Customer churn poses a big challenge in financial institutions including the field of insurance. Hence, retention
analyses are designed to predict time to churn and identify which among the customers are most likely to leave the
company so that retention campaigns and strategies may be done to address the issue. By using 6-year data of
individual insurance policyholders from an automobile insurance company in Asia, the study utilized Kaplan-Meier,
Log-rank tests and Cox-Proportional Hazard model to understand the risk of churning and to identify significant
factors affecting the said event of interest. Demographic variables, including age, marital status, region, as well as
policy and claim details, including mortgagee and claim indicator, were found to be significant in the hazard model.
Furthermore, results of K-Prototypes, a clustering algorithm used for handling mixed data types, show that customers
can be grouped into three (3) clusters and that strategies must be geared towards retaining the young, single and
working adults’ segment.

Keywords
Customer churn, Customer relationship management, Survival analysis, Customer segmentation and motor insurance