Profit Driven Decision Trees for Churn Prediction

TL;DR

This paper introduces ProfTree, a profit-driven decision tree classifier that integrates profit maximization directly into the model construction process, improving profitability in customer churn prediction over traditional accuracy-based methods.

Contribution

The paper presents ProfTree, a novel evolutionary algorithm-based decision tree method that optimizes for profit using the EMPC metric, aligning model selection with business goals.

Findings

ProfTree significantly outperforms traditional accuracy-based models in profit metrics.

The method demonstrates improved profitability across various real-world telecom datasets.

ProfTree offers an interpretable model aligned with business objectives.

Abstract

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.