Big Data, Price Discrimination, and Markets

At GigaOm, Derrick Harris has an interesting take on how data analytics are allowing New York landlords to extract maximum rents. It’s a good piece, but I think it just scratches the surface of what is going to become an increasingly important debate about the ethics of big data (where concern until now had focused primarily upon privacy issues.)

Driscrimination in admission prices
A form of price discrimination

Price discrimination is a technical (and value-neutral) term in economics. It refers to sellers charging different prices for a food or service based on factors other than the cost of providing it. In the past, price discrimination was difficult, both because it was difficult, as a practical matter to charge different prices to different customers and because sellers lacked the information they needed to determine a profit-maximizing individual price. Airlines have used discriminatory pricing since deregulation and have gotten increasingly good at it.

But there are a lot of problems inherent in price discrimination. For one thing, it is inherently a distortion of free markets. Efficient markets theory, to the extent that anyone still believes it,  assumes that all participants in a market, buyers and sellers, have equal access to the information that goes into price-setting. Price discrimination, at least as practiced in the real world, depends on the seller having information no available to the buyer. Car dealers could engage in price discrimination because only they knew what the wholesale price of the car really was and what prices were on comparable sales to other customers (a power eroded by the web.) Airlines and hotels have lots of information about available seats or rooms, marginal costs, and expected demand that lets them vary prices profitably.

The growing ability to collect and analyze vast amounts of data, plus the trend to online sales that allow customized price quotes not possible in brick-and-mortar stores, is bound to produce a lot more price discrimination. Is this necessarily bad for consumers? That’s not clear, although there definitely will be winners and losers. It is also sure to produce growing calls for regulating the practice.


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Steve Wildstrom

Steve Wildstrom is veteran technology reporter, writer, and analyst based in the Washington, D.C. area. He created and wrote BusinessWeek’s Technology & You column for 15 years. Since leaving BusinessWeek in the fall of 2009, he has written his own blog, Wildstrom on Tech and has contributed to corporate blogs, including those of Cisco and AMD and also consults for major technology companies.

4 thoughts on “Big Data, Price Discrimination, and Markets”

  1. Price discrimination doesn’t have to depend on information asymmetry. In many cases it works because consumers at different points along the demand curve have different utility functions and value different aspects of the good (aka different parts of a bundle) being sold at different amounts.

    1. You’re right, of course. But discrimination based on data analytics does require asymmetry, at least in the typical case where the seller has the analytics and the buyer doesn’t.

  2. This article contains a basic insight, but fails to develop it.

    I don’t see any new (as in: this year) examples of data/analysis is being used to price discriminate. And the claim that price discrimination distorts free markets is phony: Free markets depend on freedom, not on everyone paying the same price.

    I don’t get the impression that the reporter is very knowledgable about the practice of price discrimination, since he doesn’t develop the theme and resorts to broad generalizations: “Is this necessarily bad for consumers? That’s not clear, although there definitely will be winners and losers.” The editor should have sent this reporter back to work on this piece rather than publishing it.

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