Personalised e-customer relationship management models and system

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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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