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What data do I need to build a customer churn model?
You need a clear churn definition, a history of customers with their start dates and outcomes, and behaviour measured before the outcome date, such as purchase frequency, spend, product mix, support contacts and engagement. Customer attributes help, but behaviour over time is usually most predictive.
Build a snapshot table: for each customer at a past date, record what you knew then and whether they churned in the following window, say 90 days. Use many snapshots to give the model enough examples.
Beware of leakage from fields that are updated after leaving. Also expect class imbalance, where most customers do not churn, so use appropriate metrics such as precision, recall or lift, not just overall accuracy. Check that you hold enough churn events to learn from.
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