Churn Prediction

Churn prediction uses machine learning to identify which customers are likely to stop using your product or service. Instead of reacting after a customer leaves, churn prediction models analyze behavioral signals — declining engagement, reduced purchase frequency, support ticket patterns — to flag accounts before they churn.

Traditional churn models require months of data science work: feature engineering, model selection, training pipelines, and ongoing retraining. Most companies either can't afford the ML team or watch their churn model degrade within weeks of deployment.

EkamFlow eliminates this entire process. Connect your data warehouse, and a private churn prediction model is trained on your specific customer data. The model scores every customer in real time through a single API call — returning a churn probability alongside every other prediction you need.