MINING CUSTOMER KNOWLEDGE FOR CHANNEL AND PRODUCT SEGMENTATION

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.

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