A chart plotting the percentage of a customer cohort still active (or the revenue still retained) at each period after signup, showing how fast customers leave and where retention stabilises.
Retention(t) = (customers from a signup cohort still active in month t ÷ cohort size at month 0) × 100 — plotted for each month t to draw the curve
200 customers sign up in January. 170 are still subscribed after month 1, 140 after month 3, 130 after month 6, and 126 after month 12.
Month 1: 170 ÷ 200 = 85%. Month 3: 140 ÷ 200 = 70%. Month 6: 130 ÷ 200 = 65%. Month 12: 126 ÷ 200 = 63%. The curve drops 15 points in month one but only 7 points from month 3 to month 12 — it flattens around 63–65%, evidence that customers who survive onboarding stay.
Every retention curve starts at 100% on day zero and only moves down, unless you plot revenue, which can climb back through expansion. The shape matters more than any single point. A curve that declines steadily towards zero means customers do not stick and growth is refilling a leaky bucket. A curve that drops early then flattens means a core segment gets lasting value. A "smiling" curve that turns upwards is the strongest shape in SaaS.
The flattening point is the closest thing SaaS has to a quantitative product-market fit signal. The level where the curve goes flat — sometimes called the terminal retention rate — tells you what share of new customers become long-term customers, and it puts a hard ceiling on lifetime value. No amount of acquisition spend fixes a curve that trends to zero.
The most common mistake is reading a blended churn number instead of cohort curves. When you are adding customers quickly, new signups mask the decay of older cohorts, so a "stable 3% monthly churn" can hide cohorts losing half their customers in a year. The second trap is mixing billing periods: annual subscribers cannot churn until renewal, so plot monthly and annual cohorts separately or the curve will mislead you.
Customer-count curves and revenue curves tell different stories, and you need both. A logo retention curve always decays, but the revenue curve for the same cohort can flatten above its starting point if upgrades from surviving customers outweigh churned revenue — net negative churn. On Stripe, both come straight from billing data: group customers by signup month and track the share still subscribed and the MRR they still generate.
The retention curve is the truest picture of whether your product keeps the customers it wins. A flattening curve is the clearest quantitative evidence of product-market fit, and the height at which it flattens sets the ceiling on LTV and CAC payback. A curve trending to zero means growth is treading water — and blended churn numbers will not warn you until it is too late.
ChartMogul's SaaS Retention Report (data from over 2,100 SaaS businesses) found top-quartile companies retain about 70% of a new-customer cohort at month 12; month-3 top-quartile retention ranges from 87% (ASP under $10/month) to 98% (ASP over $500/month). Decay is near-exponential through month 11, then drops sharply in months 11–12 as annual plans renew.
A good retention curve drops in the first one to three months as poor-fit customers leave, then flattens well above zero. The flat section — your terminal retention rate — shows a durable core of customers who keep paying. A curve declining steadily towards zero signals weak product-market fit; a curve that turns upwards through reactivation or expansion is best of all.
Churn rate is a single blended number for one period across all customers, while a retention curve follows one signup cohort month by month over its lifetime. Blended churn can look stable while individual cohorts decay rapidly, because fast new-customer growth masks the losses — the curve exposes what the single number hides.
Annual subscribers cannot churn until their renewal date, so cohorts with annual plans show a cliff in months 11–12 when those renewals come due — ChartMogul's cohort data shows this pattern across SaaS broadly. Plot monthly and annual cohorts as separate curves so the cliff does not distort your read on monthly-plan retention.
Focus on the earliest months, where the curve loses most of its height: improve onboarding and time-to-value so customers reach the core benefit quickly, qualify better-fit customers at signup, and recover failed payments so involuntary churn does not masquerade as lost demand. Small early improvements lift every later point on the curve.
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