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TopMotivation And Background
The main idea of segmentation or 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). In fact, one of the most valuable pieces of information based on which a segment can be customers’ behavioural characteristics, especially past customer purchases and value-oriented attributes (Bayer, 2010, Kim et al., 2006, Wind and Lerner, 1979). In fact, customer analytics related technological advances have facilitated performing segmentation studies based on those characteristics (Bailey et al., 2009).
This study focuses on two main issues regarding customer segmentation. The first issue is related to the difficulty of obtaining homogenous segments. For customer segmentation, a wide variety of data analysis techniques, such as cluster analysis (Ghazanfari et al., 2008; Hruschka et al., 2004, Li et al., 2009, Liu and Shih, 2005, Wang, 2009, Wang et al., 2008; Xia et al., 2010), clusterwise regression (Desarbo et al., 2008), AID/CHAID (Aravamudhan, 2011; Gil-Saura and Ruiz-Molina, 2008, Jonker et al., 2004), multiple regression (Suh et al., 1999), discriminant analysis (Tsiotsou, 2006), latent class structure (Wu and Chou, 2011) and sophisticated soft computing techniques including such as fuzzy-oriented approaches (Crespo and Weber, 2005, Hu and Sheu, 2003, Kaymak, 2001, Ozer, 2001, Shin and Sohn, 2004) and neural network algorithms (Bloom, 2005, Chiu et al., 2009, Diez et al., 2008, Ha, 2007, Hsieh, 2004, Hung and Tsai, 2008, Kuo et al., 2006, Lee and Park, 2005, Potharst et al., 2001, Shin and Sohn, 2004) have been used in the related literature. Cluster-based segmentation methods, particularly hierarchical and non-hierarchical methods, have been widely used in the field. But, the hierarchical methods are criticised for non-recovery, while the non-hierarchical methods for their inability to initially determine the number of clusters (Lien, 2005). Hence, the integration of hierarchical and partitioning methods (two-stage methodology) is suggested to make the clustering results powerful for large databases (Kuo et al., 2002). None of those approaches, however, have the ability to establish non-strict customer segments that could play a significant role in today’s competitive consumer markets. Although there have been a few studies that utilised fuzzy segments they are not based on the effective two-stage methodology.