AGENT SKILLBY GUIA-MATTHIEU
Churn prediction
AT A GLANCE
Churn prediction is an agent skill for go-to-market. Detect early warning signals of customer churn through systematic analysis of usage patterns, support interactions, and relationship health. It is published by guia-matthieu in the guia-matthieu/clawfu-skills GitHub repository (128 stars) under the MIT license. Install it with: npx skills add guia-matthieu/clawfu-skills --skill churn-prediction
- SOURCE REPOSITORY
- guia-matthieu/clawfu-skills (opens GitHub in a new tab)
- REPO STARS
- SOURCE REPOSITORY
- LICENSE
- MIT
- CATEGORIES
- SKILL.MD SIZE
- 12.4 KB449 LINES
QUICK ANSWER
- WHAT IT IS01
- Detect early warning signals of customer churn through systematic analysis of usage patterns, support interactions, and relationship health. What sets it apart is how it narrows go-to-market work into one specific workflow rather than a broad, generic prompt.
- INPUTS02
- Context for go-to-market work: your goals, audience, constraints, and any source material the skill asks for.
- OUTPUT03
- A ready-to-use result for go-to-market work: the analysis, copy, or recommendations the agent produces.
INSTALL
One command adds this skill from its source repository.
npx skills add guia-matthieu/clawfu-skills --skill churn-predictionREAD THE SKILL.MD BELOW BEFORE YOU RUN IT. SKILLS INSTRUCT YOUR AGENT.
SKILL.MD
The full file, as published in the source repository. Copy it, download it or open it on GitHub.
ON THIS PAGE · 26
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 Does | You Decide |
|---|---|
| Identifies risk signals | Save vs. let go decisions |
| Calculates risk scores | Resource allocation |
| Suggests interventions | Discount/concession offers |
| Prioritizes at-risk accounts | Executive escalation timing |
| Analyzes churn patterns | Retention strategy changes |
What This Skill Does#
- Signal detection - Identify behavioral indicators of churn risk
- Risk scoring - Calculate churn probability
- Root cause analysis - Why are they likely to leave?
- Intervention planning - What actions could save them?
- 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:
| Signal | Risk Level | Weight |
|---|---|---|
| Login drop >50% | High | 15 |
| Feature usage stopped | High | 15 |
| Support tickets spike | Medium | 10 |
| Champion left | Critical | 20 |
| Negative NPS | High | 12 |
| Payment late | Medium | 8 |
| No QBR attendance | Medium | 8 |
| Competitor mentioned | High | 12 |
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#
| Category | Indicators | Typical Save Rate |
|---|---|---|
| Product Fit | Low adoption, wrong use case | 30% |
| Value Gap | Not seeing ROI, budget pressure | 45% |
| Service Issue | Support failures, unresolved bugs | 60% |
| Relationship | Champion left, no engagement | 35% |
| Competition | Actively evaluating others | 25% |
| Business Change | M&A, budget cuts, pivot | 15% |
Step 4: Prescribe Intervention#
By Root Cause:
| Cause | Primary Action | Secondary Action |
|---|---|---|
| Product Fit | Success planning | Right-size contract |
| Value Gap | ROI review | Executive sponsor call |
| Service Issue | Escalation + resolution | Service credits |
| Relationship | New champion dev | Executive mapping |
| Competition | Competitive defense | Pricing review |
| Business | Flexible terms | Pause 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 calendarOutput:
## 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 returnExample 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 deprecatedOutput:
## 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 trendSkill 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#
- Score all accounts monthly
- Alert on score drops >15 points
- Weekly review of Critical/High risk
- 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 scoringhealth-score-monitor- Continuous monitoringrenewal-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
FAQ
07 QUESTIONS
01What is the Churn prediction agent skill?
Detect early warning signals of customer churn through systematic analysis of usage patterns, support interactions, and relationship health. It is listed under go-to-market in the 100xLab agent skills directory.
02How do I install Churn prediction in Cursor, Claude Code, or Codex?
Run npx skills add guia-matthieu/clawfu-skills --skill churn-prediction in the project where you want it, then ask your agent for the skill by name. The --skill flag installs only Churn prediction, not every skill in the repository.
03What does the Churn prediction SKILL.md cover?
The SKILL.md includes sections on When to Use This Skill, Methodology Foundation, What Claude Does vs What You Decide, What This Skill Does, How to Use and Instructions. Read the full text on this page or in plain markdown at /resources/agent-skills/guia-matthieu-clawfu-skills--churn-prediction--1uvos6b/raw.
04Which agents can use Churn prediction?
Churn prediction installs with the cross-agent skills installer (npx skills add), which sets it up for agents such as Claude Code, Cursor and Codex. Each agent loads the SKILL.md when you ask for the skill by name.
05What license is Churn prediction under?
The guia-matthieu/clawfu-skills repository declares the MIT license. Read the license file in the repository before you copy or redistribute the skill.
06Is Churn prediction free to use?
Reading, copying and installing Churn prediction costs nothing: it is published in a public GitHub repository under the MIT license. Reuse and redistribution follow that license.
07Where is the source of Churn prediction?
Churn prediction comes from https://github.com/guia-matthieu/clawfu-skills, which has 128 GitHub stars. The SKILL.md is at https://github.com/guia-matthieu/clawfu-skills/blob/HEAD/skills/customer-success/churn-prediction/SKILL.md. The author is guia-matthieu.
Need an agent workflow built around your stack?
Tell us the workflow you want automated. We reply within 4 working hours (IST).
ATTRIBUTION
Churn prediction was written by its original authors and is published in guia-matthieu/clawfu-skills (opens GitHub in a new tab) under the MIT license. All rights stay with the original authors.
100xLab did not write, maintain or endorse this skill. This page lists it for discovery. Repo stars belong to the source repository, not to the skill. Review any skill before you install it.