New FTC proposals aim to address concerns about personalized pricing, warning companies to be transparent about how personal data influences online prices for consumers.
The Federal Trade Commission (FTC) has recently proposed a new enforcement policy regarding personalized pricing, cautioning companies that obscure how personal data affects the prices consumers encounter online. This initiative comes as retailers and their partners increasingly gather information about consumers, including their location, search habits, purchasing behavior, and online activities.
According to the FTC, pricing systems can leverage personal data to gauge how much an individual consumer might be willing to spend. This issue has garnered significant attention in Washington, particularly following the FTC’s publication of a proposed enforcement policy statement on August 19, 2026. While the agency acknowledges it cannot outright ban personalized pricing in all circumstances, it warns that companies failing to disclose how personal data influences pricing may violate federal consumer protection laws.
But how exactly does the information collected by companies influence the prices, discounts, or products that consumers see? Research indicates that personalized pricing can lead to different offers for different individuals, even when they are searching for the same product.
Dynamic pricing, which adjusts prices based on factors such as supply, demand, and location, is something most consumers are familiar with. For example, rideshare fares often increase during peak demand, and airline ticket prices fluctuate based on availability. However, personalized pricing takes this a step further by using specific consumer data to tailor offers. This means that two individuals searching for the same item could receive different prices based on their unique profiles.
Another related concept is price steering, where retailers may keep actual prices the same but alter the order in which products are displayed. The FTC’s research has shown that pricing tools can utilize consumer data to prioritize certain products, potentially showcasing higher-priced items first. While consumers generally expect price changes due to market conditions, they may be surprised to learn that their browsing history or purchasing patterns can also influence pricing.
In its investigation into surveillance pricing, the FTC discovered that third-party pricing companies utilize detailed consumer information to help retailers customize prices, promotions, and product rankings. These companies often work with a wide range of clients, from grocery stores to clothing retailers, and can combine first-party data with external sources, such as loyalty programs and data brokers. While not every client employs individualized pricing, the technology exists to manipulate prices based on consumer data.
A recent study by Consumer Reports examined pricing discrepancies in rideshare services like Uber and Lyft. The investigation involved 174 volunteers checking over 40 routes across the United States. It found a median price difference of 42.4% between the lowest and highest fare groups. Even when checking the same trip within minutes of each other, riders received varying prices. However, both Uber and Lyft disputed the findings, asserting that they do not use personal data to set base fares or engage in behavioral pricing.
Online grocery shopping has also revealed significant price variations. A December 2025 report from Consumer Reports, Groundwork Collaborative, and More Perfect Union indicated that nearly three-quarters of grocery items tested on Instacart were offered at different prices to different shoppers. In some cases, price differences reached as high as 23%. The researchers estimated that these discrepancies could cost a household of four approximately $1,200 annually. Instacart, however, refuted this extrapolation, stating that the pricing tests were randomized and did not rely on personal data.
Instances of personalized pricing are not new. In 2014, researchers from Northeastern University found evidence of price discrimination on nine out of 16 major retail and travel websites. For example, CheapTickets and Orbitz offered lower hotel prices to members, while Expedia and Hotels.com directed some users toward more expensive options. In 2015, ProPublica reported that The Princeton Review charged different prices for an online SAT tutoring package based on a customer’s ZIP code, with higher prices offered to individuals in areas with larger Asian populations.
While there is no guaranteed method for securing the lowest online price, consumers can take steps to limit the information available to retailers and data brokers. For instance, shoppers can compare prices while logged out of their accounts and check out as guests to avoid contributing to a shopping history. Additionally, rejecting optional advertising and tracking cookies can help minimize data collection.
Consumers should also be aware of how their location data is used. Adjusting app settings to limit precise location tracking can help reduce the information available to retailers. Furthermore, utilizing private browsing modes can create a separate session that does not rely on existing cookies, although it does not guarantee anonymity.
Before making significant purchases, it is advisable to compare prices across different retailers and check historical pricing to ensure that current deals are genuinely advantageous. A VPN can also mask a user’s public IP address, potentially altering perceived location data, although it does not erase existing accounts or shopping histories.
As the landscape of online shopping evolves, it is crucial for consumers to remain vigilant about how their personal data may influence pricing. While not every retailer employs personalized pricing, the technology exists to do so, and consumers should be proactive in protecting their information and seeking the best deals available.
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