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Overview Retention analysis measures how well you keep users engaged over time. Track whether users return after their first visit, first transaction, or any milestone event—and understand what drives long-term engagement.
Use Cases
What percentage of new users return after 7 days?
Is our retention improving over time?
How does retention compare across user segments?
Do users continue transacting after their first swap?
What’s the retention curve for NFT collectors?
Are users on different chains retained differently?
Do users who use feature X have better retention?
Does completing onboarding improve retention?
How does wallet enrichment affect engagement?
Quick Start
Define Birth Event
The event that “births” users into the cohort: Birth Event: signup_completed (or: wallet_connect, first_transaction, etc.)
Define Return Event
The event that indicates a user “returned”: Return Event: any_active_event (or: transaction, swap_completed, page_view, etc.)
Choose Time Frame
Set the cohort interval and retention window: Cohort Interval: Weekly (users grouped by week they were born) Retention Window: 8 weeks
Select Criteria
Choose when users count as retained:
On or After : Any time from that day/week onwardOn : Only on exactly that day/week Retention Criteria On or After (Default) User counts as retained if they returned on or after the specified time period:
Day 7 retention (On or After): User returned on Day 7, 8, 9, 10... → Retained ✓ User returned on Day 1-6 only → NOT Retained ✗
Best for: Understanding cumulative engagement over time.
User counts as retained only if they returned exactly on that period:
Day 7 retention (On): User returned exactly on Day 7 → Retained ✓ User returned on Day 6 or Day 8 → NOT Retained ✗
Best for: Understanding precise return patterns.
Cohort Intervals Group users by when they were “born”:
Daily Cohorts Daily cohorts over 14 days: ├── Jan 1 cohort: Users born on Jan 1 ├── Jan 2 cohort: Users born on Jan 2 ├── ... └── Jan 14 cohort: Users born on Jan 14 See: Day 1, Day 2, ... Day 14 retention for each
Best for: Short-term retention analysis, fast-moving products.
Weekly Cohorts Weekly cohorts over 8 weeks: ├── Week 1 cohort: Users born Jan 1-7 ├── Week 2 cohort: Users born Jan 8-14 ├── ... └── Week 8 cohort: Users born Feb 19-25 See: Week 1, Week 2, ... Week 8 retention for each
Best for: Most retention analyses, standard timeframe.
Monthly Cohorts Monthly cohorts over 6 months: ├── January cohort: Users born in January ├── February cohort: Users born in February ├── ... └── June cohort: Users born in June See: Month 1, Month 2, ... Month 6 retention for each
Best for: Long-term retention, subscription products.
Custom Brackets Define custom retention windows:
Custom brackets: [1, 3, 7, 14, 30, 60, 90] View retention at: ├── Day 1 ├── Day 3 ├── Day 7 ├── Day 14 ├── Day 30 ├── Day 60 └── Day 90
Reading the Retention Table │ Day 0 │ Day 1 │ Day 7 │ Day 14 │ Day 30 ───────────┼───────┼───────┼───────┼────────┼──────── Jan 1-7 │ 1,000 │ 42% │ 28% │ 22% │ 15% Jan 8-14 │ 850 │ 45% │ 30% │ 24% │ 18% Jan 15-21 │ 1,200 │ 38% │ 25% │ 20% │ - Jan 22-28 │ 950 │ 40% │ 27% │ - │ - ───────────┼───────┼───────┼───────┼────────┼──────── Average │ 1,000 │ 41% │ 28% │ 22% │ 17%
Column meanings:
Day 0 : Number of users in cohort (birth event count)Day N : Percentage who returned on/after day N
Row meanings:
Each row is a cohort (users born in that period)
Newer cohorts have fewer data points (diagonal empty)
Retention Curves Visualizing Retention 100% ─┬──────────────────────── │\ │ \ 50% ─┤ \___ │ \_____ │ \________ 0% ─┴───────────────────────── Day 0 7 14 21 28 35
Healthy retention : Curve flattens (users who stay, stay)
Concerning retention : Curve keeps declining (ongoing churn)Comparing Curves 100% ─┬──────────────────────── │\ │ \__ Pro users │ \_______________ 50% ─┤ │ \ │ \___ Free users │ \______ 0% ─┴─────────────────────────
Filters Focus on specific user segments:
Birth Event Filters Filter who enters the cohort:
Birth Event: signup_completed └── where: signup_source = "twitter" Only users who signed up from Twitter
Return Event Filters Filter what counts as “returning”:
Return Event: transaction └── where: chain = "ethereum" Users must transact on Ethereum to count as retained
User Property Filters Filter by user attributes:
User Filter: ├── plan = "pro" ├── AND country = "US" Retention for US Pro users only
Breakdowns Compare retention across segments:
By User Property Retention by plan: ├── Pro: 45% → 32% → 28% ├── Free: 38% → 22% → 15% └── Trial: 25% → 12% → 8%
By Birth Event Property Retention by signup_source: ├── Twitter: 40% → 28% → 22% ├── Discord: 52% → 38% → 32% └── Google: 35% → 20% → 14%
By Cohort Date Default view—compare how retention changes over time:
Are newer cohorts retaining better? ├── Jan cohorts: 40% → 25% → 18% ├── Feb cohorts: 42% → 28% → 22% └── Mar cohorts: 45% → 32% → 25% → Yes! Retention is improving
Web3 Retention Examples Transaction Retention Track on-chain engagement:
Birth Event: First transaction Return Event: Any transaction Question: Do users keep transacting?
Protocol Stickiness Birth Event: First swap Return Event: swap_completed Filter by: chain = "arbitrum" Question: Do Arbitrum users keep swapping?
NFT Collector Retention Birth Event: First NFT mint Return Event: Any NFT activity (mint, transfer, sale) Question: Do minters become collectors?
Feature-Driven Retention Birth Event: used_advanced_feature Return Event: any_active_event Compare to: Birth Event: did_not_use_advanced_feature Return Event: any_active_event Question: Does the feature improve retention?
Analyzing Retention Key Metrics Metric Description Good Benchmark Day 1 Next-day return >40% Day 7 Week 1 return >25% Day 30 Month 1 return >15% Plateau Stable long-term retention >10%
Signs of Healthy Retention ✅ Curve flattens (stabilizes) ✅ Newer cohorts retain better ✅ Power users have much higher retention ✅ Key features correlate with retention
Warning Signs ❌ Curve never flattens (constant churn) ❌ Newer cohorts performing worse ❌ No difference between segments ❌ Day 1 retention below 30%
Best Practices Choose Meaningful Events ✅ Good birth events: - signup_completed (clear milestone) - wallet_connect (clear commitment) - first_transaction (value delivered) ❌ Poor birth events: - page_view (too broad) - click (too minor)
Match Return to Product Social app: Return = any_active_event DeFi protocol: Return = transaction Marketplace: Return = purchase OR listing SaaS: Return = feature_used
Segment Meaningfully Compare segments that inform action:
✅ Actionable: - By signup_source (optimize marketing) - By completed_onboarding (improve onboarding) - By user_tier (price/feature adjustments) ❌ Not actionable: - By random_id - By timestamp
Allow Enough Time Retention analysis needs mature data:
For Day 30 retention with weekly cohorts: Need 5-6 weeks of data minimum For quarterly analysis: Need 6+ months of data
Saving & Sharing Save Report
Configure your retention analysis
Click Save
Name it: “Weekly Transaction Retention by Chain”
Add to Board
Save the report
Click Add to Board
Select dashboard
All reports are added to boards for organization and sharing.
Next Steps
Flows Understand user paths
Cohorts Create user segments
