> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cryptique.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Retention
> Measure how often users return and engage with your product
## 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
The event that "births" users into the cohort:
```
Birth Event: signup_completed
(or: wallet_connect, first_transaction, etc.)
```
The event that indicates a user "returned":
```
Return Event: any_active_event
(or: transaction, swap_completed, page_view, etc.)
```
Set the cohort interval and retention window:
```
Cohort Interval: Weekly (users grouped by week they were born)
Retention Window: 8 weeks
```
Choose when users count as retained:
* **On or After**: Any time from that day/week onward
* **On**: 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.
### 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.
## 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
1. Configure your retention analysis
2. Click **Save**
3. Name it: "Weekly Transaction Retention by Chain"
### 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.
## Next Steps
Understand user paths
Create user segments