# Retention churn prevention Human Guide

## What This Is For
Analyze churn, predict at-risk customers, and design retention strategies. 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 Retention churn prevention 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 retention churn prevention.
- 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 Retention churn prevention 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
- **Onboarding** — Time to first value predicts retention more than any other factor
- **Engagement loops** — Regular touchpoints (weekly reports, digests)
- **Feature adoption** — Users who adopt 3+ features churn 50% less
- **Community** — Community members have 2-3x higher retention
- **Switching costs** — Integrations and data create healthy lock-in
- **Proactive support** — Reach out before problems become cancellations
- **klaviyo-analyst** — Design retention email flows and win-back sequences
- **customer-journey-mapping** — Map retention and advocacy stages
- **google-analytics** — Cohort analysis and engagement metrics
- **cro-auditor** — Optimize cancellation flow to save more customers
- Designs health score with weighted signals (usage 25%, feature depth 20%, support 15%, billing 15%, engagement 15%, NPS 10%)
- Defines thresholds: Green (80-100), Yellow (50-79), Red (0-49)

## Decision Points And Nuance
The original skill emphasizes: Install, Churn Analysis Framework, Churn Types, Churn Rate Calculation, Benchmarks, Customer Health Scoring, Early Warning Signals, Win-Back Campaigns, Timing, Win-Back Sequence.

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
- Example 1: Build Churn Early Warning System

## Copy-And-Paste Prompt
```text
Use the Retention churn prevention 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 thatrebeccarae/claude-marketing skill entry for `retention-churn-prevention`.

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

# Retention & Churn Prevention

Analyze churn, predict at-risk customers, and design retention strategies.

## Install

```bash
git clone https://github.com/thatrebeccarae/claude-marketing.git && cp -r claude-marketing/skills/retention-churn-prevention ~/.claude/skills/
```

## Churn Analysis Framework

### Churn Types

| Type | Definition | Signal |
|------|-----------|--------|
| **Voluntary** | Customer actively cancels | Cancellation request, downgrade |
| **Involuntary** | Payment failure, card expiry | Failed charge, dunning |
| **Silent** | Stops using but does not cancel | Usage decline, no logins |

### Churn Rate Calculation

```
Monthly churn rate = Customers lost / Customers at start of month
Annual churn rate = 1 - (1 - monthly rate)^12
Net revenue retention = (Start MRR + Expansion - Contraction - Churn) / Start MRR
```

### Benchmarks

| Metric | Excellent | Good | Concerning |
|--------|----------|------|-----------|
| Monthly churn (SaaS) | <1% | 1-2% | >3% |
| Annual churn (SaaS) | <5% | 5-10% | >15% |
| Net revenue retention | >120% | 100-120% | <100% |

## Customer Health Scoring

| Signal | Weight | Healthy | At Risk |
|--------|--------|---------|---------|
| Product usage | 25% | Daily/weekly | Monthly or less |
| Feature adoption | 20% | 5+ features | 1-2 features |
| Support sentiment | 15% | Positive/none | Negative |
| Billing health | 15% | On time, expanding | Late, downgrading |
| Engagement | 15% | Opens, clicks | Ignores |
| NPS/CSAT | 10% | Promoter (9-10) | Detractor (0-6) |

## Early Warning Signals

| Timeframe | Signal | Action |
|-----------|--------|--------|
| 7 days | Login frequency drops 50%+ | In-app nudge, value reminder |
| 14 days | Key feature usage stops | CS outreach, usage tips |
| 30 days | No logins for 2+ weeks | Personal CS email, re-engagement |
| 60 days | NPS detractor, unresolved ticket | Executive escalation, save offer |
| 90 days | Cancellation signals | Retention call, custom offer |

## Win-Back Campaigns

### Timing

| Post-Churn Period | Response Rate | Approach |
|------------------|---------------|----------|
| 0-7 days | 15-25% | Immediate save, address exit reason |
| 7-30 days | 8-15% | New feature announcement, incentive |
| 30-90 days | 3-8% | Major update, significant discount |
| 90+ days | <3% | Annual check-in |

### Win-Back Sequence

```
Email 1 (Day 1): Address exit reason, offer to help
Email 2 (Day 7): New features since they left
Email 3 (Day 14): Comeback incentive (discount or extended trial)
Email 4 (Day 30): Final offer with urgency
```

## Retention Levers

1. **Onboarding** — Time to first value predicts retention more than any other factor