Group customers by acquisition period to compare behaviour patterns and identify which acquisition channels and time periods produce the best long-term value.
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Cohort analysis groups customers by common characteristics (typically acquisition date) and measures their behaviour over time. You create a cohort of all customers acquired in January 2024, another of customers acquired in February 2024, etc. Then you track metrics like retention rate, revenue, feature adoption, or churn for each cohort over subsequent months. This reveals whether your product and go-to-market are improving or degrading.
Cohort analysis is powerful because it isolates the impact of changes. If you launched a new onboarding flow in March, compare March's cohort retention to February's cohort retention. A retention improvement between the two suggests the onboarding change worked. Without cohorts, you'd compare 'overall retention this month versus last month', which mixes February's onboarding with March's onboarding, making it impossible to measure the impact of the change.
The most common cohort analysis in B2B is cohort retention: what percentage of each cohort remains six months later? A healthy SaaS business shows consistent or improving retention across cohorts. Declining retention (January cohort retains 85%, February cohort retains 80%) suggests a problem that needs investigation.
A single metric like 'overall churn' hides problems. Cohort analysis reveals whether your onboarding is improving, whether product changes increased retention, or whether customer quality changed. These insights guide product and marketing strategy.
Product launches, pricing changes, onboarding redesigns—it's hard to know if they worked because many variables affect behaviour. Cohort analysis controls for timing by comparing cohorts before and after a change, giving you confidence in causation.
If you know a cohort's retention curve (how many survive month 1, 2, 3, etc.), you can estimate lifetime value. Cohorts that retain well generate more customer lifetime value. This prediction guides how much you can afford to spend acquiring a customer.
The most useful cohort is by acquisition month or week. Create one cohort per month for 12+ months. You'll then track how each cohort behaves over time. Monthly cohorts are standard in SaaS; weekly cohorts are too granular unless you're rapidly iterating.
Decide what you'll measure: retention rate (% still customers), revenue per cohort, feature adoption, or churn. Track one main metric consistently so you can see trends. Retention is most common, but revenue cohorts matter more for a SaaS business (a cohort can have high count retention but low revenue if customers downgrade).
Plot cohorts over time. Is January's retention 90%, February's 88%, March's 86%? That declining trend suggests a problem. Is retention improving each month? That indicates your product or onboarding changes are working. Don't look at single cohorts; look at the trend.
When a cohort underperforms, investigate why. Did you change pricing or product that month? Did quality of acquired customers decline? Did a competitor launch? The why matters for fixing it. A poor cohort is only a problem if you don't understand the cause.
A productivity app tracked customer retention by cohort. Customers acquired Jan-March 2024 had 45% retention at month 3. April cohort also had 45%. May cohort had 52%. June had 55%. The trend showed improving retention. What happened in May? The team redesigned onboarding. The cohort analysis proved the redesign worked; May and subsequent cohorts retained better. They kept the new onboarding and continued improving it.
A B2B SaaS company's Jan-June 2024 cohorts all retained at 80% month 3. July cohort dropped to 72%. August dropped to 70%. What happened? They'd changed their acquisition strategy to expand into a new industry vertical. Cohort analysis revealed the new customers weren't as sticky. Investigation showed the vertical was lower-fit; they're now considering whether to continue or refocus on higher-retention verticals.
A SaaS platform with variable pricing tracked revenue per cohort. January cohort generated £50K month 1, £45K month 2, £40K month 3 (some customers downgraded). By month 12, £180K cumulative revenue. CAC was £30K per customer. Payback was 2 months (month 3 approaching cumulative revenue = CAC). This cohort analysis guided their decision to raise CAC to £45K because payback was still acceptable at under 3 months.
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A disciplined approach to experiments. Define hypotheses, design MVPs and learn before you scale.
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