Title
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
Author
Faculty/Department
Faculty of Applied Economics
Publication type
article
Publication
Amsterdam ,
Subject
Economics
Mathematics
Mass communications
Source (journal)
European journal of operational research. - Amsterdam
Volume/pages
218(2012) :1 , p. 211-229
ISSN
0377-2217
ISI
000300128800023
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of retention campaigns to prevent customers from churning, 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.
E-info
https://repository.uantwerpen.be/docman/iruaauth/79a022/88c2101.pdf
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