The Effect of the Length of the Customer Event History and the Staying Power of the Predictive Models in the Customer Churn Prediction: Case Study of Migros Sanal Market

Abstract

The customer churn prediction problem is studied for various sectors under several aspects. In this study, we consider the effect of the length of the customer event history and the staying power of the predictive models for the churn prediction problem of a leading online fast-moving consumer goods retailer in Turkey. These are important aspects of the churn prediction models as they help decision makers to determine the optimal length of the past data for predicting the customer churn as well as lifespan of the predictive models. We find that the length of the customer event history logarithmically increases the predictive power of models, validating findings in the literature in the newspaper subscription sector. Regarding the staying power of the predictive models, we conclude that the models in online fast-moving consumer goods retailing has a slightly longer lifespan that the models discussed in the literature for an Internet service provider and an insurance company.