File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Exploring business opportunities from mobile services data of customers: An inter-cluster analysis approach

TitleExploring business opportunities from mobile services data of customers: An inter-cluster analysis approach
Authors
KeywordsClustering
Customer profiling
K-means
Kohonen vector quantization
Mobile telecommunication
Issue Date2010
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/elerap
Citation
Electronic Commerce Research And Applications, 2010, v. 9 n. 3, p. 197-208 How to Cite?
AbstractWe use customer clustering to explore the behavioral patterns of customers who subscribe to mobile services. Two clustering techniques, K-means and KVQ, are used to cluster customers using knowledge about attributes that are broadly grouped under usage, revenue, services, and user categories. We used inter-cluster analysis on the clusters generated from the two techniques to compare the distribution of customers among the different categories of attributes. We observed that it was important to use multiple techniques for clustering. Our analysis discovered several interesting facts about customers, such as the imbalance between customers' usage of mobile services, subscriptions to services, and revenue contributions. These knowledge nuggets could enable mobile service providers to better align their marketing strategies with the needs of customers. © 2009 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/129445
ISSN
2023 Impact Factor: 5.9
2023 SCImago Journal Rankings: 1.338
ISI Accession Number ID
Funding AgencyGrant Number
University of Hong Kong
Funding Information:

The authors want to thank the two anonymous reviewers for their constructive suggestions which improved the quality of the paper. The first author gratefully acknowledges financial support received from the University of Hong Kong under the Seed Funding for Basic Research.

References

 

DC FieldValueLanguage
dc.contributor.authorBose, Ien_HK
dc.contributor.authorChen, Xen_HK
dc.date.accessioned2010-12-23T08:37:20Z-
dc.date.available2010-12-23T08:37:20Z-
dc.date.issued2010en_HK
dc.identifier.citationElectronic Commerce Research And Applications, 2010, v. 9 n. 3, p. 197-208en_HK
dc.identifier.issn1567-4223en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129445-
dc.description.abstractWe use customer clustering to explore the behavioral patterns of customers who subscribe to mobile services. Two clustering techniques, K-means and KVQ, are used to cluster customers using knowledge about attributes that are broadly grouped under usage, revenue, services, and user categories. We used inter-cluster analysis on the clusters generated from the two techniques to compare the distribution of customers among the different categories of attributes. We observed that it was important to use multiple techniques for clustering. Our analysis discovered several interesting facts about customers, such as the imbalance between customers' usage of mobile services, subscriptions to services, and revenue contributions. These knowledge nuggets could enable mobile service providers to better align their marketing strategies with the needs of customers. © 2009 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/elerapen_HK
dc.relation.ispartofElectronic Commerce Research and Applicationsen_HK
dc.subjectClusteringen_HK
dc.subjectCustomer profilingen_HK
dc.subjectK-meansen_HK
dc.subjectKohonen vector quantizationen_HK
dc.subjectMobile telecommunicationen_HK
dc.titleExploring business opportunities from mobile services data of customers: An inter-cluster analysis approachen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1567-4223&volume=9&issue=3&spage=197&epage=208&date=2010&atitle=Exploring+business+opportunities+from+mobile+services+data+of+customers:+an+inter-cluster+analysis+approach-
dc.identifier.emailBose, I: bose@business.hku.hken_HK
dc.identifier.authorityBose, I=rp01041en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.elerap.2009.07.006en_HK
dc.identifier.scopuseid_2-s2.0-79958127573en_HK
dc.identifier.hkuros177559en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79958127573&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume9en_HK
dc.identifier.issue3en_HK
dc.identifier.spage197en_HK
dc.identifier.epage208en_HK
dc.identifier.isiWOS:000277469100003-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridBose, I=7003751502en_HK
dc.identifier.scopusauthoridChen, X=14029590100en_HK
dc.identifier.citeulike5396826-
dc.identifier.issnl1567-4223-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats