Free tool
Cohort retention calculator
The short answer
Paste one line per cohort: how many customers started that month, then how many were still active in month 1, month 2 and so on. The calculator turns the counts into a retention table, averages it weighted by cohort size, and shows where the curve flattens. A curve that levels off means a core of customers is staying; one that keeps falling means none is yet.
On this page 7 sections
Cohort retention
Counts, not percentages. Up to 36 rows; the shareable link keeps up to 500 characters.
Cohorts read
5
Month 1 retention
61.6%
Latest month (M5)
41.0%
| Cohort | Size | M1 | M2 | M3 | M4 | M5 |
|---|---|---|---|---|---|---|
| Jan | 200 | 60% | 48% | 44% | 42% | 41% |
| Feb | 240 | 63% | 49% | 44% | 42% | |
| Mar | 180 | 58% | 47% | 43% | ||
| Apr | 260 | 64% | 50% | |||
| May | 230 | 62% | ||||
| Average | 1,110 | 62% | 49% | 44% | 42% | 41% |
The average curve flattens from month 4 (it drops less than 2 points from the month before). The level it settles at is your core retained base; that, not month 1, decides lifetime value.
Runs in your browser. Nothing you enter is sent to us; the page address keeps your numbers so you can share a result.
How it calculates
- Retention for a cohort in month n = customers from that cohort active in month n / cohort size.
- Average curve = total active across cohorts that have reached month n / total size of those cohorts. Weighting by size stops a tiny cohort from swinging the average; younger cohorts drop out of the later months they have not reached.
- Flattening month = the first month the average falls by less than 2 percentage points from the month before.
Worked example
The default data has five monthly cohorts, January to May. Size-weighted average retention runs 61.6% in month 1, 48.8% in month 2, 43.7% in month 3, 42.0% in month 4 and 41.0% in month 5. The drop shrinks from 12.8 points to 1.7 points, so the curve flattens from month 4: roughly four in ten customers form a base that stays. Read down a column to see whether newer cohorts retain better than older ones; if April's month 2 is above January's, something you changed is working.
What counts as active
- For subscriptions: still paying at the end of the month.
- For apps: did the core action at least once in the month, not merely logged in.
- For ecommerce: made a purchase in the month, or in a rolling window such as 90 days if people buy less often than monthly.
Keep the definition fixed. Changing it midway makes later cohorts look better or worse for no real reason.
Getting the data
GA4's cohort exploration builds user retention tables from your events (Explore, then Cohort exploration), and product analytics tools do the same with user IDs; see the best product analytics tools. For paying customers, your billing system or CRM is the better source because it holds the revenue. The concepts are explained in cohort analysis explained; for a single churn rate, use the churn rate calculator.
Questions
- What is cohort retention?
- Cohort retention is the share of a group of customers who started in the same period, such as the same month, that is still active a given number of months later. Comparing cohorts shows whether retention is improving over time.
- Why weight the average by cohort size?
- A simple average treats a cohort of 20 the same as a cohort of 2,000, so one small cohort can move the curve. Weighting by size gives the retention of the average customer.
- What does a flattening retention curve mean?
- It means the customers who are going to leave early have mostly left, and the rest are staying at a steady rate. The level where it flattens is a stronger guide to lifetime value than month 1 retention.
Sources
Related guides
Cohort analysis explained, with GA4 steps
What a cohort retention table shows, how to read the triangle, standard vs rolling vs cumulative retention, and how to build a cohort exploration in GA4.
SaaS metrics: LTV, CAC, payback and churn explained
How to calculate customer lifetime value, customer acquisition cost, CAC payback and churn for a SaaS or subscription business, and what the ratios mean.
Product analytics vs web analytics: which one do you need?
The difference between web analytics (sessions and sources) and product analytics (users and events over time), with examples of the questions each answers.