A novel profit maximizing metric for measuring classification performance of customer churn prediction models
Verbraken, Thomas, Verbeke, Wouter and Baesens, Bart (2012) A novel profit maximizing metric for measuring classification performance of customer churn prediction models. IEEE Transactions on Knowledge and Data Engineering (doi:10.1109/TKDE.2012.50). (In Press).
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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.
|Keywords:||data mining, classification, performance measures|
|Subjects:||H Social Sciences > HF Commerce
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
|Divisions:||Faculty of Business and Law > Southampton Management School
|Date Deposited:||27 Mar 2012 10:34|
|Last Modified:||27 Mar 2012 10:35|
|Contributors:||Verbraken, Thomas (Author)
Verbeke, Wouter (Author)
Baesens, Bart (Author)
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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