US Consumers Wary of AI-Driven Pricing Strategies

U.S. consumers are expressing frustration and confusion over the sophisticated technological methods that retailers and restaurant chains are employing to set prices. Digital price tags are becoming commonplace in grocery stores, replacing traditional paper labels, while artificial intelligence tools assist businesses in predicting customer responses to pricing adjustments.

The proliferation of terms like "dynamic pricing" and "surveillance pricing" has fueled anxiety on social media, with users questioning whether different individuals are being charged varied prices for the same goods. While consumers are accustomed to fluctuating prices for services like airfare, hotel rooms, and ride-sharing, the prospect of their everyday purchases being determined by opaque algorithms and potentially undisclosed personal data collection has struck a sensitive chord.

Katie Thomas, who leads a consumer behavior think tank at management consulting firm Kearney, noted significant tensions regarding the balance of power between companies utilizing these algorithms and consumers. However, companies argue that some consumer concerns stem from misunderstandings of how these pricing tools operate.

Walmart CEO John Furner stated in a blog post that the company's digital price tags are intended to save employee time and ensure prices are current, not to adjust costs based on external factors like weather or the time of day. He emphasized, "We price the product, not the person."

Despite such assurances, a lack of transparency regarding the customer data collected and its usage has contributed to consumer suspicion. Thomas pointed out instances where social media posts falsely suggested that Walmart's planned electronic shelf labels could scan shoppers' faces or cell phones to set individual prices.

In response to these growing concerns, lawmakers and regulators are taking action. According to the National Conference of State Legislatures, legislators in at least 30 states have considered pricing-related bills this year, some of which specifically address algorithmic and personalized pricing.

The Federal Trade Commission (FTC) has also proposed an enforcement policy warning that businesses failing to disclose their use of personal data for individualized pricing could be violating consumer protection laws.

Dynamic Pricing

Dynamic pricing involves adjusting product costs in response to shifting market conditions, such as supply and demand. This practice is common in industries like airlines, where ticket prices change as departure dates near, and in ride-sharing services through surge pricing during periods of high demand.

Data from the web data company Decodo indicates that price fluctuations extend beyond services. In 2025, the company tracked price changes across various product categories, including electronics, clothing, and home improvement supplies. Contrary to the assumption that dynamic pricing only leads to price increases, Decodo recorded that approximately half of the price changes observed in the U.S. were decreases.

Digital Price Labels

Digital displays that allow stores to update shelf prices electronically are increasingly being adopted by major U.S. retailers like Walmart and Whole Foods. While these labels simplify price changes, consumer advocates and labor unions have raised concerns that they could eventually be used for surge or personalized pricing.

Retail technology companies explain that while electronic labels facilitate frequent updates, they do not necessarily result in higher prices during peak demand. Retailers typically update these prices on a regular schedule, often weekly, to maintain consistency for shoppers. Research conducted at an unnamed grocery chain found minimal evidence of surge pricing after the implementation of these labels.

Personalized and Surveillance Pricing

Personalized pricing refers to offering different prices and discounts to customers based on factors such as their location, income, browsing history, and other individual or group data. Consumer advocacy groups and regulators sometimes use the term "surveillance pricing" for this practice.

It remains unclear how widely businesses employ personal information for price customization. An FTC staff report from early 2025 indicated that intermediaries offering price and promotion tailoring tools worked with at least 250 clients, including grocery and clothing retailers.

Several states have enacted restrictions on using shoppers' personal data for pricing. Maryland, New Jersey, and Connecticut have passed laws limiting the use of such data, with Maryland and New Jersey focusing on groceries and Connecticut covering a broader range of retail purchases.

In 2022, California prosecutors filed a lawsuit against Target, alleging that the retailer's app showed lower prices to customers before they entered stores, only to increase them once inside. Target reached a $5 million settlement and agreed to a ban on using geofencing technology to raise prices.

Target stated that the app's functionality was designed to provide accurate pricing that could vary by store and channel, asserting that it did not personalize prices based on individual data or behavior. The company affirmed its commitment to transparent pricing.

Differences in pricing are not always attributable to personal data usage. An investigation by Consumer Reports, Groundwork Collaborative, and More Perfect Union found that Instacart shoppers saw different prices for identical items at the same stores. Instacart subsequently discontinued these tests, describing them as random assignments rather than personalized pricing.

Algorithmic Pricing

Algorithmic pricing uses software to recommend or automate price setting. Businesses have utilized such systems for decades, with AI now enhancing their ability to identify sales patterns, competitor pricing, and other data to predict demand and estimate sales and profit impacts.

Research suggests a growing adoption of AI in pricing across industries. A global survey by consulting company Simon-Kucher found that 41% of business leaders used AI software for market intelligence, including competitor pricing, and 28% used it for real-time price optimization.

The National Retail Federation views algorithmic pricing as an evolution of traditional retail practices, where prices are monitored and adjusted based on factors like supply, demand, seasonality, and competitor pricing.

Some lawsuits have accused companies of using pricing algorithms to hinder competition. The FTC's 2023 antitrust lawsuit against Amazon challenged a pricing tool codenamed Project Nessie, alleging that Amazon used it to identify products for price increases, anticipating competitor imitation and thereby generating significant excess profits. Amazon has stated that the FTC's allegations mischaracterize the tool, which was intended to prevent unsustainable price matching and was ultimately scrapped.

A recent consumer lawsuit against McDonald's alleged that its pricing system used nonpublic sales data from competing locations to coordinate prices. McDonald's has called the lawsuit inaccurate, stating that the tool is made available to U.S. franchisees to assist them with setting prices at their individual restaurants.