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Randomized Markdowns and Online Monitoring

Management Science, Forthcoming

80 Pages Posted: 3 Apr 2015 Last revised: 7 Sep 2016

Ken Moon

University of Pennsylvania - The Wharton School

Kostas Bimpikis

Stanford Graduate School of Business

Haim Mendelson

Stanford University - Stanford Graduate School of Business

Date Written: September 4, 2016

Abstract

Online retail reduces the costs of obtaining information about a product's price and availability and of flexibly timing a purchase. Consequently, consumers can strategically time their purchases, weighing the costs of monitoring and the risk of inventory depletion against prospectively lower prices. At the same time, firms can observe and exploit their customers' monitoring behavior. Using a dataset tracking customers of a North American specialty retail brand, we present empirical evidence that monitoring products online is associated with successfully obtaining discounts. We develop a structural model of consumers' dynamic monitoring to find substantial heterogeneity, with consumers' opportunity costs for an online visit ranging from $2 to $25 in inverse relation to their price elasticities. Our estimation results have important implications for retail operations. The randomized markdown policy benefits retailers by combining price commitment with the exploitation of heterogeneity in consumers' monitoring costs. We estimate that the retailer's profit under randomized markdowns is 81% higher than from subgame-perfect, state-contingent pricing, because the retailer need not limit its inventory to credibly limit markdowns, which permits its jointly optimal inventory stock to expand by 133%. The welfare gain from these larger inventories splits nearly equally into retailer profit and consumer surplus. We also discuss targeting customers with price promotions using their online histories and the implications of reducing consumers' monitoring costs.

Keywords: Continuous-time stochastic game, Dynamic consumer behavior, Price commitment, Price discrimination, Revenue Management, Structural estimation

Suggested Citation

Moon, Ken and Bimpikis, Kostas and Mendelson, Haim, Randomized Markdowns and Online Monitoring (September 4, 2016). Management Science, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2589386 or http://dx.doi.org/10.2139/ssrn.2589386

Ken Moon (Contact Author)

University of Pennsylvania - The Wharton School ( email )

Jon M. Huntsman Hall
3730 Walnut St.
Philadelphia, PA 19104-6365
United States

Kostas Bimpikis

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Haim Mendelson

Stanford University - Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States
650-725-8927 (Phone)
650-725-7979 (Fax)

HOME PAGE: http://faculty-gsb.stanford.edu/mendelson/

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