Asia Pacific Journal of Information Systems (APJIS)

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Date	March 2016
Vol. No.	Vol. 26 No. 1
DOI	http://dx.doi.org/10.14329/apjis.2016.26.1.66
Page	66~79
Title	Purchase Prediction by Analyzing Users’ Online Behaviors using Machine Learning and InformationTheory Approaches
Author	Minsung Kim, Il Im, Sangman Han
Keyword	Predictive Modeling, Information Theory, Machine Learning, Random Forests
Abstract	The availability of detailed data on customers’ online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the ‘entropy’ concept from in-formation theory, while ‘random forests’ method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers’ information search behaviors differ significantly across product categories.