An empirical comparison of customer behavior modeling approaches for shopping list prediction

Date

2018-06-28

Author

Peker, Serhat
Koçyiğit, Altan
Eren, Pekin Erhan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Shopping list prediction is a crucial task for companies as it can enable to provide a specific customer a personalized list of products and improve customer satisfaction and loyalty as well. To predict customer behaviors, many studies in the literature have employed customer behavior modeling approaches which are individual-level and segment-based. However, previous efforts to predict customers' shopping lists have rarely employed these state-of-the-art approaches. In this manner, this paper introduces the segment based approach into the shopping list prediction and then presents an empirical comparison of the individual-level and the segment-based approaches in this problem. For this purpose, well-known machine learning classifiers and customers' purchase history are employed, and the comparison is performed on a real-life dataset by conducting a series of experiments. The results suggest that there is no clear winner in this comparison and the performances of customer behavior modeling approaches depend on the machine learning algorithm employed. The study can help researchers and practitioners to understand different aspects of using customer behavior modeling approaches in the shopping list prediction.

Subject Keywords

Shopping list prediction, Next basket prediction, Customer behavior models, Machine learning, Personalization

URI

https://hdl.handle.net/11511/31855

DOI

https://doi.org/10.23919/mipro.2018.8400221

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

S. Peker, A. Koçyiğit, and P. E. Eren, “An empirical comparison of customer behavior modeling approaches for shopping list prediction,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31855.