Churn Prediction Model for a Large Bank

Context
A bank aimed to develop a predictive model to identify customers at risk of churning within a two-month timeframe. This would enable proactive retention efforts.

Engagement
The team conducted extensive data analysis to define possible customer transitions and identify key variables influencing churn. We tested multiple model configurations for optimal accuracy and trained the final model on individual customer data.

Results
We built a high-performing predictive model and integrated it into the bank’s operations via an API. This solution enabled personalized churn predictions, empowering the bank to take timely and effective retention actions.

Let us know of your customer lifecycle challenges and we will share opportunities and best practices.