A customer churn prediction model in telecom industry using boosting

Lu, N; Lin, H; Lu, J; Zhang, G

A customer churn prediction model in telecom industry using boosting

Lu, N Lin, H Lu, J

Zhang, G

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Informatics, 2014, 10 (2), pp. 1659 - 1665
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Lu, N	en_US
dc.contributor.author	Lin, H	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Transactions on Industrial Informatics, 2014, 10 (2), pp. 1659 - 1665	en_US
dc.identifier.issn	1551-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37371
dc.identifier.uri	http://hdl.handle.net/10453/34985
dc.identifier.uri	http://hdl.handle.net/10453/37372
dc.description.abstract	With the rapid growth of digital systems and associated information technologies, there is an emerging trend in the global economy to build digital customer relationship management (CRM) systems. This trend is more obvious in the telecommunications industry, where companies become increasingly digitalized. Customer churn prediction is a main feature of in modern telecomcommunication CRM systems. This research conducts a real-world study on customer churn prediction and proposes the use of boosting to enhance a customer churn prediction model. Unlike most research that uses boosting as a method to boost the accuracy of a given basis learner, this paper tries to separate customers into two clusters based on the weight assigned by the boosting algorithm. As a result, a higher risk customer cluster has been identified. Logistic regression is used in this research as a basis learner, and a churn prediction model is built on each cluster, respectively. The result is compared with a single logistic regression model. Experimental evaluation reveals that boosting also provides a good separation of churn data; thus, boosting is suggested for churn prediction analysis. © 2012 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP110103733
dc.relation.ispartof	IEEE Transactions on Industrial Informatics	en_US
dc.relation.isbasedon	10.1109/TII.2012.2224355	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	A customer churn prediction model in telecom industry using boosting	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	10	en_US
utslib.for	080105 Expert Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	in_progress
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

With the rapid growth of digital systems and associated information technologies, there is an emerging trend in the global economy to build digital customer relationship management (CRM) systems. This trend is more obvious in the telecommunications industry, where companies become increasingly digitalized. Customer churn prediction is a main feature of in modern telecomcommunication CRM systems. This research conducts a real-world study on customer churn prediction and proposes the use of boosting to enhance a customer churn prediction model. Unlike most research that uses boosting as a method to boost the accuracy of a given basis learner, this paper tries to separate customers into two clusters based on the weight assigned by the boosting algorithm. As a result, a higher risk customer cluster has been identified. Logistic regression is used in this research as a basis learner, and a churn prediction model is built on each cluster, respectively. The result is compared with a single logistic regression model. Experimental evaluation reveals that boosting also provides a good separation of churn data; thus, boosting is suggested for churn prediction analysis. © 2012 IEEE.

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http://hdl.handle.net/10453/34985