/churn-prediction

name: churn-prediction
description: Identify at-risk customers using behavioral signals, engagement patterns, and health indicators before they cancel
license: MIT
metadata:
  author: ClawFu
  version: 1.0.0
  mcp-server: "@clawfu/mcp-skills"

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

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:** | 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

| 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 Compet