Optimal Pricing Strategies for an Automotive Aftermarket Retailer

Abstract

The extant retail category pricing optimization literature concentrates on grocery retailing. In contrast, this article focuses on the problem of determining profit-improving store-level prices of failure-related “hard-part” product categories at a U.S. specialty automotive part retailer with 3400 stores. There are some key institutional differences between automotive hard-part and grocery retailing: No syndicated data are available for hard parts; each subclass of a hard-part category contains variants that are ordered by quality (typically “Good,” “Better,” “Best”); there is intra- but no intersubclass competition; market shares of variants and their prices are not positively correlated within a subclass; a consumer enters the market only infrequently; and at a purchase occasion, a consumer buys one and only one variant within a subclass and one and only one unit of that variant. Using two years of weekly sales histories from 800 stores, the authors develop store-level demand models for 23 subclasses of a hard part and employ these with available product cost data to set prices of variants of each subclass at each store that increase profit. The authors test the model-recommended prices for 10 subclasses in a field experiment involving 500 stores, leading to a projection of an annual increase of more than $610,000 in the retailer's profit from these 10 subclasses if the new prices are applied at all stores. The empirical analysis also yields new insights into asymmetric price competition across quality variants and deviations of actual from optimal prices that run counter to previous grocery retailing-based findings.

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