Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/9403
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dc.coverage.spatialen_US
dc.date.accessioned2013-06-10T06:58:17Z-
dc.date.available2013-06-10T06:58:17Z-
dc.date.issued2013-06-10-
dc.identifier.urihttp://hdl.handle.net/10603/9403-
dc.description.abstractiv newlineABSTRACT newlineData mining in customer intelligence is increasingly used for customer newlineacquisition, customer segmentation, customer retention and cross selling. newlineIncorporation of data mining for detailed analysis of customer s preferences, and newlinebuying habits is a need of every business enterprise. This helps organizations to newlineadopt strategies, which will attract customers and increase sales. Customer newlineRelationship Management (CRM) systems are adopted by the organisations in order newlineto achieve success in the business and also to formulate business strategies, which newlinecan be formulated based on the predictions given by the data mining tools. CRM is a newlineprocess by which an organization maximizes customer satisfaction in an effort to newlineincrease loyalty and retain customers business over their lifetimes. On the other newlinehand, customer segmentation is the grouping of customers into different groups newlinebased on their common attributes and it is the main part of CRM. Treating customers newlineto their preferred levels of service forms the basis of segmentation. The key goal of newlinecustomer segmentation is identifying and achieving profitable sectors and provides newlineproducts and services that are the customer s common need. Segmentation analysis newlinehas as inputs customer transactional, demographic and psychographic data. newlineWhen data mining tools and techniques are applied on the data newlinewarehouse based on customer records, they search for the hidden patterns and trends. newlineIn order to analyze CRM data, one needs to explore the data from different angles newlineand look at its different aspects. Business organizations need to determine products newlineand services most preferred by customers and to develop techniques to communicate newlineand distribute them to customers on time. newlinev newlineData mining is a technology that helps businesses to predict future trends newlineand behaviours, allowing them to make proactive, knowledge-driven newlinedecisions.Predicting customer behavior is important only to the extent that effective newlineaction can be taken based on the predictions. The unprecedented growth of newlinecompetitionen_US
dc.format.extent2.5 mben_US
dc.languageEnglishen_US
dc.relationen_US
dc.rightsuniversityen_US
dc.titleSEGMENTATION OF CUSTOMERS FOR PREDICTION ANALYSIS USING SUPERVISED AND UNSUPERVISED DATA MINING TECHNIQUESen_US
dc.title.alternativeen_US
dc.creator.researcherBALAJI Sen_US
dc.description.noteen_US
dc.contributor.guideDr S.K.SRIVATSAen_US
dc.publisher.placeChennaien_US
dc.publisher.universityVels Universityen_US
dc.publisher.institutionSchool of Computing Sciencesen_US
dc.date.registered02-11-2009en_US
dc.date.completed30-11-2012en_US
dc.date.awarded01-04-2013en_US
dc.format.dimensionsen_US
dc.format.accompanyingmaterialNoneen_US
dc.source.universityUniversityen_US
dc.type.degreePh.D.en_US
Appears in Departments:School of Computing Sciences

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01-title.pdfAttached File100.31 kBAdobe PDFView/Open
02-certificate.pdf101.86 kBAdobe PDFView/Open
04-declaration.pdf99.81 kBAdobe PDFView/Open
05-acknowledgement.pdf102.67 kBAdobe PDFView/Open
06-contents.pdf119.36 kBAdobe PDFView/Open
07-list of tables.pdf97.81 kBAdobe PDFView/Open
08-list of figures.pdf98.82 kBAdobe PDFView/Open
09-abbreviations.pdf98.93 kBAdobe PDFView/Open
10-chapter1.pdf313.39 kBAdobe PDFView/Open
11-chapter2.pdf192.25 kBAdobe PDFView/Open
12-chapter3.pdf495.7 kBAdobe PDFView/Open
13-chapter4.pdf530.57 kBAdobe PDFView/Open
14-chapter5.pdf961.49 kBAdobe PDFView/Open
15-conclusions.pdf110.57 kBAdobe PDFView/Open
16-bibliography.pdf135.19 kBAdobe PDFView/Open


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