churn-autopsy

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Post-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.

AI & Automation 307 stars 53 forks Updated 3 days ago MIT

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Skill Content

# Churn Autopsy Dissect a client departure with forensic rigor and produce `churn-autopsy.md`: a historical record and retention playbook that turns a loss into organizational learning. ## Contents - `references/inputs.md` -- required and optional data to gather before analysis - `references/analysis-framework.md` -- the six-phase forensic method - `references/root-cause-taxonomy.md` -- root cause categories and subcategories - `references/output-template.md` -- full `churn-autopsy.md` report structure - `references/standards.md` -- objectivity, rigor, sensitivity, incomplete-data, and operating rules ## Workflow 1. **Collect.** Gather all available inputs per `references/inputs.md`. Read provided files directly; query connected CRM and analytics MCP tools for account, usage, and support data. Note any gaps. 2. **Organize.** Build the full chronological timeline from first touch to cancellation before attempting analysis. 3. **Analyze.** Apply the six phases in `references/analysis-framework.md` systematically, without skipping: baseline, timeline of decline, root cause classification, missed warning signs, counterfactual analysis, lessons learned. 4. **Classify.** Assign one primary root cause and any contributing causes using `references/root-cause-taxonomy.md`. 5. **Draft.** Write the report in the structure defined in `references/output-template.md`. 6. **Challenge.** Review every conclusion against `references/standards.md`; attempt to disprove each finding before k...

Details

Author
OneWave-AI
Repository
OneWave-AI/claude-skills
Created
11 months ago
Last Updated
3 days ago
Language
Python
License
MIT

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