Customer churn prediction for a motor insurance company

Abstract

Customer churn poses a significant challenge in various industries, including motor insurance. Retaining customers within insurance companies is much more challenging than in any other industry as policies are generally renewed every year. The main aim of this research is to identify the risk factors associated with churn, establish who are the churning customers and to model time until churn. The dataset used includes 72,445 policy holders and covers a period of one year. The data comprises information related to premiums, claims, policies and policy holders. The random forest algorithm turns out to be a very effective model for forecasting customer churn, reaching an accuracy rate of 91.18%. On the other hand, survival analysis was used to model time until churn and it was concluded that approximately 90% of the policy holders survived for the first five years while the majority of the policy holders survived till the end of the policy period. These results could be used to target the identified customers in marketing campaigns aimed at reducing the rate of churn while increasing profitability.