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This guide focuses on the United States. It explains how to compare two offers, what federal guidance does and does not cover, and one state-specific disclosure rule in New York. It is general consumer information, not a legal conclusion about a particular purchase.
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First identify why the prices differ
Before deciding that two shoppers were quoted different prices for the same reason, make the offers comparable. Check the exact product or service, seller, sales channel, location, time, quantity, and any promotion or account requirements. An online listing and an in-store shelf tag, for example, are not a clean comparison if they were checked at different times or in different locations.
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A price that changes over time or by location
Dynamic pricing means a price changes with conditions such as demand or inventory, or varies by locality or time. A seller might therefore show different prices to shoppers checking at different moments or from different places without having calculated an individual price for either person. FTC staff guidance says businesses may use demand- or inventory-based pricing when pricing information is not misleading; the specific federal rule discussed below has a narrower scope than retail pricing generally.
A discount with stated eligibility conditions
A promotion may be available only to shoppers who meet a stated requirement, such as joining a rewards program. That is different from silently assigning a price based on an individual shopper’s profile. In FTC staff guidance for covered transactions, the price available to everyone should be reflected in the total price until the shopper qualifies for the discount. Look for the requirement and check whether the displayed or checkout price depends on it.
A price set using an individual’s personal data
Personalized pricing is the use of personal data to set a price based on an estimate of what an individual consumer is willing to pay. FTC staff says the data could include location, demographics, browsing patterns, shopping history, mouse movements, or items left in a cart. The key distinction is not simply that two people saw different numbers: it is whether information about a person was used to determine that person’s price.
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The FTC’s initial study described hypothetical examples. Those examples are not proof that a particular named retailer used the tactics described, and an observed difference between two shoppers does not by itself establish that personalization occurred.
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Different prices in sales to competing businesses
The Robinson-Patman Act is a separate issue from ordinary consumer shopping. The FTC’s guide describes statutory tests for qualifying commodity sales to competing business purchasers, including sales of goods of like grade and quality to at least two purchasers within approximately the same period and a reasonable possibility of injury to competition. The guide also identifies potential defenses such as cost differences, including volume-related savings, and good-faith price concessions to meet a competitor. This is not a general rule that every difference between the prices paid by individual retail customers is illegal.
What U.S. federal rules say—and what they do not say
The FTC’s August 2026 draft on personalized pricing
In August 2026, the Federal Trade Commission announced a draft enforcement policy statement and sought public comment on personalized pricing. FTC Chairman Andrew Ferguson said: “The FTC does not have the legal authority to ban personalized pricing in all circumstances, but businesses that fail to tell consumers how their personal data is being used to set a price may be in violation of the FTC Act and other laws we enforce.” The stated deadline for public comments was September 18, 2026.
That announcement described a draft policy, not a blanket federal ban on personalized pricing. The sources available for this article establish the proposed statement and its comment deadline, but do not establish whether it was later finalized or revised. Do not treat the announcement as a final nationwide rule or as a determination about a particular store’s conduct.
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The federal fee rule is limited to tickets and short-term lodging
The FTC’s Rule on Unfair or Deceptive Fees took effect May 12, 2025. Its scope is live-event tickets and short-term lodging—not all retail goods. FTC guidance for those covered contexts says businesses may use demand- or inventory-based dynamic pricing as long as pricing information is not misleading. The rule also addresses prominent total-price displays and conditional promotions. It should not be read as a general ban on different prices or as a universal disclosure requirement for every store.
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Separate rules may apply in particular states
Federal guidance does not answer every state-law question. The law that applies can depend on where the shopper and seller are, the transaction, and the conduct involved. The New York example below is one specific state requirement, not a nationwide standard. If a price difference matters to a complaint or claim, check the law applicable to the place and transaction rather than assuming one state’s rule applies elsewhere.
New York’s disclosure rule is a state-specific example
New York’s Attorney General said the state Algorithmic Pricing Disclosure Act took effect on November 10, 2025. The Attorney General said the law requires most companies using customers’ personal data to set algorithmic prices to disclose that use. The release quoted the required disclosure as: “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA.”
This is a disclosure example for New York, not proof that all personalized pricing is prohibited there, and not a requirement that automatically applies to shoppers across the country. A shopper elsewhere should not expect to see this exact notice solely because two prices differ. Whether a business or transaction falls within a particular law depends on its scope and applicable rules.
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A careful comparison can rule out common explanations before you infer that a seller tailored a price to an individual. Record what you can verify rather than relying on memory or a screenshot without context.
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- Match the item or service. Compare the same model, size, configuration, ticket type, room type, or other relevant unit. Similar-looking options may have different terms.
- Match the seller and channel. Confirm that both prices come from the same retailer and sales channel. A marketplace listing, an app offer, and a store’s own shelf price may not be interchangeable.
- Note location and time. Record where and when each offer appeared. Prices can vary by locality or change with demand or inventory, so comparisons from different places or moments may not isolate the cause.
- Match quantity and purchase conditions. Check whether one price requires buying more than one item, selecting a particular delivery or service option, or meeting another stated condition.
- Check promotion eligibility. Look for a rewards account, membership, coupon, or other requirement. Compare the regular price with the conditional offer and verify what you must do to qualify.
- Review the final price before paying. Check the checkout total and the terms that apply. In the specific covered ticketing and short-term lodging contexts, FTC guidance addresses prominent total-price displays; that does not establish one disclosure rule for every retail transaction.
- Keep contemporaneous details. If you plan to ask the seller about a discrepancy, save the offer, the time, the location or channel, and the applicable terms. That gives the seller something concrete to investigate; it does not, on its own, prove that personal data determined a price.
After these checks, describe what you observed precisely: for example, that the same item appeared at two prices under matched conditions. Avoid treating the comparison as proof of a specific algorithm or data practice unless you have evidence of that cause.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the reported grocery-price figures do—and do not—show
A New York Attorney General release described an experiment by Consumer Reports and Groundwork Collaborative involving 437 shoppers across four cities. As reported by the Attorney General, the experiment found that nearly three-quarters of grocery items examined were offered at multiple prices, and that the average difference between the highest and lowest price for an individual good was 13 percent. The release also said the study estimated that differences could translate to $1,200 a year for a typical family of four.
Those figures belong to that named experiment as reported in the Attorney General’s January 8, 2026 release. They are not a universal rate of price variation, a current estimate for every grocery store, or a forecast for every household. The reported findings do not establish that every observed price difference came from personal-data profiling; they should not be used as proof that a particular retailer charged a particular shopper an individualized price.
Separately, FTC staff reported in 2025 that the intermediaries examined in its initial study worked with at least 250 clients. The FTC described the study as initial and ongoing, and its examples as hypothetical. That figure describes the intermediaries examined—not the number of retailers proven to personalize prices or the share of shoppers affected.
Can you stop a personalized price from appearing?
The FTC’s draft statement mentioned steps consumers might take, such as using a VPN or a private browsing session, or choosing a retailer whose prices are static or widely offered. Those are possibilities mentioned in the draft, not demonstrated fixes. The available information does not show that a VPN or private browsing reliably changes a displayed price, prevents profiling, or produces a lower offer.
If you want to compare offers, use the same product, seller, channel, location, time, quantity, and eligibility conditions. A comparison may help you see whether the price changes across contexts, but it cannot by itself tell you which data a seller used or whether the price was personalized. Do not assume that changing a privacy setting will reveal a “true” price.
When a price difference may warrant a question or complaint
A price discrepancy is most useful to investigate when you can identify the offer, the terms, and the point where the amounts diverged. Start with the retailer’s customer service and ask it to explain the specific difference. If the issue concerns a required disclosure, a misleading price presentation, or a relevant state law, the appropriate consumer-protection agency may depend on your location and the type of transaction.
- Keep the listing, receipt, or checkout details and note when and where you saw each price.
- Include the product or service details, sales channel, quantity, and any account or promotion terms in your question.
- Separate what you know from what you suspect. Two different offers are an observation; personal-data use is a possible explanation that may require additional evidence.
- For a legal concern, identify the state and transaction type before relying on a rule. The federal ticket-and-lodging fee rule and New York’s disclosure law have limited, distinct scopes.
This approach cannot determine legal liability on its own. It gives a seller or relevant agency a clearer account of the transaction to review.
How to interpret the answer you find
When evaluating a claim about different prices, ask what kind of evidence supports it. A binding rule, agency guidance, a draft policy, and a reported experiment do not have the same legal status or prove the same thing. Also check the geography and transaction type: a rule for New York, live-event tickets, short-term lodging, or qualifying business sales does not automatically govern an ordinary retail purchase elsewhere.
The practical takeaway is to establish the cause before drawing a conclusion. A price can change because of timing, location, inventory, or a stated promotion; personal-data-based pricing raises a different set of disclosure and consumer-protection questions. The available federal announcement does not establish a blanket ban, and the examples and figures described here do not prove that a particular store personalized a particular shopper’s price.
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