Will You Stay or Will You Go? Churn Prediction for an App-Delivered International Calling Service

Abstract: In markets with fierce competition and low switching costs, the capability to keep customers is just as important as the capability to get new customers. One of the cornerstones of efficiently keeping customers from churning is the ability to identify customers that are more prone to leaving the company than others. This way, churn prevention resources and measures can be efficiently directed towards the customer segments where the impact is the greatest. This paper investigates how churn can be predicted for an individual customer in an app-delivered international calling service (Rebtel). It also investigates which the underlying behavioral drivers of churn are and how these findings can be used to efficiently prolong the lifetime for customers using Rebtel’s subscription services when calling to India. The method applied to identify churn is the machine learning algorithm Random Forest. It is found that the Random Forest algorithm is well suited for classifying which customers that are about to churn by identifying complex customer behavior patterns indicating that a certain customer is about to leave the service. When investigating individual customer features to isolate drivers of churn, the method of using the Random Forest algorithm is not the best suited approach and contributes with limited findings. The recommendations to Rebtel is that a churn prediction model implementing the Random Forest algorithm should be used to segment which users, among users calling to India, that should be incentivized to stay as customers in order to decrease churn.