Here\u2019s how it works: You group users who share a meaningful characteristic (e.g., everyone who completed onboarding) and then track how those cohorts behave relative to one another. If one group consistently retains better, engages more deeply, or churns less, you\u2019ve identified a behavior that likely contributes to long-term stickiness.<\/p>
With product analytics<\/a> tools, you can segment users, quantify retention deltas, and pinpoint which feature interactions create long-term users versus the ones people bounce off right before they churn.<\/p> While you can slice data infinitely, I focus on two specific cohort types that yield the highest ROI. One diagnoses your product\u2019s health<\/a>; the other validates the value of your features.<\/p> This type of cohort analysis groups users based on when they signed up or made a purchase. I use acquisition cohorts to audit the long-term health of our user onboarding<\/a>. For example, to measure the impact of a June feature release, I compare the retention rate of the June cohort against the May cohort. This isolates variables such as:<\/p> Behavioral cohorts tell you why users disengage. These are cohorts based on specific actions users took (or failed to take) inside your product. I run experiments comparing different cohorts to prove feature adoption<\/a> value:<\/p> If Cohort A has higher retention, you have found a correlation. This proves that getting users to the reporting feature early is critical for customer stickiness<\/a>. You define these behaviors using custom events<\/a> in your analytics tool.<\/p> Running cohort analysis is straightforward. Analytics platforms handle the heavy lifting (like date alignment, statistical calculations, and data visualization<\/a>) so you can focus on interpreting the results.<\/p> Open the Retention page from your analytics dashboard or through the ‘Create Report’ button.<\/p> Then, decide whether to analyze at the user level or the company level. For B2C products or individual user interactions, choose user-level analysis. For B2B SaaS where you track accounts, you can switch to company-level to measure how entire organizations engage with your product<\/a> over time.<\/p> Next, specify which actions matter for your retention analysis<\/a>. The starting metric defines who enters your cohort. This metric could be “Signed Up,” “Completed Onboarding,” or “Created First Project.”<\/p> The returning metric defines what retention means for your product\u2014typically actions like “Logged In,” “Used Core Feature,” or “Invited Team Member.”<\/p> You can pull these metrics from three sources:<\/p> Decide whether to count unique users or total occurrences. Unique users count each person once per cohort entry, which works for most retention analysis. If a user signs up on Monday, they’re measured from that Monday forward.<\/p> On the other hand, the total count allows users to re-enter your cohort multiple times during the analysis period, helping track repeated behaviors, such as weekly feature usage<\/a>.<\/p> Filter your data to focus on the cohorts that matter most. You can segment by:<\/p> For example, you can filter to show only users who completed a specific action during onboarding, and compare their retention with those who didn’t.<\/p> Click ‘Run Query’ and configure your view settings. Choose your cohort chart period (last 7 days, 30 days, or custom range), select platform (web, mobile, or both), and set your data point interval (daily, weekly, or monthly).<\/p> You’ll see two visualizations:<\/p> For example, if your starting event is “Created Report” and returning event is “Viewed Report,” a row showing October 6th with 15 users means those 15 people created their first report that day.<\/p> If 66.7% returned the same day and 33.3% returned the next day, you know that one-third of users came back within 24 hours to view their work: a strong signal of user engagement.<\/p> Here’s how we decode cohort tables to find valuable insights.<\/p> The retention table<\/a> is a grid that tells a story of how users drop off. When you open a retention report<\/a>, read it in these three directions to gain a deeper understanding and diagnose different problems:<\/p> Vertical read: <\/strong>Scan down a specific column, such as “Month 2.” You are comparing how well you retained January’s users vs. March’s users at the same point in their lifecycle. If numbers increase as you go down, your product is getting stickier. If they drop, you’re either attracting lower-quality leads or recent product changes are creating user friction<\/a>.<\/p> Horizontal read:<\/strong> Read a single row from left to right to see how a specific group behaves over time. Your goal is to flatten the curve. If the line stabilizes at 20%, you have a sustainable business with a core user base. If it trends to zero, you probably have a product-market fit<\/a> problem.<\/p> Diagonal read:<\/strong> The diagonal represents calendar time. Let\u2019s say you see a sudden drop across multiple cohorts (rows) at the same specific point (diagonal). It indicates an external variable, such as a server outage or a pricing change<\/a>.<\/p> Cohort analysis tells you what is happening; qualitative data<\/a> tells you why. Combine the data<\/a> with these tools:<\/p> I recommend using behavioral cohorts<\/a> to compare retention between users who use a specific feature (e.g., “Export Report”) and those who don’t. If the “Export” group retains 40% better, that is your habit-forming feature.<\/p> To drive feature adoption<\/a>, direct users to this feature using subtle native tooltips<\/a>. For example, if a user hits Day 30 without touching your “Reports” tab but has logged in 15+ times, display a tooltip that says “See how teams like yours use Reports to cut review time by 40%.”<\/p> Cohort analysis without action is trivia. Apply the scientific method to your retention strategy<\/a> by treating every fix as a hypothesis you can validate:<\/p> Start by finding your steepest drop-off point. Use a product growth platform<\/a> to deploy a fix, and watch whether the curve flattens. Then iterate to increase customer retention.<\/p> These three mistakes will lead you to the wrong conclusions and waste time fixing problems that don’t exist. Avoid them to practice effective cohort analysis.<\/p> ClearCalcs<\/a>, a structural design platform, faced an issue: new users couldn’t find the right calculator within 10 minutes and churned before completing their calculations. Their NPS data<\/a> showed that existing customers praised the simplicity, while new users were overwhelmed by the number of options.<\/p> ClearCalcs used cohort analysis to segment activation by account type and analyzed it at the company level.<\/p> They discovered that paying accounts weren’t activating<\/a> until five months or later. The pattern: one person would purchase, add six or seven colleagues to the account, and those invited users wouldn’t log in for months.<\/p> They built personalized onboarding<\/a> flows. Welcome surveys segmented<\/a> users by role, goals, and company size.<\/p> Based on these answers, ClearCalcs routed users to specific calculator types relevant to their job.<\/p> CEO Chris Borzillo describes their approach:<\/p> \u201cWe use Userpilot to find 10,000 ways of how not to onboard someone\u2026in the hope that the 10,001 will show us the right way.\u201d<\/p><\/blockquote> The results: activation improved across every segment they tracked. Cohort analysis helped ClearCalcs to identify the problem, test solutions<\/a>, and validate which changes actually drove users to value faster.<\/p> We’ve come to the end of this guide to cohort analysis. With segmenting retention by behavior, you will identify their activation triggers faster, validate product changes with real data, and compound small retention wins into structural growth.<\/p>![\"Use](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/retention-report_5c6abce568492774e7286085cd7fa66e.gif)
<\/picture>Types of cohort analysis<\/h2>
1. Acquisition cohorts (the “when”)<\/h3>
![\"Cohort](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/cohort-analysis-dashboard-comparing-acquisition-cohorts_bbf828dc7bb5341b1a53d9f4bbeee443_800.png)
<\/picture>2. Behavioral cohorts (the “why”)<\/h3>
![\"Cohort](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/cohort-charts-in-userpilot-comparing-behavior-cohorts_dd5a753a72af50c0e499daebb854af40_800.png)
<\/picture>How to perform cohort analysis?<\/h2>
Navigate to retention reports and select your analysis level<\/h3>
![\"Getting](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/getting-started-to-create-a-retention-report_7e6022905d6a741512e1aca5ada10ec4_800.png)
<\/picture>![\"Choose](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/choose-the-level-youd-like-to-analyze-the-data_2326eda4fed1db2ea0c3f323cb015172_800.png)
<\/picture>Define your starting and returning metrics<\/h3>
Choose your measurement method<\/h3>
Apply filters to isolate specific segments<\/h3>
Analyze your retention trend and cohort table<\/h3>
![\"Choose](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/choose-your-preferred-data-presentation-option-as-you-run-your-query_3d95ca872370fba730bb6b1fea11d0d1_800.png)
<\/picture>![\"Analyze](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/analyze-your-retention-trend-and-retention-table-in-userpilot_d9bf909847dfab2b2ae7114918cbd7bd_800.png)
<\/picture>![\"Example](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/example-of-a-retention-table-showing-how-retention-measures-user-engagement-over-time_c2870df2684f9389e6d2b21c1f3ce37a_800.png)
<\/picture>Best practices when looking at customer cohort analysis<\/h2>
Identify patterns by decoding horizontal and vertical reads<\/h3>
![\"Example](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/example-of-a-retention-table_66a3f55220d7dfea8349fc3bc5a2160c_800.png)
<\/picture>Combine cohort analysis with qualitative data to get the full picture<\/h3>
![\"Combine](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/3-qualitative-steps_03adc6713acb256f01b265bcd097e8f7_800.png)
<\/picture>Use behavioral cohorts to drive feature adoption<\/h3>
![\"Example](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/tooltip-example_cb244e0c6b26def3e660eed3c0d4f36c_800.png)
<\/picture>Improve customer retention rates based on insights from cohort analysis<\/h3>
Common traps to avoid when performing cohort analysis<\/h2>
Cohort analysis example<\/h2>
![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/clearcalcs-activation-cohorts-by-segment_f2634c55546841d5d754bb29616c1944_800.png){kind=spacer}
<\/picture><\/div>![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/activation-cohort-analysis_f2634c55546841d5d754bb29616c1944_800.png)
<\/picture><\/div><\/div>![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/clearcalssegmentationbyrole_14fcf30f2433957db6153fc5c6bc2a63_800.png){kind=spacer}
<\/picture><\/div>![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/segmentationbygoal-clearcalcs_14fcf30f2433957db6153fc5c6bc2a63_800.png){kind=spacer}
<\/picture><\/div>![](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/companysize_14fcf30f2433957db6153fc5c6bc2a63_800.png)
<\/picture><\/div><\/div>![\"How](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/12\/segmentingtheflowsbycalculationtype_ccc29ad60f0c24b5a9a2c8e89c7618a5_800.png)
<\/picture>Start measuring retention the way it actually works<\/h2>