{"id":113394,"date":"2025-02-27T15:00:22","date_gmt":"2025-02-27T15:00:22","guid":{"rendered":"https:\/\/userpilot.com\/blog\/cohort-retention-analysis\/"},"modified":"2026-03-19T12:05:40","modified_gmt":"2026-03-19T12:05:40","slug":"cohort-retention-analysis","status":"publish","type":"post","link":"https:\/\/userpilot.com\/blog\/cohort-retention-analysis\/","title":{"rendered":"Cohort Retention Analysis 101: How to Measure User Retention?"},"content":{"rendered":"<p data-pm-slice=\"1 1 []\">What is cohort retention analysis? And how do you leverage it to reduce churn, including early churn through improved <a href=\"https:\/\/userpilot.com\/blog\/best-user-onboarding-experience\/\" target=\"_blank\" rel=\"noopener\">user onboarding experiences<\/a>, and <a href=\"https:\/\/userpilot.com\/solutions\/customer-retention-software\" target=\"_blank\" rel=\"noopener noreferrer\">boost your retention rates<\/a>?<\/p>\n<p>These are the main questions I will break down in this article, plus share a few intuitive tools to get the job done.<\/p>\n<h2>What is cohort retention analysis?<\/h2>\n<p>Cohort retention analysis is the process of <a href=\"https:\/\/userpilot.com\/blog\/customer-engagement-score\/\" target=\"_blank\" rel=\"noopener noreferrer\">tracking user engagement <\/a>with the product over time. However, instead of looking at the user population as a whole, it looks at specific user groups linked to specific events or timeframes.<\/p>\n<p>Have a look at the cohort analysis table below.<\/p>\n<p>The users are grouped into daily cohorts, based on when they subscribed to the product.<\/p>\n<p>From the chart, we can easily see that the retention rate for users who subscribed to the product on October 20, 2023 is 51.1% after 2 days, 85.1% after 4 days, and so on.<\/p>\n<figure style=\"width: 2850px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" src=\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/78bfa9c0-1821-4933-ae25-7a84e192aa7c-scaled.png\" alt=\"Cohort retention analysis in Userpilot.\" width=\"2850\" height=\"1650\" \/><figcaption class=\"wp-caption-text\">Cohort retention analysis<\/figcaption><\/figure>\n<h2>Why use cohort analysis to measure customer retention?<\/h2>\n<p>There are a number of benefits of using cohort analysis to track customer retention. Here are a few key ones:<\/p>\n<ul>\n<li>By measuring how well different groups are retained, cohort analysis helps you evaluate your retention strategies, identify the most successful ones, and make data-driven decisions to\u00a0and <a href=\"https:\/\/userpilot.com\/blog\/drive-retention\/\" target=\"_blank\" rel=\"noopener noreferrer\">improve your retention efforts<\/a>.<\/li>\n<li>Cohort analysis enables teams to track and <a href=\"https:\/\/userpilot.com\/blog\/analyze-customer-behavior\/\">analyze customer behavior<\/a> over a specific period and gain insights into long-term behavior trends.<\/li>\n<li>With cohort analysis, you can compare retention rates for different cohorts and identify behavior patterns or product changes that could be impacting them, like the frequency of use or updates.<\/li>\n<li>By analyzing cohort retention rates, SaaS businesses can establish the lengths of the customer relationship, predict the <a href=\"https:\/\/userpilot.com\/blog\/calculate-lifetime-value-saas\/\" target=\"_blank\" rel=\"noopener noreferrer\">customer lifetime value, <\/a>and use them to forecast future revenue.<\/li>\n<\/ul>\n<h2>Different types of customer retention cohort analysis<\/h2>\n<p>There are three key types of cohort retention data analysis: acquisition cohort, behavioral cohort, and predictive cohort analysis. Let\u2019s break them down and explore how each one can <a href=\"https:\/\/userpilot.com\/blog\/drive-retention\/\">drive better retention<\/a> insights.<\/p>\n<h3>Acquisition cohorts<\/h3>\n<p>Acquisition cohort analysis divides users into cohorts based on acquisition events. Examples of acquisition events include signing up for the product or subscribing to a paid version.<\/p>\n<p>This kind of analysis allows organizations to identify long-term retention patterns for each cohort and compare the effectiveness of various <a href=\"https:\/\/userpilot.com\/blog\/customer-retention-strategies\/\" target=\"_blank\" rel=\"noopener noreferrer\">retention strategies<\/a> over time. It also enables them to evaluate the impact of their acquisition and marketing campaigns.<\/p>\n<h3>Behavioral cohorts<\/h3>\n<p>Behavioral cohorts are made up of users who have or haven\u2019t completed a specific event within a specific time frame. For example, you could group your users based on whether they completed the <a href=\"https:\/\/userpilot.com\/blog\/onboarding-checklist-template\/\" target=\"_blank\" rel=\"noopener noreferrer\">onboarding checklists <\/a>or not.<\/p>\n<p>Behavioral cohorts analysis gives teams more granular insights than user acquisition cohorts because it tells them not only how many users have been retained but also why.<\/p>\n<h3>Prediction cohorts<\/h3>\n<p>Prediction cohorts are groups of users who are likely to show certain behaviors or patterns in the future, like churn. They are built using machine learning algorithms to analyze historical <a href=\"https:\/\/userpilot.com\/blog\/behavioral-data-types\/\" target=\"_blank\" rel=\"noopener noreferrer\">behavior data<\/a>.<\/p>\n<p>This type of cohort analysis enables teams to predict user behavior and act proactively to change it. For example, they could use them to maximize <a href=\"https:\/\/userpilot.com\/blog\/upsell-opportunities-saas\/\" target=\"_blank\" rel=\"noopener noreferrer\">upsell opportunities<\/a> by engaging users with contextual in-app messages.<\/p>\n<h2>How do you calculate cohort retention?<\/h2>\n<p>To calculate the <a href=\"https:\/\/userpilot.com\/blog\/retention-rate-meaning\/\" target=\"_blank\" rel=\"noopener noreferrer\">retention rate,<\/a> you divide the number of active users within a time period by the total number of users in the cohort.<\/p>\n<figure style=\"width: 1824px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" src=\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/05e30cff-dc43-41ee-9dad-ba0413a96be8.png\" alt=\"Formula to calculate cohort retention.\" width=\"1824\" height=\"1027\" \/><figcaption class=\"wp-caption-text\">Formula to calculate cohort retention.<\/figcaption><\/figure>\n<p>So if we go back to the retention table above, 47 users subscribed to the product on October 20, 2023. That\u2019s the total cohort size. On Day 1 (2nd day), there were 24 active users. Dividing 24 by 47 gives us a cohort retention rate of 51.1%.<\/p>\n<h2>What is a retention cohort analysis example?<\/h2>\n<p>Let\u2019s imagine you\u2019re a <a href=\"https:\/\/userpilot.com\/blog\/product-owner-vs-product-manager\/\" target=\"_blank\" rel=\"noopener noreferrer\">product manager or product owner<\/a> of a social media automation tool, like Hootsuite. You want to identify user behaviors that affect their retention.<\/p>\n<p>One of your hypotheses is that users who connect to a social media account have a higher retention rate.<\/p>\n<p>You start the analysis by choosing an acquisition cohort, like the new customers who signed up for the product in January 2023.<\/p>\n<p>Next, you break down the cohort into two behavioral cohorts: those who have connected to a social media account and those who haven\u2019t.<\/p>\n<p>After that, you have to plot the two cohort data over a specific time period and compare them. If there\u2019s a big discrepancy between the two, your hypothesis may be right.<\/p>\n<p>All that is left to do is act on the insights. For example, you could ensure that users do add their social accounts by adding the task to the new <a href=\"https:\/\/userpilot.com\/solutions\/user-onboarding-software\/\">user onboarding<\/a> checklist or prompting existing customers with <a href=\"https:\/\/userpilot.com\/blog\/in-app-messaging\/\" target=\"_blank\" rel=\"noopener noreferrer\">in-app messages.<\/a><\/p>\n<figure style=\"width: 1824px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" src=\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/b26f2a5e-557c-4cd7-9938-0a25e292f167.png\" alt=\"Cohort retention analysis example.\" width=\"1824\" height=\"1110\" \/><figcaption class=\"wp-caption-text\">Cohort retention analysis example.<\/figcaption><\/figure>\n<h2>How to analyze cohorts to improve the customer retention rate?<\/h2>\n<p>The example above shows you how cohort analysis can help you improve your retention figures. Let\u2019s look at different ways you can leverage your own <a href=\"https:\/\/userpilot.com\/blog\/cohort-analysis-saas\/\" target=\"_blank\" rel=\"noopener noreferrer\">cohort analysis <\/a>to streamline your customer retention strategy and boost customer retention rates.<\/p>\n<h3>Measure the effectiveness of new users\u2019 onboarding experience<\/h3>\n<p>The quality of <a href=\"https:\/\/userpilot.com\/blog\/customer-onboarding\/\" target=\"_blank\" rel=\"noopener noreferrer\">user onboarding<\/a> will have an impact on your retention figures.<\/p>\n<p>So what can you do if you see user engagement drop dramatically within 2 weeks of signing up for the product?<\/p>\n<p>First, choose the acquisition cohorts that clearly show the trend. How are they different from others where the drop is not so significant? Were there any changes introduced to the onboarding process before you started experiencing the drop?<\/p>\n<p>If yes, they could be the culprit.<\/p>\n<p>If not, you may need to look at user engagement with the onboarding assets in more detail.<\/p>\n<p>Start by breaking down the acquisition cohorts into two user segments: retained customers and churned ones. Next, use your <a href=\"https:\/\/userpilot.com\/blog\/product-usage-analytics-saas\/\" target=\"_blank\" rel=\"noopener noreferrer\">product analytics <\/a>to compare their engagement patterns. I recommend using funnel analysis to compare conversion rates between the two segments at each step of the onboarding process.<\/p>\n<figure style=\"width: 2940px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" src=\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/5dbee13a-2bb2-4d7e-9894-a2192d5dd682-scaled.png\" alt=\"Funnel analysis in Userpilot.\" width=\"2940\" height=\"1699\" \/><figcaption class=\"wp-caption-text\">Funnel analysis in <a href=\"https:\/\/userpilot.com\/userpilot-demo\/\">Userpilot<\/a>.<\/figcaption><\/figure>\n<h3>Pinpoint successful behaviors that increase user engagement and retention<\/h3>\n<p>You can use a similar process to identify<a href=\"https:\/\/userpilot.com\/blog\/user-behavioral-analysis\/\" target=\"_blank\" rel=\"noopener noreferrer\"> successful user behaviors<\/a> that are correlated with higher retention rates.<\/p>\n<p>First, use acquisition cohort analysis to identify the most successful users.<\/p>\n<p>Next, analyze their behavior patterns in detail using your analytics tool. Pay attention to the behaviors that distinguish them from the churned users. To ensure consistency, look at the same period of time for each cohort, for example, the first 2 weeks or a month.<\/p>\n<p>In the same way, you can identify churn warning signals.<\/p>\n<p>Once you\u2019ve identified these specific behaviors, you can create custom events and then visualize their impact on a <a href=\"https:\/\/userpilot.com\/blog\/retention-curve\/\">retention curve<\/a> to verify your findings.<\/p>\n<figure style=\"width: 2850px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" src=\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/f1326167-2620-4484-8db1-2389ef28d7c8.png\" alt=\"Trends analysis in Userpilot.\" width=\"2850\" height=\"1286\" \/><figcaption class=\"wp-caption-text\">Trends analysis<\/figcaption><\/figure>\n<h3>Identify drop-offs in the user journey<\/h3>\n<p>Identifying the drop-off points in the <a href=\"https:\/\/userpilot.com\/blog\/b2b-saas-customer-journey-map\/\" target=\"_blank\" rel=\"noopener noreferrer\">user journey <\/a>is the first step when tackling declining retention.<\/p>\n<p>To identify such drop-off points, all you have to do is plot the retention curve for a particular acquisition cohort. For example, with the social media automation tool, you could plot their daily engagement.<\/p>\n<p>The curve will show you immediately when the users experience friction. This could be after a day, two, or five. However, it won\u2019t tell you why users drop off.<\/p>\n<figure style=\"width: 2880px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" src=\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/55f71961-5aba-4320-9334-144dc1192d20-scaled.png\" alt=\"Retention curve in Userpilot.\" width=\"2880\" height=\"1226\" \/><figcaption class=\"wp-caption-text\">Retention curve<\/figcaption><\/figure>\n<p>For that, you will need to look at customer behavior in more detail using session replays to understand if they&#8217;re facing friction, rage clicking on a button, or failing to take the next step.<\/p>\n<figure style=\"width: 1334px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" src=\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/a77a4d13-60bc-49c7-bf8e-3082428fc201.png\" alt=\"Session replays in Userpilot. \" width=\"1334\" height=\"916\" \/><figcaption class=\"wp-caption-text\">Session replays<\/figcaption><\/figure>\n<h3>Track how a new feature launch impacted retention rates<\/h3>\n<p>Let\u2019s imagine that you\u2019ve <a href=\"https:\/\/userpilot.com\/blog\/how-to-measure-new-feature-success\/\" target=\"_blank\" rel=\"noopener noreferrer\">launched a new feature<\/a> in May and hope that this will increase your <a href=\"https:\/\/userpilot.com\/blog\/good-retention-rate-in-saas\/\" target=\"_blank\" rel=\"noopener noreferrer\">retention rate.<\/a><\/p>\n<p>Start by looking at acquisition cohorts from May onwards. We can see that the retention rate has indeed started growing since the new feature launch.<\/p>\n<p>Does it mean that the increase can be attributed to the new feature? Not quite.<\/p>\n<p>Correlation is not the same as causality. The retention boost could be a pure coincidence or there could be other factors that have had an impact.<\/p>\n<p>To test your hypothesis, you need to dig a bit deeper and use product analytics to analyze the engagement with the feature.<\/p>\n<p>If the retained users engaged with the feature, it may be the reason for the improved retention data, assuming there were no other significant changes to the product introduced at the same time.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cohort retention analysis is an essential tool for product and marketing teams aspiring to reduce customer churn and maximize customer lifetime value. If you want to see how to carry out detailed retention analysis in Userpilot and use the insights to boost user engagement, book the demo!<\/p>\n","protected":false},"author":56,"featured_media":262494,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[290,770],"tags":[1837,1669,460,687,285,614,406,1862,615,316],"class_list":["post-113394","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-updated","category-ux-analytics","tag-boost-engagement","tag-churn-analysis","tag-cohort-analysis-for-saas","tag-customer-churn","tag-customer-retention","tag-improve-retention","tag-lifetime-value","tag-retention-analysis","tag-retention-rate","tag-user-engagement"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Cohort Retention Analysis 101: How to Measure User Retention?<\/title>\n<meta name=\"description\" content=\"Cohort retention analysis is an essential tool for teams aspiring to reduce churn and maximize customer lifetime value. 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