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Working Paper

Developing Optimal Store-level Pricing Strategies for an Automotive Aftermarket Retailer

Murali K. Mantrala, P. B. (Seethu) Seetharaman, Rajeeve Kaul, Srinath Gopalakrishna, and Antonie Stam, 2006 [06-105]

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Much of extant retail pricing research has focused on grocery retailers, neglecting other major retailing sectors that face very different demand environments. One such sector is automotive aftermarket retailing, in which highly specialized “hard parts” are stocked, the vast majority of which sell five or fewer units a year.

In this paper, authors Mantrala, Seetharaman, Kaul, Gopalakrishna, and Stam create a model for optimal store-level pricing of specialty hard parts for a large U.S. specialty automotive after-market chain. They use two years’ worth of weekly store-level sales data from 800 stores located across the U.S. to formulate and estimate a heterogeneous multinomial logit model of store-level demand for three quality variants (good, better, and best) for 27 subclasses of hard parts.

The model offers insights into store-level SKU price elasticities and generates profit-maximizing prices for the three quality variants in the multiple subclasses. This price optimization is done separately for each store in the sample, meeting the collaborating company’s stated goal of micromarketing store-level SKU prices.

The team finds that in many stores, the company would benefit from lowering its prices, while in a smaller fraction of stores, the retailer would benefit from raising the prices. The team also finds that the retailer would benefit from reducing the size of the gap between the prices of its "good" and its "best" variants.

Contrary to findings in the packaged-goods industry, they find that changing the price of the highest-quality variant affects the demand for a lower-quality brand significantly less than the amount by which changing the price of the lower-quality variant affects the demand for the highest-quality variant, a difference that may be due to the durable nature of automotive hard parts.

About the authors

Murali K. Mantrala is Sam M. Walton Distinguished Professor of Marketing at the College of Business, University of Missouri-Columbia. P.B. Seetharaman is Associate Professor of Management at the Jesse H. Jones Graduate School of Management, Rice University. Rajeeve Kaul is Director of Pricing Optimization at AutoZone. Srinath Gopalakrishna is Associate Professor of Marketing and Antonie Stamis Leggett & Platt Distinguished Professor of Information Systems, both at the College of Business, University of Missouri-Columbia.

Included in the following collections:
MSI Reports 2006

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