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A novel profit maximizing metric for measuring classification performance of customer churn prediction models

A novel profit maximizing metric for measuring classification performance of customer churn prediction models
A novel profit maximizing metric for measuring classification performance of customer churn prediction models
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.
data mining, classification, performance measures
1041-4347
Verbraken, Thomas
40def165-29ac-4a4d-8820-f434ea123b96
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Verbraken, Thomas
40def165-29ac-4a4d-8820-f434ea123b96
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

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).

Record type: Article

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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More information

Accepted/In Press date: 11 February 2012
e-pub ahead of print date: 2 March 2012
Keywords: data mining, classification, performance measures
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 336464
URI: http://eprints.soton.ac.uk/id/eprint/336464
ISSN: 1041-4347
PURE UUID: 3214f016-436a-4e11-8c04-34ebd873e69f
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 27 Mar 2012 10:34
Last modified: 29 Oct 2019 01:51

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