Optimizing Retail Assortments

Authors:
Robert P. Rooderkerk;Harald J. van Heerde;Tammo H. A. Bijmolt
Affiliations:
TiasNimbas Business School and Tilburg School of Economics and Management, Tilburg University, 5000 LE Tilburg, The Netherlands/ and Tuck School of Business at Dartmouth, Hanover, New Hampshire 03 ...;School of Communication, Journalism, and Marketing, Massey University, Auckland 0745, New Zealand/ and CentER, Tilburg University, 5000 LE Tilburg, The Netherlands;Faculty of Economics and Business, University of Groningen, 9700 AB Groningen, The Netherlands
Venue:
Marketing Science
Year:
2013

Citing 18
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Abstract

Retailers face the problem of finding the assortment that maximizes category profit. This is a challenging task because the number of potential assortments is very large when there are many stock-keeping units SKUs to choose from. Moreover, SKU sales can be cannibalized by other SKUs in the assortment, and the more similar SKUs are, the more this happens. This paper develops an implementable and scalable assortment optimization method that allows for theory-based substitution patterns and optimizes real-life, large-scale assortments at the store level. We achieve this by adopting an attribute-based approach to capture preferences, substitution patterns, and cross-marketing mix effects. To solve the optimization problem, we propose new very large neighborhood search heuristics. We apply our methodology to store-level scanner data on liquid laundry detergent. The optimal assortments are expected to enhance retailer profit considerably 37.3%, and this profit increases even more to 43.7% when SKU prices are optimized simultaneously.