A Product Placement Framework to Improve Diversity in Retail Businesses

Abstract

Retail stores have become a part of our daily lives and play a vital economic role in society. It has been reported that shelf-space allocation decisions in a retail store significantly impact the retailer’s revenue. So, developing approaches for efficient product placement in available shelf space in retail stores is one of the key research issues. Several research efforts have been made to propose approaches for improving product placement in retail stores based on the knowledge of customer purchase history. Traditionally, several dynamic programming model based approaches were proposed to solve this problem. More recently, data mining approaches, like frequent pattern mining and utility pattern mining, have been employed to improve product placement based on the patterns extracted from customer purchase transactions. In this thesis, we address the issue of product placement in a retail store by considering the diversity of products. Providing diverse options to customers can be useful in increasing the long term sustainability of retail stores. Other recommendation systems have also used increasing diversity to improve user interest. In the context of retail stores, a store that can cater to the needs of a diverse customer base (by providing diverse recommendations) has a higher chance to stay relevant for customers in the long term, thus, facilitating long term sustainability of retail businesses. We have proposed an approach for facilitating the placement of items in a diversified manner in a given retail store based on the concept hierarchy that exists among the items without compromising the revenue. In the proposed approach, a concept hierarchy based approach is employed to determine the diversity value of the itemset quantitatively. The diversity of the given itemset captures the extent of its items belonging to multiple categories. By combining the notion of utility and diversity, we propose a framework to compute diverse net revenue of the itemset. Given a set of transactions, price values of items, and concept hierarchy, we propose a methodology to build the Concept Hierarchy Utility Itemset Index (CHUI), which contains potential itemsets with high revenue and diversity. Next, we propose the approach to perform product placement based on the knowledge of itemsets in the CHUI. We conduct experiments on a real dataset, namely, Instacart retail dataset (containing 49,688 items and 1,31,208 transactions), to demonstrate the proposed approach’s overall effectiveness