churn-analysis

Generate structured root-cause post-mortem analyses for customer churn events.

Updated May 2, 2026
One-click install
npx skills add https://github.com/marius-bughiu/ooligo --skill churn-analysis-marius-bughiu
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: churn-analysis
Source: https://github.com/marius-bughiu/ooligo/tree/main/apps/web/public/artifacts/churn-analysis-skill
Command: npx skills add https://github.com/marius-bughiu/ooligo --skill churn-analysis-marius-bughiu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the inconsistency of free-text CSM churn post-mortems that prevent RevOps from aggregating root causes across quarters without time-consuming manual recoding of unstructured notes.

Core Features & Use Cases

  • Structured Post-Mortem Generation: Produces a standardized markdown document with triggering event, root cause classification, missed signals, deviation from success plan, and prevention recommendations for every churned account.
  • Bias-Guarded Classification: Uses a two-pass evidence extraction and classification process to prevent hindsight bias, taxonomy creep, and over-attribution to common narratives like unsubstantiated champion departure.
  • Aggregatable Outputs: Ties all classifications to a team-defined churn taxonomy and prevention action library, so RevOps can roll up root causes and recommended actions across quarters without recoding data.
  • Use Case: After a customer churns, run this skill to turn the CSM's notes and CRM data into a consistent analysis that feeds directly into quarterly RevOps churn reports.

Quick Start

Invoke the churn-analysis skill with the churned account's CRM ID, churn date, and optional CSM notes to generate a structured root-cause post-mortem document for RevOps review.

Frequently Asked Questions about churn-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I standardize customer churn post-mortems for RevOps aggregation?

Generate structured churn post-mortems by processing CRM data, CSM notes, support tickets, and Gong call summaries to build a 180-day pre-churn timeline, extracting evidence across two passes to classify root causes against a defined taxonomy.

Why does free-text CSM churn analysis prevent rolling up root causes across quarters?

Free-text CSM churn analysis prevents rolling up root causes because unstructured notes require manual recoding, suffer from hindsight bias, and lack a standardized churn taxonomy, making quarterly RevOps aggregation inconsistent and time-consuming.

How do I prevent hindsight bias when classifying customer churn root causes?

Prevent hindsight bias in churn root cause classification by enforcing a two-pass evidence extraction and classification workflow tied to a team-defined churn taxonomy, eliminating over-attribution to common narratives like unsubstantiated champion departure.

What data sources do I need for a structured churn root-cause analysis?

Structured churn root-cause analysis requires CRM data, CSM notes, support tickets, Gong call summaries, and product usage metrics to construct a 180-day pre-churn timeline and extract evidence for classification.

Can I use this churn analysis workflow for non-renewal and close-lost events?

Yes, this churn analysis workflow applies to both post-close-lost and non-renewal churn events, processing account data to generate standardized markdown post-mortem documents with root cause classifications and prevention recommendations.