metric close to your final goal: Track results sooner and easier by choosing a metric closer to the final conversion in your funnel.
Control for seasonality and trends: Account for external factors influencing your results by incorporating seasonality and trends into your analysis.
Use a unified testing platform: Streamline the A/B testing process and improve data accuracy with a unified testing platform.
For data-driven marketers looking to improve their site’s user experience, A/B testing is one pillar of experience optimization used to help increase conversion rates. Relied on as a way to experiment with large and small changes before going “all in,” it is an important tool for deciding what experiences get applied to all users.
Therefore, not analyzing the results properly could mean the difference between potentially massive uplifts and diminishing returns.
By addressing some common A/B testing pitfalls and introducing more reliable approaches to measuring the efficacy of your experiences, you can be sure to leave here knowing how to optimize for success.
Look at Conversions Instead of Unique Conversions

Marketers have gotten very used to setting Conversion Rate as a main KPI, but when it comes to testing, it simply does not tell the whole story. While Conversion Rate means the number of unique users that converted, your business goals should be set up for more conversions, not necessarily more convertors.
Look at the table below, and ask yourself, which variation performs better?
It is safe to say that Variation A performed better, resulting in more conversions and an uplift of 25%. So, even though it might be easier to think about percentages than small fractions, is it worth losing out on the meaningful data?
Don’t opt for the less optimized path.
To conclude: If your objective is an action that can only be done once, like registering to a site or becoming a newsletter subscriber, Conversion Rate and Conversions per User should show the same result (one with percentages and the other with fractions). However, if the objective is to add an item to a cart or purchase, Conversion Rate is an inferior metric, as it assumes you’re looking for convertors and not conversions — and multiple conversions by a user do not weigh more than a single conversion.
Average by Users, not Sessions

Google Analytics taught us to analyze behavior through the prism of Sessions. For instance, an eCommerce brand measuring revenue would look to RPV (revenue per session), and a media site, average number of pageviews per session.
However, if you’re choosing the variation once for each user, you should average results by number of users. Averaging by sessions will many times result in a bias in favor of the worst variation.
As an example, here is the result of a test where the primary metric is clicking the Add to Cart button. After two users entered the test, one was served with the Control and converted four times, not returning to the site. The other was served with Variation A and converted five times, returning for another session, in which he converted twice.
When dividing conversions by sessions, the Control won, with 4 conversions per session against Variation A’s 3.5. Though, Conversions per User paints a different story, with Variation A winning against the Control 7 – 4.
Would it make sense to punish Variation A, which ultimately produced more conversions because it led to an additional session that had a small number of conversions?
To conclude: If you’re comparing two alternatives, averaging results by users, and not sessions will allow you to optimize for a better user experience on your site.
Choose the right primary metric

When performing a test, you usually have one metric in mind that is the primary — the winning variation will be determined by the success of each variation for this metric. This can include any user activity, such as clicking a specific button, adding items to the cart, completing a purchase, etc.
A marketer for an eCommerce site once asked:
“Why do you allow so many metrics to choose from? I only need one: Revenue.”
As with any business relying on revenue, it makes sense as an approach that everything contributing to this goal would be good, and everything negatively impacting it, bad. However, conversion funnel methodology contradicts this model for thinking as each step of the funnel is aimed towards advancing the user to the next step in the funnel, meaning results should be optimized accordingly.
Here’s a simplified version of an eCommerce site conversion funnel.
After analyzing tests run earlier in the conversion funnel on a new homepage navigation element of an eCommerce site, after seven days, this became quite obvious:
There is no clear winner yet for the primary metric, as the number of purchases was too small. Though, when looking at clicks, enough data was collected to determine Variation A as a winner. And so we see the mathematical reason to select a closer, less delayed conversion.
Furthermore, selecting a conversion farther down the funnel will cause significant results to arrive a lot later because many of the users that advanced in the funnel will now experience the same site. It is not unlikely the rest of the experience and conversion rate will be the same.
Hence, the difference between their performance will be small.
This is what we refer to as Immediate Reward and Delayed Reward.
Clicks on a variation is immediate
Purchases on a test in the checkout page are immediate reward
Purchases on a test in the homepage are delayed reward
To conclude: eCommerce Conversion Rate Optimization allows marketers to more quickly improve conversion rates. By setting objectives that are achieved more immediately, results are easier to track, and key metrics easier to improve.
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                                }</style><div class="lesson-content-sidebar-list"><ol><li class=""> <a href="https://www.dynamicyield.com/lesson/client-side-testing-and-personalization/">Client-side testing and personalization explained</a> <span class="mark-as-read" data-id="120262"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/server-side-testing-and-personalization/">Server-side testing and personalization explained</a> <span class="mark-as-read" data-id="130448"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/frequentists-approach-to-ab-testing/">The Frequentist Approach to A/B Testing</a> <span class="mark-as-read" data-id="117864"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/bayesian-approach-to-ab-testing/">The Bayesian Approach to A/B Testing</a> <span class="mark-as-read" data-id="116912"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/optimization-analytics-in-experimentation/">The role of optimization analytics in experimentation</a> <span class="mark-as-read" data-id="97849"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/conversion-attribution-kpi/">Why session-based attribution is flawed in A/B tests</a> <span class="mark-as-read" data-id="20206"></span> <span class="article-read-mark">Already read</span></li><li class="current"> <a href="https://www.dynamicyield.com/lesson/maximum-returns-with-3-ab-testing-rules/">Choosing the right optimization KPI for your A/B tests</a> <span class="mark-as-read" data-id="25170"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/5-reasons-multivariate-testing-sucks/">The complex nature of running multivariate tests</a> <span class="mark-as-read" data-id="29682"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/outliers-detection/">Outliers detection in A/B testing</a> <span class="mark-as-read" data-id="91549"></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/advanced-experimentation/">Advanced Experimentation</a></div><h1 class="article-title">Choosing the right optimization KPI for your A/B tests</h1><h2 class="article-excerpt">By addressing some common A/B testing pitfalls and introducing more reliable approaches to measuring the efficacy of your experiences, you can be sure to optimize for success.</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;">Focus on conversions, not unique conversions: Measure all conversions, not just the number of people who converted for the first time.</li><li style="margin: 10px 0;">Average results by users, not sessions: Avoid biasing results in favor of the worst variation by averaging based on users, not sessions.</li><li style="margin: 10px 0;">Choose a primary metric close to your final goal: Track results sooner and easier by choosing a metric closer to the final conversion in your funnel.</li><li style="margin: 10px 0;">Control for seasonality and trends: Account for external factors influencing your results by incorporating seasonality and trends into your analysis.</li><li style="margin: 10px 0;">Use a unified testing platform: Streamline the A/B testing process and improve data accuracy with a unified testing platform.</li></ul></div><p></p><p>For data-driven marketers looking to improve their site&rsquo;s user experience, <a href="https://www.dynamicyield.com/lesson/introduction-to-ab-testing/">A/B testing</a> is one pillar of <a href="https://www.dynamicyield.com/article/experience-optimization/">experience optimization</a> used to help increase conversion rates. Relied on as a way to experiment with large and small changes before going &ldquo;all in,&rdquo; it is an important tool for deciding what experiences get applied to all users.</p><p>Therefore, not analyzing the results properly could mean the difference between potentially massive uplifts and diminishing returns.</p><p>By addressing some <a href="http://online-behavior.com/testing/success" target="_blank" rel="noopener noreferrer">common A/B testing pitfalls</a> and introducing more reliable approaches to measuring the efficacy of your experiences, you can be sure to leave here knowing how to optimize for success.</p><h2>Look at Conversions Instead of Unique Conversions</h2><p>Marketers have gotten very used to setting <a href="https://marketing.dynamicyield.com/benchmarks/conversion-rate/">Conversion Rate</a> as a main KPI, but when it comes to testing, it simply does not tell the whole story. While <a href="/glossary/conversion-rate/">Conversion Rate means</a> the number of unique users that converted, your business goals should be set up for more <strong>conversions</strong>, not necessarily more <strong>convertors</strong>.</p><p>Look at the table below, and ask yourself, which variation performs better?</p><p><img fetchpriority="high" decoding="async" src="/wp-content/uploads/2017/10/table2_test.jpg" alt="table2_test" width="1330" height="346" class="aligncenter size-full wp-image-25216" /></p><p>It is safe to say that Variation A performed better, resulting in more conversions and an uplift of 25%. So, even though it might be easier to think about percentages than small fractions, is it worth losing out on the meaningful data?</p><p>Don&rsquo;t opt for the less optimized path.</p><p><strong>To conclude:</strong> If your objective is an action that can only be done once, like registering to a site or becoming a newsletter subscriber, Conversion Rate and Conversions per User should show the same result (one with percentages and the other with fractions). However, if the objective is to add an item to a cart or purchase, Conversion Rate is an inferior metric, as it assumes you&rsquo;re looking for <strong>convertors</strong> and not <strong>conversions </strong>&#8212; and multiple conversions by a user do not weigh more than a single conversion.</p><h2>Average by Users, not Sessions</h2><p>Google Analytics taught us to analyze behavior through the prism of Sessions. For instance, an eCommerce brand measuring revenue would look to RPV (revenue per session), and a media site, average number of pageviews per session.</p><p>However, if you&rsquo;re choosing the variation once for each user, you should average results by number of users. Averaging by sessions will many times result in a <strong>bias in favor of the worst variation</strong>.</p><p>As an example, here is the result of a test where the primary metric is clicking the Add to Cart button. After two users entered the test, one was served with the <a href="https://www.dynamicyield.com/glossary/control-group/">Control</a> and converted four times, not returning to the site. The other was served with Variation A and converted five times, returning for another session, in which he converted twice.</p><p><img decoding="async" src="/wp-content/uploads/2017/10/table3_test.jpg" alt="table3_test" width="1330" height="335" class="aligncenter size-full wp-image-25217" /></p><p>When dividing conversions by sessions, the Control won, with 4 conversions per session against Variation A&rsquo;s 3.5. Though, Conversions per User paints a different story, with Variation A winning against the Control 7 &#8211; 4.</p><p>Would it make sense to punish Variation A, which ultimately produced more conversions because it led to an additional session that had a small number of conversions?</p><p><b>To conclude</b>: If you&rsquo;re comparing two alternatives, averaging results by users, and not sessions will allow you to optimize for a better user experience on your site.</p><h2>Choose the right primary metric</h2><p>When performing a test, you usually have one metric in mind that is the primary &#8212; the winning variation will be determined by the success of each variation for this metric. This can include any user activity, such as clicking a specific button, adding items to the cart, completing a purchase, etc.</p><p>A marketer for an eCommerce site once asked:</p><p>&ldquo;<strong>Why do you allow so many metrics</strong> <strong>to choose from? I only need one: Revenue.&rdquo; </strong></p><p>As with any business relying on revenue, it makes sense as an approach that everything contributing to this goal would be good, and everything negatively impacting it, bad. However, <a href="/glossary/conversion-funnel/">conversion funnel</a> methodology contradicts this model for thinking as each step of the funnel is aimed towards advancing the user to the next step in the funnel, meaning results should be optimized accordingly.</p><p>Here&rsquo;s a simplified version of an eCommerce site conversion funnel.</p><p><img decoding="async" src="/wp-content/uploads/2017/10/Funnel.jpg" alt="eCommerce Conversion Funnel -- Dynamic Yield" width="1330" height="793" class="aligncenter size-full wp-image-25206" /></p><p><a href="https://www.dynamicyield.com/lesson/ab-testing-analysis/">After analyzing tests</a> run earlier in the conversion funnel on a new homepage navigation element of an eCommerce site, after seven days, this became quite obvious:</p><p><img loading="lazy" decoding="async" src="/wp-content/uploads/2017/10/table1_test.jpg" alt="table1_test" width="1330" height="352" class="aligncenter size-full wp-image-25215" /></p><p>There is no clear winner yet for the primary metric, as the number of purchases was too small. Though, when looking at clicks, enough data was collected to determine Variation A as a winner. And so we see the mathematical reason to select a closer, less delayed conversion.</p><p>Furthermore, selecting a conversion farther down the funnel will cause significant results to arrive a lot later because many of the users that advanced in the funnel will now experience the same site. It is not unlikely the rest of the experience and conversion rate will be the same.</p><p>Hence, the difference between their performance will be small.</p><p>This is what we refer to as <em>Immediate Reward</em> and <em>Delayed Reward</em>.</p><ul><li>Clicks on a variation is immediate</li><li>Purchases on a test in the checkout page are immediate reward</li><li>Purchases on a test in the homepage are delayed reward</li></ul><p><strong>To conclude:</strong> <a href="https://www.dynamicyield.com/lesson/the-economics-of-ecommerce-conversion-optimization/">eCommerce Conversion Rate Optimization</a> allows marketers to more quickly improve conversion rates</a>. By <a href="https://www.dynamicyield.com/lesson/optimization-objective/">setting objectives</a> that are achieved more immediately, results are easier to track, and key metrics easier to improve.</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/advanced-experimentation/">Advanced Experimentation</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: #ffe8b1" class="article-next-link pagination-link" href="https://www.dynamicyield.com/lesson/5-reasons-multivariate-testing-sucks/" title="The complex nature of running multivariate tests">The complex nature of running multivariate tests</a><a rel="prev" style="background-color: #ffe8b1" class="article-previous-link pagination-link" href="https://www.dynamicyield.com/lesson/conversion-attribution-kpi/" title="Why session-based attribution is flawed in A/B tests">Why session-based attribution is flawed in A/B tests</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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var current_selection_content, top_offset = 0;

if($(window).width() > 980) {
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$('.leftsidemenu #navigation ul li a, .leftsidemenu #navigation .mobile.current-selection').on('click', function() {
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$('.leftsidemenu #navigation ul li a').on('click', function() {
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$(window).bind('mousewheel', function() {
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$('.customer').on('click', function() {
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$(window).load(function(){
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var partners_filter = jQuery('#navigation');

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	window.location.hash = thisHash;
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			function getUrlParameter(sParam) {
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