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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/99931

    Title: Mining Customer Knowledge for Channel and Product Segmentation
    Authors: Liao, Shu-Hsien;Chen, Yin-Ju;Yang, Hsiao-Wei
    Contributors: 淡江大學管理科學學系
    Date: 2013-07-01
    Issue Date: 2015-01-14 12:03:24 (UTC+8)
    Publisher: Philadelphia: Taylor & Francis Inc.
    Abstract: Segmentation is particularly challenging in current markets. Hence, companies operating on consumer markets face significant implementation complexities. However, successful implementation of market segmentation is reported problematic, despite being extensively researched and widely acknowledged as a powerful concept in practice. The desired outcome, and the knowledge discovery of market segmentation, is to reap the benefits of competitive advantage. This study takes Computers/Communications/Consumer (3C) products as an example and uses a two-step data mining approach to the cluster analysis and association rules to analyze customer channels and product segmentation. Moreover, we look at what kinds of products and brands customers of different segments prefer and how these preferences differ in relation to varying channel types. Thus, this study finds some 3C product-buying behavior patterns, including customer purchase preferences and customer purchase demands, in order to generate different 3C segmentation marketing alternatives.
    Relation: Applied Artificial Intelligence 27(7), pp.635-655
    DOI: 10.1080/08839514.2013.813195
    Appears in Collections:[Department of Management Sciences] Journal Article

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