Cohort Analysis
A technique that groups users who share a common characteristic (usually acquisition date) and tracks their behaviour over time to reveal retention, engagement, and revenue trends.
What is Cohort Analysis?
Cohort analysis groups users who share a common starting point (usually their signup week/month) and tracks their behaviour over time. It reveals whether product improvements are actually being experienced by real users, not just new cohorts.
Reading a cohort retention table
`
Cohort | Week 0 | Week 1 | Week 2 | Week 4 | Week 8
Jan | 100% | 45% | 32% | 22% | 18%
Feb | 100% | 48% | 35% | 25% | 20%
Mar | 100% | 52% | 40% | 31% | 26%
`
Each row is an acquisition cohort. Each column is how many users were active N weeks after signup. Improving numbers from Jan → Mar indicates the product is getting better at retaining users.
What to look for
- Flattening curves — retention stabilising above 0% = you have a retained core
- Improving cohorts — later cohorts retain better = product improvements are working
- Cliff at day 1/3 — massive early drop-off = onboarding problem
- Revenue cohorts — expansion MRR in older cohorts = strong monetisation
Free templates for Cohort
Frequently asked questions
What's the difference between cohort analysis and funnel analysis?
Funnel analysis measures conversion through a sequence of steps at a point in time. Cohort analysis measures what happens to a group of users over time. Use funnels to find where users drop off in a flow; use cohorts to measure whether product changes improved long-term retention.
How many users do I need for reliable cohort analysis?
At least 100–200 users per cohort for statistical reliability. With smaller cohorts, retention curves are noisy. Early-stage companies should focus on qualitative interviews alongside cohort data rather than trusting small-sample cohort curves.
Apply Cohort Analysis to your real product data
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Related terms
Churn Rate
The percentage of customers or revenue lost in a given period — the primary indicator of whether a product retains the value it delivers.
Retention Rate
The percentage of users who continue using a product over a defined time period — the most important signal of product-market fit and sustainable growth.
A/B Testing
A controlled experiment that compares two versions of a product element (A = control, B = variant) to determine which performs better on a defined metric.