Cohort Analysis

GATE: Confirm brief before proceeding.

Phase 2: Design

Cohort Table Structure

Build a triangular matrix:

• Rows = cohorts (e.g., Jan signups, Feb signups)
• Columns = lifecycle periods (Month 0, Month 1, Month 2...)
• Cells = metric value or percentage

Analysis Framework

1. Retention curve — how each cohort declines over time (or does not)
2. Cohort comparison — are newer cohorts retaining better than older ones?
3. Inflection points — where does the biggest drop-off happen?
4. Stabilization — at what period do cohorts flatten out?
5. Segment overlays — do specific segments (channel, plan, geography) retain differently?

GATE: Present the analysis framework and confirm before building templates.

Phase 3: Build

Deliverables

1. Cohort Analysis Template

• Spreadsheet or table structure ready for data entry
• Formulas for calculating retention percentages
• Conditional formatting rules (green = good retention, red = steep drop)
• Chart template for visualizing retention curves

2. Interpretation Guide

• How to read the cohort table (rows, columns, diagonal patterns)
• What "good" retention looks like for the business type
• Common patterns and what they mean:
  • Improving rows = product is getting better
  • Steep Month 1 drop = onboarding problem
  • Flat curves after Month 3 = healthy core retention

3. Segmented Analysis Plan

• Which segments to overlay (acquisition source, pricing tier, geography)
• How to split cohorts for sub-analysis
• Comparison template for segment vs. segment

4. Action Recommendations Framework

• If Month 1 retention is below X%, focus on onboarding
• If retention flattens at Y%, invest in expansion over acquisition
• If newer cohorts are worse, investigate product or market changes

Phase 4: Polish

Reporting Cadence

• Monthly: update the cohort table with latest period data
• Quarterly: full analysis with segment overlays and trend narrative
• Trigger: run ad hoc analysis when a major change is made (pricing, onboarding, product)

Presentation Template

One-page summary with: cohort table, retention curve chart, top 3 insights, and recommended actions.

Example 1: SaaS Monthly Retention Cohorts

Cohorts by signup month. Metric: % of users active in each subsequent month. Goal: identify if onboarding improvements in March improved Month 1 retention vs. January cohort.

Example 2: E-commerce Revenue Cohorts

Cohorts by first purchase month. Metric: cumulative revenue per cohort. Goal: understand which acquisition channels produce higher lifetime value customers.

Anti-Patterns

• Using averages instead of cohorts — "Average retention is 40%" hides whether things are getting better or worse. Always break by cohort.
• Too many cohorts — 52 weekly cohorts on one chart is unreadable. Start monthly, go weekly only for specific investigations.
• Ignoring cohort size — a cohort of 5 users with 80% retention is not meaningful. Note sample sizes.
• Comparing different lifecycle stages — Month 3 of a January cohort and Month 1 of a March cohort are not comparable. Align by period.
• Analysis without action — cohort data is diagnostic. Always end with "So what do we do about it?"

Recovery

• Not enough data: Need at minimum 3 cohorts with 3 periods each. If data is thin, start tracking now and revisit in 3 months.
• No customer-level data: Use aggregate proxies (monthly active users vs. signups) for a rough cohort view. Plan to implement proper tracking.
• Retention looks terrible: Normalize expectations — B2C apps often see 20-30% Month 1 retention. Compare to industry benchmarks before panicking.
• User cannot interpret the table: Walk through one row in detail, explaining what each cell means for that specific cohort.