Cancellation Reason Taxonomy Builder

The problem

Raw cancellation comments and churn data are often too messy to act on. Turning thousands of qualitative feedback rows into a rigorous, auditable taxonomy usually leads to invented motives or lost nuance.

What it does

• Builds an evidence ledger with source IDs and locators for every claim.
• Categorizes feedback into primary reasons and secondary drivers with strict inclusion/exclusion boundaries.
• Generates a coded table with confidence scores and a review queue for ambiguous cases.
• Creates a repeatable monthly protocol and migration map for longitudinal tracking.
• Reconciles conflicting sources by identifying decision owners rather than smoothing over data gaps.

Why this beats prompting it yourself

General prompts tend to hallucinate categories or "fill in the blanks" when data is missing. This skill enforces a strict traceability mandate, ensuring every taxonomy item is grounded in a specific source ID and separates human inference from stated facts.

Use cases

• Transforming raw churn survey exports into a board-ready loss analysis.
• Developing a standardized coding guide for customer success teams to tag tickets.
• Building a repeatable monthly process for tracking product-market fit shifts.
• Auditing existing cancellation categories against raw customer verbatim.

Known limitations

Does not predict individual churn or infer sensitive personal traits. Large datasets require batch processing and reconciliation steps.