Title of article :
Mining customer knowledge to implement online shopping and home delivery for hypermarkets
Author/Authors :
Liao، نويسنده , , Shu-Hsien and Chen، نويسنده , , Yin-ju and Lin، نويسنده , , Yi-tsun، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
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.
Keywords :
Electronic commerce , Database marketing , DATA MINING , Association Rule , home delivery , On-line shopping , Cluster analysis
Journal title :
Expert Systems with Applications
Journal title :
Expert Systems with Applications