Mining customer knowledge to implement online shopping and home delivery for hypermarkets

Authors:
Shu-hsien Liao;Yin-ju Chen;Yi-tsun Lin
Affiliations:
Department of Management Sciences and Decision Making, Tamkang University, No. 151, Yingjuan Road, Danshuei Jen, Taipei Country 251, Taiwan, ROC;Department of Management Sciences and Decision Making, Tamkang University, No. 151, Yingjuan Road, Danshuei Jen, Taipei Country 251, Taiwan, ROC;Department of Management Sciences and Decision Making, Tamkang University, No. 151, Yingjuan Road, Danshuei Jen, Taipei Country 251, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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.