A Soft Computing Approach to Customer Segmentation

A Soft Computing Approach to Customer Segmentation

Abdulkadir Hiziroglu (Yildirim Beyazit University, Turkey)
Copyright: © 2016 |Pages: 28
DOI: 10.4018/978-1-5225-0075-9.ch006
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Abstract

There are a number of traditional models designed to segment customers, however none of them have the ability to establish non-strict customer segments. One crucial area that can meet this requirement is known as soft computing. Although there have been studies related to the usage of soft computing techniques for segmentation, they are not based on the effective two-stage methodology. The aim of this study is to propose a two-stage segmentation model based on soft computing using the purchasing behaviours of customers in a data mining framework and to make a comparison of the proposed model with a traditional two-stage segmentation model. Segmentation was performed via neuro-fuzzy two stage-clustering approach for a secondary data set, which included more than 300,000 unique customer records, from a UK retail company. The findings indicated that the model provided stronger insights and has greater managerial implications in comparison with the traditional two-stage method with respect to six segmentation effectiveness indicators.
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2. Literature Review

The conceptual usage of the term “segmentation” has been attributed to Wendell R. Smith (1956), and in his pioneering article, he considered the differences between the strategies of differentiation and segmentation. Following his work, some other authors, such as Wind (1978), Myers and Tauber (1977), Wilkie and Cohen (1977), Beane and Ennis (1987), Yankelovich and Meer (2006), Dolnicar (2004), Sun (2009) and Tynan and Drayton (1987) also provided broad reviews of segmentation research. The main idea of segmentation or clustering is to group similar customers. A segment can be described as a set of customers who have similar characteristics of demography, behaviours, values, and so on (Nairn and Berthon, 2003, Bailey et al., 2009).

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