  Customer retention campaigns increasingly rely on predictive models to detect
potential churners in a vast customer base. From the perspective of machine
learning, the task of predicting customer churn can be presented as a binary
classification problem. Using data on historic behavior, classification
algorithms are built with the purpose of accurately predicting the probability
of a customer defecting. The predictive churn models are then commonly selected
based on accuracy related performance measures such as the area under the ROC
curve (AUC). However, these models are often not well aligned with the core
business requirement of profit maximization, in the sense that, the models fail
to take into account not only misclassification costs, but also the benefits
originating from a correct classification. Therefore, the aim is to construct
churn prediction models that are profitable and preferably interpretable too.
The recently developed expected maximum profit measure for customer churn
(EMPC) has been proposed in order to select the most profitable churn model. We
present a new classifier that integrates the EMPC metric directly into the
model construction. Our technique, called ProfTree, uses an evolutionary
algorithm for learning profit driven decision trees. In a benchmark study with
real-life data sets from various telecommunication service providers, we show
that ProfTree achieves significant profit improvements compared to classic
accuracy driven tree-based methods.
