Personalized Pricing: Who Wins, Who Pays?
TL;DR
Background: Personalized pricing uses data about a customer to estimate what that person will pay. Maryland and New Jersey have enacted restrictions on the practice in grocery sales, while Connecticut has adopted a broader retail restriction. The Federal Trade Commission (FTC) has also proposed guidance warning that undisclosed personalized pricing is likely unfair or deceptive.
But… The effects on consumers depend on the market and how sellers set prices. In the three major empirical studies the FTC cites in its proposed guidance, most consumers paid less, received a lower offer, or gained from personalization, even when consumers lost as a group. Nor is the practice as widespread as the debate suggests: A 2018 European Commission study of 160 online retailers found personalized price differences in just 6% of tests, with a median pricing difference below 1.6%.
Moreover… Disclosure has costs. Requiring sellers to explain each tailored price could discourage discounts for price-sensitive customers, while evidence that such explanations change shopping decisions is weak. A price tag tells a shopper what they can pay. It says nothing about what anyone else pays.
KEY TAKEAWAYS
The Price Is…Complicated
Price discrimination means charging different buyers different prices for the same product for reasons other than cost. Student discounts, matinee tickets, airline fares, and grocery coupons are familiar examples. Personalized pricing uses data about an individual to estimate what that person will pay.
Its effects depend on the market. Richard Schmalensee and Lars Stole explain that a monopolist’s price discrimination can raise total welfare only if it increases sales. Models by Jacques-François Thisse and Xavier Vives show that, among competing firms, it can drive down both prices and profits. As Daniel J. Gilman puts it, “The welfare effect of price discrimination is known to be ambiguous and thus a matter of empirical measurement.”
The alternative matters, too. John M. Yun argues that, in the absence of personalized pricing, sellers would usually use another form of price discrimination, rather than charge everyone the same price.
Three Studies, No Easy Verdict
Three studies cited in the FTC’s personalized-pricing guidance find mixed effects. It should be noted, however, that none tests personalized posted prices for everyday shoppers in a U.S. retail store.
In a ZipRecruiter experiment, Jean-Pierre Dubé and Sanjog Misra estimate that personalization could have raised profits by 86% over the firm’s actual pricing. Optimizing a single price would have raised profits by 55%; personalization added 19% over that optimized price. About three in five potential customers would receive a lower offer. But the study examines a hiring platform with a single seller, not competing retailers.
Benjamin Reed Shiller estimates that prices based on browsing histories could have raised Netflix’s 2006 profits by 12.99%. Yet “most consumers receive lower prices when prices are personalized, and hence the majority are slightly better off.” His results are based on a simulation of personalized pricing because at the time of his study Netflix did not engage in personalized pricing.
Nicholas Buchholz and his co-authors find that 62.5% of riders on Liftago, a Prague taxi platform, gain from personalization. Riders least sensitive to price and wait times bear most of the losses. Total consumer surplus falls by a further 2.25% compared with platform-set uniform pricing.
These settings reveal real tradeoffs, but they do not establish what personalized pricing would do in a competitive U.S. retail market or support a presumption that personalization harms consumers.
Competition Spoils the Plot
Competition can change who gains from personalized pricing. Andrew Rhodes and Jidong Zhou find that when all consumers already buy a product and all firms personalize, consumers gain as a group while industry profits fall. If uniform pricing leaves some buyers out, personalization can bring them in even as total consumer surplus falls. Brian C. Albrecht finds that when competing firms have the same complete information, competition can push prices for contested buyers down to the cost of serving them.
More data does not automatically mean more harm. Dubé and Misra find that “allowing the firm to target on more granular data can be good for consumer surplus.” Ginger Zhe Jin, Liad Wagman, and Mengyi Zhong call the tradeoffs “complex.” Their review identifies the greatest risk as when one firm has much more data than its rivals.
Many sellers leave money on the table rather than vary prices. Alberto Cavallo found no evidence that large U.S. retailers varied online prices by a shopper’s location or browsing history. Stefano DellaVigna and Matthew Gentzkow found that most large U.S. food, drug, and general-merchandise chains charged nearly uniform prices across stores. The median chain gave up about $16 million a year in profit by doing so.
The Price Tag Said What?
A price tag tells a shopper what they can pay, not what everyone else pays. Yet the FTC’s deception theory assumes shoppers expect the same price for all and treats silence about personalization as misleading. The International Center for Law & Economics (ICLE) argues that this goes too far. In International Harvester (1984), the FTC distinguished a misleading “half-truth” from a “pure omission,” a subject on which the seller “has simply said nothing.” Pure omissions generally are not deceptive.
Unfairness requires proof of substantial injury that consumers cannot reasonably avoid and that benefits to consumers or competition do not outweigh. ICLE says the FTC’s hypothetical examples never apply that test. In FTC v. Corpay (2026), the 11th U.S. Circuit Court of Appeals upheld an unfairness finding over undisclosed fleet-card fees based on survey evidence, internal documents, and customer complaints.
More Fine Print Means Fewer Deals
A typical supermarket stocks more than 31,000 products, Gilman notes. Explaining how it sets every tailored price would be impractical and could lead sellers to drop targeted discounts. The explanations might do little for shoppers: An OECD study found no significant effect of pricing disclosures on purchasing decisions in experiments in Dublin, Ireland and Santiago, Chile.
Petra Persson shows how firms can bury useful information in a flood of disclosures, a risk Frank Easterbrook and Daniel Fischel identified decades ago. Disclosure rules can also change what sellers offer. Allison Nicoletti and Christina Zhu found lower approval rates for loans covered by federal mortgage-disclosure rules. ICLE has made a similar point about uniform-pricing mandates for grocery suppliers: They discourage discounts.
For more on this issue, see “ICLE Comments on the FTC’s Personalized Pricing Proposed Enforcement Policy Statement.”