Winbda Churn Analyzer
Overview
Analyzes churn patterns, identifies root causes, and creates actionable retention strategies.
Workflow
Step 1: Gather Context
- Business model: SaaS, service, product?
- Current churn rate: Monthly/annual?
- Customer segments: Different tiers or types?
- Known reasons: Why do customers say they leave?
- Available data: What customer data do you have?
Step 2: Analyze Churn
Churn calculation:
Monthly churn = Customers lost / Starting customers × 100
Annual churn = 1 - (1 - monthly churn)^12
Revenue churn = MRR lost / Starting MRR × 100
Analysis dimensions:
- By segment (plan tier, industry, size)
- By tenure (when do they leave?)
- By engagement (usage before churn)
- By acquisition source (where they came from)
- By season (time patterns)
Output Format
# Churn Analysis: [Company]
## Current State
- **Monthly churn**: [X%]
- **Annual churn**: [X%]
- **Revenue impact**: $[X] lost/month
- **Industry benchmark**: [X%]
## Churn Patterns
### By Tenure
| Period | Churn Rate | Insight |
|--------|-----------|---------|
| Month 1 | X% | [early churn = onboarding problem] |
| Month 2-3 | X% | [value realization gap] |
| Month 6+ | X% | [engagement decay] |
### By Segment
[Breakdown by customer type]
## Root Cause Analysis
1. **[Cause 1]** — [% of churn] — [evidence]
2. **[Cause 2]** — [% of churn] — [evidence]
3. **[Cause 3]** — [% of churn] — [evidence]
## Retention Strategy
### Quick Wins (This Month)
1. [Action]: Expected impact [X% reduction]
2. [Action]: Expected impact [X% reduction]
### Medium-Term (This Quarter)
1. [Action]: Expected impact [X% reduction]
### Long-Term (This Year)
1. [Action]: Expected impact [X% reduction]
## Financial Impact
If churn reduces from [X%] to [Y%]:
- Monthly revenue saved: $[X]
- Annual impact: $[X]
- LTV improvement: [X%]
Quality Checklist
- Churn calculated correctly (logo + revenue)
- Patterns identified by multiple dimensions
- Root causes ranked by impact
- Retention strategies are specific and actionable
- Financial impact quantified