Profit Driven Decision Trees for Churn Prediction

By Sebastiaan Höppner, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Tim Verdonck. 2017

129 citations recorded by the source corpus.

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.

Subfields: Information Systems, Marketing

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Cristina Molero-Río
2. Dolores Romero Morales
3. Emilio Carrizosa

In Marketing

1. Emilio Carrizosa
2. Dolores Romero Morales

Citations in the corpus

• Mathematical optimization in classification and regression trees (2021, background): “The first research avenue we touch on consists of expanding the family of criteria under consideration: on the top of accuracy and sparsity, there may be other important requirements on T , such as fairness, to ensure that T protects sensitive groups (Romei and Ruggieri 2014), or explainability, to ”