{"id":18010,"date":"2022-03-11T18:39:02","date_gmt":"2022-03-11T18:39:02","guid":{"rendered":"https:\/\/productcollective.com\/?p=18010"},"modified":"2022-03-16T12:36:05","modified_gmt":"2022-03-16T12:36:05","slug":"ab-testing-in-product-management","status":"publish","type":"post","link":"https:\/\/productcollective.com\/ab-testing-in-product-management\/","title":{"rendered":"A\/B Testing in Product Management: Definition &#038; Overview"},"content":{"rendered":"\r\n<p><span style=\"font-weight: 400;\">Data is critical to the decision-making process of product managers (PMs). And there are so many ways to gather data\u2014from qualitative feedback to usability studies.<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">One time-tested tool for gathering real-time user insights is A\/B testing. A\/B testing in product management allows PMs to test specific elements of the user experience, such as copy, user interface, or design. A\/B tests are popular because they provide immediate and easy-to-analyze insights, and they reflect actual user interaction (instead of theoretical actions).<\/span><\/p>\r\n<h3><strong><span style=\"color: #ed3130;\">What is A\/B testing in product management?<\/span><\/strong><\/h3>\r\n<p><span style=\"font-weight: 400;\">In this article, we\u2019ll first break down what A\/B testing is, then we\u2019ll focus on how to <\/span><span style=\"font-weight: 400;\">learn A\/B testing for product managers.<\/span><\/p>\r\n<h4>A\/B testing definition<\/h4>\r\n<p><span style=\"font-weight: 400;\">A\/B testing (also called split testing) is an experiment meant to determine which variation of a variable drives the most impact. For example, you might run an A\/B test to determine which landing page layout drives a higher click-through rate (CTR) or lower bounce rate.\u00a0<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">A\/B tests require:<\/span><\/p>\r\n<ul>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Two or more variables (e.g., design, call to action, messaging) to test<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A randomized audience (so as to get the most unbiased results)<\/span><\/li>\r\n<\/ul>\r\n<p><span style=\"font-weight: 400;\">A <\/span><a href=\"https:\/\/productcollective.com\/success-metrics-new-products-2\/\"><span style=\"font-weight: 400;\">success metric<\/span><\/a><span style=\"font-weight: 400;\"> (e.g., conversions, clicks, time on page)<\/span><\/p>\r\n<h3><span style=\"color: #ed3130;\"><b>Why is product A\/B testing important for product managers?<\/b><\/span><\/h3>\r\n<p><span style=\"font-weight: 400;\">For the most part, A\/B testing has been used in marketing and advertising to find the ideal combination of variables that perform the best. But A\/B testing can be used by product managers as well.\u00a0<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">Often, product experimentation happens in the design phase. Research participants are shown a mockup and asked to interact with it or provide feedback. With <\/span><span style=\"font-weight: 400;\">A\/B testing, product managers<\/span><span style=\"font-weight: 400;\"> can launch multiple versions of a layout or design to figure out which works better. Not only does this save time from one-on-one research, it also provides objective feedback on which performs better. With design-based research, people are reacting to a concept or idea, not a live product. With A\/B testing, you gain insight on what works when the design or layout is put into the hands of real users.<\/span><\/p>\r\n<h3><strong><span style=\"color: #ed3130;\">How to do A\/B testing in product management: 5 tips to nail A\/B test in product management<\/span><\/strong><\/h3>\r\n<p><span style=\"font-weight: 400;\">Trying to find the right design for your product but running into conflicting opinions? An A\/B test can provide you with tie-breaking user insights. Here are five steps to leverage<\/span><span style=\"font-weight: 400;\"> A\/B testing for product management<\/span><span style=\"font-weight: 400;\">:<\/span><\/p>\r\n<h4>1. Develop a hypothesis<\/h4>\r\n<p><span style=\"font-weight: 400;\">The first step to successful A\/B testing is to develop a hypothesis. Think back to when you learned the scientific method. A hypothesis is what you expect to happen. A good hypothesis is something you can prove either correct or incorrect.<\/span><\/p>\r\n<p><b>Three questions to ask yourself when developing a hypothesis:<\/b><\/p>\r\n<ul>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">How do you think users will interact with each variation?\u00a0<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What do you expect the results to be?<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Why do you think that?<\/span><\/li>\r\n<\/ul>\r\n<p><span style=\"font-weight: 400;\">Let\u2019s use the example of call-to-action (CTA) button color. If your current button color is green, you might want to test that against a different color, like orange. This is what your hypothesis would look like: \u201cIf we use an orange CTA button, we\u2019ll see an increase in conversions since many other SaaS companies are seeing orange buttons outperform other colors.\u201d<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">If you don\u2019t have a clear hypothesis or rationale for your hypothesis, it might not be an element you\u2019re ready to test.<\/span><\/p>\r\n<h4>2. Decide if the test is worth it<\/h4>\r\n<p><span style=\"font-weight: 400;\">Now that you know your hypothesis, think about the potential impact. Will the results of this test drive significant results? If not, is there a different test you could run that might make more of an impact?<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">While the motto \u201cEverything is worth testing\u201d is a great one, it\u2019s also worth thinking about effort vs. impact. If the test will take time and effort, but not measurably move the needle, it\u2019s probably not worth it.<\/span><\/p>\r\n<h4>3. Design the test<\/h4>\r\n<p><span style=\"font-weight: 400;\">Once you\u2019ve established your hypothesis and decided it\u2019s an impactful test, it\u2019s time to design your test.\u00a0<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">You\u2019ll need to determine the following:<\/span><\/p>\r\n<ul>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Who is the audience for this test? This could be a subset or all of your user base or prospects.<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">How long will you run the test for? You may be short on time, which can be a consideration for determining the length of your test. But you usually want to run a test until you see a difference that has statistical significance.<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What will the test look like?\u00a0<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What tool will you use for testing? (Check out our A\/B testing tool recommendations below.)<\/span><\/li>\r\n<\/ul>\r\n<p><span style=\"font-weight: 400;\">With this criteria in mind, you can build and deploy your A\/B test.<\/span><\/p>\r\n<h4>4. Run the test<\/h4>\r\n<p><span style=\"font-weight: 400;\">This is the fun part! It\u2019s time to push your test live. Once the test is up and running, you can start thinking about other tests you might run and get those in your backlog.<\/span><\/p>\r\n<h4>5. Measure the results<\/h4>\r\n<p><span style=\"font-weight: 400;\">Once you\u2019ve hit the predetermined length of time\u2014or have a large enough sample size\u2014you can measure your test KPIs.<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">Since A\/B tests are only two variables and drive equal traffic to both, you\u2019ll simply compare one to the other. Which one drove better results?<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">In the case of our CTA button, we might want to look at two different measures of success: Clicks and subsequent conversions. You may find that just because the initial measure of success (a click) was higher doesn\u2019t mean that the downstream impact (conversion or interaction) is higher.<\/span><\/p>\r\n<h3><span style=\"color: #ed3130;\"><b>7<\/b><b> product management A\/B testing tools<\/b><\/span><\/h3>\r\n<p><span style=\"font-weight: 400;\">A\/B testing tools can do a lot of the heavy lifting when it comes to running the actual split test.\u00a0<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">When it comes to <\/span><span style=\"font-weight: 400;\">A\/B testing for product managers<\/span><span style=\"font-weight: 400;\">, here are some of the most popular tools you can use:<\/span><\/p>\r\n<h4>VWO<\/h4>\r\n<p><a href=\"https:\/\/vwo.com\/\"><span style=\"font-weight: 400;\">VWO<\/span><\/a><span style=\"font-weight: 400;\"> is a conversion optimization tool that\u2019s great for A\/B testing. VWO lets you run multiple tests at the same time by allocating traffic to each test. This reduces interference and allows for cleaner data. VWO also allows you to run tests based on a user\u2019s on-site behavior. For example, VWO can automatically enroll someone in a test once they click on an element of your site or based on the amount of time they\u2019ve spent on each page.<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">A key benefit to VWO is its asynchronous code, which reduces loading times while tests are running.\u00a0<\/span><\/p>\r\n<h4>Google Optimize<\/h4>\r\n<p><a href=\"https:\/\/optimize.google.com\/optimize\/home\/\"><span style=\"font-weight: 400;\">Google Optimize<\/span><\/a><span style=\"font-weight: 400;\"> is a great free option for A\/B testing. If you have Google Analytics set up on your web-based application, you can use that data to identify areas of your product that need improvement. From there, you can test and measure different versions. Google Optimize uses Bayesian statistical methods to model the real-world performance of your experiments.<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">Why would you not use Google Optimize? To get the most out of the platform, you need a certain amount of technical prowess.\u00a0<\/span><\/p>\r\n<h4>Optimizely<\/h4>\r\n<p><a href=\"https:\/\/www.optimizely.com\/\"><span style=\"font-weight: 400;\">Optimizely<\/span><\/a><span style=\"font-weight: 400;\"> is a powerful tool for A\/B testing that was built for enterprise organizations. Optimizely uses a visual editor to make getting your A\/B test up and running simple\u2014even without developer support. Exclusion groups allow you to safely run multiple experiments at the same time. Optimizely\u2019s program management functionality allows you to scale experimentation and collaboration by collecting ideas, prioritizing projects, and managing experiments across your organization.<\/span><\/p>\r\n<h4>Omniconvert<\/h4>\r\n<p><a href=\"https:\/\/www.omniconvert.com\/\"><span style=\"font-weight: 400;\">Omniconvert<\/span><\/a><span style=\"font-weight: 400;\"> is a conversion rate optimization tool for developers, startups, and ecommerce companies. It uses CSS and Javascript to give you complete control over variation code. You can also reuse code between variations to make it easier to run new experiments, or leverage cookies, data layer attributes, or UTM parameters for advanced segmentation.\u00a0<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">A big differentiator for Omniconvert\u2019s CRO tool (Explore) is that it uses a CDN cache bypass, allowing you to run live tests immediately. Due to its technical nature, Omniconvert works best for product teams that have a developer who can help create and deploy A\/B tests.<\/span><\/p>\r\n<h4>LaunchDarkly<\/h4>\r\n<p><a href=\"https:\/\/launchdarkly.com\/\"><span style=\"font-weight: 400;\">LaunchDarkly<\/span><\/a><span style=\"font-weight: 400;\"> allows you to continuously improve your software by gathering data on the impact of new features.\u201d\u00a0<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">Something that\u2019s unique to LaunchDarkly\u2019s model is that they make experimentation part of the normal delivery process. LaunchDarkly builds A\/B tests into feature flags. Each time you <\/span><a href=\"https:\/\/productcollective.com\/let-users-know-new-features\/\"><span style=\"font-weight: 400;\">launch a new feature<\/span><\/a><span style=\"font-weight: 400;\">, it\u2019s wrapped in a feature flag. You can launch different variations and LaunchDarkly will identify the winning variant, then you can roll out the ideal version to all instances.\u00a0<\/span><\/p>\r\n<h4>Inspectlet<\/h4>\r\n<p><a href=\"https:\/\/www.inspectlet.com\/feature\/ab-testing\"><span style=\"font-weight: 400;\">Inspectlet<\/span><\/a><span style=\"font-weight: 400;\"> is a lot like the other A\/B testing tools in that it\u2019s easy to install via a short Javascript snippet, you can build tests using a visual editor, and it tracks your experiment parameters. What\u2019s neat about Inspectlet is it also includes session recordings and dynamic heatmaps. These tools will help you understand the answers to these questions:<\/span><\/p>\r\n<ul>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Are there sections users are spending a lot of time on?\u00a0<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Where are they clicking?\u00a0<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Where are they scrolling without stopping?\u00a0<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Where are people getting confused or frustrated?<\/span><\/li>\r\n<\/ul>\r\n<p><span style=\"font-weight: 400;\">These insights can help you pinpoint areas for testing.\u00a0<\/span><\/p>\r\n<h4>AB Tasty<\/h4>\r\n<p><a href=\"https:\/\/www.abtasty.com\/\"><span style=\"font-weight: 400;\">AB Tasty<\/span><\/a><span style=\"font-weight: 400;\"> is another A\/B and multivariate testing tool. The functionality is a lot like the other A\/B testing tools above, with two standout features:<\/span><\/p>\r\n<ol>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AB Tasty sends experiment performance data directly to your analytics tool for easy analysis.<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AB Tasty offers an ROI dashboard that tracks the impact of your tests compared to the original. This helps you attribute gains in revenue to your experiments\u2014making it easy to get buy-in from stakeholders for the next experiment.<\/span><\/li>\r\n<\/ol>\r\n<h3><span style=\"color: #ed3130;\"><b>Key Takeaways<\/b><\/span><\/h3>\r\n<p><span style=\"font-weight: 400;\">If you\u2019re a product manager, run experiments regularly. A\/B test results can help you improve the customer experience in your current product\u2014and make sure new product development meets user\u2019s expectations. Try leveraging an A\/B testing tool to make experimentation quick and simple.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>Data is critical to the decision-making process of product managers (PMs). And there are so many ways to gather data\u2014from qualitative feedback to usability studies. One time-tested tool for gathering real-time user insights is A\/B testing. A\/B testing in product management allows PMs to test specific elements of the user experience, such as copy, user [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":18011,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"inline_featured_image":false,"footnotes":""},"categories":[372],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A\/B Testing in Product Management: Definition &amp; Overview<\/title>\n<meta name=\"description\" content=\"A\/B testing in product management allows PMs to test specific elements of the user experience, such as copy, user interface, or design.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/productcollective.com\/ab-testing-in-product-management\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A\/B Testing in Product Management: Definition &amp; 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