Personalised e-customer relationship management models and system

Lin, H

Personalised e-customer relationship management models and system

Lin, H

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Publication Type:: Thesis
Issue Date:: 2013

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Field	Value	Language
dc.contributor.author	Lin, H
dc.date.accessioned	2014-03-25T03:44:19Z
dc.date.available	2014-03-25T03:44:19Z
dc.date.issued	2013
dc.identifier.uri	http://hdl.handle.net/10453/24205
dc.description	University of Technology, Sydney. Faculty of Engineering and Information Technology.	en_US
dc.description.abstract	Electronic Customer Relationship Management (eCRM), and analytical eCRM in particular, is currently one of the most active research topics in the area of customer marketing and customer analytics. The goal of this research is to develop an integrated analytical eCRM framework with a personalised intelligent recommendation approach through the analysis of telecom and banking products/services and customer needs. The development of the personalised recommendation approach combines two technology streams: 1) data mining techniques, which enable the prediction of customer buying behaviour patterns, and 2) fuzzy measure and community-based collaborative filtering recommendation techniques, which automatically combine the predicted buying behaviours and needs-preference requirements of customers to provide relevant and needs-based offers in an uncertain environment. The delivery of this framework effectively improve the quality of customer relationship management in terms of reducing the cost of customer acquisition, increasing customer retention and maximising customer lifetime value, which will enable and support a business strategy to build a long-term, profitable relationship with specific customers. The research contributes to both recommender system research and eCRM research and develops frontier technologies that can be applied across industries.	en_US
dc.format	Thesis (PhD)	en_US
dc.language.iso	en	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/24205/2/02whole.pdf
dc.rights	au.edu.uts.lib/ppc
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Electronic customer relationship management.	en
dc.subject	Fuzzy measure.	en
dc.subject	Customer relations.	en
dc.subject	Mathematical models.	en
dc.subject	Predictive models.	en
dc.subject	eCRM.	en
dc.subject	Personalised recommender system.	en
dc.subject	Data mining.	en
dc.title	Personalised e-customer relationship management models and system	en_US
dc.type	Thesis
utslib.copyright.status	open_access

Abstract:

Electronic Customer Relationship Management (eCRM), and analytical eCRM in particular, is currently one of the most active research topics in the area of customer marketing and customer analytics. The goal of this research is to develop an integrated analytical eCRM framework with a personalised intelligent recommendation approach through the analysis of telecom and banking products/services and customer needs. The development of the personalised recommendation approach combines two technology streams: 1) data mining techniques, which enable the prediction of customer buying behaviour patterns, and 2) fuzzy measure and community-based collaborative filtering recommendation techniques, which automatically combine the predicted buying behaviours and needs-preference requirements of customers to provide relevant and needs-based offers in an uncertain environment. The delivery of this framework effectively improve the quality of customer relationship management in terms of reducing the cost of customer acquisition, increasing customer retention and maximising customer lifetime value, which will enable and support a business strategy to build a long-term, profitable relationship with specific customers. The research contributes to both recommender system research and eCRM research and develops frontier technologies that can be applied across industries.

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http://hdl.handle.net/10453/24205