Cohort Analysis

Cohort analysis groups customers by a shared starting point — usually their signup month — and follows each group over time. Instead of one blended average that mixes new and old customers, it shows how a specific batch behaves month by month, making retention, churn, and revenue trends far easier to see.

How it works

Each cohort is a set of customers who started in the same period. You then measure a metric for that cohort across the following months:

1. Group customers by their join month (the cohort).
2. Track a value for each cohort over time — customers still active, MRR retained, or revenue per customer.
3. Compare cohorts side by side to spot whether newer ones behave better or worse than older ones.

A retention cohort might show that of customers who joined in January, 90% are still active after one month, 80% after three, and 70% after six.

Why it matters

Blended averages hide problems. If you're growing fast, a flood of new customers can mask heavy churn in older groups, because the average is dominated by fresh signups who haven't had time to leave. Cohort analysis separates the two and answers questions a single number can't:

• Is retention improving for newer cohorts than older ones?
• When in the lifecycle do customers typically cancel?
• Does a product or pricing change actually move churn?

What it reveals

Because it isolates each group, cohort analysis is the foundation for accurate LTV estimates and for reading net revenue retention over a customer's full lifetime. A revenue cohort that trends upward over time signals that expansion is outpacing churn — the healthiest pattern a subscription business can show.