Retail Analytics for Product Placement in Brick-and-mortar stores


Authors: Parul Chaudhary, Anirban Mondal & Polepalli Krishna Reddy
Venue: International Journal of Data Science and Analytics
Link: https://link.springer.com/article/10.1007/s41060-020-00221-5

It is a well-established fact in the retail industry that the sale of items, and consequently, retailer revenue are significantly impacted by the method followed for item placement on the retail store shelves. In this scenario, if the decisions concerning the placement of products are carried out in an ad hoc manner by means of rudimentary methods, the retailer would miss the opportunity to improve their revenue. The problem assumes even more significance in case of medium-to-large retail stores, some of which even have floor space exceeding a million square feet e.g., New South China Mall (Dongguan, China), Siam Paragon Mall (Bangkok, Thailand), West Edmonton Mall (Edmonton, Alberta, Canada) and Berjaya Times Square Mall (Kuala Lumpur, Malaysia). Furthermore, customers often tend to buy sets of items (i.e., itemsets) instead of individual purchases.

This project focuses on utility mining and examines research issues and challenges for addressing the effective placement of itemsets in retail stores for maximizing the revenue of the retailer. In particular, this project concerns research issues including (but not limited to) products/items of different sizes, retail slot premiumness, itemset diversification, retail inventory management, market segmentation, spatial placement of itemsets and so on.

More details about this project can be found in the following publications:

  • Anirban Mondal, Samant Saurabh, Parul Chaudhary, Raghav Mittal and P. Krishna Reddy. A retail itemset placement framework based on Premiumness of Slots and Utility Mining. IEEE Access, 2021
  • Parul Chaudhary, Anirban Mondal, Polepalli Krishna Reddy. An improved scheme for determining top-revenue itemsets for placement in retail businesses. International Journal of Data Science and Analytics, 10(4): 359-375, 2020 
  • Raghav Mittal, Anirban Mondal, Parul Chaudhary and P. Krishna Reddy. An Urgency-aware and Revenue-based Itemset Placement Framework for Retail Stores. Proceedings of the Database and Expert Systems Applications (DEXA) Conference, 2021 
  • Anirban Mondal, Raghav Mittal, Vrinda Khandelwal, Parul Chaudhary and P.K. Reddy. PEAR: A Product Expiry-Aware and Revenue-Conscious Itemset Placement Scheme. Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2021
  • Pooja Gaur, P. Krishna Reddy, Mittapally Kumara Swamy and Anirban Mondal. A Revenue-Based Product Placement Framework to Improve Diversity in Retail Businesses.
  • Chinmay Bapna, Polepalli Krishna Reddy, Anirban Mondal. Improving Product Placement in Retail with Generalized High-Utility Itemsets. Proceedings of the International Conference on Data Science and Advanced Analytics (DSAA), 2020: 60-69 
  • Parul Chaudhary, Anirban Mondal, Polepalli Krishna Reddy. An Efficient Premiumness and Utility-Based Itemset Placement Scheme for Retail Stores. Database and Expert Systems Applications – 30th International Conference, DEXA 2019, Linz, Austria, August 26-29, 2019
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