Churn Risk Analyzer
Prompt
Analyze churn risk for: Account: {account_name}. Usage data: {usage_trends}. Support history: {support_data}. Engagement signals: {engagement}. Contract details: {contract}. Recent events: {events}. Score risk (1-10), identify top warning signals, compare against healthy account benchmarks, and recommend specific intervention actions with priority and timeline.Variables
account_name
usage_trends
support_data
engagement
contract
events
About This Prompt
Evaluate customer health signals and predict churn probability with intervention recommendations.
Example Output
**Churn Risk Analysis: TechStart Inc**
**Overall Churn Risk: 7/10 (High)**
**Predicted Churn Probability: 65% within 90 days**
**Warning Signals Detected:**
Signal
Severity
Detail
--------
----------
--------
Usage decline
Critical
MAU dropped 45% over 2 months (28→15)
Executive sponsor left
High
VP of Ops (champion) left company in Feb
Support ticket spike
Medium
8 tickets in March (avg: 2/month), 3 unresolved
Login frequency
High
Admin login dropped from daily to weekly
No feature adoption
Medium
Zero usage of 3 features launched in Q1
**Benchmark Comparison:**
Metric
TechStart
Healthy Avg
Status
--------
-----------
-------------
--------
MAU trend
-45%
+5%
Critical
Support tickets/mo
8
2.3
Elevated
Feature adoption rate
0%
35%
Critical
NPS (last survey)
22
52
Below
Days since exec contact
45
14
Overdue
**Intervention Plan (Priority Order):**
**1. IMMEDIATE (This Week):**
- Identify new executive sponsor / power user
- Escalate unresolved support tickets to senior engineering
- CSM outreach call: acknowledge challenges, offer dedicated support session
**2. WITHIN 2 WEEKS:**
- Schedule onboarding refresh for new stakeholders
- Create custom ROI report showing value delivered to date
- Offer 1:1 training on unused features
**3. WITHIN 30 DAYS:**
- Executive-to-executive outreach (our VP to their VP)
- Discuss contract flexibility if needed (quarterly billing, scope adjustment)
- If risk persists: consider retention offer
...
Usage Tips
- Weight usage decline most heavily
- Check for champion/sponsor changes
- Compare to cohort benchmarks not absolute numbers
- Act fast — intervention within 48 hours of risk detection