Digital pricing fuels fears faster than evidence of grocery price surges

Electronic shelf labels and AI tools are intensifying concerns about how retailers set prices, even as evidence of widespread grocery price surges remains limited. Regulators and lawmakers are moving to curb the use of personal data in pricing.

U.S. shoppers are increasingly uneasy about how prices are being set in supermarkets, on delivery apps and across major retail chains. The anxiety is not only about inflation. It is also about technology: digital shelf labels, algorithmic pricing tools and artificial intelligence systems that can change what consumers pay, sometimes in ways that are difficult to see or understand.

The core distinction, as pricing experts and regulators see it, is between dynamic pricing and personalised pricing. Dynamic pricing adjusts prices to changing market conditions such as demand, supply and competitor behaviour. Personalised pricing goes further, using customer data to tailor offers or prices to individuals or groups. The Federal Trade Commission said in January that third-party pricing services may draw on information such as location, demographics, browsing history, shopping behaviour and even mouse movements when helping businesses set individualised prices or promotions.

That distinction matters because many of the public fears surrounding digital labels are not yet backed by evidence of widespread surge pricing in grocery stores. Research reported by the Associated Press found that after one unnamed supermarket chain introduced electronic shelf labels, there was virtually no demand-driven price surge. Retailers often use the labels to update prices centrally and on a regular schedule, rather than throughout the day. Asa Farquhar of Relex Solutions said changing grocery and general merchandise prices repeatedly in a single day is still rare.

Still, the spread of electronic shelf labels has become a flashpoint because the technology removes a practical barrier to frequent repricing. It is already common in Europe and Asia, and it is now appearing more often at large U.S. chains such as Walmart and Whole Foods. Critics say the labels could one day be used for surge pricing or more advanced forms of customer-specific pricing, while retailers argue the systems are mainly designed to improve accuracy and save staff time. Walmart chief executive John Furner said in a blog post last month that the company prices products, not people, and denied using the labels to raise prices based on the weather, time of day or a shopper’s identity.

The concern is sharpened by the way consumer data can be used outside the shop itself. The FTC’s study of surveillance pricing found that intermediaries offering pricing and promotion tools had at least 250 clients, including grocery and apparel companies. Separate state laws in Maryland, New Jersey and Connecticut have now moved to restrict the use of shoppers’ personal data in price-setting, with the first two focusing on groceries. Maryland’s law, in particular, reflects a growing political belief that consumers should not be charged differently because of information inferred from their behaviour or circumstances.

There have also been high-profile disputes that have deepened public suspicion. California prosecutors sued Target in 2022 over allegations that its app showed lower prices before shoppers entered stores and higher prices after they were inside. Target later agreed to a $5 million settlement and a ban on geofencing to increase prices. The company has said the app was intended to provide accurate pricing across channels and stores, not to personalise prices by user data. Similarly, Instacart ended pricing tests after an investigation by Consumer Reports, Groundwork Collaborative and More Perfect Union found different shoppers could see different prices for identical goods at the same stores, although that did not prove individual profiling.

Artificial intelligence is adding another layer of concern because it can make pricing systems more responsive and more opaque. Simon-Kucher, the consulting firm, reported that in a survey of business leaders across 28 countries, 41 per cent said they used AI to gather market intelligence such as competitors’ prices, while 28 per cent used it for real-time price optimisation. The National Retail Federation says this is simply a more sophisticated version of what retailers have always done: monitor demand, seasonality and rivals’ prices. But antitrust regulators and consumer advocates worry that algorithmic tools can be used to co-ordinate pricing, damp competition or extract higher margins.

Those worries are visible in the legal disputes now moving through the courts. The FTC’s antitrust case against Amazon alleges that a pricing tool known as Project Nessie helped the company identify products whose prices could be raised while competitors followed, producing more than $1 billion in excess profits. Amazon has rejected that characterisation and says the tool was an abandoned experiment aimed at preventing unsustainably low prices. McDonald’s is also facing a consumer lawsuit over claims that its pricing software relied on non-public sales data from competing locations. The company has said the case is full of inaccuracies.

For now, the most immediate takeaway is that pricing technology is advancing faster than public trust. Retailers and technology suppliers insist that much of the anxiety rests on misunderstandings about how their tools work. Yet lawmakers, regulators and consumer groups are moving as if the risks are real, or at least plausible enough to justify guardrails. The result is a broader debate over who should control pricing power in an era when software can read markets, test responses and adjust prices at scale.