Retention Analysis
Retention Analysis helps you understand how well you keep users engaged over time. It answers the question: "Of the users who did X on Day 0, what percentage came back and did Y on Day 1, Day 2, and so on?"
Using cohort-based analysis, you group users by when they first acted and then measure what share of them returns in each following period.
Two Modes
A switch at the top of the page selects how you want to look at retention:
- Standard - one analysis across all cohorts, with a curve, a cohort matrix, and derived insights.
- Compare cohorts - pick two or more saved cohorts and lay their retention side by side.
Common Use Cases
| Use Case | Cohort Entry Event | Return Event | What It Measures |
|---|---|---|---|
| Product Stickiness | Sign Up | Page View | Are users coming back after signing up? |
| Purchase Retention | Purchase | Purchase | Are customers buying again? |
| Feature Adoption | Feature X Used | Feature X Used | Is the feature sticky? Do users keep using it? |
| Onboarding Success | Signup Completed | Core Action | Do onboarded users engage with the product? |
| Content Engagement | First Article Read | Article Read | Do readers come back for more content? |
| App Engagement | App Install | App Open | Are users opening the app regularly? |
Accessing Retention Analysis
Navigate to Analytics → Retention from the sidebar.
The Config Bar
Both modes share the same set of controls at the top of the page.
Cohort entry event vs. Return event
These two events are the foundation of every retention analysis, and the difference matters:
- Cohort entry event - the action that defines when a user "starts". Users are grouped into cohorts by the day they first did this event (signup, first purchase, app install, and so on).
- Return event - the action that counts as coming back. For each following period, we check whether a user did this event.
The two can be the same (measuring repeat behaviour, like purchase → purchase) or different (measuring engagement after an entry point, like signup → session started).
Interval and Periods
| Setting | Options | Description |
|---|---|---|
| Interval | Daily, Weekly, Monthly | The size of each retention period. |
| Periods | 7, 14, 30 | How many periods to track. |
Choosing an interval:
- Daily - best for high-frequency products (social apps, games).
- Weekly - a good balance for most products.
- Monthly - best for low-frequency products (B2B, utilities).
Min cohort size
Sets the smallest cohort that counts, with options 1, 10, 25, or 50 (default 10). Cohorts below this size are excluded from the headline numbers and averages.
This guard matters because tiny cohorts distort everything. With a single-digit cohort, one user returning reads as 100% retention, and that noisy 100% would otherwise drag the average up and make retention look far healthier than it is. Requiring a minimum size keeps the summary honest.
Export CSV
Export the retention data as CSV for further analysis or reporting. The button is enabled once you have run an analysis.
Standard Mode
KPI Row
- Users analysed - total users across all included cohorts.
- Day 1 - retention at the first period.
- Day 7 - retention at period seven.
- Day N - retention at the final period you selected.
(Labels follow your interval - weeks or months instead of days.)
The Retention Curve
The curve shows retention over time as a size-weighted average of all cohorts. Weighting matters: a plain average would let a 2-user cohort sitting at 100% count just as much as a 996-user cohort sitting under 1%, which would badly misrepresent reality. Weighting by cohort size means each cohort influences the curve in proportion to how many users it actually contains.
The curve starts at D0 = 100% by definition - on the entry day, every user in the cohort is present.
The Cohort Matrix
Below the curve, the matrix shows one row per cohort and one column per period:
- The D0 column is omitted. It is always 100% by definition, so it is left out to keep attention on the periods that vary.
- An "All cohorts" row is pinned at the top as a weighted reference line - the same size-weighted average as the curve - so you can read each cohort against the overall trend.
- Heat scale - cells are tinted by retention. Zero cells are neutral: rather than being tinted at the cold end of the scale, they show a muted en dash, with the exact value on hover. This keeps genuine zeros from reading as "just low".
- Size bar - each cohort shows a bar for its size, and a warning icon appears on any cohort below your Min cohort size.
Two toggles adjust the matrix:
- Hide all-zero cohorts - removes rows that never returned.
- Show users - switches cells between absolute user counts and percentages.
Example Matrix (Weekly)
| Cohort | Week 1 | Week 2 | Week 3 | Week 4 |
|---|---|---|---|---|
| All cohorts | 49% | 35% | 30% | 26% |
| Jan 1-7 | 45% | 32% | 28% | 25% |
| Jan 8-14 | 48% | 35% | 30% | 27% |
| Jan 15-21 | 52% | 38% | 33% | - |
| Jan 22-28 | 50% | 36% | - | - |
Reading this:
- 45% of users from Jan 1-7 returned in Week 1, 32% in Week 2.
- The pinned "All cohorts" row gives the weighted benchmark to compare each cohort against.
- Newer cohorts have higher Week 1 rates, so retention appears to be improving.
What This Means
The What this means section turns the numbers into plain-language insights, typically covering:
- The day-1 (first period) retention rate.
- The habit-loop reading - whether the tail has flattened out, which is the signal that you have reached a core of habitual users who keep coming back.
- The best-performing cohort.
- How many cohorts were excluded for being below the minimum size.
Compare Cohorts Mode
Compare cohorts mode lets you place different behavioural cohorts next to each other.
- Switch to Compare cohorts.
- Choose your Cohort entry event and Return event.
- Pick 2 or more saved cohorts from the list.
- Run the comparison.
What you see:
- Per-cohort KPI cards - the headline retention numbers for each selected cohort.
- Comparison curve - each cohort's retention overlaid on one chart. The lines are drawn with straight segments connecting each interval, because retention is only defined at the interval points, not in between. Any cohort below the minimum size is drawn dashed with a warning, so small samples are visually flagged.
- Small-sample banner - an amber banner appears when one or more selected cohorts fall below the minimum size, reminding you to read those lines with caution.
- Key milestones table - Day 1 / Day 7 / Day 14 retention for each cohort, with the +/- point difference versus the first selected cohort so you can see who retains better or worse and by how much.
Use Cases for Cohort Comparison
| Comparison | What It Reveals |
|---|---|
| Power Users vs Casual Users | How engagement level affects retention |
| iOS vs Android Users | Platform-specific retention patterns |
| Paid vs Free Users | Impact of monetization on retention |
| Campaign A vs Campaign B | Which acquisition channel has stickier users |
| Feature X Users vs Non-Users | Whether a feature improves retention |
Creating Cohorts for Comparison
Cohorts are created in Analytics → Cohorts. You can define cohorts based on:
- Events performed (e.g., "used feature X")
- Event frequency (e.g., "purchased 3+ times")
- User properties (e.g., "country = US")
- Time windows (e.g., "in the last 30 days")
See Cohorts for details on creating behavioral cohorts.
Key Metrics Explained
Day 1 / Week 1 / Month 1 Retention
The most critical retention metric. Shows how many users return after their first period.
Benchmarks vary by industry:
- Consumer apps: 25-40% D1 retention is good
- B2B products: 60-80% W1 retention is typical
- Games: 30-40% D1, 15-20% D7 is healthy
Retention Curve Shape
- Steep early drop, then flat - normal. You lose casual users early but keep engaged ones. The flat tail is the habit loop.
- Continuous decline - a problem. Even engaged users are leaving.
- Flat from the start - unusual. Either a very sticky product or a data issue.
Best Practices
Choose Meaningful Events
Good cohort entry events: signup completed, first meaningful action, onboarding completed.
Good return events: a core product action (not just page views), something that signals real engagement, or the same event as entry when measuring repeat behaviour.
Respect the Minimum Cohort Size
Leave Min cohort size at 10 or higher unless you have a specific reason. Small cohorts read as noisy 100% values and mislead the averages.
Focus on Early Retention
Day 1 and Week 1 retention are the strongest predictors of long-term retention. If users do not return early, they rarely come back.
Segment Your Analysis
Use Compare cohorts mode to compare retention by acquisition channel, user type, or feature usage. See Cohorts for creating behavioral segments.
Common Issues
Cohort Sizes Vary Too Much
Use the percentage view (turn off Show users) and rely on the size-weighted "All cohorts" row rather than eyeballing individual small cohorts.
Return Event Too Broad
If you use a generic event like page_view, retention can look artificially high. Use more meaningful engagement events.
Date Range Too Short
With monthly retention, you need at least 3-4 months of data for useful insights.
Related
- Cohorts - Create behavioral cohorts
- Funnel Analysis - Track conversion flows
- Event Explorer - Find events for analysis