Predicting Customer Churn Rate in the iGaming Industry using Supervised Machine Learning

Abstract: Mr Green is one of the leading online game providers in the European market. Their mission is to o˙er entertainment and a superior user experience to their customers. To be able to better understand each individual customer and the entire customer life cycle the concept of churn rate is essential, which is also an important input value when calculating the return on marketing e˙orts. This thesis analyzes the feasibility to use 24 hours of initial data on player characteristics and behaviour to predict the probability of each customer churning or not. This is done by examining various supervised machine learning models to determine which model best captures the customer behaviour. The evaluated models are logistic regression, random forest and linear discriminant analysis, as well as two ensemble methods using stacking and voting classifiers. The main finding is that the best accuracy is obtained using a voting ensemble method with the three base models logistic regression, random forest and linear discriminant analysis weighted as w = (0.005, 0.80, 0.015). With this model the attained accuracy is 75.94 %.