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churn-analysis

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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.

plugins/pm-cs/skills/churn-analysis/SKILL.md

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# 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).

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