Retention from month 3 to month 12 determines LTV — not month 1. Measure it, or you'll pay CAC twice for the same customer.
In 30 seconds: Simulate the retention curve month by month by cohort and separate real growth from revenue that only comes from CAC burned on new logos. Deterministic calculation with auditable formulas. The result is indicative — adjust the assumptions to reflect your real operation.
Active users in month m = Cohort size × (1 − monthly churn)^m
Retention (%) in month m = (1 − churn)^m × 100
Monthly revenue in month m = Active users × ARPU
Cumulative revenue = Σ Monthly revenue from month 1 to horizon
A cohort of 500 users enters in January with $299 ARPU and 6% monthly churn.
Month 3 — retention: (1 − 0.06)^3 = 83.1% → 415 active users.
Month 6 — retention: 69.0% → 345 active users.
Month 12 — retention: 47.6% → 238 active users.
Cumulative 12-month revenue ≈ $1,055,000 — useful for comparing against the initial investment in acquiring that cohort.
Month-3 (M3) retention is the most reliable early indicator of product health: cohorts that survive the first 90 days tend to stick around much longer.
The exponential curve is a floor: reality is often better due to late activation or reactivations, or worse due to shocks (pricing changes, outages).
Compare cohorts month over month to spot product improvements or regressions. If March's cohort retains better at 3 months than February's, something shifted in your favor.
If your M12 retention is below 30%, your business depends heavily on acquiring new customers to grow — retention is a priority lever.
To project future revenue from an acquisition campaign before spending it.
When comparing acquisition channels: an organic cohort usually retains better than a paid one.
To set retention goals by milestone (M3, M6, M12) and give the product team a quantitative north star.
Before changing pricing: simulate with higher projected churn to estimate how much current MRR is at risk.
In post-incident analysis: if a product failure pushed monthly churn from 5% to 9% in one month, this calculator quantifies the damage in 12-month cumulative revenue.
Assuming a single cohort represents all. Cohorts from different months can behave very differently — always compare at least 3.
Taking last month's churn as a constant monthly churn rate. Better to use the average monthly churn of the last 3-6 months.
Ignoring cumulative revenue and looking only at retention: a cohort with high churn but high ARPU can be more profitable than one with low churn and low ARPU.
Confusing user retention with revenue retention. If you have downgrades, users stay but revenue falls.
Healthy cohorts retain 85-90% at M3, 75-85% at M6 and 65-75% at M12. Cohorts with M12 retention < 50% indicate a serious fit issue.
Typical retention is much lower: 20-30% at M3 and 5-15% at M12. The strategy is to maximize revenue per user in the first 90 days or build habit.
Highly seasonal cohort usage: back-to-school cohorts retain better than mid-year ones. Model each seasonal cohort separately.
Supply- vs demand-side retention diverges: sellers who make a successful sale in M1 retain at twice the rate. Useful to model two separate cohorts.
Methodology and assumptions
Retention(t) = Active customers in month t ÷ Customers in month 0 · GRR = 1 − Revenue churn
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This calculator projects how a customer cohort fades month by month and how much revenue it leaves along the way. From cohort size, ARPU, churn and a horizon, it builds the retention curve, the cohort triangle, the bottom-up cumulative LTV and the half-life (the month the cohort drops to 50%).
It is aimed at product, growth and finance teams in SaaS and subscriptions who want to go beyond average churn: the same churn rate produces very different economics depending on whether losses concentrate early (smile curve) or spread evenly, and this engine models both.
Constant model: Retention(m) = (1 − churn)^m
Weibull model: Retention(m) = e^(−(m/η)^β), with β = 0.7 by default
Two-phase model: early churn until the crossover month, stable churn afterward
Bottom-up LTV = Σ ARPU × Retention(m) × Gross margin, for m = 1 … horizon
NRR(m) = Retention(m) × (1 + cumulative expansion), with gradual expansion ramp-in
In the default Weibull model, β below 1 produces the 'smile curve': the cancellation rate is high in the first months and decreases among those who stay. The scale parameter η is auto-calibrated from your churn so that 12-month retention matches the constant model — making both models comparable from the same input.
LTV is built by summing each projected month's real margin within the horizon, instead of the ARPU ÷ churn approximation, which overstates value when early churn is front-loaded.
Hypothetical example for illustration. The numbers reproduce exactly when entered into the calculator on this page.
Worked example: a 500-customer cohort with $400 ARPU, 5% base monthly churn, a 24-month horizon and the default Weibull model (70% gross margin).
Month 2: 419 customers remain active (83.9% retention) — the early drop concentrates onboarding losses.
Month 7: 328 active (65.6%). Month 13: 261 active (52.2%).
Half-life: 14.2 months — where the cohort crosses 50%.
Month 24: 184 customers still active (36.8%). Cohort cumulative revenue: $2,703,142; cumulative gross margin: $1,892,199.
Bottom-up LTV: $1,892,199 ÷ 500 = $3,784 per initial customer within the 24-month horizon.
Reading: although the 'average' churn is 5%, the Weibull curve shows much faster early losses and slower late ones — half the cohort left before month 15, yet a third is still present at two years.
The curve's shape matters more than average churn: two businesses with the same 12-month churn have different LTVs if one loses its customers in the first quarter and the other loses them spread out. Early decay is attacked with onboarding; late decay, with product and customer success.
Use half-life to compare cohorts against each other: if recent cohorts have shorter half-lives than older ones, acquisition quality or onboarding degraded, even if the business's aggregate churn doesn't show it yet.
The horizon-bounded bottom-up LTV is deliberately conservative: it assumes no revenue beyond what is projected. Comparing it against your CAC gives a more honest payback reading than closed-form LTV.
From theory to calculation
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Last updated: July 19, 2026
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