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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/64932

    Title: Mining customer knowledge to implement online shopping and home delivery for hypermarkets
    Authors: Liao, Shu-Hsien;Chen, Yin-Ju;Lin, Yi-Tsun
    Contributors: 淡江大學經營決策學系
    Keywords: Data mining;Association rule;Cluster analysis;On-line shopping;Home delivery;Electronic commerce;Database marketing
    Date: 2011-04
    Issue Date: 2011-10-20 16:11:35 (UTC+8)
    Publisher: Kidlington: Pergamon
    Abstract: With advances in modern technology, the Internet population has increased year by year globally. For young customers who consider convenience and speed as prerequisites, online shopping has become a new type of consumption. In addition, business-to-customer (B2C) home delivery markets have taken shape gradually, because virtual stores have risen and developed, e.g. mail-order, TV marketing, e-commerce. To integrate the above statements, this study combines online shopping and home delivery, and attempts to use association rules to determine unknown bundling of fresh products and non-fresh products in a hypermarket. Customers are then divided up in clusters by clustering analysis, and the catalog is design based on each of the cluster’s consumption preferences. By this method, to increase the catalogue’s attraction to customers, hypermarkets are offered an online shopping and home delivery business model for sales services and propositions. With such a model, we can expect to attract more customers open up more broad markets, and earn the higher profits for hypermarkets.
    Relation: Expert Systems with Applications 38(4), pp.3982–3991
    DOI: 10.1016/j.eswa.2010.09.059
    Appears in Collections:[管理科學學系暨研究所] 期刊論文

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