Segmentation of the on-line shopping market using neural networks

Abstract The characterization and analysis of on-line customers' needs and expectations, regarding the Internet as a new marketing channel, is considered a prerequisite to the realization of the expected growth of the consumer-oriented electronic commerce market. The aim of the present study is twofold: to carry out an exploratory segmentation of this market that can throw some light upon its structure, and to characterize the on-line shopping adoption process. The Self-Organizing Map (SOM), an unsupervised neural network model devised by Kohonen (Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69; Kohonen, T., (1995). Self-organizing maps. Berlin: Springer) will be used as part of a tandem approach to segmentation, which involves the factor analysis of the observable variables in the data to be analyzed, prior to clustering. The SOM is shown to be a powerful data visualization tool, able to assist the data analysis, providing supervised methods with useful explanatory capabilities. It is also applied, in a completely unsupervised mode, to discover the clusters or segments that naturally occur in the data. The SOM is proposed as a flexible clustering model able to accommodate both Finer Segmentation and Normative Segmentation approaches. Within the latter, a cluster-partition is proposed and analysed, and high-level customer profiles, of potential interest to on-line marketers, are derived and described in marketing terms.

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