Please use this identifier to cite or link to this item: https://repository.iimb.ac.in/handle/2074/20032
Title: Assortment realization for retail stores
Authors: Balki, Shubham 
Sridar, Varun 
Keywords: Airline industry;Retail stores;Product substitutability;Consumer behavior;Consumer behaviour
Issue Date: 2019
Publisher: Indian Institute of Management Bangalore
Series/Report no.: PGP_CCS_P19_164
Abstract: To perform an assortment rationalization for duty free retail stores at airports on the basis of past sales performance, product substitutability and insights into consumer choices. Buying and merchandizing form the heart of any retail organization. While the world sees the sales operations as a front, there is a complex engine running at the back to support the sales. Deciding what to put on the shelves/website, at what price to put, how much to promote and how to display the products form the job description of a Buying and Merchandizing manager. As of 2018, 47% of customers viewed duty free and travel retailing stores as a part of their travel experience1 , while 44% felt the variety of products made duty free stores a great place for shopping. In this scenario, deciding the right mix of products to keep at the store in keeping with maintaining the customer experience proves to be all the more vital. This can be done by understanding customer preferences of different categories of products, as well as product interactions (products which are purchased together, products which can be substituted etc.) and relating consumer profiles to purchase patterns. The methodology followed for the assortment rationalization was as follows: • Performing preliminary data analysis to derive preliminary insights around top selling products, store preferences of customers etc. • Identifying product substitutability between products by identifying correlations in purchasing patterns or through a clustering analysis. • Identifying products sold together frequently by identifying correlations in purchasing patterns, as well as through a lift analysis. • Identifying important products through network analysis. • Developing a logistic regression model to assign the probability of purchase of specific products as per consumer profiles.
URI: https://repository.iimb.ac.in/handle/2074/20032
Appears in Collections:2019

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