Automated Customer Churn Prediction and Intervention
Design a system that identifies at-risk customers based on usage patterns, support interactions, and engagement signals, then triggers retention actions.
Proactively identify and intervene with at-risk customers before they churn, reducing churn rate by 25%.
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Prompt objective
Proactively identify and intervene with at-risk customers before they churn, reducing churn rate by 25%.
Real use case
A subscription business loses 8% of customers monthly. By the time they notice (cancellation), it's too late. Early warning signals exist in usage data but nobody connects them.
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Prompt
Act as a customer success data scientist. Design a churn prediction and intervention system for [COMPANY NAME] with [NUMBER] active subscribers. **Context:** - Subscription model: [MONTHLY/ANNUAL] at [AMOUNT]/month - Monthly churn rate: [PERCENTAGE]% - Average customer lifetime: [MONTHS] months - Data sources: [PRODUCT ANALYTICS], [CRM], [SUPPORT TICKETS], [BILLING] - Intervention budget: [AMOUNT] per month for retention offers **Deliverables (numbered):** 1. Churn signals: define [NUMBER] predictive signals with weights (login frequency decline, feature usage drop, support ticket spike, payment issues, competitor mentions, NPS decline, contract end approaching) 2. Risk scoring model: calculate daily churn risk score (0-100) per customer based on signal weights, segment by risk level (critical: >[SCORE], high: [SCORE]-[SCORE], medium: [SCORE]-[SCORE]) 3. Intervention playbook: define specific actions per risk level -- critical: CSM call within 24h + retention offer; high: personalized email + feature walkthrough; medium: targeted content + check-in survey 4. Retention offers: tiered offer structure (discount %, extended trial, feature upgrade, dedicated support) matched to churn reason and customer value 5. Intervention tracking: log every intervention (type, date, offer, outcome), measure intervention success rate, and calculate ROI of retention spend 6. Root cause analysis: aggregate churn reasons by segment, identify product gaps causing churn, and feed insights to product team monthly 7. Dashboard: at-risk customer list with risk scores, intervention pipeline, churn forecast for next [NUMBER] days, and retention campaign performance **Constraints:** - Must exclude customers who are already in cancellation flow - Must respect offer limits (max [NUMBER] offers per customer per quarter) - Must not trigger interventions for customers who have already renewed
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- 1Replace the key placeholders first: COMPANY NAME, NUMBER, MONTHLY/ANNUAL, AMOUNT.
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