Produce a structured churn analysis that separates avoidable from unavoidable churn. Use when investigating why customers are leaving, identifying at-risk segments, calculating net revenue retention, or building a retention intervention plan. Produces a churn report with rate calculations, categorised reasons by avoidability, segment breakdown, timing analysis, early warning signals, and prioritised interventions ranked by estimated impact.
📚 Based on Net revenue retention & churn cohort analysis
▸Time period — being analysed (e.g. Q1, last 12 months)
▸Total customers at start of period — and customers churned
▸ARR or revenue lost — to churn
▸Churn reasons data — exit survey results, CSM notes, support data, or sales loss reasons
▸Customer segments — by tier, industry, cohort, or product line
▸Current retention rate — if known
▸Any recent changes — pricing, product, support model — that may have affected churn
✅ The bar it holds itself to
Every skill in this library self-verifies — these are this skill's own quality checks, straight from its definition.
✓Churn rate is correctly calculated (churned ÷ starting cohort, not end-of-period total)
✓Avoidable and unavoidable churn are separated — interventions target avoidable churn only
✓Churn reasons are customer-reported, not internally assumed
✓Segment analysis identifies which segments over-index — not just averages
✓Early warning signals are specific and detectable, not generic ("low engagement")
✓Interventions link directly to the top churn reasons — no recommendations without a root cause match
⚠️ What it refuses to do
Do not mix avoidable and unavoidable churn in intervention plans — recommending product fixes for customers who churned due to company shutdown wastes resources
Do not calculate churn rate using end-of-period customer count as the denominator — this understates churn; always divide churned customers by the starting cohort
Do not rely solely on exit survey data for churn reasons — response rates are typically low and self-selection biases the sample toward customers who are engaged enough to complete a survey
Do not recommend interventions without linking them to a specific churn reason — interventions disconnected from root causes will not move retention
Do not report only gross revenue churn — without net revenue retention (NRR), a healthy-looking retention number can hide a shrinking revenue base
Install
npx pm-claude-skills add --agent claude # or codex · cursor · gemini · hermes
# or one-line MCP (every skill, any client):
claude mcp add pm-skills -- npx -y pm-claude-skills-mcp
Example output
Input: SMB SaaS, $49/mo. Monthly logo churn rose from 3% to 5% over two quarters. Most cancellations happen in month 2-3. Top stated reasons: 'too hard to set up' and 'didn't see value'. Annual plans churn far less than monthly. · generated by claude-sonnet-4-6