Churn Risk Analyzer

Identify customers most likely to churn before they leave. Uses behavioral signals, usage patterns, and engagement data to score accounts and recommend retention actions.

3,891 stars
Complexity: easy

About this skill

The Churn Risk Analyzer is an AI agent skill designed to proactively identify customers most likely to churn before they leave. It systematically processes various data sources, including usage metrics, support tickets, billing history, and engagement signals, to construct a comprehensive risk profile for each customer account. Based on a weighted scoring framework, the skill categorizes accounts into risk levels—Critical, At Risk, Healthy, and Thriving—enabling businesses to prioritize their retention efforts effectively. This skill provides actionable recommendations tailored to each at-risk account, such as executive sponsor calls, custom success plans, feature training, or loyalty offers. By automating the identification and classification of churn risks, it empowers customer success, sales, and account management teams to intervene strategically. The output is a detailed retention report, including a ranked list of at-risk accounts, specific actions, estimated revenue at risk, and a retention calendar, making it an invaluable tool for maintaining and growing customer relationships.

Best use case

This skill is primarily used by customer success managers, account managers, and sales teams to proactively manage customer retention. It helps them identify and prioritize accounts that show signs of dissatisfaction or disengagement, allowing for timely intervention. Businesses looking to reduce customer attrition, optimize customer lifetime value, and improve overall customer health will benefit most, particularly when they have access to quantitative customer data.

Identify customers most likely to churn before they leave. Uses behavioral signals, usage patterns, and engagement data to score accounts and recommend retention actions.

A prioritized retention report identifying at-risk customers with specific, actionable recommendations and estimated revenue at risk.

Practical example

Example input

Analyze my customer data for churn risk. I have a CSV with usage metrics, support ticket counts, billing history, and email engagement for my top 100 accounts. Please identify the most critical ones and suggest actions.

Example output

Here is your Churn Risk Report:

**🔴 Critical Accounts:**
1. Acme Corp (Score: 85) - Estimated Revenue at Risk: $50,000
   * Actions: Executive sponsor call, Custom success plan, Pricing review.
2. Global Solutions (Score: 78) - Estimated Revenue at Risk: $35,000
   * Actions: Feature training session, Roadmap preview, Loyalty offer.

**🟡 At Risk Accounts:**
3. InnovateTech (Score: 62) - Schedule check-in this week.

... A detailed 30/60/90 day retention calendar will follow.

When to use this skill

  • During customer success reviews and quarterly retention planning.
  • When usage data, support ticket logs, or other engagement metrics are available.
  • For prioritizing proactive outreach to at-risk accounts.
  • To understand revenue at risk and develop targeted retention strategies.

When not to use this skill

  • When no customer-specific behavioral or usage data is available.
  • For initial customer acquisition strategies (it's focused on retention).
  • If the goal is purely to generate new leads or identify expansion opportunities for healthy accounts.
  • In highly qualitative B2C scenarios without structured engagement data.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/afrexai-churn-analyzer/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/1kalin/afrexai-churn-analyzer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/afrexai-churn-analyzer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Churn Risk Analyzer Compares

Feature / AgentChurn Risk AnalyzerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityeasyN/A

Frequently Asked Questions

What does this skill do?

Identify customers most likely to churn before they leave. Uses behavioral signals, usage patterns, and engagement data to score accounts and recommend retention actions.

How difficult is it to install?

The installation complexity is rated as easy. You can find the installation instructions above.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Churn Risk Analyzer

Identify customers most likely to churn before they leave. Uses behavioral signals, usage patterns, and engagement data to score accounts and recommend retention actions.

## When to Use
- Customer success reviews
- Quarterly retention planning
- When usage data or support ticket logs are available
- Proactive outreach prioritization

## How It Works

### 1. Gather Data
Ask the user for available data sources:
- **Usage metrics** (logins, feature adoption, API calls)
- **Support tickets** (frequency, sentiment, resolution time)
- **Billing history** (downgrades, late payments, discount requests)
- **Engagement signals** (email opens, meeting attendance, NPS scores)

If no structured data, work from what the user describes qualitatively.

### 2. Score Each Account
Apply this risk framework:

| Signal | Weight | High Risk Indicator |
|--------|--------|-------------------|
| Usage decline (30d) | 25% | >30% drop |
| Support ticket spike | 20% | 2x+ above baseline |
| Champion departure | 20% | Key contact left |
| Contract timing | 15% | <90 days to renewal |
| Payment behavior | 10% | Late/disputed invoices |
| Engagement drop | 10% | No response to last 3 outreach |

**Score**: 0-100 (higher = more likely to churn)

### 3. Categorize
- **🔴 Critical (75-100)**: Immediate intervention needed
- **🟡 At Risk (50-74)**: Schedule check-in this week
- **🟢 Healthy (25-49)**: Monitor monthly
- **💚 Thriving (0-24)**: Expansion opportunity

### 4. Recommend Actions
For each at-risk account, suggest specific retention plays:
- Executive sponsor call
- Custom success plan
- Feature training session
- Pricing review / loyalty offer
- Roadmap preview (show upcoming value)

### 5. Output
Generate a retention report with:
- Ranked list of accounts by churn risk
- Top 3 actions per critical account
- Estimated revenue at risk
- 30/60/90 day retention calendar

## Integration Notes
- Works with CSV exports, CRM data, or manual input
- Pair with the [AfrexAI Context Packs](https://afrexai-cto.github.io/context-packs/) for industry-specific retention strategies ($47/pack)
- For a full AI-powered retention system built for your business, try the [AI Revenue Calculator](https://afrexai-cto.github.io/ai-revenue-calculator/) to see what you're leaving on the table

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