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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.

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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?

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Quick Start

1

Define Birth Event

The event that “births” users into the cohort:
Birth Event: signup_completed
(or: wallet_connect, first_transaction, etc.)
2

Define Return Event

The event that indicates a user “returned”:
Return Event: any_active_event
(or: transaction, swap_completed, page_view, etc.)
3

Choose Time Frame

Set the cohort interval and retention window:
Cohort Interval: Weekly (users grouped by week they were born)
Retention Window: 8 weeks
4

Select Criteria

Choose when users count as retained:
  • On or After: Any time from that day/week onward
  • On: Only on exactly that day/week

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Retention Criteria

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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.

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On

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.

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Cohort Intervals

Group users by when they were “born”:

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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.

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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.

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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.

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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

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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)

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Retention Curves

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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)

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Comparing Curves

100% ─┬────────────────────────
      │\
      │ \__ Pro users
      │    \_______________
  50% ─┤
      │  \
      │   \___ Free users
      │       \______
   0% ─┴─────────────────────────

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Filters

Focus on specific user segments:

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Birth Event Filters

Filter who enters the cohort: 
Birth Event: signup_completed
  └── where: signup_source = "twitter"

Only users who signed up from Twitter

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Return Event Filters

Filter what counts as “returning”: 
Return Event: transaction
  └── where: chain = "ethereum"

Users must transact on Ethereum to count as retained

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User Property Filters

Filter by user attributes: 
User Filter:
├── plan = "pro"
├── AND country = "US"

Retention for US Pro users only

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Breakdowns

Compare retention across segments:

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By User Property

Retention by plan:
├── Pro: 45% → 32% → 28%
├── Free: 38% → 22% → 15%
└── Trial: 25% → 12% → 8%

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By Birth Event Property

Retention by signup_source:
├── Twitter: 40% → 28% → 22%
├── Discord: 52% → 38% → 32%
└── Google: 35% → 20% → 14%

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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

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Web3 Retention Examples

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Transaction Retention

Track on-chain engagement: 
Birth Event: First transaction
Return Event: Any transaction

Question: Do users keep transacting?

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Protocol Stickiness

Birth Event: First swap
Return Event: swap_completed
Filter by: chain = "arbitrum"

Question: Do Arbitrum users keep swapping?

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NFT Collector Retention

Birth Event: First NFT mint
Return Event: Any NFT activity (mint, transfer, sale)

Question: Do minters become collectors?

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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?

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Analyzing Retention

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Key Metrics

MetricDescriptionGood Benchmark
Day 1Next-day return>40%
Day 7Week 1 return>25%
Day 30Month 1 return>15%
PlateauStable long-term retention>10%

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Signs of Healthy Retention

✅ Curve flattens (stabilizes)
✅ Newer cohorts retain better
✅ Power users have much higher retention
✅ Key features correlate with retention

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Warning Signs

❌ Curve never flattens (constant churn)
❌ Newer cohorts performing worse
❌ No difference between segments
❌ Day 1 retention below 30%

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Best Practices

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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)

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Match Return to Product

Social app: Return = any_active_event
DeFi protocol: Return = transaction
Marketplace: Return = purchase OR listing
SaaS: Return = feature_used

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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

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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

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Saving & Sharing

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Save Report

  1. Configure your retention analysis
  2. Click Save
  3. Name it: “Weekly Transaction Retention by Chain”

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Add to Board

  1. Save the report
  2. Click Add to Board
  3. Select dashboard
All reports are added to boards for organization and sharing.

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Next Steps

Flows

Understand user paths

Cohorts

Create user segments