churn-analysis
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.
npx skills add mohitagw15856/pm-claude-skills --skill churn-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Churn Analysis Skill Produce a structured churn analysis that goes beyond the headline rate — identifying why customers leave, which segments are most at risk, and what interventions will have the highest impact on retention. ## Reads from / Writes to the Brain If a [`professional-brain`](../professional-brain/SKILL.md) (`brain/`) exists, ground in it instead of re-asking for what you already know: - **Read first:** `context.md` (metric definitions — what "churn" means here), `knowledge/`, and related segment `entities/`. Run `python3 ../professional-brain/scripts/brain_query.py ./brain "churn"` and carry each fact's provenance tag through. - **📥 Propose to the Brain:** after producing, propose recording the headline retention finding to `knowledge/` (`[data]`), any retention decision to `decisions/`, and at-risk drivers as `hypotheses/`. Show them, get a yes, then write with `../professional-brain/scripts/brain_write.py … --commit` (append-only, dry-run by default). ## Required Inputs Ask for these if not already provided: - **Time period** being analysed (e.g. Q1, last 12 months) - **Total customers at start of period** and **customers churned** - **ARR or revenue lost** to ch
- Reads from / Writes to the Brain
- Required Inputs
- Churn Categories
- Output Format
- Headline Numbers
- Churn Breakdown by Category
- Churn Reasons — Avoidable Churn Only
- Churn by Segment
- By Tier
- By Cohort (Acquisition Year)
- By Industry / Use Case (if data available)
- Timing Analysis
- Early Warning Signals
- Intervention Recommendations
What does the churn-analysis skill do?
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.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill churn-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.