Abstract
It is a well-established fact that strategic placement of items on the shelves of a retail store significantly impacts the revenue of the retailer. Consumer goods sold in retail stores can be classified into essential and typically low-priced convenience items, and non-essential and high-priced shopping items. Notably, the lower-priced convenience items are critical to ensuring consumer foot-traffic, thereby also driving the sales of shopping items. Moreover, users typically buy multiple items together (i.e., itemsets) to facilitate one-stop shopping. Hence, it becomes a necessity to strategically index and place itemsets that contain both convenience items and shopping items. In this regard, we propose a consumer-good-type aware and revenue-conscious itemset indexing scheme for efficiently retrieving high-revenue itemsets containing both convenience and shopping items. Moreover, we propose an itemset placement scheme, which exploits our indexing scheme, for improving retailer revenue. Our performance study with two real datasets shows that our framework is indeed effective in improving retailer revenue w.r.t. a reference scheme.
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Mittal, R., Mondal, A., Reddy, P.K. (2023). A Consumer-Good-Type Aware Itemset Placement Framework for Retail Businesses. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13935. Springer, Cham. https://doi.org/10.1007/978-3-031-33374-3_22
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DOI: https://doi.org/10.1007/978-3-031-33374-3_22
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