churn-autopsy
FeaturedPost-mortem analysis when a client churns. Takes client history, engagement data, support tickets, usage logs, and exit feedback to produce a comprehensive churn autopsy with root cause classification, timeline of decline, and preventive measures.
Install
Quality Score: 90/100
Skill Content
Details
- Author
- OneWave-AI
- Repository
- OneWave-AI/claude-skills
- Created
- 11 months ago
- Last Updated
- 3 days ago
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
churn-autopsy
Find why customers leave before deciding how to keep them: build a ledger of every customer who left from the person's own data, score the five suspects (selection, promise, delivery, experience, economics) against that ledger, and rank the causes by how many customers each one explains. Use whenever someone says "clients keep leaving", "100% churn", "customers do not come back", "retention is bad", "nobody renews", "hamare clients ruk nahi rahe", "clients chhod ke ja rahe hain", "is it our quality or our UX", "help me find the root cause of churn", or asks for a retention plan, a loyalty offer, a win-back campaign or a discount before the cause is known. Also use when an owner arrives already certain of the cause ("it is the dashboard", "the team is slow", "our reports are confusing"). Stand down when the ask is a prompt (noguess owns it), a plan for a business with no customers yet (business-blueprint owns it), or a fix to one named thing one customer complained about.
churn-postmortem
When a customer has churned, downgraded, or was nearly lost, and the user needs to know why it really happened, when it first became visible in data they already had, and what to change so the next one does not repeat. Also use when the user mentions 'what went wrong', 'accounts we lost', 'why did we lose', 'why did we lose them', 'churn post-mortem', 'loss review', 'post mortem on Acme', 'root cause analysis', 'we lost Northwind', 'they never renewed', 'churn reasons this quarter', 'could we have saved them', 'was it preventable', 'this account was green and churned', 'downgrade analysis', or 'quarterly churn review'. Use this whenever a loss has already happened and someone is trying to learn from it rather than relitigate it, even if they don't say 'post-mortem' — including a save that nearly failed. For live risk on an open account, see churn-risk. For feedback themes across the base, see voice-of-customer. For rebuilding the score this loss defeated, see health-score-designer.
churn-analyst
Finds churn patterns, scores account health from behavioural signals and designs retention interventions. Use when diagnosing why customers leave or building an at-risk model. Not for renewal outreach - use renewal-strategist.