File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Hybrid models using unsupervised clustering for prediction of customer churn

TitleHybrid models using unsupervised clustering for prediction of customer churn
Authors
KeywordsChurn
Clustering
Data mining
Decision trees
Lift
Prediction
Rules
Issue Date2009
PublisherLawrence Erlbaum Associates, Inc. The Journal's web site is located at http://www.leaonline.com/loi/joce
Citation
Journal Of Organizational Computing And Electronic Commerce, 2009, v. 19 n. 2, p. 133-151 How to Cite?
AbstractChurn management is one of the key issues handled by mobile telecommunication operators. Data mining techniques can help in the prediction of churn behavior of customers. Various supervised learning techniques have been used to study customer churn. However, research on the use of unsupervised learning techniques for prediction of churn is limited. In this article, we use two-stage hybrid models consisting of unsupervised clustering techniques and decision trees with boosting on two different data sets and evaluate the models in terms of top decile lift. We examine two different approaches for hybridization of the models for utilizing the results of clustering based on various attributes related to services usage and revenue contribution of customers. The results indicate that the use of clustering led to improved top decile lift for the hybrid models compared with the benchmark case when no clustering is used. It is also shown that using cluster labels as inputs to the decision trees is a preferred method of hybridization. Out of the five unsupervised clustering techniques used, none is found to dominate others. But interesting attributes and rules that can help marketing experts identify churners from the data are obtained from the best hybrid models. © Taylor & Francis Group, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/60227
ISSN
2015 Impact Factor: 0.944
2015 SCImago Journal Rankings: 0.440
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorBose, Ien_HK
dc.contributor.authorChen, Xen_HK
dc.date.accessioned2010-05-31T04:06:16Z-
dc.date.available2010-05-31T04:06:16Z-
dc.date.issued2009en_HK
dc.identifier.citationJournal Of Organizational Computing And Electronic Commerce, 2009, v. 19 n. 2, p. 133-151en_HK
dc.identifier.issn1091-9392en_HK
dc.identifier.urihttp://hdl.handle.net/10722/60227-
dc.description.abstractChurn management is one of the key issues handled by mobile telecommunication operators. Data mining techniques can help in the prediction of churn behavior of customers. Various supervised learning techniques have been used to study customer churn. However, research on the use of unsupervised learning techniques for prediction of churn is limited. In this article, we use two-stage hybrid models consisting of unsupervised clustering techniques and decision trees with boosting on two different data sets and evaluate the models in terms of top decile lift. We examine two different approaches for hybridization of the models for utilizing the results of clustering based on various attributes related to services usage and revenue contribution of customers. The results indicate that the use of clustering led to improved top decile lift for the hybrid models compared with the benchmark case when no clustering is used. It is also shown that using cluster labels as inputs to the decision trees is a preferred method of hybridization. Out of the five unsupervised clustering techniques used, none is found to dominate others. But interesting attributes and rules that can help marketing experts identify churners from the data are obtained from the best hybrid models. © Taylor & Francis Group, LLC.en_HK
dc.languageengen_HK
dc.publisherLawrence Erlbaum Associates, Inc. The Journal's web site is located at http://www.leaonline.com/loi/joceen_HK
dc.relation.ispartofJournal of Organizational Computing and Electronic Commerceen_HK
dc.rightsJournal Of Organizational Computing And Electronic Commerce. Copyright © Lawrence Erlbaum Associates, Inc.en_HK
dc.subjectChurnen_HK
dc.subjectClusteringen_HK
dc.subjectData miningen_HK
dc.subjectDecision treesen_HK
dc.subjectLiften_HK
dc.subjectPredictionen_HK
dc.subjectRulesen_HK
dc.titleHybrid models using unsupervised clustering for prediction of customer churnen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1091-9392&volume=19&spage=133&epage=151&date=2009&atitle=Hybrid+Models+Using+Unsupervised+Clustering+For+Prediction+Of+Customer+Churnen_HK
dc.identifier.emailBose, I: bose@business.hku.hken_HK
dc.identifier.authorityBose, I=rp01041en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/10919390902821291en_HK
dc.identifier.scopuseid_2-s2.0-70449568735en_HK
dc.identifier.hkuros160466en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70449568735&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume19en_HK
dc.identifier.issue2en_HK
dc.identifier.spage133en_HK
dc.identifier.epage151en_HK
dc.identifier.isiWOS:000265336500005-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridBose, I=7003751502en_HK
dc.identifier.scopusauthoridChen, X=8043429800en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats