Cohort analysis groups users who share a common starting point — like the month they signed up — and tracks how each group behaves over time. It reveals retention, churn, and revenue patterns that blended, all-user averages completely hide.
What Is a Cohort?
A cohort is a group of users who share a characteristic or experience within a defined period — for example:
- All customers who signed up in January 2025
- Users who made their first purchase during a Black Friday sale
- Subscribers who joined after a specific marketing campaign
The word "cohort" comes from ancient Roman military units — in business, you're grouping users to battle for retention rather than territory.
Cohort Analysis Example
The classic view is a retention table: each row is a signup-month cohort, each column is how many of them are still active N months later.
| Cohort | Month 0 | Month 1 | Month 2 | Month 3 |
|---|---|---|---|---|
| Jan signups | 100% | 68% | 55% | 48% |
| Feb signups | 100% | 72% | 60% | 54% |
| Mar signups | 100% | 80% | 71% | — |
Reading down a column shows whether retention is improving cohort-over-cohort (here it is — March's onboarding clearly works better). A single blended "60% retention" number would have masked that trend entirely.
Types of Cohort Analysis
| Type | Groups users by |
|---|---|
| Time-based | When they became customers (signup week/month) |
| Behavior-based | Actions they took (used a feature, hit a milestone) |
| Size-based | Purchase amount or company size |
How to Do a Cohort Analysis
- Define your cohorts: pick the shared characteristic (e.g. signup month).
- Choose a metric: retention rate, revenue, or engagement.
- Track it over time: measure each cohort across successive periods.
- Compare cohorts: read down the columns to spot trends and act on them.
Metrics to Track
| Metric | What it shows |
|---|---|
| Retention rate | % still active over time |
| Churn rate | % who stopped using the product |
| Customer Lifetime Value | Revenue per customer over their lifetime |
| ARPU | Average revenue per user |
| Engagement | Login frequency, feature usage |
Why Cohort Analysis Matters
- Accurate LTV: see how customer value actually develops over time.
- Product insight: identify which features drive long-term retention.
- Marketing optimization: find which channels bring the best-retaining customers.
- Churn prevention: spot exactly when and why customers leave.
- Forecasting: project revenue from real cohort behavior, not averages.
Tools for Cohort Analysis
Google Analytics (basic web cohorts), Mixpanel and Amplitude (event/product cohorts), Tableau (custom visualization), or direct SQL queries for full control.
Cohort Analysis FAQ
What is cohort analysis in simple terms?
It's grouping users by a shared starting point — usually when they signed up — and watching how each group behaves over time. Instead of one blended average, you see how January's customers compare to March's at the same age.
What is an example of cohort analysis?
A retention table where each row is a signup-month cohort and each column shows the % still active 1, 2, 3 months later. Comparing rows reveals whether newer cohorts retain better than older ones.
What are the types of cohort analysis?
Three main types: time-based (grouped by signup date), behavior-based (grouped by an action taken), and size-based (grouped by spend or company size).
Why is cohort analysis better than overall averages?
Blended averages mix new and old users, hiding trends. Cohort analysis isolates each group so you can see whether retention, revenue, or engagement is genuinely improving over time.
