Predicting Marketing Churn Using Machine Learning Models

Abstract: For any organisation that engages in marketing actions there is a need to understand how people react to communication messages that are sent. Since the introduction of General Data Protection Regulation, the requirements for personal data usage have increased and people are able to effect the way their personal information is used by companies. For instance people have the possibility to unsubscribe from communication that is sent, this is called Opt-Out and can be viewed as churning from communication channels. When a customer Opt-Out the organisation loses the opportunity to send personalised marketing to that individual which in turn result in lost revenue.  The aim with this thesis is to investigate the Opt-Out phenomena and build a model that is able to predict the risk of losing a customer from the communication channels. The risk of losing a customer is measured as the estimated probability that a specic individual will Opt-Out in the near future. To predict future Opt-Outs the project uses machine learning algorithms on aggregated communication and customer data. Of the algorithms that were tested the best and most stable performance was achieved by an Extreme Gradient Boosting algorithm that used simulated variables. The performance of the model is best described by an AUC score of 0.71 and a lift score of 2.21, with an adjusted threshold on data two months into the future from when the model was trained. With a model that uses simulated variables the computational cost goes up. However, the increase in performance is signicant and it can be concluded that the choice to include information about specic communications is considered relevant for the outcome of the predictions. A boosted method such as the Extreme Gradient Boosting algorithm generates stable results which lead to a longer time between model retraining sessions.