On August 19, 2026, the FTC released a proposed enforcement policy statement addressing personalized pricing, the practice of using personal data to set or adjust prices based on what a business believes a particular consumer is willing to pay.

The FTC is not proposing an outright ban on personalized pricing. Instead, the agency is signaling that companies deploying these practices without meaningful transparency could face enforcement under Section 5 of the FTC Act, which prohibits unfair or deceptive acts or practices.

For companies increasingly using AI, behavioral analytics, customer profiles, and other data-driven tools to optimize pricing, the distinction matters.

What Is Personalized Pricing?

Personalized pricing goes beyond ordinary dynamic pricing.

Prices routinely fluctuate because of supply and demand, geography, taxes, market conditions, or characteristics inherent to a transaction. Rideshare surge pricing is one familiar example.

The FTC is focused on something different: changing the price offered to a particular consumer based on personal information about that individual.

Modern businesses may have access to enormous amounts of information about consumers—from purchase histories and browsing behavior to location data, financial information, household characteristics, and data purchased from third parties.

Combined with increasingly sophisticated analytics and AI systems, that information can potentially be used to predict a consumer’s willingness to pay.

For example, a company might determine that one customer is unlikely to comparison shop and therefore display a higher price to that customer than another person viewing the same product at the same time.

The FTC’s concern is that consumers may reasonably assume they are seeing the same generally available price as everyone else when, behind the scenes, the price has actually been calculated specifically for them.

The FTC Is Not Banning Personalized Pricing

The proposed policy statement contains an important limitation: the FTC acknowledges that Congress has not given the agency authority to prohibit personalized pricing in every circumstance.

Instead, the Commission plans to use its existing enforcement authority.

Section 5 of the FTC Act prohibits unfair or deceptive acts or practices in commerce. According to the proposed statement, personalized pricing could implicate Section 5 when a company misrepresents—or fails to disclose—how a consumer's personal information is affecting the price offered to that consumer.

That means the regulatory issue may not simply be whether a company personalizes prices, but whether consumers understand that personalization is happening and how their information is being used.

Disclosure May Become the Critical Compliance Issue

The FTC's proposed approach places substantial emphasis on transparency.

Where consumers reasonably expect a price to be generally available rather than personalized, the FTC says businesses engaging in personalized pricing should clearly and conspicuously disclose:

• that the price is personalized;

• the basis for the personalization; and

• the types of consumer data being used to determine the price.

A vague statement that a consumer has received a “special” or “personalized” offer may not be enough.

The FTC gives an example of a potentially sufficient disclosure: informing a customer that the price is based on the customer's estimated willingness to pay derived from previous purchases made through the same account.

The takeaway is significant. If personalization materially affects price, businesses may need to explain not only that personalization exists, but what is driving it.

The FTC Is Also Looking at the Data Behind the Price

Pricing practices are only part of the equation.

The FTC also warns that the collection and use of personal information itself may trigger Section 5 concerns.

A company that collects, uses, or shares consumer information for personalized pricing without adequate disclosure or consent could potentially face scrutiny. The same may be true where a business relies on data obtained from another source without sufficiently verifying that consumers consented to its collection or use for that purpose.

This creates a broader compliance issue for companies purchasing datasets or relying on third-party advertising, analytics, data-broker, or AI infrastructure.

Businesses may need to understand not only what information their pricing systems use, but also:

Where did the data come from? What did consumers consent to? And does that consent actually cover its use in individualized pricing?

The FTC’s Examples Show Where the Risk Could Be Highest

The proposed statement provides several hypothetical examples of personalized pricing that could raise Section 5 concerns if implemented without adequate disclosure.

Among them:

• a food-delivery platform charging someone more because data suggests the person cannot easily leave home;

• a grocery service charging more for milk because data indicates children live in the household;

• a hotel increasing a consumer's price because data suggests the person is traveling for a funeral or other unavoidable event;

• a rideshare platform charging more because it knows the customer does not have competing rideshare apps installed;

• a rideshare service increasing the price of transportation to a medical facility based on information suggesting a medical emergency;

• a retailer increasing the price of a security system after learning through court records that the customer was recently a crime victim; and

• a retailer charging a consumer more online because location data shows the consumer is currently inside or near one of its physical stores.

These examples point toward a broader concern: using data to identify circumstances in which a consumer may have less bargaining power, fewer alternatives, greater urgency, or a higher willingness to pay.

What This Means for AI-Powered Pricing

The proposal is particularly relevant as businesses increasingly incorporate AI into pricing, marketing, and customer analytics.

A company may not have an employee manually deciding that Customer A should pay more than Customer B. Instead, an algorithm may process hundreds of variables and automatically determine the price displayed to each consumer.

That does not eliminate the underlying compliance risk.

Companies deploying AI-driven pricing tools should understand what data enters the model, how that information influences pricing decisions, whether consumers would reasonably expect the resulting price differences, and what disclosures appear before the transaction.

This also makes vendor diligence increasingly important.

If a third-party pricing platform promises to “optimize willingness to pay,” for example, businesses should understand exactly how that optimization works before deploying it.

Practical Steps for Businesses

Companies using—or considering—personalized pricing should begin reviewing their practices now.

That review should include identifying whether prices actually vary between similarly situated consumers, documenting what data influences those differences, reviewing consumer-facing disclosures, assessing consent mechanisms, and evaluating third-party data and pricing vendors.

Businesses should also distinguish between traditional dynamic pricing and true consumer-level personalization.

A price that increases for everyone because demand has increased is fundamentally different from a price that increases only for a particular person because an algorithm predicts that person will tolerate the increase.

As pricing technology becomes more sophisticated, understanding that distinction will become increasingly important.

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This blog post is for informational purposes only and is not legal advice. Please consult with a Launch Legal attorney regarding your specific situation.