Identify customers most likely to churn before they leave. Uses behavioral signals, usage patterns, and engagement data to score accounts and recommend retention actions.
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Identify customers most likely to churn before they leave. Uses behavioral signals, usage patterns, and engagement data to score accounts and recommend retention actions.
Ask the user for available data sources:
If no structured data, work from what the user describes qualitatively.
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)
For each at-risk account, suggest specific retention plays:
Generate a retention report with:
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