Browsing by Author "Erhan Eren,P."
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Conference Object Citation Count: 6An empirical comparison of customer behavior modeling approaches for shopping list prediction(Institute of Electrical and Electronics Engineers Inc., 2018) Peker, Serhat; Kocyigit,A.; Erhan Eren,P.; Software EngineeringShopping 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. © 2018 Croatian Society MIPRO.Conference Object Citation Count: 3A methodology for product segmentation using sale transactions(Institute of Electrical and Electronics Engineers Inc., 2018) Peker, Serhat; Kocyigit,A.; Erhan Eren,P.; Software EngineeringThis paper presents a novel methodology for product segmentation using customers' transactions on products. The proposed methodology introduces FMC model, and utilizes this model's features and clustering algorithms to group products into segments. The applicability of the proposed approach has been demonstrated on data collected by a supermarket chain. The results show that the proposed methodology provides an efficient tool that can be used to identify different product segments and to gain valuable insights about these distinct groups. The resulting product segments can help managers in the inventory management and developing marketing strategies. © 2018 Croatian Society MIPRO.