Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70061
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorNg, V-
dc.creatorNg, IDA-
dc.date.accessioned2017-11-13T02:16:36Z-
dc.date.available2017-11-13T02:16:36Z-
dc.identifier.isbn978-3-540-40550-4-
dc.identifier.isbn978-3-540-45080-1-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10397/70061-
dc.description4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003, Hong Kong, China, March 21-23, 2003en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectLoyaltyen_US
dc.subjectNeuro-fuzzyen_US
dc.subjectRegression analysisen_US
dc.subjectCustomer relationship managementen_US
dc.titleCustomer loyalty on recurring loansen_US
dc.typeConference Paperen_US
dc.identifier.spage653-
dc.identifier.epage660-
dc.identifier.volume2690-
dc.identifier.doi10.1007/978-3-540-45080-1_88-
dcterms.abstractCustomer Loyalty has long been a pressing issue in today’s competitive commercial arena. It is increasingly important as companies emphasize more on their customer relationship management. In this paper, we investigate the segmentation of bank customers in terms of their loyalty level; the sample dataset is the personal finance customers from a bank. Based on a loyalty definition, a model on the customer loyalty will be formulated. This loyalty function classifies customers into four levels. Statistical and neuro-fuzzy techniques will also be deployed to explore the relationships of customer demographics, loan approval information and transaction information towards customer loyalty.-
dcterms.bibliographicCitationLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2003, v. 2690, p. 653-660-
dcterms.isPartOfLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)-
dcterms.issued2003-
dc.relation.conferenceInternational Conference on Intelligent Data Engineering and Automated Learning [IDEAL]-
dc.identifier.eissn1611-3349-
dc.identifier.rosgroupidr15296-
dc.description.ros2002-2003 > Academic research: refereed > Refereed conference paper-
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