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Customer Churn Early Warning System

Table output Has example

Detect trouble before cancellation.

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Design a churn early warning system. Our product and customer lifecycle: [PRODUCT_AND_LIFECYCLE]. Data we have (logins, features used, tickets, billing, NPS, contacts, contract dates): [AVAILABLE_DATA]. Past churned customers and what happened before they left: [CHURN_HISTORY].

Produce:
1. A signals table: Signal | Definition | Data source | Lead time before churn (from my history) | Weight | Threshold.
2. A health score model (0 to 100) with weights and a worked calculation for two fictional accounts (one healthy, one at risk). Keep it simple enough to maintain in a spreadsheet.
3. Alert rules: score drops of more than a set amount, champion departure, three negative tickets, payment failures, decreased logins, and silence after outreach; with owners and response times.
4. A play for each alert: who contacts the customer, what they say, what to offer, and how to record the outcome. Provide two scripts (60 words).
5. Validation: back-test the score on past churned and retained customers, measuring how many churners it would have flagged and how many false alarms; show the method.
6. Governance: monthly review, model update, privacy of customer data.

State limitations: with small data the model is a guide, not a prediction. Do not invent my data.

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Health score (0 to 100): usage 40, support 20, engagement 20, billing 10, relationship 10.
Account A: weekly logins 12 against a norm of 15 gives 32 of 40; tickets resolved quickly, 18 of 20; attended last review, 20 of 20; paid on time, 10; champion in place, 10. Total 90, healthy.
Account B: logins 3 against 15 gives 8 of 40; three unhappy tickets, 6 of 20; missed two meetings, 5 of 20; late payment, 4; champion left, 2. Total 25, at risk.
Alert: score under 50 triggers a call within two days.
Back-test: of 20 churned customers, 14 scored under 50 three months before leaving, so 70 percent flagged; 9 of 60 retained customers also scored under 50, a 15 percent false alarm rate.

• churn