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.
Download workflow config (JSON)How the data moves
This is a handoff plan, not an automatic integration. Review outputs before the next step. Unlabeled arrows show order, not a verified data format. Use the capability links to explore tool substitutions.
Step 1
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.
Step 2
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.
Step 3
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.
Step 4
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.
Step 5
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.
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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