Data Cohort Analysis

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

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