Build a Customer Churn Early-Warning System
Set up a system that flags at-risk accounts before they churn — giving your team time to intervene with the right response.
Time Required
1–2 days
Expected Result
A weekly report of at-risk accounts ranked by churn probability, with suggested interventions for each — delivered to your CS team automatically.
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Define Your Churn Signals
Use Claude to analyze your churn history and identify the leading indicators: login frequency drop, feature usage decline, support ticket volume, NPS score drop, and contract renewal date proximity.
Build the Health Score Model
Create an Airtable AI formula that calculates a health score (0–100) for each account by weighting the churn signals based on their predictive value.
Automate Data Collection
Use Zapier or Make to pull usage data from your product, NPS responses, and support tickets into Airtable on a weekly basis to keep health scores current.
Generate Intervention Playbooks
For each risk level (yellow: 50–69, red: below 50), use Claude to write a standard intervention playbook: who reaches out, what they say, what they offer, and the escalation path.
Deliver the At-Risk Report
Set up a weekly Zapier automation that pulls accounts below 70 and emails the CS team a ranked list with the health score, top risk factor, and recommended first action for each.