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

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

Signal Sources (provide what you have)

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

Calibration

What does "healthy" look like? — Describe a healthy customer (e.g., "logs in daily, uses 3+ features, responds to emails within 24h")
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:

Signal	Calculation	Risk Weight
Ticket volume spike	>2x their average in last 30 days	High
Unresolved tickets	Open tickets older than 7 days	High
Escalation language	Keywords: "cancel", "frustrated", "alternative", "not working", "disappointed"	Critical
Response time degradation	Your avg response time to this customer trending up	Medium
Repeat issues	Same problem reported 2+ times	High

1B: Communication Signal Analysis

From Slack/email history:

Signal	Calculation	Risk Weight
Gone silent	No messages in 30+ days (was previously active)	High
Decreasing frequency	Message frequency dropped >50% vs prior 90 days	Medium
Negative sentiment shift	Tone changed from positive to neutral/negative	Medium
Champion disengagement	Primary contact stopped responding	Critical
New stakeholder questions	New person asking basic "what does this do?" questions	Medium (potential reorg)

1C: Usage Signal Analysis (if data available)

Signal	Calculation	Risk Weight
Login drop	Active users down >30% vs prior month	High
Feature abandonment	Stopped using a key feature they previously used regularly	High
Shallow usage	Only using 1 feature when they're paying for many	Medium
No growth	Same number of seats/users for 6+ months	Low
Export spike	Sudden increase in data exports	Critical (may be migrating)

1D: Commercial Signal Analysis

Signal	Calculation	Risk Weight
Discount request	Asked for pricing reduction	High
Downgrade inquiry	Asked about lower tier	Critical
Payment failure	Failed payment not resolved in 7+ days	High
Contract approaching renewal	<60 days to renewal with no renewal discussion	Medium
Competitor mention	Mentioned a competitor in any channel	High

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

Tier	Score	Label	Action Urgency
Red	70-100	Critical risk — likely to churn	This week
Orange	40-69	Elevated risk — needs attention	Within 2 weeks
Yellow	20-39	Early warning — monitor closely	Within 30 days
Green	0-19	Healthy — no action needed	Routine check-in

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

markdown

# 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

clients/<client-name>/customer-success/churn-risk/risk-report-[YYYY-MM-DD].md

Scheduling

Run weekly:

bash

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

Cost

Component	Cost
All signal analysis	Free (LLM reasoning)
Slack/email parsing	Free
Total	Free

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"

churn-risk-detector

NPX Install

Tags

SKILL.md Content