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Title
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Social network analysis for customer churn prediction
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Author
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Abstract
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| This study examines the use of social network information for customer churn prediction. An alternativemodeling approach using relational learning algorithms is developed to incorporate social network effectswithin a customer churn prediction setting, in order to handle large scale networks, a time dependent classlabel, and a skewed class distribution. An innovative approach to incorporate non-Markovian networkeffects within relational classifiers and a novel parallel modeling setup to combine a relational and non-relational classification model are introduced. The results of two real life case studies on large scale telcodata sets are presented, containing both networked (call detail records) and non-networked (customerrelated) information about millions of subscribers. A significant impact of social network effects, includingnon-Markovian effects, on the performance of a customer churn prediction model is found, and theparallel model setup is shown to boost the profits generated by a retention campaign. |
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Language
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English
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Source (journal)
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Applied soft computing. - Place of publication unknown
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Publication
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Place of publication unknown
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2014
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ISSN
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1568-4946
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DOI
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10.1016/J.ASOC.2013.09.017
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Volume/pages
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14
:C
(2014)
, p. 431-446
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ISI
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000327529200011
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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