Optimisation of R&R contests for a life insurance company using predictive and prescriptive analytics¶

Project Title: Optimization of Reward & Recognition Contests for Insurance Agents
Client: Large Indian Life Insurance Company
Institution: Indian Institute of Management Bangalore (IIMB)
Timeline: December 2019 – April 2020
Technologies: R, Excel, Google Colab
Recognition: Highly Commended Project – BAI Batch 2019–20, IIM Bangalore

POC: Contest Insights Dashboard

Project report: Download from DCAL IIMB website

Problem Statement¶

The client aimed to improve the effectiveness of its Reward and Recognition (R&R) contests for insurance agents. The challenge was to design contests that would maximize agent performance while staying within budget constraints and accounting for agent diversity in capacity and motivation.

Solution¶

A comprehensive analytics framework was developed to optimize contest design:

Sales Forecasting: Built a regression model that explained 97% of the variation in agent sales.
Agent Segmentation: Clustered agents based on their sales capacity and influencing factors.
Impact Quantification: Measured the lift in sales generated by different contest parameters.
Simulation & Optimization: Simulated cumulative sales across clusters under various contest scenarios and identified optimal parameters using budget and operational constraints.

Impact¶

The solution enabled data-driven contest design, improving fairness, motivation, and ROI. It was recognized as a top project in the IIMB Business Analytics & Intelligence program and contributed to more effective sales planning for the client.