Dashboard Templates
Track how well you retain users over time with cohort-based retention charts built from your own event data.
Retention measures whether the users a product acquires keep coming back, usually tracked as the percentage of users still active a set number of days after their first visit or transaction, such as Day-7, Day-30, or Day-90 retention. Cohort analysis groups users by when they first appeared, a weekly or monthly cohort, and compares how each group’s retention holds up over time, making it possible to see whether retention is improving as a product changes.
For onchain and DeFi apps, retention is often harder to sustain than in typical consumer apps because switching costs are low and incentive-driven usage, like airdrops or points programs, can inflate short-term activity without producing lasting engagement. That’s why teams increasingly track retention drivers and re-engagement signals alongside the core retention curves.
What’s included
Day-7, Day-30, and Day-90 retention: three KPI numbers, last 90 days, showing the share of users still active 7, 30, and 90 days after their first event.
Resurrection rate: a KPI number for users who went quiet for 30 or more days and then came back, a win-back signal.
Weekly retention cohorts: a built-in cohort heatmap tracking users who return to fire the same connect event again.
Weekly retention cohorts by activity: a table version of the heatmap using any event, not just connect, to define a return.
Retention by first feature: a table showing day-N retention broken down by the first custom event each user ever fired, revealing which entry points retain best.
Retention drivers: a table comparing day-30 retention for users who did each custom event against your overall baseline, in percentage points.
Re-entry events: a table showing what events resurrected users fire first, hinting at what brings people back.
Use this dashboard to see whether the users you acquire keep coming back, which features predict long-term retention, and what wins users back after they go quiet.
Frequently Asked Questions
1. What is cohort retention analysis?
Cohort retention analysis groups users by when they first appeared, then tracks what percentage of each cohort is still active in the days or weeks after, revealing whether retention is improving over time.
2. How is retention rate calculated?
Retention rate is the percentage of users from a starting cohort who are still active N days after their first event, calculated as the number of users active on day N divided by the total size of the original cohort.
3. What is a good retention rate for a Web3 or DeFi app?
Retention benchmarks vary widely, but Day-30 retention in the 20 to 40 percent range is common for engaged DeFi products, while incentive-driven apps often see retention drop sharply once rewards end. Comparing retention across cohorts over time matters more than any single benchmark.
4. What is the difference between retention and churn?
Retention and churn are two views of the same behavior: retention measures the share of users who stay active, while churn measures the share who stop. A 70 percent Day-30 retention rate is the same as a 30 percent Day-30 churn rate.
5. What charts are included in the User Retention & Cohorts Dashboard?
Day-7, Day-30, and Day-90 retention KPIs, a Resurrection rate KPI, a weekly retention cohort heatmap, and tables for retention by activity, by first feature, retention drivers, and re-entry events.
6. What’s the difference between the cohort heatmap and the cohort table?
The heatmap tracks users who return to fire the same connect event again. The table version uses any event to define a return, giving a broader view of activity.
7. What is resurrection rate?
It’s the share of users who went quiet for 30 or more consecutive days and then came back, a signal for how well re-engagement efforts are working.
8. Which chart shows what brings churned users back?
Re-entry events: it shows the events resurrected users fire first after their gap, hinting at what wins them back.
9. Why does Day-90 retention look sparse?
Day-90 retention needs a 90 day window to populate, so in a 90 day lookback it only captures users whose very first event happened right at the start of the range. Use a longer date range for more data points.
10. Which features predict the best retention?
Check the Retention by first feature and Retention drivers tables. They show day-N retention broken down by each user’s first custom event, and compare retention for users of each event against the overall baseline.