Market segmentation via structured click stream analysis
Accurate market segmentation has been the basis for successful customization of products and services. To date, however, the marketing management literature has focused mainly on the exploration of segmentation variables, but lagged behind in the development of practical means for segmentation mechanisms using contemporary information technology. Motivated by this shortcoming, the current study attempts to devise an effective method that allows for systematic collection and analysis of online customers' click stream data to facilitate market segmentation. Cohen's CAD theory was employed in conjunction with artificial neural network models to provide the analytical foundation of this research. To test the effectiveness of the proposed method, a sizable online field experiment utilizing a disguised 7-ELEVEN Website was conducted, and 912 useful click streams collected. The results from the subsequent data analysis supported the feasibility of the current work, but also identified the needs for further study.
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