FTC Proposes Enforcement Policy Statement On Personalized Pricing: What Businesses Need To Know

ior alerts, New Jersey's Fair Price Protection Act (signed on July 23, 2026) bans personalized algorithmic pricing for grocery retailers and delivery platforms, and Maryland bans surveillance pricing for food retailers (effective October 1, 2026). Connecticut requires disclosures for certain personalized algorithmic pricing practices (effective October 1, 2026). In addition, New York's One Fair Price Act passed the state legislature on June 10, 2026, and awaits the governor's signature. California AG Rob Bonta has opened investigations under the California Consumer Privacy Act's purpose limitation provisions, treating pricing-related data use as potentially exceeding disclosed purposes. Companies operating in multiple jurisdictions must now navigate both the FTC's disclosure-based enforcement approach and these emerging state-law prohibitions.

In addition, congressional scrutiny is intensifying. The U.S. House of Representatives Committee on Oversight and Government Reform launched a formal investigation into AI-driven pricing in March 2026, and a coalition of 16 state AGs – led by New York and Tennessee – urged the FTC to issue a separate rule targeting personalized or surveillance pricing. The bipartisan attention to this issue significantly increases the risk that enforcement actions or rulemaking may follow quickly.

Key Provisions of the Policy Statement

Disclosure Requirements

The policy statement's core requirement is that where consumers reasonably expect prices will not vary based on personal data, businesses engaging in personalized pricing must provide clear and conspicuous disclosures of the following:

1. the fact that the price is personalized
2. the basis for that personalization
3. the types of data on which the personalization is based

Failure to make these disclosures is, in the FTC's view, "likely to constitute an unfair or deceptive act or practice in violation of Section 5."

Adequacy of Disclosures

The FTC provides guidance on what constitutes adequate versus inadequate disclosure:

• Telling a consumer that they have been shown only a "specially selected" price would likely be misleading because it omits important information about the nature and basis of personalization.
• A clear and conspicuous disclosure that a personalized price is based on a consumer's estimated willingness to pay derived from data about that consumer's previous purchases from the same retailer through the same login account – if accurate and complete – would likely be sufficient.

Data Practices and Consent

The policy statement addresses data collection and consent requirements:

• Businesses that collect, use or disclose consumers' personal data for personalized pricing without adequate disclosures or without obtaining consent may violate Section 5.
• Businesses that base personalized prices on personal data without sufficiently verifying that consumers consented to the collection of those data for that specific purpose may also violate Section 5.

The FTC's Legal Theory

Deception Under Section 5

The FTC applies its long-standing three-part test for deception. An act or practice is deceptive if it involves a representation, omission or practice that is 1) material, 2) likely to mislead a consumer acting reasonably under the circumstances and 3) to the consumer's detriment.

The FTC identifies several scenarios constituting deception, including:

• representing that a price is static or widely offered when it is in fact personalized
• failing to disclose that a price is personalized when a consumer reasonably believes it is static or widely offered
• misleading consumers as to the basis for personalization or the effect of that personalization on the price shown

Unfairness Under Section 5

An act or practice is unfair if it 1) causes substantial injury to consumers, 2) is not reasonably avoidable by consumers, and 3) is not outweighed by countervailing benefits to consumers or competition. The FTC's reasoning includes the following:

• Substantial Injury. The higher price paid due to personalized pricing may constitute substantial injury.
• Not Reasonably Avoidable. Consumers may not be able to avoid the higher price if the fact or nature of personalization has been concealed from them.
• Countervailing Benefits. Economic research suggests more sophisticated, personalized pricing practices are less likely to benefit consumers.

Other Applicable Laws

The policy statement notes that personalized pricing practices may also implicate:

• Restore Online Shoppers' Confidence Act (ROSCA)
• Rule Against Unfair or Deceptive Fees (16 C.F.R. Part 464)
• Fair Credit Reporting Act (cited as an analogy for disclosure requirements in industries with established personalized pricing)

Illustrative Examples of Problematic Practices

The policy statement identifies specific examples of personalized pricing practices likely to raise enforcement concerns:

1. a food delivery company quoting a higher price to consumers based on data suggesting they are less likely or unable to leave their homes to purchase food
2. a grocery chain charging a delivery customer a higher price for milk based on data showing several children live in the customer's household
3. a hotel charging a higher price based on data indicating the consumer is traveling for a funeral or other can't-miss personal business
4. a ride-share company charging a user more based on data revealing the user has not installed any competitor apps
5. a ride-share company charging more for transport to a medical facility based on data suggesting a life-threatening medical emergency
6. a retailer charging more for a home-security camera system based on court filings indicating the customer has recently been a crime victim
7. a retailer charging more for a product on its website based on data revealing the consumer is inside one of the retailer's physical locations or parking lots

These examples share a common theme: the use of data indicating consumer vulnerability or reduced alternatives to extract higher prices without disclosure.

The Bottom Line

AI is now a risk multiplier when used in pricing, merchandising, bundling or fee presentation. The FTC has emphasized that machine learning and automated experimentation enable granular consumer segmentation, rapid A/B price testing and optimization processes that are largely invisible to consumers. Companies that deploy AI pricing tools should treat those tools as heightening – not reducing – their disclosure and governance obligations under this policy statement.

Although the policy statement does not confer any rights and does not bind the FTC or the public, it provides clear signals about the FTC's enforcement priorities.