Year: 2013
Type: article
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.
Source: Applied Artificial Intelligence
Authors: Shu-Hsien Liao, Yin-Ju Chen, Hsiao-Wei Yang
Institution: Tamkang University
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: bronze
Funder: National Science Council
Award: G1923519799
OpenAlex ID: W2035925705