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Dynamic pricing changes when market conditions change, such as demand, timing or available inventory. Surveillance pricing uses information about a person or group—such as location, browsing behaviour or inferred willingness to pay—to tailor a price, discount or even the products shown. A price that moved is not, by itself, evidence of surveillance pricing. The useful question is what caused the offer to change: a market condition affecting many shoppers, or data about the buyer.
The two approaches can overlap. A seller might start with a demand-based price and then adjust the offer for a segment identified from consumer data. Because the U.S. Federal Trade Commission (FTC) has not adopted one formal definition of “surveillance pricing,” treat the terms as practical descriptions rather than mutually exclusive technical categories.
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Dynamic pricing: a market response
Dynamic pricing recalculates an offer as conditions around the sale change. The same item or service can cost more at a busy time, less when demand falls, or change as inventory becomes scarce. Every shopper may see the same updated price, or a broad group may receive it at once. A unique price for one person is not required.
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- Demand: A spike in requests can push a price up; a quiet period can trigger a reduction.
- Timing: Prices may vary by hour, day, season or a sales window.
- Availability: Limited inventory, capacity or delivery slots can change the offer.
- Market-wide events: A seller may update prices for everyone in a region or for all customers using a channel.
These inputs can be measured automatically, but automation does not make the pricing surveillance pricing. The defining feature is the reason for the change, not the fact that software made it.
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Surveillance pricing: a buyer-specific offer
Surveillance pricing centers on consumer-related information. A company or an intermediary uses observed or inferred characteristics to estimate what an individual or segment might pay, then changes the price, discount, ranking or product selection shown to that audience. The information can come from the company’s own systems, outside data providers, or both.
Data that may be used
In a January 2025 release, FTC staff said its initial analysis found that intermediaries could access a broad range of information and that retailers can use consumer information to tailor prices. The examples named by the agency included location, demographics, browsing patterns, shopping history, mouse movements on a webpage and products left in an online cart. These are examples of possible inputs, not a finding that every retailer uses every category.
What can be personalized
Personalization is not limited to the number beside a product. The FTC materials discuss tailored:
- prices for the same good or service;
- discounts, coupons or eligibility for a promotion;
- product rankings and the order in which choices appear;
- the selection of products or services presented at all.
A shopper could therefore pay the same listed price as another shopper but receive a smaller discount or never see the lower-priced alternative. Looking only for different headline prices misses part of the issue.
Side-by-side distinction
| Question | Dynamic pricing | Surveillance pricing |
|---|---|---|
| What drives the offer? | Market conditions such as demand, timing or availability. | Consumer characteristics, behaviour or inferred preferences, sometimes combined with market conditions. |
| Must the offer be unique? | No. It can move for everyone or a broad group. | Individual or segmented targeting is central to the FTC staff description. |
| Can personal data matter? | It may be irrelevant to the market adjustment. | Personal or inferred data is the mechanism that makes the offer buyer-specific. |
| Can the methods overlap? | Yes. A market-based price can be further adjusted for a segment. | Yes. FTC staff says surveillance pricing can incorporate or overlap with dynamic pricing. |
| What should a consumer investigate? | Whether a market factor and the timing explain the move. | Whether information about the buyer appears to determine the price, discount or products shown. |
This is a working distinction, not a legal test. The FTC’s issue spotlight says it is not attempting to establish one formal definition of surveillance pricing.
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How the approaches can combine
Imagine a hotel with fewer rooms available for a holiday weekend. Raising the public rate as rooms sell is dynamic pricing. If the hotel then gives a different rate to people whose browsing history suggests they will pay more, the second step is surveillance pricing. Both calculations could run in the same system, so seeing a market-driven increase does not rule out a personal-data adjustment.
The reverse is also possible. A retailer could segment shoppers using consumer data but still apply the same demand signal to every segment. In that case, the price is both segmented and responsive to market conditions. Asking which label is “correct” is less useful than identifying each input that affected the result.
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January 2025 initial findings
In January 2025, FTC Chair Lina M. Khan described the agency’s initial staff findings this way: “Initial staff findings show that retailers frequently use people’s personal information to set targeted, tailored prices for goods and services—from a person’s location and demographics, down to their mouse movements on a webpage.” The statement concerned an initial staff perspective, not a final adjudication that all retailers use surveillance pricing.
The analysis drew on documents from Mastercard, Accenture, PROS, Bloomreach, Revionics and McKinsey & Co. The agency said the 6(b) study information would be published only in aggregated or anonymized form to protect confidential business information. That qualification matters: the public material describes capabilities, data flows and hypothetical mechanisms rather than a complete census of retail practice.
The hypothetical thermometer example
One staff example described a consumer profiled as a new parent being shown higher-priced baby thermometers. It illustrates how a profile could influence a recommendation or price, but it is not proof that a named retailer charged an actual shopper that amount. Do not use the example as evidence that any particular store has done so.
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No prevalence number
The FTC material mentions intermediaries with “at least 250 clients.” That figure describes client relationships for those firms; it is not a percentage of retailers, a count of affected shoppers or a prevalence estimate for the market. The agency’s January 2025 update also said that more work remained. The responsible conclusion is that the practice is technically possible and under examination, not that every changing online price is personalized.
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Can a store charge different people different prices?
In many markets, a seller can offer different prices, discounts or product assortments to different customers, subject to consumer-protection, competition, contract and sector-specific rules. A price difference alone does not establish unlawful conduct or explain its cause. The legal question can depend on the data used, the disclosure made, the affected group and the applicable law.
The FTC’s August 19, 2026 announcement sought public comment on a draft enforcement policy statement about personalized pricing—described there as using personal data to set a price according to what a company believes an individual is willing to spend. The release was corrected on August 31, 2026, and comments were due September 18, 2026. As of October 16, 2026, that cited action establishes a draft statement and a completed comment process, not a final rule or a blanket ban.
The draft addresses potential FTC Act concerns when firms fail to disclose that personal data is being used in pricing. FTC Chairman Andrew Ferguson summarized the proposed position in the release: “When consumers see a listed price, they expect it to be same price that everyone else sees, not the retailer’s estimate of how much they are willing to pay based on their personal data.” The quotation expresses the chairman’s view about the draft; it does not make every form of personalized pricing illegal or give the draft binding force.
Does browsing history affect the price you see?
It can be one possible input, according to the categories listed in the FTC’s initial findings, but an observed difference does not prove that browsing history caused it. Prices can change between two checks because inventory, demand, timing, taxes, delivery options, currency or location changed. A seller may also show different discounts or products without changing the base price.
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There is no universal consumer-facing test that reveals an online pricing algorithm’s inputs. Treat a comparison as evidence to preserve and investigate, not as proof of surveillance.
A practical way to investigate a suspicious difference
Use a controlled comparison before drawing conclusions. The goal is to separate ordinary market movement from buyer-specific treatment.
- Record the offer. Save the displayed price, discount, product identifier, seller, currency, taxes, delivery fee, stock message, date and exact time. A screenshot alone may omit details that change the total.
- Hold market conditions constant. Check whether the item, quantity, delivery speed, membership status and payment method are identical. Note whether the seller reports a low-stock warning or a limited-time promotion.
- Compare broad conditions. Look at the offer from the same location and network, then from a different location only if you can account for taxes, shipping and currency. A regional price is not automatically a personal price.
- Compare account states. If practical, check while signed out and signed in, or with and without a loyalty benefit. Record the account’s membership, prior purchases and saved cart because those can alter a legitimate discount.
- Test a clean session cautiously. A separate browser profile or private window can show whether the presentation changes when existing cookies and session data are absent. It does not reveal server-side records, device fingerprints or third-party data, and it cannot prove that a difference was caused by a cookie.
- Check more than the headline price. Compare coupons, shipping options, product rankings and the alternatives displayed. A different selection can be a form of personalization even when the first price matches.
- Repeat at the same time. Run the comparison quickly enough that demand and inventory are unlikely to move. If the price changes repeatedly for everyone, a market explanation becomes more plausible; if only an identified account consistently receives a different offer under otherwise equal conditions, document that pattern without claiming certainty about the algorithm.
- Ask for an explanation. Request the seller’s pricing, promotion and personalization disclosures in writing. A support reply may identify a membership rule or regional tax, or it may leave the cause unresolved.
- Keep an evidence log. Store screenshots, page addresses, timestamps, account state and correspondence together. This makes a later complaint more useful than an unsupported assertion that “the price changed.”
Privacy measures and their limits
The FTC’s August 2026 release said informed consumers might consider a VPN or private browsing session. Those are possible privacy measures, not proven ways to prevent surveillance pricing, and neither exposes all data a company may already hold. A VPN can alter the apparent network location; private browsing can limit local session storage. Neither guarantees a different price, anonymity or a clean profile.
You can also review account privacy settings, decline optional personalization where a service offers that choice, remove saved items before a controlled comparison and avoid treating a logged-in test as independent from your purchase history. These steps may reduce some signals, but a seller can still use information held on its servers or supplied by data partners. Do not interpret an unchanged price as proof that no profiling occurred, or a changed price as proof that profiling did.
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Evidence that points toward each explanation
| Observation | More consistent with a market adjustment | More consistent with buyer-specific targeting |
|---|---|---|
| Several shoppers see the same change at the same time. | Yes, especially when demand, timing or inventory also changed. | Not by itself; a segment could still be targeted broadly. |
| Only one account receives a different coupon while the public price matches. | Less likely, unless the account has a stated loyalty or eligibility rule. | More consistent with individualized or segmented treatment. |
| The product list or ranking changes with browsing or profile state. | Possible if availability or merchandising changed. | More consistent when the change tracks consumer attributes or inferred preferences. |
| A private-window check shows another price. | Still possible if the sessions occurred at different times or locations. | A clue worth recording, not conclusive proof. |
| The seller explains a published surge, time or inventory rule. | Supports the market explanation. | Does not exclude an additional personal-data adjustment. |
Common mistakes when interpreting changing prices
Calling every algorithmic change surveillance pricing
Software can update a price solely because demand or availability changed. The algorithmic nature of the update says nothing about whether personal data was used.
Treating two screenshots as causal evidence
Screenshots show that offers differed, not why. Without matching time, location, inventory, account state and terms, the comparison cannot isolate the input.
Confusing a discount with a different base price
A targeted coupon, loyalty benefit or cart promotion can produce different totals while the listed price remains identical. Record each component separately.
Assuming a privacy tool guarantees fairness
Private browsing and a VPN may change some signals, but they do not erase server-side history or establish what a seller used. The FTC did not claim that they reliably prevent personalized prices.
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The August 2026 FTC announcement concerns a draft enforcement policy statement. It is not a final rule, and the release does not establish a general prohibition on personalized pricing.
What a transparent seller should explain
A clear notice should identify whether a price or promotion depends on location, account status, loyalty history, demand, inventory or other consumer information. It should distinguish a changing public price from a private coupon and explain material eligibility conditions. If a recommendation system changes which products appear, the disclosure should not imply that the list is a neutral catalogue when it is tailored.
Consumers cannot independently verify every model input, so disclosure is especially important where an offer is individualized. The FTC’s draft policy discussion focuses on potential concerns when firms fail to disclose personal-data use; that is an agency proposal, not a finding that every undisclosed adjustment violates the FTC Act.
Bottom line for shoppers
Start with the driver, not the label. If demand, time or inventory explains a change that many shoppers receive, it is dynamic pricing. If a seller uses information about you or a segment you belong to to alter the price, discount or products shown, it fits the FTC staff’s description of surveillance pricing. The methods can coexist, and public evidence currently supports caution rather than a claim that every online price is personalized. Document the conditions, ask what data and rules apply, and treat privacy tools as limited experiments—not guarantees.
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