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A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models
May 2013 (vol. 25 no. 5)
pp. 961-973
Thomas Verbraken, Katholieke Universiteit Leuven, Leuven
Wouter Verbeke, University of Edinburgh, Edinburgh
Bart Baesens, Katholieke Universiteit Leuven, Leuven and University of Southampton, Highfield Southampton
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.
Index Terms:
Business,Knowledge engineering,Area measurement,Receivers,Data engineering,Educational institutions,performance measures,Data mining,classification
Citation:
Thomas Verbraken, Wouter Verbeke, Bart Baesens, "A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 5, pp. 961-973, May 2013, doi:10.1109/TKDE.2012.50
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