{"id":12011,"date":"2021-12-29T18:00:00","date_gmt":"2021-12-29T12:30:00","guid":{"rendered":"https:\/\/www.vue.ai\/blog\/?p=12011"},"modified":"2024-06-07T11:38:53","modified_gmt":"2024-06-07T06:08:53","slug":"a-b-testing-4-common-mistakes-ecommerce-retailers-make","status":"publish","type":"post","link":"https:\/\/www.vue.ai\/blog\/ai-in-retail\/a-b-testing-4-common-mistakes-ecommerce-retailers-make\/","title":{"rendered":"A\/B Testing: 4 Common Mistakes Ecommerce Retailers Make"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_65 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<p class=\"ez-toc-title\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69f4b819ab8b9\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69f4b819ab8b9\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/a-b-testing-4-common-mistakes-ecommerce-retailers-make\/#what-ab-testing-really-means\" title=\"What A\/B Testing Really Means\">What A\/B Testing Really Means<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/a-b-testing-4-common-mistakes-ecommerce-retailers-make\/#we-have-listed-the-top-4-mistakes-that-retailers-should-look-out-for\" title=\"We have listed the top 4 mistakes that retailers should look out for:\">We have listed the top 4 mistakes that retailers should look out for:<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/a-b-testing-4-common-mistakes-ecommerce-retailers-make\/#1-testing-without-goals-or-hypothesis\" title=\"1. Testing Without Goals or Hypothesis\">1. Testing Without Goals or Hypothesis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/a-b-testing-4-common-mistakes-ecommerce-retailers-make\/#2-testing-too-many-factors-at-one-go\" title=\"2. Testing Too Many Factors At One Go\">2. Testing Too Many Factors At One Go<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/a-b-testing-4-common-mistakes-ecommerce-retailers-make\/#3-target-audience-no-clarity-no-segmentation\" title=\"3. Target Audience: No Clarity, No Segmentation\">3. Target Audience: No Clarity, No Segmentation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/a-b-testing-4-common-mistakes-ecommerce-retailers-make\/#4-comparing-different-days-results\" title=\"4. Comparing Different Days\u2019 Results&nbsp;\">4. Comparing Different Days\u2019 Results&nbsp;<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/a-b-testing-4-common-mistakes-ecommerce-retailers-make\/#conclusion\" title=\"Conclusion\">Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">4<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span>\n<p>eCommerce retailers prefer A\/B testing because it takes the guesswork out of site planning, strategies, and experiments, providing them with proven data that they can use to make better decisions. With A\/B testing, retailers can discover what action on the website can lead to the desired outcome \u2014 it is a method of discovering what design, content, or functionality is more successful with the site visitors. It allows retailers to test and tweak pages or a particular element on a page to determine what positively affects consumer behavior.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"what-ab-testing-really-means\"><\/span>What A\/B Testing Really Means<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>This is a proven method that helps retailers avoid expensive mistakes. Effective A\/B testing ideally involves:<\/p>\n\n\n\n<ul><li>Repeatedly testing improvements until the best possible version is achieved &#8211; each test is built based on the results of the previous tests, meaning, the overall website can be improved bit by bit, to resonate with the customer\u2019s interests and meet business goals.<\/li><li>Running experiments and measuring data to identify cause and effect &#8211; retailers can test the hypothesis in small, controlled environments, to identify winning strategies before scaling those efforts.<\/li><\/ul>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"512\" src=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_337811585-1024x512.jpg\" alt=\"Meaning of A\/B testing \" class=\"wp-image-12094\" srcset=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_337811585-1024x512.jpg 1024w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_337811585-300x150.jpg 300w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_337811585-768x384.jpg 768w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_337811585-1536x769.jpg 1536w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_337811585-2048x1025.jpg 2048w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_337811585-150x75.jpg 150w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>For example, let\u2019s assume an eCommerce manager working for a fashion brand wants to identify the right <a href=\"https:\/\/www.vue.ai\/products\/product-recommendations\/\">recommendation<\/a> module that aids conversions.&nbsp;<\/p>\n\n\n\n<p>In order to do that, they can try A\/B testing two different types of recommendation modules to see which module works the best. The manager can do this by exposing two user groups to journeys with these modules with keeping everything else constant. This will help gauge the impact of the modules and compare the performance of one against the other.&nbsp;<\/p>\n\n\n\n<p>At the same time, many retailers and marketers tend to make a few mistakes when it comes to A\/B testing that can prove detrimental to their business.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"we-have-listed-the-top-4-mistakes-that-retailers-should-look-out-for\"><\/span>We have listed the top 4 mistakes that retailers should look out for:<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1-testing-without-goals-or-hypothesis\"><\/span>1. Testing Without Goals or Hypothesis<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The first step in conducting an A\/B test is to identify the business problem that needs to be solved and the quantifiable metric\/KPI(click-through rate, conversion rate, etc.) that will be used to ascertain if the goals of the experiment are met.&nbsp;<\/p>\n\n\n\n<p>A testing hypothesis is a statement that clearly states what changes need to be made, why they must be made, and its expected impact.<\/p>\n\n\n\n<p>&nbsp;Once a goal is identified, retailers can begin generating A\/B testing ideas and hypotheses to create customer journey variations. The three components that make a strong hypothesis are:<\/p>\n\n\n\n<ul><li>Defining a problem<\/li><li>Describing a proposed solution<\/li><li>Setting criteria for success\/failure and using metrics to measure results<\/li><\/ul>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_1350310139-1024x860.jpg\" alt=\"A\/b testing with hypothesis \" class=\"wp-image-12095\" width=\"577\" height=\"484\" srcset=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_1350310139-1024x860.jpg 1024w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_1350310139-300x252.jpg 300w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_1350310139-768x645.jpg 768w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_1350310139-1536x1291.jpg 1536w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_1350310139-2048x1721.jpg 2048w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_1350310139-150x126.jpg 150w\" sizes=\"(max-width: 577px) 100vw, 577px\" \/><\/figure><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2-testing-too-many-factors-at-one-go\"><\/span>2. Testing Too Many Factors At One Go<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>It\u2019s very possible to test too many variables at once. Whenever retailers test too many variables at the same time, they won&#8217;t know what&#8217;s affecting conversions and may miss out on an improvement.<\/p>\n\n\n\n<p>With solid research and a hypothesis, retailers can change multiple elements in the same test. For example, they can introduce a whole new layout for their product page, where most of the page&#8217;s elements are changed. <\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3-target-audience-no-clarity-no-segmentation\"><\/span>3. Target Audience: No Clarity, No Segmentation<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>When testing, retailers should always keep their target audience in mind and design experiments FOR them. They are the ones who validate what works or doesn&#8217;t work on the website. To truly understand the audience, retailers should create shopper personas and map their understanding of them to the shopper behavior on the site to identify possible tests that can be run.<\/p>\n\n\n\n<p>When it comes to A\/B tests, the more control retailers have, the better. Whenever retailers do any sort of analysis on the website, whether for split tests or not, they need to understand and consider segments.&nbsp;<\/p>\n\n\n\n<p>The most common segments that e-commerce teams use during A\/B testing are Returning visitors, New visitors, Visitors divided by countries, Visitors divided by traffic sources, Different days, Different devices, Different browsers.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_311929856-1024x1024.jpg\" alt=\"A\/B testing segments \" class=\"wp-image-12097\" width=\"574\" height=\"574\" srcset=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_311929856-1024x1024.jpg 1024w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_311929856-300x300.jpg 300w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_311929856-150x150.jpg 150w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_311929856-768x768.jpg 768w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_311929856-1536x1536.jpg 1536w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_311929856-2048x2048.jpg 2048w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_311929856-640x640.jpg 640w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/09\/shutterstock_311929856-96x96.jpg 96w\" sizes=\"(max-width: 574px) 100vw, 574px\" \/><\/figure><\/div>\n\n\n\n<p>The important thing to know about segments is that visitors from two different segments cannot be compared.<\/p>\n\n\n\n<p>For example, let&#8217;s assume on a particular day, a website has 100 new visitors as well as 100 returning visitors. How they browse, how long they stay on the site, how much they purchase for, etc., will vary from one segment to another.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4-comparing-different-days-results\"><\/span>4. Comparing Different Days\u2019 Results&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The regular traffic on a website will vastly differ from holiday traffic. Even a single day of holiday traffic can affect the test results which can lead to retailers optimizing their website for people who visit the site only during the holidays.&nbsp;<\/p>\n\n\n\n<p>Retailers should avoid these days\u2019 data as they might skew the test results or make sure they run tests for a longer period of time if these days fall within the test duration.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The primary goal of A\/B testing, more than revenue or engagement, is to derive as many insights as possible. Leveraging these insights can help retailers understand different aspects of customer behavior.<\/p>\n\n\n\n<p>It is important to analyze performance metrics to get a comprehensive understanding of what worked and what did not.&nbsp;<\/p>\n\n\n\n<p>Vue.ai\u2019s <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">personalization engine<\/a> allows retailers to create personalized eCommerce shopper journeys and A\/B test them to optimize for winning ones. <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">Vue<\/a>.ai&#8217;s personalization engine provides in-built analytics to track all relevant metrics that enable you to understand the performance of different customer journeys.\u00a0<\/p>\n\n\n\n<div class=\"wp-block-image .wp-block-image figcaption{text-align: center;}\"><figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2021\/12\/ab-testing-1-5.png\" alt=\"Design and run statistically significant A\/B tests \" class=\"wp-image-12677\" width=\"532\" height=\"321\" \/><figcaption>Design and run statistically significant A\/B tests<\/figcaption><\/figure><\/div>\n\n\n\n<p>There is no single journey that can fit all customers. Testing multiple customer journeys allows you to gain key insights into customer behavior and identify the best-suited journey for the audience.&nbsp;<\/p>\n\n\n\n<p>The idea is to continuously experiment with different combinations of experiences with highly customized content across different audiences to maximize the retailers\u2019 business goals. Vue.ai supports a frequentist approach to A\/B testing where the Vue.ai user is completely in control of executing\/monitoring and concluding A\/B tests.<\/p>\n\n\n\n<p><strong>Related Articles:&nbsp;<\/strong><\/p>\n\n\n\n<p><a href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/a-b-testing-personalization-retail\/\">Retailer\u2019s Guide: How A\/B Testing Can Increase AOV By 16.5%<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/best-practices-ab-testing-ecommerce\/\">6 Best Practices To Follow While A\/B Testing For eCommerce<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/ai-enabled-segmentation-retail\/\">How Brands Can Use AI-Enabled Segmentation To Understand Shoppers Better<\/a><\/p>\n\n\n\n<p><strong>Read More about <a href=\"https:\/\/www.vue.ai\/products\/virtual-dressing-room\/\">Virtual dressing room<\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">4<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span> eCommerce retailers prefer A\/B testing because it takes the guesswork out of site planning, strategies, and experiments, providing them with proven data that they can use to make better decisions. With A\/B testing, retailers can discover what action on the website can lead to the desired outcome \u2014 it is a method of discovering what  &#8230;<\/p>\n","protected":false},"author":23,"featured_media":12661,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1162],"tags":[1437,1163,1165,1164,1435,1220,29,1221],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A\/B Testing: 4 Common Mistakes eCommerce Retailers Make | Vue.ai<\/title>\n<meta name=\"description\" content=\"Read about the 4 common mistakes eCommerce retailers tend to make while A\/B testing, that can prove detrimental to their business.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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