Objectives:
For most players, renewal remains the primary source of revenue. Besides, it is the quality of the claim experience that decides the subsequent behavior of the policyholder.
For instance, a particular general insurance company was interested in clients who were likely to renew their policies. With an aim to maximize the number of renewals, here is how TCG Digital enabled the general insurance company to assess the risk that clients making late premium payments were facing and basis that develop an incentive plan for sales agents to maximize the total net revenue.
Challenges:
In a challenging economic environment with stagnating growth, customer retention and timely renewal of policies remain the key areas of concerns. The insurance company was experiencing mid-term cancellations, late premium payments, and also losing customers to its competitors. All of these were leading to a decrease in profitability.
Solution:
TCG Digital suggested a renewal propensity model (ML Model) to the client. The insurance company could then use advanced analytics to profile customers into high, medium, and low risk segments by applying algorithms such as Decision Trees, Random Forest, XG Boost, or Logistic Regression and also identify policyholders who were likely to go for renewal. This also helped them to concentrate on the target clients who were at high risk of non-renewal well in time. Furthermore, we provided the company with a retention plan to win back churned clients.
Benefits / Impacts:
- This model allowed the insurance company to calculate the potential total net revenue from a group of renewed clients.
- The company was able to identify high-risk customers and increase customer retention by 2%.
- The client made effective use of the model to formulate a strategic plan for its sales agents to increase renewal collection and gauge profitability trends in the long run.
