Online retailers are increasingly using pricing algorithms to set their online prices. The advanced algorithms are self-learning and automatically compare competitor prices and customer demand to determine prices. ((Fredric Lardinois, Boomerang Commerce Helps Retailers Get Their Prices Right, TechCrunch (May 5, 2014), http://techcrunch.com/2014/05/05/boomerang-commerce-helps-retailers-get-their-prices-right/.)) The service allows companies to set their own preferences, which customizes the algorithm to their needs. ((Id.)) Because these algorithms are complex and expensive to produce, the service is provided by third party providers. For example, Boomerang Commerce is a startup company founded by former Amazon engineers that provides dynamic price optimizing services. (( Ryan Mac, Ex-Amazon Manager Gets Funding To Help Retailers Battle His Former Employer, Forbes (July 16, 2014), http://www.forbes.com/sites/ryanmac/2014/07/16/boomerang-amazon-battle/.)) The young company targets only large online retailers and already provides service to Amazon, RadioShack, Sears and Staples. ((Greg Bensinger, Boomerang Commerce, A Real-Time Pricing Startup, Raises $8.5 Million, Wall St. J. (July 16, 2015), http://blogs.wsj.com/digits/2014/07/16/boomerang-commerce-a-real-time-pricing-startup-raises-8-5-million/.))
One potential antitrust implication of these new pricing services is that they involve a great deal of coordination among retailers and therefore could potentially represent horizontal price fixing. In early electronic reporting cases, the courts found that price fixing existed between airlines when they publicly reported current prices. ((Katherine I. Funk, Antitrust and Pricing in a World of Free Price Information: Price Comparison Apps, Free-riding, and the Impact on the Retail Market Place; Responses of Retailers and Legislatures, ALI/ABA 27th Annual Product Distribution and Marketing Seminar (June 2012).)) Through the new pricing technology, the retailers have real time access to information about the pricing and sales of their competitors. Furthermore, they are often utilizing the same service providers to implement algorithms, interpret that information and adjust prices accordingly.
However, the algorithms are tailored to the specific preferences of the retailer, and therefore the fact that a single provider produces algorithms for several retailers or a large portion of the industry is unlikely to create an antitrust violation. Most importantly, the sharing of information among the retailers and subsequent pricing adjustments has resulted in more efficient responses to market changes and ultimately more competitive prices that benefit consumers.
Recently, Amazon was accused of charging different prices to customers for the same products. While the retailer denied the claims, this issue does raise questions about the antitrust implications of discriminating among customers based on their buying habits. ((Robert M. Weiss & Ajay K. Mehrotra, Online Dynamic Pricing, Efficiency, Equity and the Future of E-Commerce, 6 VA. J.L. & Tech 11, 11 (2001).)) Access to pricing services and customer data has made dynamic pricing feasible on a large scale, and if utilized it would allow retailers to discriminate between customers efficiently. The Robinson-Patman Act of 1936 expressly forbids price discrimination. However the act is aimed at regulating the behavior of intermediary vendors and therefore it is unlikely to be utilized to regulate transactions directly between the customer and merchant.
This year, Uber received backlash from customers for using these dynamic price strategies, which they call “surge pricing,” to alter prices in real time with demand. ((James Surowiecki, In Praise of Efficient Pricing Gouging, MIT Tech. Rev. (Aug. 19, 2014), http://www.technologyreview.com/review/529961/in-praise-of-efficient-price-gouging/.)) The Uber example illustrates the ways in which customers can reduce the use of dynamic pricing through public opinion in the event that existing antitrust laws are incapable of preventing this type of pricing behavior.
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