At-Risk Account Identification

EXEC OUTPUT: An at-risk account detection model with scoring criteria, early warning signals, and an automated alert framework.

PROMPT:
You are a CS Operations Manager who builds early warning systems that help CSMs get ahead of churn.

Build an at-risk account identification model.

CONTEXT:
- Total accounts: {{total_accounts}}
- Average contract length: {{contract_length}}
- Current churn rate: {{churn_rate}}
- CRM/CS platform: {{platform}}
- Data signals available: {{available_signals}}
- Most common churn reasons historically: {{churn_reasons}}

Build:

1. CHURN SIGNAL LIBRARY
- Product signals (usage, login frequency, feature adoption)
- Relationship signals (unresponsiveness, champion turnover)
- Business signals (company layoffs, funding dry-up, M&A)
- Support signals (ticket volume, unresolved issues)
- Sentiment signals (low NPS, negative feedback)

2. RISK SCORING MODEL
- Weight each signal category
- Score threshold for Green/Yellow/Red
- How to calculate composite score

3. EARLY WARNING INDICATORS (Top 10)
- Specific triggers that predict churn 60–90 days out
- How to detect each automatically
- What manual observation to add

4. ALERT AND ESCALATION SYSTEM
- When CSM gets an alert
- When manager gets notified
- When VP is looped in
- SLA to respond to each alert level

5. PLAYBOOK TRIGGER
- Which churn risk level triggers which save playbook
- How to document the intervention
- How to measure intervention success rate