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


Peker S., KOÇYİĞİT A. , Erhan Eren P. E.

41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018, Opatija, Hırvatistan, 21 - 25 Mayıs 2018, ss.1220-1225 identifier

  • Doi Numarası: 10.23919/mipro.2018.8400221
  • Basıldığı Şehir: Opatija
  • Basıldığı Ülke: Hırvatistan
  • Sayfa Sayıları: ss.1220-1225

Özet

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.