Cohort Analysis
Framework
IRON LAW: Aggregate Metrics Hide Cohort Differences
A 70% monthly retention rate OVERALL can mask that January cohort retains
at 85% while June cohort retains at 50%. Aggregate metrics blend improving
and deteriorating cohorts together, hiding both problems and progress.
ALWAYS analyze by cohort before drawing conclusions.Core Concepts
Cohort: A group of users who share a common characteristic in a specific time period. Most common: acquisition cohort (grouped by signup month).
Retention Matrix: Rows = cohorts (by signup month), Columns = time periods after signup (Month 0, 1, 2...). Cells = % of cohort still active.
Month 0 Month 1 Month 2 Month 3
Jan cohort 100% 65% 48% 40%
Feb cohort 100% 60% 42% 35%
Mar cohort 100% 70% 55% 48% ← Improvement!Retention Types
| Type | Definition | Use Case |
|---|---|---|
| N-day | % active on exactly day N | Games, daily-use apps |
| N-day bounded | % active within first N days | General product usage |
| Week/Month | % active in week/month N | SaaS, subscriptions |
| Unbounded | % who ever return after day N | Low-frequency products |
Analysis Steps
Phase 1: Define Cohort and Activity
- Cohort definition: signup date, first purchase date, or other milestone
- Activity definition: login, purchase, specific action — must match the product's core value
- Time granularity: daily (for daily-use products), weekly, or monthly
Phase 2: Build Retention Matrix
- Group users into cohorts
- For each cohort, calculate retention at each time period
- Visualize as a heatmap (darker = higher retention)
Phase 3: Identify Patterns
- Retention curve shape: Does it flatten (good — stable core users) or keep declining (bad — everyone eventually churns)?
- Cohort comparison: Are newer cohorts retaining better or worse than older ones?
- Drop-off cliff: Is there a specific period where retention drops sharply? (e.g., Day 1 → Day 7 drops 50%)
Phase 4: Connect to Actions
- What changed for the improving/deteriorating cohorts? (product update, marketing channel shift, onboarding change)
- Can you isolate the cause through A/B test or event analysis?
Phase 5: LTV Projection
- Use cohort retention curves to project future revenue per cohort
- LTV = Σ (retention_month_n × ARPU_month_n) for all future months
Output Format
# Cohort Analysis: {Product}
## Cohort Definition
- Cohort: {signup month / first purchase}
- Activity: {what counts as "active"}
- Period: {daily / weekly / monthly}
## Retention Matrix
| Cohort | M0 | M1 | M2 | M3 | M4 | M5 | M6 |
|--------|-----|-----|-----|-----|-----|-----|-----|
| {month} | 100% | {%} | {%} | {%} | {%} | {%} | {%} |
## Key Findings
1. {retention curve shape}
2. {cohort trend — improving or deteriorating}
3. {critical drop-off point}
## Cohort Comparison
| Metric | Oldest Cohort | Newest Cohort | Delta |
|--------|-------------|-------------|-------|
| M1 retention | {%} | {%} | {±pp} |
| M3 retention | {%} | {%} | {±pp} |
| Projected LTV | ${X} | ${X} | {%} |
## Recommendations
1. {action to improve retention at critical drop-off point}Gotchas
- Define "active" carefully: Login ≠ value delivery. A user who logs in but doesn't complete the core action (purchase, send message, create document) shouldn't count as "retained."
- Cohort size matters: A cohort of 10 users with 50% retention is meaningless (5 users). Ensure cohorts have statistically meaningful sizes.
- Survivorship bias in aggregates: "Average retention is improving" may just mean you have more new users (who are always at M0 = 100%) diluting the denominator.
- Seasonal cohorts behave differently: December cohorts (holiday shoppers) often retain worse than March cohorts (organic discovery). Compare same-season cohorts YoY.
- Retention ≠ engagement depth: A user who returns once per month but uses for 5 hours vs one who returns daily for 30 seconds — same retention, very different engagement. Layer in activity depth metrics.
References
- For SQL retention query templates, see
references/retention-sql.md - For LTV projection from cohort data, see
references/cohort-ltv.md