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Predicting customer purchase behavior in the e-commerce context

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Abstract

Predicting customer purchase behavior is an interesting and challenging task. In the e-commerce context, meeting this challenge requires confronting many problems not observed in the traditional business context. Recommender system technology has been widely adopted by e-commerce websites. However, a traditional recommendation algorithm cannot perform well the predictive task in this context. This study intends to build a predictive framework for customer purchase behavior in the e-commerce context. This framework, known as CustOmer purchase pREdiction modeL (COREL), may be understood as a two-stage process. First, associations among products are investigated and exploited to predicate customer’s motivations, i.e., to build a candidate product collection. Next, customer preferences for product features are learned and subsequently used to identify the candidate products most likely to be purchased. This study investigates three categories of product features and develops methods to detect customer preferences for each of these three categories. When a product purchased by a particular consumer is submitted to COREL, the program can return the top n products most likely to be purchased by that customer in the future. Experiments conducted on a real dataset show that customer preference for particular product features plays a key role in decision-making and that COREL greatly outperforms the baseline methods.

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Notes

  1. In this paper, we use the term motivation as shorthand for motivations for purchasing products.

  2. In this paper, we use the term “product feature” to refer both to product attributes (e.g., color, size, etc.) and to the related information displayed on e-commerce websites (e.g., ratings, reviews, sales, etc.).

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Acknowledgments

This work was supported by Major Program of National Natural Science Foundation of China (No. 91218301), National Natural Science Foundation of China (No. 71473201) and Humanities and Social Science Foundation of Ministry of Education of China (No. 14YJAZH063).

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Authors and Affiliations

  1. School of Information, Southwestern University of Finance and Economics, Chengdu, China

    Jiangtao Qiu

  2. The Rawls College of Business Administration, Texas Tech University, Lubbock, TX, USA

    Zhangxi Lin

  3. School of Humanities, Southwestern University of Finance and Economics, Chengdu, China

    Yinghong Li

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  1. Jiangtao Qiu
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  2. Zhangxi Lin
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Correspondence to Jiangtao Qiu.

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Qiu, J., Lin, Z. & Li, Y. Predicting customer purchase behavior in the e-commerce context. Electron Commer Res 15, 427–452 (2015). https://doi.org/10.1007/s10660-015-9191-6

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