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 calendar

Output:

Churn Risk Assessment: MediaTech Corp

🔴 CRITICAL RISK - 78/100

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

Risk Signal Analysis

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

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

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

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

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

Preventability Assessment

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

• 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 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