Design of a predictive model for customer retention in the cancellation of an insurance policy

Insurance companies need to define actions to improve the persistence indicators of customers in subscription services. We want to fully understand the behavior of cancellations in mass sales channels with the retention tasks performed inside the company. As a proposal to increase the retention of customers calling to cancel, a logistic regression model and a decision tree model were used on real data through the CRISP-DM methodology. After comparing the models, the logistic regression model gives better results since its accuracy of prediction is 87.21%; this allows us to propose strategies to increase the customer retention rate.