Abstract
In recent years, the rise of electronic commerce and direct-to-consumer (D2C) has significantly “showroomed” retail stores, and there is a need to maximize the consumer experience through actual products in stores. Therefore, this study aims to identify important products by quantitatively analyzing and discussing retailers' purchase history data, and to propose marketing measures focusing on value communication in stores. Specifically, we defined the degree of similarity between products based on consumer preferences from ID-POS data and constructed a network model between products using social network analysis techniques. In addition, we used community detection and centrality indices to extract similar product groups and identify important products.
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Acknowledgment
This work was supported by JSPS KAKENHI Grant Numbers 21K13385, 21H04600.
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Nakashima, J., Namatame, T., Otake, K. (2024). Identification of Important Products in Electronics Retail Stores Using a Product-To-Product Network. In: Coman, A., Vasilache, S. (eds) Social Computing and Social Media. HCII 2024. Lecture Notes in Computer Science, vol 14704. Springer, Cham. https://doi.org/10.1007/978-3-031-61305-0_22
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DOI: https://doi.org/10.1007/978-3-031-61305-0_22
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