# Cohort Analysis > Creates cohort analysis frameworks for understanding retention, revenue, and behavior patterns over time. Use when measuring how user groups perform across their lifecycle. - Skill: `reaperinvest/cohort-analysis` (Agent Skill) - Install (CLI): `npx skillmds@latest add reaperinvest/cohort-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/reaperinvest/cohort-analysis/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - Author: reaperinvest (https://skillmd.com/u/reaperinvest) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/reaperinvest/cohort-analysis --- # Cohort Analysis ## When to Use This Skill Use this skill when you need to: - Understand customer retention trends by signup month - Compare revenue performance across acquisition cohorts - Identify which customer groups behave differently over time - Build a repeatable cohort analysis framework for ongoing use **DO NOT** use this skill for one-time snapshot metrics, individual customer analysis, or real-time monitoring. This is for longitudinal group-based analysis. --- ## Core Principle AVERAGES LIE — COHORT ANALYSIS REVEALS WHETHER YOUR BUSINESS IS ACTUALLY IMPROVING BY COMPARING HOW DIFFERENT GROUPS BEHAVE OVER THE SAME LIFECYCLE STAGE. --- ## Phase 1: Brief ### Required Inputs | Input | What to Ask | Default | |-------|------------|---------| | **Analysis goal** | "What are you trying to understand? (retention, revenue, engagement, churn)" | Customer retention | | **Cohort definition** | "How should cohorts be grouped? (signup month, acquisition channel, plan tier)" | Signup month | | **Time granularity** | "Weekly, monthly, or quarterly cohorts?" | Monthly | | **Observation window** | "How many periods to track each cohort?" | 6 months | | **Data source** | "Where is your customer data? (Stripe, CRM, database, spreadsheet)" | Spreadsheet or Stripe | | **Metric** | "What metric per cohort? (active users, revenue, orders, logins)" | Active users (retention) | **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.