Customer online shopping behaviours analysis using bayesian networks

Authors:
Zi Lu;Jie Lu;Chenggang Bai;Guangquan Zhang
Affiliations:
Faculty of Resources & Environment Sciences, Hebei Normal University, China;Faculty of Information Technology, University of Technology, Sydney, Australia;Department of Automatic Control, Beijing University of Aeronautics and Astronautics, China;Faculty of Information Technology, University of Technology, Sydney, Australia
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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Abstract

This study applies Bayesian network technique to analyse the relationships among customer online shopping behaviours and customer requirements. This study first proposes an initial behaviour-requirement relationship model as domain knowledge. Through conducting a survey customer data is collected as evidences for inference of the relationships among the factors described in the model. After creating a graphical structure, this study calculates conditional probability distribution among these factors, and then conducts inference by using the Junction-tree algorithm. A set of useful findings has been obtained for customer online shopping behaviours and their requirements with motivations. These findings have potential to help businesses adopting more suitable online system development.