Problem Statement

The client is one of the leading healthcare providers in North America. The company wanted to understand the characteristics of churned customers and the parameters that led to it. They were looking for a model that would predict the churn probability of their new enquiries. The churn predictor model had to be integrated with their CRM tool which would help them to understand the probability of churn when a new customer acquisition happens in a particular branch.

Our Solution

Both in-house transactional and external demographic historic data were used to develop a machine learning algorithm, which helped us to understand the various parameters that led to customer churn and create a churn probability predictor.

This was integrated with their CRM tool. When new customer details are entered in the client’s CRM, the predictive ML-based algorithm works at the back end and predicts the probability of churn of that customer in a pop-up window. For high Churn probability, the front office executive offered different attractive packages, discounts, and special offers to retain the customer.

Result & Value Adds

• Increased sales conversion by 30%
• Increased cross-selling by 13%
• Reduced churn by 13% during 8 months period
• Increased customer loyalty by 15% during 8 month period due to the great customer experience