From Transactions to Trends: Predict When a Customer Is About to Stop Buying

Customer churn can be predicted by applying linear regression to monthly purchase data to detect declining engagement. This approach identifies "silent churn" by analyzing the trend of a customer's spending over time. A negative slope on the regression line signifies that a customer is buying less, while a positive slope indicates increasing engagement. The method involves aggregating transactions, normalizing data, and calculating the slope's angle in degrees to quantify the trend.