How to Build a Subscription Churn Early-Warning System With Apify and Google Sheets

Most SaaS founders discover churn the same way: the cancellation email arrives. By that point, the decision was made weeks ago. The customer had already stopped logging in, stopped using key features, maybe even clicked "downgrade" before closing the tab. You just didn't know. This guide shows you how to build a churn early-warning system that catches those signals — before the cancellation — using Apify to pull usage data and Google Sheets to score risk automatically. The Problem: You're Diagnosing Churn Retrospectively Most subscription businesses track churn as a lagging metric — customers leave, you count them, you calculate the rate. This is useful for reporting but useless for retention. The decision to cancel is rarely impulsive. It follows a pattern: Login frequency drops Core feature usage declines The user stops completing their primary workflow Sometimes they try to downgrade first This behavioral decay typically unfolds over 2 to 4 weeks before cancellation. That window is