feat: v12.0.0 — 150-skill milestone, 15 new skills across 10 bundles
Adds 15 new skills reaching the 150-skill milestone: Data & Analytics (pm-data): - cohort-analysis: retention curves, LTV projection, behavioural segmentation, SQL reference queries - data-pipeline-spec: ETL/ELT design with SLAs, DQ rules, error handling, compliance Customer Success (pm-cs): - renewal-playbook: health snapshot, value story, commercial scenarios, objection responses, 16-week timeline - customer-success-plan: joint success plan with milestones, mutual commitments, escalation path People & Leadership (pm-people): - 360-feedback-template: survey instrument + narrative report with strengths and development themes - team-health-check: Spotify-model assessment across 7 dimensions with facilitation guide Operations (pm-operations): - risk-register: L×I scoring, RAG heat map, mitigation and contingency plans - raci-matrix: role definitions, decision map, anti-pattern guide, communication template Marketing & GTM (pm-gtm): - social-media-strategy: audience profile, content pillars, KPIs, 4-week starter calendar - product-positioning-doc: April Dunford-style positioning, messaging hierarchy, persona messaging Discovery (pm-discovery): - customer-journey-map: stage-by-stage journey with touchpoints, emotions, and prioritised opportunities Delivery (pm-delivery): - user-story-writer: Given/When/Then ACs, edge cases, definition of done, epic decomposition Advanced (pm-advanced): - ai-ethics-review: fairness, bias, transparency, privacy, safety, accountability, societal impact Sales (pm-sales): - partnership-proposal: mutual value, commercial model, joint GTM plan, governance Design (pm-design): - design-system-audit: component coverage, token consistency, WCAG, adoption, remediation roadmap Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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name: cohort-analysis
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description: "Structure a cohort analysis for retention, LTV, or behavioural patterns. Use when asked to run a cohort analysis, analyse retention by cohort, segment users by behaviour over time, or calculate lifetime value by acquisition period. Produces a complete cohort analysis framework with methodology, cohort definitions, retention curves, and prioritised interventions."
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---
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# Cohort Analysis Skill
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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.
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## Required Inputs
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Ask the user for these if not provided:
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- **Analysis goal** (retention improvement / LTV modelling / behavioural segmentation / churn prediction)
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- **Product or feature being analysed**
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- **Cohort definition** — what groups users? (acquisition month, signup channel, plan tier, feature adoption)
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- **Observation window** — how many periods to track? (e.g. 12 months, 8 weeks)
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- **Key metric** — what are you measuring per cohort? (retention rate, revenue, engagement score, feature usage)
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- **Available data** — what tables/metrics are available? (paste schema or describe)
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- **Baseline** — any existing retention benchmarks or goals?
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## Output Structure
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---
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# Cohort Analysis: [Product / Feature]
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**Analysis type:** [Retention / LTV / Behavioural / Churn]
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**Cohort definition:** [Acquisition month / Signup channel / Plan tier / Feature adoption date]
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**Observation window:** [X months / weeks]
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**Primary metric:** [Metric name]
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**Date prepared:** [Date]
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---
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## 1. Cohort Definitions
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| Cohort | Period | Size | Description |
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|---|---|---|---|
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| [Cohort 1] | [Jan 2025] | [N users] | [e.g. Users who signed up in Jan 2025 via organic] |
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| [Cohort 2] | [Feb 2025] | [N users] | [...] |
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**Cohort logic:**
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- Cohort entry event: [First sign-up / First purchase / Feature activation]
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- Cohort exit criteria: [Churned / Downgraded / No activity for 30 days]
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- Exclusions: [Trial users / Internal test accounts / Users with < X days of data]
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---
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## 2. Retention Curve
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**How to read:** Each cell shows what % of the cohort performed the key metric in period N.
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| Cohort | Period 0 | Period 1 | Period 2 | Period 3 | Period 6 | Period 12 |
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|---|---|---|---|---|---|---|
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| Jan 2025 | 100% | [X%] | [X%] | [X%] | [X%] | [X%] |
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| Feb 2025 | 100% | [X%] | [X%] | [X%] | [X%] | [X%] |
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| [Trend] | — | [↑/↓ vs prior] | [...] | [...] | [...] | [...] |
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**Retention plateau:** [At what period does retention flatten? What % does it flatten at?]
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**Key observations:**
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- [e.g. Period 1 → Period 2 drop is the largest — average X% churn in first 30 days]
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- [e.g. Cohorts acquired via [channel] retain X% better at Period 6]
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- [e.g. Retention has improved from X% → Y% at Period 3 comparing oldest to newest cohort]
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---
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## 3. LTV Projection (if applicable)
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**ARPU per period:** [£/$/€ X per active user per month]
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**Retention curve used:** [Which cohort or blended average]
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| Period | Retained % | Revenue per user | Cumulative LTV |
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|---|---|---|---|
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| Month 1 | [X%] | [£X] | [£X] |
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| Month 3 | [X%] | [£X] | [£X] |
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| Month 6 | [X%] | [£X] | [£X] |
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| Month 12 | [X%] | [£X] | [£X] |
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**Blended LTV:** [£X at 12 months — based on blended retention across cohorts]
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**LTV by segment:**
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| Segment | LTV (12M) | vs Baseline |
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|---|---|---|
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| [Organic] | [£X] | [+X%] |
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| [Paid] | [£X] | [-X%] |
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| [Enterprise] | [£X] | [+X%] |
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---
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## 4. Behavioural Segmentation
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Group cohorts by behaviour patterns, not just acquisition date:
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| Segment | Definition | Size | Retention (P6) | LTV (12M) |
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|---|---|---|---|---|
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| **Power users** | [Used core feature ≥ 3x/week in first 30 days] | [X%] | [X%] | [£X] |
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| **Casual users** | [Used 1–2x/week in first 30 days] | [X%] | [X%] | [£X] |
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| **Dormant** | [Logged in but did not use core feature] | [X%] | [X%] | [£X] |
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| **Never activated** | [Signed up but never completed onboarding] | [X%] | [X%] | [£X] |
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**Activation threshold insight:** [What action — taken within the first X days — most strongly predicts retention? This is the "aha moment" to optimise for.]
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---
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## 5. Leading Indicators of Churn
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List the signals that appear **before** users churn, so teams can intervene:
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| Signal | How early does it appear? | Churn correlation | Intervention |
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| [No login for 7 days] | [7 days before churn] | [Strong] | [Re-engagement email sequence] |
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| [Support ticket with escalation] | [14 days before churn] | [Moderate] | [CSM outreach within 48 hours] |
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| [Feature usage dropped >50% WoW] | [10 days before churn] | [Strong] | [In-app nudge with use-case tutorial] |
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---
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## 6. Cohort Comparison: What's Changed Over Time
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Compare oldest and newest cohorts to assess whether product improvements are showing up in retention:
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| Metric | [Oldest cohort — e.g. Jan 2024] | [Newest cohort — e.g. Jan 2025] | Change |
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|---|---|---|---|
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| Period 1 retention | [X%] | [X%] | [↑/↓ X pp] |
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| Period 3 retention | [X%] | [X%] | [↑/↓ X pp] |
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| Activation rate | [X%] | [X%] | [↑/↓ X pp] |
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| Avg. sessions in first 30 days | [X] | [X] | [↑/↓] |
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**Verdict:** [Are more recent cohorts performing better or worse? What shipped in that period that might explain the change?]
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---
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## 7. Recommendations
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Prioritise by impact on retention curve:
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| # | Recommendation | Target segment | Expected impact | Effort | Priority |
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|---|---|---|---|---|---|
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| 1 | [e.g. Redesign onboarding to hit activation milestone in day 1, not day 7] | [Never-activated segment] | [+X pp P1 retention] | [Medium] | P1 |
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| 2 | [e.g. Launch re-engagement sequence at day 7 inactivity trigger] | [Dormant segment] | [+X pp P2 retention] | [Low] | P1 |
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| 3 | [e.g. Introduce power-user features earlier to accelerate habit formation] | [Casual users] | [+X pp P6 LTV] | [High] | P2 |
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---
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## 8. SQL Reference (if applicable)
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Provide the core cohort query so data teams can replicate or extend the analysis:
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```sql
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-- Retention cohort query
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SELECT
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DATE_TRUNC('month', u.created_at) AS cohort_month,
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DATE_TRUNC('month', e.event_date) AS activity_month,
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DATEDIFF('month', u.created_at, e.event_date) AS period,
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COUNT(DISTINCT e.user_id) AS retained_users,
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COUNT(DISTINCT c.user_id) AS cohort_size,
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ROUND(COUNT(DISTINCT e.user_id) * 100.0 / COUNT(DISTINCT c.user_id), 1) AS retention_rate
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FROM users u
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JOIN events e ON u.user_id = e.user_id
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JOIN (
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SELECT user_id, DATE_TRUNC('month', created_at) AS cohort_month
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FROM users
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WHERE created_at >= '[start_date]'
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) c ON u.user_id = c.user_id AND DATE_TRUNC('month', u.created_at) = c.cohort_month
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WHERE e.event_type = '[key_retention_event]'
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GROUP BY 1, 2, 3
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ORDER BY 1, 3;
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```
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---
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## Quality Checks
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- [ ] Cohort definition is unambiguous — the same user cannot appear in two cohorts
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- [ ] Retention curve shows a clear plateau, or the analysis notes that the window is too short to see one
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- [ ] LTV projection uses observed retention, not assumed
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- [ ] Behavioural segments are mutually exclusive and exhaustive
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- [ ] Recommendations are tied to specific cohort or segment findings — not generic growth advice
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- [ ] Leading indicators are observable in production data, not just in theory
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## Example Trigger Phrases
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- "Run a cohort analysis for our SaaS product"
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- "Analyse retention by acquisition month for the last 12 cohorts"
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- "What's the LTV of users who came via paid vs organic?"
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- "Build a cohort retention model showing period 0 through period 12"
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- "Segment users by behaviour and show me which group retains best"
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