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New insights into churn prediction in the telecommunication sector: a profit driven data mining approach

New insights into churn prediction in the telecommunication sector: a profit driven data mining approach
New insights into churn prediction in the telecommunication sector: a profit driven data mining approach
Customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of customer retention campaigns and to reduce the costs associated with churn. Although cost reduction is their prime objective, churn prediction models are typically evaluated using statistically based performance measures, resulting in suboptimal model selection. Therefore, in the first part of this paper, a novel, profit centric performance measure is developed, by calculating the maximum profit that can be generated by including the optimal fraction of customers with the highest predicted probabilities to attrite in a retention campaign. The novel measure selects the optimal model and fraction of customers to include, yielding a significant increase in profits compared to statistical measures.

In the second part an extensive benchmarking experiment is conducted, evaluating various classification techniques applied on eleven real-life data sets from telecom operators worldwide by using both the profit centric and statistically based performance measures. The experimental results show that a small number of variables suffices to predict churn with high accuracy, and that oversampling generally does not improve the performance significantly. Finally, a large group of classifiers is found to yield comparable performance.
Data mining churn prediction, profit, input selection, oversampling, telecommunication sector
0377-2217
211-229
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732
Dejaeger, Karel
491728b5-118c-4661-b003-3787037c8f2d
Hur, Joon
af55d884-fd84-48ee-a03b-7e884d504dce
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Martens, David
42e7e141-fb3d-4ead-8e3a-96b39bab65f9
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732
Dejaeger, Karel
491728b5-118c-4661-b003-3787037c8f2d
Martens, David
42e7e141-fb3d-4ead-8e3a-96b39bab65f9
Hur, Joon
af55d884-fd84-48ee-a03b-7e884d504dce
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Verbeke, Wouter, Dejaeger, Karel, Hur, Joon and Baesens, Bart , Martens, David (ed.) (2012) New insights into churn prediction in the telecommunication sector: a profit driven data mining approach. European Journal of Operational Research, 218 (1), 211-229. (doi:10.1016/j.ejor.2011.09.031).

Record type: Article

Abstract

Customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of customer retention campaigns and to reduce the costs associated with churn. Although cost reduction is their prime objective, churn prediction models are typically evaluated using statistically based performance measures, resulting in suboptimal model selection. Therefore, in the first part of this paper, a novel, profit centric performance measure is developed, by calculating the maximum profit that can be generated by including the optimal fraction of customers with the highest predicted probabilities to attrite in a retention campaign. The novel measure selects the optimal model and fraction of customers to include, yielding a significant increase in profits compared to statistical measures.

In the second part an extensive benchmarking experiment is conducted, evaluating various classification techniques applied on eleven real-life data sets from telecom operators worldwide by using both the profit centric and statistically based performance measures. The experimental results show that a small number of variables suffices to predict churn with high accuracy, and that oversampling generally does not improve the performance significantly. Finally, a large group of classifiers is found to yield comparable performance.

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More information

Published date: 1 April 2012
Keywords: Data mining churn prediction, profit, input selection, oversampling, telecommunication sector
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 336471
URI: http://eprints.soton.ac.uk/id/eprint/336471
ISSN: 0377-2217
PURE UUID: 789d2d1d-2079-40fb-8be7-47483946d0e0
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 27 Mar 2012 11:44
Last modified: 17 Dec 2019 01:47

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