Using tree-based methods for churn-related problems in I-Gaming

Abstract

Customer churn occurs when an existing client stops doing business with a company. For example, this could mean closing some type of account, cancelling a subscription or membership, or not renewing a contract. In the betting industry, one main reason for churning is self-exclusion. The focus in this dissertation is on the use of tree-based methods for classification, specifically for classifying the reasons for customer churn, and self-exclusion. The theory behind the classification and regression tree algorithm will be discussed, particularly classification trees. Decision trees will be used as building blocks to understand the theory behind random forests and boosting. These two techniques are constructed from an ensemble of decision trees. The performance of the classification methods mentioned will be explored by applying them on two real-life datasets coming from the betting industry. The performance of these three techniques will be compared to the performance of the benchmark of statistical models for classification - logistic regression.