A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models

2012/03/06 by Thomas Verbraken, Wouter Verbeke, Bart Baesens · 6 citations
Business, Management and Accounting · Computer Science · #Customer churn and segmentation #Data Mining Algorithms and Applications #Imbalanced Data Classification Techniques

paper · doi:10.1109/tkde.2012.50

openalex publication_date 2012/03/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

The interest for data mining techniques has increased tremendously during the past decades, and numerous classification techniques have been applied in a wide range of business applications. Hence, the need for adequate performance measures has become more important than ever. In this paper, a cost-benefit analysis framework is formalized in order to define performance measures which are aligned with the main objectives of the end users, i.e., profit maximization. A new performance measure is defined, the expected maximum profit criterion. This general framework is then applied to the customer churn problem with its particular cost-benefit structure. The advantage of this approach is that it assists companies with selecting the classifier which maximizes the profit. Moreover, it aids with the practical implementation in the sense that it provides guidance about the fraction of the customer base to be included in the retention campaign.

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• Cost-Sensitive Evaluation for Binary Classifiers
• Two-stage consumer credit risk modelling using heterogeneous ensemble learning
• Ensemble of Example-Dependent Cost-Sensitive Decision Trees
• e-Profits: A Business-Aligned Evaluation Metric for Profit-Sensitive Customer Churn Prediction
• RadarSeq: A Temporal Vision Framework for User Churn Prediction via Radar Chart Sequences
• The foundations of cost-sensitive causal classification

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