About the Cohort Retention Calculator
This cohort retention calculator turns raw counts into a retention table. Paste one line per signup cohort — the number of users who signed up, followed by how many of them were still active in month 1, month 2 and so on — and it calculates each cohort’s retention percentage, a signup-weighted average retention curve and the retention rate at the month you choose.
Cohort analysis is the standard way product, growth and finance teams judge whether a product keeps users. A single churn rate mixes old loyal customers with new ones; cohorts separate them, so you can see whether newer cohorts retain better after a product change and whether the curve flattens (a sign of product-market fit) or keeps sliding toward zero.
Use the same activity definition for every month (for example “logged in” or “paid”). Recent cohorts naturally have fewer months of data; the average curve for each month only includes cohorts old enough to have reached it, weighted by their signups.
With the default inputs, the weighted retention at chosen month is 36.82%. Change any value above to recalculate instantly.
How to use the cohort retention calculator
- 1Export monthly signups and active users per cohort from your analytics tool.
- 2Paste one line per cohort, oldest first: signups, then month 1, month 2 … active users.
- 3Choose the month you want to report (for example month 3).
- 4Read the weighted retention, the cohort table and the retention curve.
- 5Compare newer cohorts with older ones to see whether retention is improving.
Formula and method
Each cohort’s retention for month k is its active users that month divided by the users who originally signed up. The average curve is weighted by cohort size rather than a simple average of percentages, so a cohort of 2,000 users counts twice as much as one of 1,000.
For every month, only cohorts old enough to have data for that month are included in both the numerator and the denominator. This avoids the common mistake of dragging the curve down by counting recent cohorts as zero retention for months they have not lived through yet.
- k
- Months since signup (month 0 is the signup month)
- Signups
- Users who joined in the cohort period
- Active
- Cohort users meeting your activity definition in month k
Worked examples
Four monthly cohorts, month-3 retention
Month-1 retention is (600 + 700 + 560 + 640) ÷ 4,200 = 59.5%. Only the first two cohorts have reached month 3, so month-3 retention is (380 + 430) ÷ (1,000 + 1,200) = 36.8%. Month 4 uses the oldest cohort alone: 340 ÷ 1,000 = 34%.
Flattening curve after month 2
Month-2 retention is (150 + 190 + 180) ÷ 1,650 = 31.5%. After that the curve loses only a point or two per month, reaching 26.4% by month 5 — the flattening pattern investors look for.
Frequently asked questions
What is a cohort retention table?+
It is a grid where each row is a group of users who signed up in the same period and each column is a month since signup. Each cell shows the share of that group still active, so you can compare cohorts and see how retention decays over time.
What is a good month-1 retention rate?+
It varies widely by product. Consumer apps often keep 20–40% of users after one month, while B2B SaaS measured by paying accounts commonly retains 85–95% month to month. Compare against your own earlier cohorts first.
Why weight by signups instead of averaging percentages?+
A simple average treats a 50-user cohort the same as a 5,000-user cohort, which can distort the curve. Weighting by signups gives the retention of all users combined, which is what you need for forecasting.
What does a flattening retention curve mean?+
If retention stops falling and levels off, a core group of users has found lasting value. A curve that flattens above zero is a common signal of product-market fit; one that keeps sliding toward zero suggests users eventually leave.
Should I measure logo or revenue retention?+
Both are useful. User or logo retention shows whether people keep using the product, while revenue retention also captures upgrades and downgrades. You can paste revenue by cohort instead of users to get a revenue retention table.