What Is Cohort Analysis?

Definition: Group users by a shared characteristic, then track behavior over time

Why it matters: Reveals trends that overall metrics hide

Common Cohort Types

1. Time-Based Cohorts

Group by: Sign-up month (Jan 2025, Feb 2025...)

Track: Retention, engagement, revenue per cohort

Use case: "Are newer users more engaged than older users?"

2. Behavior-Based Cohorts

Group by: First action taken (watched video, read article, purchased)

Track: Conversion paths, feature adoption

Use case: "Which onboarding action leads to best retention?"

3. Segment-Based Cohorts

Group by: Acquisition channel, pricing tier, geography

Track: Channel effectiveness, LTV by segment

Use case: "Do paid ads bring better users than organic?"

Reading a Retention Cohort Table

Rows: Cohorts (e.g., Jan 2025 sign-ups)
Columns: Time periods (Week 0, 1, 2, 3...)
Values: Percentage of cohort still active

Example:

• Jan 2025: 100% → 60% → 45% → 40%
• Feb 2025: 100% → 70% → 55% → 50%

Insight: Feb cohort has better retention!

Key Metrics to Track

• Retention rate: % who return after N days/weeks
• Churn rate: % who leave in each period
• Cohort LTV: Total revenue per user in cohort
• Time to value: How long until first key action

Common Mistakes

❌ Comparing cohorts of different sizes - Use percentages, not absolute numbers

❌ Not waiting for cohorts to mature - Week 1 data from yesterday is incomplete

❌ Ignoring seasonal effects - Dec cohorts may behave differently than June