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Using Sequence Analysis to Classify Web Usage Patterns across Websites

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4 Author(s)
Qiqi Jiang ; Dept. of Inf. Syst., City Univ. of Hong Kong, Hong Kong, China ; Chuan-Hoo Tan ; Chee Wei Phang ; Wei, K.K.

This study applies sequence analysis to identify the distinct web browsing patterns based on 200 China users' 30-days web usage. Our results reveal four key, unique web navigation behavior categories, namely search-information browsing, social-information browsing, ecommerce-information browsing, and direct browsing. Of these, the ratio of ecommerce activities in the social-information cluster is higher than the others, with the exception of the ecommerce-information cluster. To test the robustness of the proposed method based on our classification, we also summarize the characteristics of each category after they were segmented according to two demographic indicators, i.e. gender and occupation. Different online shopping behaviors are also discussed through the proposed classified groups. Complementing the extant methods which are based on within-website categorization of consumers, the demonstration of the sequence analysis application to e-commerce affords a deeper, integrated understanding of an individual's online activity and behavior (i.e., navigation across multiple websites).

Published in:

System Science (HICSS), 2012 45th Hawaii International Conference on

Date of Conference:

4-7 Jan. 2012

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