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Past Issue • Jul-Sep 2012

Using Business Analytics to Improve Product Assortment

Retailers with multiple stores of commodity products face the problem of product assortment that incorporates the varying geographic and demographic conditions of the locations they serve. How can such retailers use the sheer volume of available transactional data to their best advantage? In this article, Professors Xue Bai, Sudip Bhattacharjee, Fidan Boylu and Ram Gopal of University of Connecticut, present a data mining and optimisation based decision model for product assortment across multiple stores of a company.

Global and Local Patterns of Sales Merchandising managers and retail buying executives frequently decide on the assortment of products to be carried in a retail store. Product variety is important for decision making, and so are the consumer demographics ser ved by the retail store as well as meeting the inventor y constraints at the location. While consumer products and brand name items can be marketed using pricing, promotions, bundling and other techniques, this is usually not the case for commercially used commodity products such as oil, steel and plastics, where markets are competitive and pricing and product differentiation are not salient sales tools. For retailers selling such products, product assortment and availability are important determinants of sales success, while price is usually closely tied to the cost of the product.

ABOUT THE AUTHORS

  • Ram-D-Gopal-feb7

    Ram Gopal

    GE Endowed Professor of Business and Head of the Department of Operations and Information management in the School of Business, University of Connecticut and a visiting scholar at the Indian School of Business (ISB).
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