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churn-prediction

guia-matthieu
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About

This skill helps developers identify at-risk customers likely to cancel by analyzing behavioral signals, engagement patterns, and health indicators. It's useful for building early warning systems, prioritizing customer support, and conducting churn risk reviews. The tool is based on established churn analysis methodologies to detect product engagement decay and relationship deterioration.

Quick Install

Claude Code

Recommended
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npx skills add guia-matthieu/clawfu-skills -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/guia-matthieu/clawfu-skills
Git CloneAlternative
git clone https://github.com/guia-matthieu/clawfu-skills.git ~/.claude/skills/churn-prediction

Copy and paste this command in Claude Code to install this skill

Documentation

Churn Prediction

Detect early warning signals of customer churn through systematic analysis of usage patterns, support interactions, and relationship health.

When to Use This Skill

  • Monthly/quarterly churn risk reviews
  • Prioritizing CSM intervention
  • Building early warning systems
  • Post-mortem analysis on lost customers
  • Executive churn reporting

Methodology Foundation

Based on Lincoln Murphy's Churn Analysis and ProfitWell Retention Research, analyzing:

  • Product engagement decay
  • Support sentiment trends
  • Payment behavior changes
  • Relationship deterioration
  • Competitive signals

What Claude Does vs What You Decide

Claude DoesYou Decide
Identifies risk signalsSave vs. let go decisions
Calculates risk scoresResource allocation
Suggests interventionsDiscount/concession offers
Prioritizes at-risk accountsExecutive escalation timing
Analyzes churn patternsRetention strategy changes

What This Skill Does

  1. Signal detection - Identify behavioral indicators of churn risk
  2. Risk scoring - Calculate churn probability
  3. Root cause analysis - Why are they likely to leave?
  4. Intervention planning - What actions could save them?
  5. Pattern recognition - Learn from past churned accounts

How to Use

Assess churn risk for this customer:

Account: [Company Name]
Contract: $[ARR], Renewal: [Date]
Tenure: [Months]

Usage Signals:
- Login frequency: [trend]
- Feature adoption: [% and trend]
- Active users: [current vs licensed]
- Key feature usage: [specific metrics]

Support Signals:
- Recent tickets: [count and nature]
- CSAT trend: [improving/stable/declining]
- Escalations: [any open or recent]
- Sentiment: [last few interactions]

Relationship Signals:
- Champion status: [engaged/disengaged/left]
- Exec sponsor: [status]
- NPS response: [score and comments]
- QBR attendance: [pattern]

Financial Signals:
- Payment status: [current/late]
- Contract discussions: [any mentions of changes]
- Competitor mentions: [any signals]

Instructions

Step 1: Evaluate Leading Indicators

30-60 Day Warning Signs:

SignalRisk LevelWeight
Login drop >50%High15
Feature usage stoppedHigh15
Support tickets spikeMedium10
Champion leftCritical20
Negative NPSHigh12
Payment lateMedium8
No QBR attendanceMedium8
Competitor mentionedHigh12

Step 2: Calculate Churn Probability

Risk Score Formula:

Churn Risk = Sum of weighted signals / 100

Score Ranges:
- 0-20: Low Risk (normal attention)
- 21-40: Moderate Risk (proactive outreach)
- 41-60: High Risk (intervention required)
- 61-80: Critical Risk (executive escalation)
- 81-100: Imminent Churn (save or plan exit)

Step 3: Identify Root Cause Category

CategoryIndicatorsTypical Save Rate
Product FitLow adoption, wrong use case30%
Value GapNot seeing ROI, budget pressure45%
Service IssueSupport failures, unresolved bugs60%
RelationshipChampion left, no engagement35%
CompetitionActively evaluating others25%
Business ChangeM&A, budget cuts, pivot15%

Step 4: Prescribe Intervention

By Root Cause:

CausePrimary ActionSecondary Action
Product FitSuccess planningRight-size contract
Value GapROI reviewExecutive sponsor call
Service IssueEscalation + resolutionService credits
RelationshipNew champion devExecutive mapping
CompetitionCompetitive defensePricing review
BusinessFlexible termsPause option

Step 5: Create Save Plan

90-Day Save Framework:

  • Days 1-7: Triage and stabilize
  • Days 8-30: Address root cause
  • Days 31-60: Rebuild value perception
  • Days 61-90: Secure commitment

Examples

Example 1: High-Risk Account

Input:

Account: MediaTech Corp
Contract: $96K ARR, Renewal: April 15 (45 days)
Tenure: 18 months

Usage Signals:
- Logins: Down 65% last 30 days
- Feature adoption: 35% (was 60%)
- Active users: 8 of 25 licensed
- Core feature: Stopped using analytics module

Support Signals:
- Tickets: 8 this month (normally 2)
- CSAT: Dropped from 4.5 to 3.2
- Escalation: 1 open (data export issue)
- Sentiment: Last 3 interactions negative

Relationship Signals:
- Champion: VP Marketing left 6 weeks ago
- Exec sponsor: Never established
- NPS: 4 (was 8 six months ago)
- QBR: Cancelled last two

Financial Signals:
- Payment: Current
- Mentions: "Evaluating options" in last call
- Competitor: Saw CompetitorX demo on their calendar

Output:

## Churn Risk Assessment: MediaTech Corp

### 🔴 CRITICAL RISK - 78/100

**Churn Probability: 75-85%**
**Time to Action: Immediate**

---

### Risk Signal Analysis

| Signal | Finding | Points |
|--------|---------|--------|
| Usage drop 65% | Critical | 15/15 |
| Feature stopped | Analytics abandoned | 15/15 |
| Champion left | No replacement | 20/20 |
| NPS Detractor | Dropped 4 points | 12/12 |
| Competitor eval | Demo scheduled | 12/12 |
| No QBR | 2 cancelled | 8/8 |
| **Total** | | **78/100** |

### Root Cause Analysis

**Primary: Relationship Void** (Champion departure)
- VP Marketing left 6 weeks ago
- No executive sponsor ever established
- Organizational knowledge lost
- No one internally championing value

**Secondary: Value Gap** (Product not delivering)
- Usage collapse suggests not getting value
- Analytics module abandoned = key feature unused
- May have been champion-dependent usage

**Tertiary: Active Competition**
- CompetitorX demo on calendar
- "Evaluating options" language
- Classic exit signals

### Competitor Intelligence

**CompetitorX Positioning:**
- Likely pitching: Simpler interface, lower price
- Their weakness: Less robust analytics
- Our defense: Depth of features, integration

### 90-Day Save Plan

**WEEK 1: Emergency Triage**

| Day | Action | Owner |
|-----|--------|-------|
| 1 | Resolve data export escalation | Support |
| 1 | CSM call to understand situation | CSM |
| 2 | Identify new potential champion | CSM |
| 3 | VP CS call to express commitment | VP CS |
| 5 | Executive sponsor outreach (their CMO) | CRO |

**WEEK 2-4: Stabilize**
- [ ] Close escalation completely
- [ ] Meet new champion (find one!)
- [ ] Re-onboard inactive users
- [ ] Document value delivered to date
- [ ] Competitive battle card preparation

**WEEK 5-8: Rebuild Value**
- [ ] ROI review presentation
- [ ] Success planning session
- [ ] Showcase new features
- [ ] Get 3 quick wins documented

**WEEK 9-12: Secure Renewal**
- [ ] QBR with exec attendance
- [ ] Renewal discussion
- [ ] Multi-year incentive if appropriate
- [ ] Reference/case study request (confidence signal)

### Save Probability Assessment

| If We... | Save Probability |
|----------|------------------|
| Do nothing | 15% |
| Standard outreach | 25% |
| Execute full plan | 45% |
| Add discount/concession | 55% |
| Exec-to-exec + plan | 60% |

### Decision Point

**Recommended: Full save effort**
- $96K ARR worth 60-90 hours of CSM time
- Cost to acquire replacement: ~$30K
- Reputation risk if churns

**If No Traction by Day 30:**
- Prepare for graceful offboarding
- Offer reduced contract if budget issue
- Maintain relationship for potential return

Example 2: Cohort Churn Analysis

Input:

Analyze churn patterns from these 10 churned accounts:

1. AlphaCo: Left after 8 months, champion left, low usage
2. BetaTech: Left after 14 months, pricing, competitor win
3. GammaCorp: Left after 6 months, wrong fit, never adopted
4. DeltaInc: Left after 24 months, budget cuts, loved product
5. EchoSys: Left after 10 months, support issues, 3 escalations
6. FoxtrotLLC: Left after 18 months, competitor, champion left
7. GolfCo: Left after 4 months, implementation failed
8. HotelGrp: Left after 12 months, didn't see ROI
9. IndiaInc: Left after 9 months, champion left, low NPS
10. JulietCorp: Left after 20 months, M&A, product deprecated

Output:

## Churn Cohort Analysis: Q4 Losses

### Summary
- **Total Churned**: 10 accounts
- **Total ARR Lost**: [Calculate from data]
- **Average Tenure**: 12.5 months

---

### Root Cause Distribution

| Root Cause | Count | % | Avg Tenure |
|------------|-------|---|------------|
| Champion Left | 4 | 40% | 11.3 mo |
| Competitor | 3 | 30% | 17.3 mo |
| Product/Fit | 2 | 20% | 5.0 mo |
| Business Change | 2 | 20% | 22.0 mo |
| Service/Support | 1 | 10% | 10.0 mo |
| Value/ROI | 2 | 20% | 10.0 mo |

*Note: Some accounts had multiple causes*

### Key Insights

**1. Champion Dependency is Critical (40%)**
- 4 of 10 churns involved champion departure
- Average: Churned 3-4 months after champion left
- **Action**: Multi-threading program required

**2. Early Churn = Fit Problem**
- 3 accounts churned <6 months
- All had adoption/implementation issues
- **Action**: Improve qualification + onboarding

**3. Competitor Wins Correlate with Tenure**
- Competitor losses at 14, 18, 20 months
- Long enough to evaluate alternatives
- **Action**: Value reinforcement at 12-month mark

**4. Business Change is Uncontrollable**
- 2 churns from M&A/budget cuts
- Both were "happy" customers
- **Action**: Accept, maintain relationship

### Early Warning Signal Validation

| Signal | Present Before Churn | Lead Time |
|--------|---------------------|-----------|
| Champion left | 4/10 (40%) | 3-4 months |
| Usage drop >40% | 7/10 (70%) | 6-8 weeks |
| NPS drop | 6/10 (60%) | 2-3 months |
| Missed QBR | 5/10 (50%) | 3-4 months |
| Support spike | 3/10 (30%) | 4-6 weeks |

**Best Predictor**: Usage drop >40% (70% correlation)

### Preventability Assessment

| Account | Preventable? | What Would Have Helped |
|---------|--------------|------------------------|
| AlphaCo | Likely | Champion backup plan |
| BetaTech | Possibly | Competitive defense earlier |
| GammaCorp | Unlikely | Better qualification |
| DeltaInc | No | Business change |
| EchoSys | Likely | Faster escalation resolution |
| FoxtrotLLC | Possibly | Multi-thread + compete |
| GolfCo | Likely | Implementation oversight |
| HotelGrp | Likely | Proactive ROI review |
| IndiaInc | Likely | Champion backup |
| JulietCorp | No | M&A out of control |

**Preventability Rate**: 60% (6/10 could have been saved)

### Recommendations

**Process Changes:**
1. Implement champion backup contact rule (2+ contacts)
2. Add 12-month value review to CSM playbook
3. Create competitive defense triggers
4. Improve implementation success metrics

**Investment Areas:**
1. CSM capacity for proactive outreach
2. Competitive intelligence
3. Champion development program

**Metrics to Track:**
- Champion backup coverage %
- Time to first value
- Competitive mention alerts
- 12-month NPS trend

Skill Boundaries

What This Skill Does Well

  • Systematic risk signal analysis
  • Probability scoring with clear logic
  • Root cause categorization
  • Intervention planning

What This Skill Cannot Do

  • Access actual customer data
  • Predict exact churn timing
  • Know internal customer dynamics
  • Replace relationship intuition

When to Escalate to Human

  • Strategic accounts (top 10%)
  • Complex multi-product relationships
  • Escalation requiring legal/exec involvement
  • Pricing concession decisions

Iteration Guide

Follow-up Prompts

  • "Create a 30-60-90 save plan for this account."
  • "What competitive response should we prepare?"
  • "Which of my at-risk accounts should I prioritize?"
  • "Analyze the pattern across all my churned accounts."

Monitoring Cadence

  1. Score all accounts monthly
  2. Alert on score drops >15 points
  3. Weekly review of Critical/High risk
  4. Quarterly pattern analysis

Checklists & Templates

At-Risk Account Checklist

  • Usage trend analyzed (30/60/90 day)
  • Support sentiment reviewed
  • Champion status confirmed
  • NPS collected or requested
  • Competitor signals checked
  • Financial health verified
  • Save plan documented

References

  • Lincoln Murphy's Churn Analysis Framework
  • ProfitWell Retention Benchmarks
  • Gainsight Customer Health Methodology
  • ChurnZero Predictive Analytics

Related Skills

  • account-health - Broader health scoring
  • health-score-monitor - Continuous monitoring
  • renewal-management - Renewal process

Skill Metadata

  • Domain: Customer Success
  • Complexity: Advanced
  • Mode: centaur
  • Time to Value: 20-30 min per account
  • Prerequisites: Customer data, history, context

GitHub Repository

guia-matthieu/clawfu-skills
Path: skills/customer-success/churn-prediction
0
ai-skillsanthropicclaude-codeclaude-skillsmarketingmcp-server

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