cohort-analysis
# Cohort Analysis Skill This Claude Code item structures comprehensive cohort analyses for product teams analyzing user retention, lifetime value, behavioral patterns, and churn. Use it when asked to segment users by acquisition period or channel, track retention curves over time, estimate LTV by cohort, or identify behavioral trends across user groups. The skill produces a complete framework including cohort definitions, retention matrices, trend analysis, and prioritized interventions ready for presentation to product leadership or data teams.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/cohort-analysis && cp -r /tmp/cohort-analysis/plugins/pm-data/skills/cohort-analysis ~/.claude/skills/cohort-analysisSKILL.md
# Cohort Analysis Skill
This skill produces a structured cohort analysis covering retention curves, LTV estimation, behavioural segmentation, and actionable interventions. Output is ready to present to product leadership or share with growth and data teams.
## Required Inputs
Ask the user for these if not provided:
- **Analysis goal** (retention improvement / LTV modelling / behavioural segmentation / churn prediction)
- **Product or feature being analysed**
- **Cohort definition** — what groups users? (acquisition month, signup channel, plan tier, feature adoption)
- **Observation window** — how many periods to track? (e.g. 12 months, 8 weeks)
- **Key metric** — what are you measuring per cohort? (retention rate, revenue, engagement score, feature usage)
- **Available data** — what tables/metrics are available? (paste schema or describe)
- **Baseline** — any existing retention benchmarks or goals?
## Output Structure
---
# Cohort Analysis: [Product / Feature]
**Analysis type:** [Retention / LTV / Behavioural / Churn]
**Cohort definition:** [Acquisition month / Signup channel / Plan tier / Feature adoption date]
**Observation window:** [X months / weeks]
**Primary metric:** [Metric name]
**Date prepared:** [Date]
---
## 1. Cohort Definitions
| Cohort | Period | Size | Description |
|---|---|---|---|
| [Cohort 1] | [Jan 2025] | [N users] | [e.g. Users who signed up in Jan 2025 via organic] |
| [Cohort 2] | [Feb 2025] | [N users] | [...] |
**Cohort logic:**
- Cohort entry event: [First sign-up / First purchase / Feature activation]
- Cohort exit criteria: [Churned / Downgraded / No activity for 30 days]
- Exclusions: [Trial users / Internal test accounts / Users with < X days of data]
---
## 2. Retention Curve
**How to read:** Each cell shows what % of the cohort performed the key metric in period N.
| Cohort | Period 0 | Period 1 | Period 2 | Period 3 | Period 6 | Period 12 |
|---|---|---|---|---|---|---|
| Jan 2025 | 100% | [X%] | [X%] | [X%] | [X%] | [X%] |
| Feb 2025 | 100% | [X%] | [X%] | [X%] | [X%] | [X%] |
| [Trend] | — | [↑/↓ vs prior] | [...] | [...] | [...] | [...] |
**Retention plateau:** [At what period does retention flatten? What % does it flatten at?]
**Key observations:**
- [e.g. Period 1 → Period 2 drop is the largest — average X% churn in first 30 days]
- [e.g. Cohorts acquired via [channel] retain X% better at Period 6]
- [e.g. Retention has improved from X% → Y% at Period 3 comparing oldest to newest cohort]
---
## 3. LTV Projection (if applicable)
**ARPU per period:** [£/$/€ X per active user per month]
**Retention curve used:** [Which cohort or blended average]
| Period | Retained % | Revenue per user | Cumulative LTV |
|---|---|---|---|
| Month 1 | [X%] | [£X] | [£X] |
| Month 3 | [X%] | [£X] | [£X] |
| Month 6 | [X%] | [£X] | [£X] |
| Month 12 | [X%] | [£X] | [£X] |
**Blended LTV:** [£X at 12 months — based on blended retention across cohorts]
**LTV by segment:**
| Segment | LTV (12M) | vs Baseline |
|---|---|---|
| [Organic] | [£X] | [+X%] |
| [Paid] | [£X] | [-X%] |
| [Enterprise] | [£X] | [+X%] |
---
## 4. Behavioural Segmentation
Group cohorts by behaviour patterns, not just acquisition date:
| Segment | Definition | Size | Retention (P6) | LTV (12M) |
|---|---|---|---|---|
| **Power users** | [Used core feature ≥ 3x/week in first 30 days] | [X%] | [X%] | [£X] |
| **Casual users** | [Used 1–2x/week in first 30 days] | [X%] | [X%] | [£X] |
| **Dormant** | [Logged in but did not use core feature] | [X%] | [X%] | [£X] |
| **Never activated** | [Signed up but never completed onboarding] | [X%] | [X%] | [£X] |
**Activation threshold insight:** [What action — taken within the first X days — most strongly predicts retention? This is the "aha moment" to optimise for.]
---
## 5. Leading Indicators of Churn
List the signals that appear **before** users churn, so teams can intervene:
| Signal | How early does it appear? | Churn correlation | Intervention |
|---|---|---|---|
| [No login for 7 days] | [7 days before churn] | [Strong] | [Re-engagement email sequence] |
| [Support ticket with escalation] | [14 days before churn] | [Moderate] | [CSM outreach within 48 hours] |
| [Feature usage dropped >50% WoW] | [10 days before churn] | [Strong] | [In-app nudge with use-case tutorial] |
---
## 6. Cohort Comparison: What's Changed Over Time
Compare oldest and newest cohorts to assess whether product improvements are showing up in retention:
| Metric | [Oldest cohort — e.g. Jan 2024] | [Newest cohort — e.g. Jan 2025] | Change |
|---|---|---|---|
| Period 1 retention | [X%] | [X%] | [↑/↓ X pp] |
| Period 3 retention | [X%] | [X%] | [↑/↓ X pp] |
| Activation rate | [X%] | [X%] | [↑/↓ X pp] |
| Avg. sessions in first 30 days | [X] | [X] | [↑/↓] |
**Verdict:** [Are more recent cohorts performing better or worse? What shipped in that period that might explain the change?]
---
## 7. Recommendations
Prioritise by impact on retention curve:
| # | Recommendation | Target segment | Expected impact | Effort | Priority |
|---|---|---|---|---|---|
| 1 | [e.g. Redesign onboarding to hit activation milestone in day 1, not day 7] | [Never-activated segment] | [+X pp P1 retention] | [Medium] | P1 |
| 2 | [e.g. Launch re-engagement sequence at day 7 inactivity trigger] | [Dormant segment] | [+X pp P2 retention] | [Low] | P1 |
| 3 | [e.g. Introduce power-user features earlier to accelerate habit formation] | [Casual users] | [+X pp P6 LTV] | [High] | P2 |
---
## 8. SQL Reference (if applicable)
Provide the core cohort query so data teams can replicate or extend the analysis:
```sql
-- Retention cohort query
SELECT
DATE_TRUNC('month', u.created_at) AS cohort_month,
DATE_TRUNC('month', e.event_date) AS activity_month,
DATEDIFF('month', u.created_at, e.event_date) AS period,
COUNT(DISTINCT e.user_id) AS retained_users,
COUNT(DISTINCT c.user_id) AS cohort_size,
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