# Churn prediction Human Guide

## What This Is For
Detect early warning signals of customer churn through systematic analysis of usage patterns, support interactions, and relationship health. It gives the agent a clearer input/output frame for go-to-market work: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Churn prediction agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for churn prediction.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Churn prediction guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- Monthly/quarterly churn risk reviews
- Prioritizing CSM intervention
- Building early warning systems
- Post-mortem analysis on lost customers
- **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
- **Days 1-7**: Triage and stabilize
- **Days 8-30**: Address root cause
- **Days 31-60**: Rebuild value perception

## Decision Points And Nuance
The original skill emphasizes: When to Use This Skill, Methodology Foundation, What Claude Does vs What You Decide, What This Skill Does, How to Use, Instructions, Step 1: Evaluate Leading Indicators, Step 2: Calculate Churn Probability, Step 3: Identify Root Cause Category, Step 4: Prescribe Intervention.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- > Detect early warning signals of customer churn through systematic analysis of usage patterns, support interactions, and relationship health.
- Building early warning systems
- 30-60 Day Warning Signs:
- 41-60: High Risk (intervention required)
- Exec sponsor: Never established
- GammaCorp: Left after 6 months, wrong fit, never adopted
- **Action**: Multi-threading program required
- Early Warning Signal Validation

## Copy-And-Paste Prompt
```text
Use the Churn prediction human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the guia-matthieu/clawfu-skills skill entry for `churn-prediction`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# 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 |