Unit economics
See how a cohort decays month over month at your churn rate.
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How do you calculate cohort retention from a monthly churn rate?
Customers left after m months = cohort size × (1 − monthly churn)^m. At 4% monthly churn, a 500-customer cohort keeps 306 customers (61%) after 12 months and 188 (38%) after 24. Average lifetime is 1 ÷ monthly churn, so 4% means about 25 months. Halve churn to 2% and 78% survive the first year.
At 4.0% monthly churn, only 306 of your original 500 customers (61%) remain after a year, implying an average lifespan of about 25 months. Small churn reductions extend that curve dramatically.
Monthly churn compounds, 4%/month is nearly 40% a year. Because lifespan is 1 ÷ churn, halving churn roughly doubles customer lifetime and LTV.
About this calculator
A single "monthly churn rate" hides how brutal compounding decay actually is on a cohort of real customers. This visualizer takes a starting cohort size and a monthly churn rate, then shows exactly how many customers remain at 3, 6, 12 and 24 months, plus the average customer lifetime that churn rate implies, the number that quietly determines your LTV.
How to use it
- Enter your starting cohort size, the number of customers who signed up in a given period.
- Enter your monthly logo churn rate for that cohort.
- Read how many customers survive at 3, 6, 12 and 24 months, plus the percentage retained at each point.
- Note the average customer lifespan the tool derives, this is the number to plug into any LTV calculation.
Methodology
Survivors at month m are calculated as cohort size times (1 minus monthly churn) raised to the power of m, standard exponential decay. This is the correct way to model churn: a fixed percentage of the remaining base is lost each month, not a fixed percentage of the original cohort, so the absolute number lost shrinks over time even as the rate stays constant.
Average customer lifespan is derived as 1 divided by the monthly churn rate (as a decimal). At 4% monthly churn that's 25 months; at 2% it's 50 months, this is why halving churn roughly doubles both lifespan and lifetime value, a disproportionate lever compared to most growth tactics.
The tool grades churn into three bands, 2% or under as good, up to 4% as a caution zone, and above 4% as concerning, reflecting that monthly churn compounds annually (4% monthly compounds to nearly 40% annual churn, not 48%, because the base shrinks each month).
This is a single-cohort model: it assumes a constant churn rate applied uniformly to everyone in the cohort. Real cohorts often churn faster in the first few months (early-life churn) and slower once customers are embedded, so treat the curve as a simplified average, not a precise prediction for any individual account.
Cohort decay and customer lifetime
Survivors at month m = Cohort size × (1 − c)^m Retained % at month m = (1 − c)^m × 100 Average lifetime (months) = 1 ÷ c Annual churn = 1 − (1 − c)^12
- Cohort size
- Customers who started paying in the same period (for example, all January sign-ups).
- c
- Monthly logo churn rate as a decimal (4% = 0.04): customers lost in the month ÷ customers at the start of the month.
- m
- Months since the cohort started. The tool shows 3, 6, 12 and 24.
- Average lifetime
- Expected months a customer stays at a constant churn rate; the number used in simple LTV formulas.
The tool counts customers, not revenue, and applies one constant rate to every month. Real cohorts rarely behave that way: ChartMogul found accounts expand most in year one and churn tends to increase in year two. Stripe's billing analytics shows the same compounding with 10% monthly churn: 90%, 81%, 73%, 66% and 59% retained over the first five months.
Worked example: 500 customers at 4% monthly churn
A self-serve SaaS product signs 500 paying customers in January. Its monthly logo churn has been running at about 4%.
- Monthly survival factor = 1 − 0.04 = 0.96.
- Month 3: 500 × 0.96³ = 500 × 0.885 = 442 customers (88%).
- Month 6: 500 × 0.96⁶ = 500 × 0.783 = 391 customers (78%).
- Month 12: 500 × 0.96¹² = 500 × 0.613 = 306 customers (61%), so annual churn is about 39%.
- Month 24: 500 × 0.96²⁴ = 500 × 0.375 = 188 customers (38%).
- Average lifetime = 1 ÷ 0.04 = 25 months.
- At 2% churn instead: 392 customers (78%) remain at month 12 and lifetime doubles to 50 months.
Two points of monthly churn are the difference between keeping 306 and 392 of the same 500 customers after a year, and between a 25-month and a 50-month average lifetime. Lifetime feeds LTV directly, so the same change roughly doubles customer value; the LTV to CAC calculator shows what that does to acquisition limits.
Median monthly user churn by ARPU, with the 12-month survival it implies
| ARPU cohort | Median monthly user churn | Retained after 12 months | Implied lifetime |
|---|---|---|---|
| Under $10 | 6.2% | 46% | 16 months |
| $10–$25 | 5.8% | 49% | 17 months |
| $25–$50 | 8.3% | 35% | 12 months |
| $50–$100 | 8.4% | 35% | 12 months |
| $100–$250 | 7.4% | 40% | 14 months |
| $250 and up | 6.1% | 47% | 16 months |
Median monthly customer churn by company size and ARPA (ChartMogul, 2022)
| Segment | Median monthly customer churn |
|---|---|
| Under $300k ARR | 6.5% |
| $1M–$3M MRR | 3.7% |
| ARPA under $25 a month | 6.1% |
| ARPA above $500 a month | 2.2% |
What is a good monthly churn rate for a cohort?
It depends mostly on price point and customer size:
- Low-priced self-serve products: 6% to 8% monthly user churn is typical in the Baremetrics data, which means roughly a third to a half of a cohort is left after a year.
- Higher-ARPA B2B products: ChartMogul's 2022 data showed 2.2% median monthly churn above $500 ARPA. At that rate 77% of a cohort survives the first year.
- Guideline: Baremetrics suggests 3% to 5% monthly churn for SaaS selling to small businesses and under 1% for enterprise products.
This tool marks 2% or less as good and above 4% as concerning, which fits B2B SaaS. Compare against your own price band, not a general average. For the revenue side, where expansion can offset lost logos, use the net revenue retention forecaster, and for retention fixes see the B2B SaaS churn playbook.
Cohort churn in India vs the US
For rupee subscriptions, separate involuntary churn (failed UPI AutoPay mandates, expired cards, unpaid invoices) from customers who chose to cancel. Recurring card and UPI mandates can fail for reasons unrelated to the product, and then the fix is in billing, not onboarding. Indian SMB products at low price points should compare against the low-ARPU rows above. If cohort data lives across Razorpay, a CRM and spreadsheets, a RevOps audit is a sensible first step.
US B2B SaaS sold on annual contracts does not churn monthly in practice: customers leave at renewal. Convert annual logo churn to a monthly equivalent with 1 − (1 − annual churn)^(1/12) before entering it, rather than dividing by 12. Card-based billing still produces failed-payment churn, so the voluntary and involuntary split matters in both markets.
Common cohort churn mistakes
- Dividing annual churn by 12 to get a monthly rate. 39% annual churn is about 4% a month, not 3.25%.
- Mixing cohorts. A blended churn rate across old and new customers understates churn for new sign-ups.
- Counting trial or free users in the starting cohort, which inflates early churn.
- Using revenue churn in a logo model. This tool tracks customers; revenue churn belongs in an NRR or MRR view.
- Assuming churn stays constant. Early-life churn is often higher, and ChartMogul found churn tends to rise again in year two.
- Treating 1 ÷ churn as a forecast for small cohorts, where a handful of cancellations moves the rate a lot.
Sources
- SaaS Financial Benchmarks: Churn, Growth and Retention Rates by ARPU, Baremetrics. Accessed 2026-10-17.
- What Is a Good Customer Churn Rate?, ChartMogul. Accessed 2026-10-17.
- The SaaS Retention Report: The New Normal For SaaS, ChartMogul. Accessed 2026-10-17.
- Billing analytics, Stripe Docs. Accessed 2026-10-17.
FAQ
Because churn is applied to whatever's left, not to the original cohort. Losing 4% of 500 customers in month one is 20 people; losing 4% of the 480 who remain in month two is only 19.2. The absolute losses taper even though the percentage rate never changes.
It's 1 divided by the monthly churn rate. It matters because customer lifetime value is typically calculated as ARPA times gross margin times average lifespan, so this single derived number feeds directly into your LTV, and therefore your LTV:CAC ratio.
No, this tool tracks logo count only, how many customers remain, not revenue per customer. If your surviving customers expand their spend over time, actual retained ARR can look much healthier than retained logo count alone.
It varies heavily by segment. Baremetrics' SaaS financial benchmarks (updated 2026) suggest 3-5% monthly customer churn for SaaS companies that sell to small businesses and under 1% monthly for enterprise products. Compare your number against your own segment's typical range rather than a single universal target.
Annual churn = 1 − (1 − monthly churn)^12. At 4% monthly, that is 1 − 0.96^12 = 38.7%, not 48%. Going the other way, monthly churn = 1 − (1 − annual churn)^(1/12).
It groups customers by the month (or quarter) they started and tracks what share of each group is still paying over time. Comparing cohorts shows whether recent sign-ups retain better or worse than older ones, which a single blended churn rate hides.
With a constant monthly churn rate, the time a customer stays follows a geometric distribution whose mean is 1 ÷ c. At 5% monthly churn that is 20 months. It is a simplification: if early churn is high and later churn low, the real average differs.
Both answer different questions. Logo churn shows whether customers keep finding value; revenue churn shows the financial effect. If large customers stay and small ones leave, revenue churn can be much lower than logo churn.
At the 2.2% median monthly churn ChartMogul reported for products above $500 ARPA, about 77% of a cohort remains after a year. At the 6% to 8% typical of low-priced products in Baremetrics data, 35% to 49% remains. Judge your cohort against products at a similar price.
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