Cohort analysis groups customers by when they signed up and tracks each group over time to reveal how retention truly behaves.
Cohort retention (period n) = (customers from the cohort still active in period n ÷ customers in the cohort at start) × 100
A January cohort of 100 customers has 92 active after month one, 85 after month two, and 80 after month three.
Month 1 = 92%, Month 2 = 85%, Month 3 = 80% retention — a curve that is flattening, suggesting the early churn has settled
Cohort analysis groups customers by a shared starting point — usually their signup or first-payment month — and then follows each group separately as it ages. Instead of one blended retention or revenue number that mixes customers acquired years apart, you see how the January cohort behaves in month one, month two, and so on, alongside every other cohort. The pattern that emerges is far more honest than any single average.
Its power is that it isolates the effect of time and of changes you made. Blended metrics flatter you when growth is fast, because a flood of new customers masks the decay of old ones; cohorts strip that illusion out. If the retention curve for recent cohorts flattens higher than older ones, an onboarding or product change is working — and you can see it months before it shows up in headline churn.
In SaaS, cohort analysis usually takes two forms: retention cohorts, which track the share of each group still active over time, and revenue cohorts, which track the MRR each group generates. Revenue cohorts can curve back upward when expansion outweighs churn — the visual signature of negative churn and net revenue retention above 100%. Cohort analysis is also a core Mowt feature, computed automatically from your billing data.
Cohort analysis is the antidote to misleading blended averages, which hide decay behind new-customer growth. By following each group as it ages, it shows whether retention is genuinely improving, pinpoints when customers tend to churn, and reveals whether revenue cohorts curve back up through expansion — the clearest visual proof of durable, self-sustaining growth and the fastest way to see whether a product change actually moved the needle.
There is no single cohort benchmark, but a healthy B2B SaaS revenue-cohort curve flattens (and ideally turns upward through expansion) rather than decaying to zero; month-12 logo retention around 85–90% for annual cohorts and revenue cohorts trending toward net retention above 100% are strong markers (sources: ChartMogul retention benchmarks; SaaS Capital 2025).
Blended retention mixes customers acquired at different times into one number, which fast growth can flatter. Cohort analysis follows each signup group separately as it ages, so it exposes decay and improvement that a blended average hides.
A retention cohort tracks the share of customers still active over time; a revenue cohort tracks the MRR each group generates. Revenue cohorts can curve upward when expansion outweighs churn — the signature of negative churn.
Cohort revenue curves are the most accurate basis for lifetime value, because they show how much a real group of customers actually pays over time rather than relying on a single averaged churn assumption.
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