Imported from gooseworks-ai/goose-skills (skills/research/composites/churn-risk-detector/SKILL.md). Install upstream with npx skills add gooseworks-ai/goose-skills --skill churn-risk-detector. Copyright stays with the author.

Churn Risk Detector

Surface accounts at risk of churning before it's too late. Aggregates signals from support, communication, and usage patterns into a scored risk report with specific save actions.

Built for: Early-stage teams with no CS platform (no Gainsight, no ChurnZero). You have a spreadsheet of customers, a Slack channel, and a support inbox. This skill turns those raw signals into an actionable churn risk list.

When to Use

• "Which customers are at risk of churning?"
• "Run the weekly churn risk scan"
• "Flag accounts I should worry about"
• "Who haven't we heard from in a while?"
• "Produce a customer health report"

Phase 0: Intake

Account Data

1. Customer list — CSV or sheet with: company name, primary contact email, contract value (MRR/ARR), contract start date, renewal date (if known)
2. Product/service type — What are they paying for? (Helps calibrate expected engagement)

Signal Sources (provide what you have)

3. Support tickets — Export from Intercom, Zendesk, or email (CSV with: customer, date, subject, status, resolution time)
4. Slack channel history — Customer Slack channel or shared channel messages
5. NPS/CSAT scores — Recent survey results with scores and comments
6. Usage data — Any metrics you track: logins, API calls, features used, active users (CSV export)
7. Email/communication log — Last touchpoints per account (dates + context)
8. Billing data — Payment failures, downgrades, discount requests

Calibration

9. What does "healthy" look like? — Describe a healthy customer (e.g., "logs in daily, uses 3+ features, responds to emails within 24h")
10. Known churn reasons — Why have customers churned in the past? (helps weight signals)

Phase 1: Signal Extraction

1A: Support Signal Analysis

From support ticket data, calculate per account:

1B: Communication Signal Analysis

From Slack/email history:

1C: Usage Signal Analysis (if data available)

1D: Commercial Signal Analysis

Phase 2: Risk Scoring

Scoring Model

Each account gets a composite risk score (0-100):

Risk Score = Σ (signal_weight × signal_present)

Weights:
  Critical signal = 25 points each
  High signal     = 15 points each
  Medium signal   = 8 points each
  Low signal      = 3 points each

Score cap: 100

Risk Tiers

Phase 3: Save Play Generation

For each Red and Orange account, generate a specific save play:

Save Play Template

ACCOUNT: [Company Name]
RISK TIER: [Red/Orange]
RISK SCORE: [X/100]
MRR/ARR: $[X]

SIGNALS DETECTED:
- [Signal 1] — [Evidence: specific data point]
- [Signal 2] — [Evidence]
- [Signal 3] — [Evidence]

ROOT CAUSE HYPOTHESIS:
[1-2 sentences: What do you think is actually going wrong?
 E.g., "Champion left the company and new stakeholder hasn't been onboarded"
 or "They hit a technical limitation with [feature] that's blocking their primary use case"]

RECOMMENDED SAVE PLAY:
1. [Immediate action — e.g., "Schedule a call with [contact] this week"]
2. [Follow-up — e.g., "Send a personalized Loom showing how to solve [specific issue]"]
3. [Structural fix — e.g., "Assign a dedicated onboarding session for new stakeholder"]

TALK TRACK:
"[2-3 sentences the CSM/founder can use to open the conversation naturally,
 without saying 'we noticed you might be churning']"

ESCALATION TRIGGER:
If [specific condition] by [date], escalate to [founder/CEO call].

Phase 4: Output Format

# Churn Risk Report — Week of [DATE]
Total accounts scanned: [N]
Data sources: [list what was available]

---

## Risk Summary

| Tier | Count | Total MRR at Risk |
|------|-------|-------------------|
| 🔴 Red (Critical) | [N] | $[X] |
| 🟠 Orange (Elevated) | [N] | $[X] |
| 🟡 Yellow (Early Warning) | [N] | $[X] |
| 🟢 Green (Healthy) | [N] | $[X] |

**Total MRR at risk (Red + Orange):** $[X] ([Y]% of total MRR)

---

## 🔴 Critical Risk Accounts

### [Company Name 1] — Score: [X]/100 | MRR: $[X]
**Signals:** [bullet list]
**Root cause:** [hypothesis]
**Save play:** [specific actions]
**Owner:** [who should act]
**Deadline:** [date]

### [Company Name 2] — ...

---

## 🟠 Elevated Risk Accounts

### [Company Name] — Score: [X]/100 | MRR: $[X]
**Signals:** [bullet list]
**Recommended action:** [1-2 sentences]

---

## 🟡 Early Warning Accounts

| Account | Score | Key Signal | Suggested Action |
|---------|-------|------------|-----------------|
| [Name] | [X] | [Signal] | [Action] |
| [Name] | [X] | [Signal] | [Action] |

---

## Trends vs Last Week

- Accounts moved Red → Green: [list — wins!]
- Accounts moved Green → Yellow/Orange: [list — new risks]
- Accounts churned since last report: [list]

---

## Signal Distribution

| Signal Type | Accounts Affected |
|------------|-------------------|
| Support ticket spike | [N] |
| Gone silent | [N] |
| Usage decline | [N] |
| Competitor mention | [N] |
| Payment issue | [N] |
| Champion disengagement | [N] |

---

## Recommended Focus This Week

1. **[Account]** — [Why + what to do]
2. **[Account]** — [Why + what to do]
3. **[Account]** — [Why + what to do]

Save to risk-report-[YYYY-MM-DD].md in the current working directory.

Scheduling

Run weekly:

0 8 * * 1 python3 run_skill.py churn-risk-detector --client <client-name>

Cost

Tools Required

• Input data from CSV/sheets (support tickets, usage, NPS)
• Optional: Slack channel reading for communication signals
• No external API costs — pure analysis

Trigger Phrases

• "Which customers are at risk?"
• "Run the churn risk scan"
• "Weekly customer health report"
• "Flag at-risk accounts"