Cohort & Segment Analysis
Perform cohort analysis and customer segmentation to identify behavioral patterns, retention trends, and high-value groups.
Your overall retention is 85% and everyone celebrates. But the January cohort retained at 92% while the March cohort retained at 71%. The averages hide the story — and without cohort analysis, you're optimizing for a number that doesn't represent any actual customer group.
Who it's for: product analysts measuring feature adoption by user cohort, growth teams optimizing onboarding for different customer segments, data analysts building retention cohort models, marketing teams identifying high-value customer segments, subscription businesses tracking cohort-level churn and expansion
Example
"Analyze retention by signup cohort for the past 6 months" → Cohort retention table with monthly breakdown, segment comparison (enterprise vs SMB vs self-serve), churn inflection points identified (Week 3 and Month 2), behavioral patterns distinguishing retained vs churned users, and targeted recommendations per segment
New here? 3-minute setup guide → | Already set up? Copy the template below.
# Cohort & Segment Analysis
## Your Role
You are an expert customer analytics specialist. Your job is to reveal behavioral patterns through cohort analysis and segmentation.
## Core Principles
- Segment by behavior, not just demographics
- Recent cohorts reflect current product-market fit
- Identify "aha moments" that predict retention
- Compare acquisition channels by LTV, not just volume
- Every segment needs its own strategy
## Instructions
Produce: cohort retention curves, revenue by cohort, segment profiles, high-value group identification, at-risk patterns, and segment-specific recommendations.
## Commands
- "Cohort analysis by [dimension]" - Retention and revenue curves
- "Customer segmentation" - Behavioral clustering
- "High-value segments" - Best customer identification
- "Churn predictors by segment" - Early warning indicators
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What This Does
Analyzes customer data by cohort (sign-up month, acquisition channel) and segments to reveal retention patterns, behavioral differences, and lifetime value trends that aggregate metrics hide.
Quick Start
Step 1: Download the Template
Click Download above to get the CLAUDE.md file.
Step 2: Prepare Customer Data
Export: customer list with sign-up dates, revenue, usage, and key attributes for segmentation.
Step 3: Start Using It
claude
Say: "Run a cohort analysis on this customer data. Group by sign-up month, show retention and revenue trends per cohort."
Analysis Output
| Section | Content |
|---|---|
| Cohort Retention | Month-by-month retention curves per cohort |
| Revenue Cohorts | Revenue per cohort over time |
| Segment Profiles | Behavioral clusters with characteristics |
| High-Value Segments | Groups with highest LTV and why |
| At-Risk Patterns | Early indicators of churn by segment |
| Recommendations | Segment-specific strategies |
Tips
- Recent cohorts tell you about current product-market fit: Improving retention = product is getting better
- Look for "aha moments": What do high-retention cohorts do that low-retention ones don't?
- Segment by behavior, not just demographics: Usage patterns predict outcomes better than company size
- Compare acquisition channels: Which channels bring the highest-LTV customers?
Commands
"Run cohort analysis by sign-up month"
"Segment customers by usage pattern and compare retention"
"Which acquisition channel produces highest-LTV customers?"
"Identify early warning signs for churn in each segment"
Troubleshooting
Cohorts look too similar Try different dimensions: "Segment by plan type, company size, or first feature used"
Not enough data Say: "Use 6-month retention as proxy — we don't have enough data for 12-month cohorts yet"
Results are confusing Ask: "Summarize the single most important insight from this cohort analysis"