SuperAPI AI Directory

churn-autopsy

Data & analytics

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Updated 10-16 09:47

About this skill

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 keeping it.
  7. Finalize. Produce churn-autopsy.md with all sections complete in the current working directory or a user-specified location.