A leading insurance firm sought FlatworldEdge's expertise in predictive modeling to enhance their understanding of policy renewal risks and cross-selling opportunities. The firm was struggling with manual predictions of policy renewals and identification of potential cross-sell customers from Motor to Health insurance.

FlatworldEdge provided a solution that resulted in improved policy segmentation, increased process transparency, reduced costs, and enhanced customer lifetime value. The innovative approach revolutionized the way the insurance company managed renewals and cross-selling, providing them with a competitive edge in the marketplace.

Story of the Customer 

The customer is a leading provider in health and vehicle insurance, operating in a highly competitive digital marketplace. They were dealing with complex policy renewals and struggling to predict customer renewals due to costly and time-consuming manual processes.

In an effort to navigate these challenges, the client sought a predictive model that would help understand the risks associated with renewal, identify potential revenue opportunities, and increase the lifetime value of their customers. Identifying opportunities for cross-selling, particularly from motor to health insurance, was a crucial part of their strategy to enhance customer value.

The Challenge 

  • The client faced issues with predicting insurance renewals, a manual and resource-draining process.
  • Identifying potential customers for cross-selling from motor to health insurance was a major challenge.
  • The lack of transparency and control in business processes hindered informed decision-making and resource optimization.

The Solution 

  • Data from various business processes was analyzed to select features influencing customer behavior for policy renewal and cross-selling.
  • Standard and ensemble predictive models were built, with visualizations highlighting factors influencing renewal behavior.
  • Optimum resource utilization was achieved, reducing costs, and customer lifetime value was improved by predicting their interest in future products or services.

The Result 

  • Risk-based policy segmentation improved efficiency and cut down operational costs.
  • Greater process control and transparency optimized resource utilization.
  • Predictive modeling identified potential cross-sell opportunities, enhancing customer lifetime value.