Predicting Churn with Machine Learning

Customer churn is one of the most critical metrics for any subscription-based business. In this post, we explore how DataFold built a machine learning pipeline to predict churn before it happens.

The Problem

Every business loses customers. The question is whether you can identify who is about to leave — and act before they do.

Our Approach

We used a combination of behavioral signals, usage patterns, and engagement metrics to train a gradient boosting model that predicts churn probability with over 90% accuracy.

Key Takeaways

• Early warning signals exist in usage data weeks before a customer churns
• Feature engineering matters more than model complexity
• Actionable predictions require tight integration with your customer success workflow