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Measure the stickiness of your product with different cohorts of users.

Measure the stickiness of your product with different cohorts of users.

Understanding user retention and engagement allows you to identify which user cohorts are more likely to take specific actions within a given time period. You can select which event to use to calculate week-on-week user retention. This gives product teams better insight into retention rates for specific features and user flows. For example, measure retention based on:
  • Transaction completions
  • Feature usage
  • Wallet connections
  • Custom events specific to your app

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How to analyze user retention

Retention analysis shows how many users come back to your app over time. This guide walks you through reading retention charts and improving user stickiness.

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What is a cohort retention chart?

A cohort is a group of users who started using your app in the same time period (e.g., users who first connected in Week 1). Retention tracks what percentage of each cohort returns in subsequent weeks. This example shows: of users who started in Week 0, 40% returned in Week 1, 25% in Week 2, etc.

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Step 1: Create a Retention chart

Retention is a chart type you add to a dashboard (board), not a separate nav page.
  1. Go to the Formo Dashboard
  2. Select your project
  3. Open Dashboards in the left navigation, then open or create a board
  4. Click Add Chart and choose the Retention chart type

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Step 2: Choose the entry and retention events

Configure how cohorts are formed and what counts as “coming back”:
For crypto apps, transaction retention is usually the most meaningful metric because it shows who’s actually using your app.
You can also narrow the cohort with the Segment filter: device, browser, OS, country, volume, revenue, points, referrer, referrer URL, referral, builder codes, or UTM parameters. Separately, a cohort label filter restricts the cohort to wallets that carried a given label as of their entry week. Alternatively, switch Retention by to User label to retain on a wallet label value (e.g. open_interest > 10000) instead of an event.

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Step 3: Read the retention matrix

The retention chart displays:
  • Rows: Cohorts, grouped by start week, plus a pinned “Mean retention” row (the unweighted average across all visible cohorts)
  • Columns: Weeks after the cohort’s start (Week 0 through however many weeks have fully elapsed)
  • Cells: Percentage of the cohort still active in that week
Retention Chart

Cohort retention shows which user groups stick around.

Color coding: cells shade from light to dark green as the retention percentage increases (darkest at 100%). Week 0 is always 100% by definition. Weeks whose observation window hasn’t fully elapsed yet are shown blank or dashed rather than colored.

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Step 4: Identify patterns

Healthy retention curve:
  • Sharp drop in Week 1 (normal)
  • Gradual stabilization by Week 3-4
  • Flat line after stabilization (loyal users)
Concerning patterns:
  • Continuous decline without stabilization
  • Large drop-offs in later weeks
  • Significant variance between cohorts

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Step 5: Compare cohorts

Look for cohorts with better or worse retention: Questions to ask:
  • Did a product change improve retention for newer cohorts?
  • Do users from certain campaigns retain better?
  • Is there seasonal variation in retention?

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Improving retention

Based on your retention analysis: If Week 1 drop-off is too high:
  • Improve onboarding experience
  • Send follow-up notifications
  • Add incentives for early engagement
If long-term retention is declining:
  • Add new features or content
  • Implement re-engagement campaigns
  • Analyze churned users for common patterns
If certain cohorts retain better:
  • Identify what made them different
  • Replicate successful acquisition channels
  • Apply learnings to current users

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Using retention with other features