cohort-analysis

name	cohort-analysis
description	Creates cohort analysis frameworks for understanding retention, revenue, and behavior patterns over time. Use when measuring how user groups perform across their lifecycle.
allowed-tools	Read Write Glob
metadata	{"author":"matthewhitcham","version":"1.0"}

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.

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Core Principle

AVERAGES LIE — COHORT ANALYSIS REVEALS WHETHER YOUR BUSINESS IS ACTUALLY IMPROVING BY COMPARING HOW DIFFERENT GROUPS BEHAVE OVER THE SAME LIFECYCLE STAGE.

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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.

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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.

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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

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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.

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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.

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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?"

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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.