期刊
APPLIED SOFT COMPUTING
卷 14, 期 -, 页码 431-446出版社
ELSEVIER
DOI: 10.1016/j.asoc.2013.09.017
关键词
Data mining; Social networks; Customer churn prediction; Relational classification; CDR data; Profit
资金
- Flemish Research Council (Odysseus grant) [B.0915.09]
- National Bank of Belgium [NBB/10/006]
This study examines the use of social network information for customer churn prediction. An alternative modeling approach using relational learning algorithms is developed to incorporate social network effects within a customer churn prediction setting, in order to handle large scale networks, a time dependent class label, and a skewed class distribution. An innovative approach to incorporate non-Markovian network effects 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 (customer related) information about millions of subscribers. A significant impact of social network effects, includingn on Markovian effects, on the performance of a customer churn prediction model is found, and the parallel model setup is shown to boost the profits generated by a retention campaign. (C) 2013 Elsevier B.V. All rights reserved.
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