Surveillance Pricing: You’re Watching Consumers — and Government Is Watching You

Federal and state governments are escalating scrutiny of “surveillance pricing” and AI-enabled pricing practices, particularly where pricing relies on consumer data, opaque algorithms or insufficient price transparency. Although traditional dynamic pricing based on market conditions remains lawful, regulators are increasingly focused on personalized pricing tied to consumer data, price experimentation and how prices and fees are disclosed to consumers.

Against this backdrop, continued and increasingly aggressive government scrutiny is expected — from the FTC and Congress, as well as state attorneys general — of pricing practices that rely on consumer data, algorithmic decision‑making or shadowy pricing mechanics, even where companies maintain that prices are driven by traditional market factors rather than individualized profiling.

For companies that utilize variable pricing, ticketing fees, loyalty programs and algorithmic revenue management, this government activity creates near‑term compliance risk and controversy, even absent attempts at statutory or regulator limitations.

Federal regulators define “surveillance pricing” as pricing practices that use detailed consumer personal data — including location, browsing history, demographics or behavioral inferences — to set individualized prices or offers for the same product or service. The FTC has emphasized that advances in data collection and machine learning have made such pricing scalable and difficult for consumers to detect.

Critically, regulators distinguish dynamic pricing, which responds to market conditions (inventory, demand, seasonality), from personalized or surveillance pricing, which responds to characteristics of the individual consumer rather than the market as a whole. This distinction is increasingly central to enforcement, legislation and congressional oversight.

The FTC’s enforcement and surveillance pricing work

In 2024, the FTC launched a Section 6(b) study to examine how companies and intermediaries use consumer data to implement surveillance pricing and algorithmic decision‑making. The FTC continues to maintain public resources describing this work and its consumer protection rationale.

In testimony before Congress in April, FTC leadership confirmed that staff work on surveillance pricing continues and that the agency is assessing whether additional disclosures may be required when pricing is highly personalized or driven by consumer data.

The commission has also paired its surveillance pricing focus with aggressive enforcement on price transparency, particularly in live event ticketing. The FTC recently announced a settlement with a ticket exchange to resolve allegations that it failed to clearly and conspicuously disclose mandatory fees as required under the FTC Act and the agency’s rule governing unfair or deceptive fees. The FTC emphasized that total ticket prices must be disclosed upfront and prominently at all stages of the purchase process.

The implication is that pricing enforcement risk is no longer theoretical but an active priority grounded in FTC rule violations and Section 5 authority.

Going forward, the FTC is expected to aggressively pursue surveillance pricing and related deceptive pricing theories. Even where companies deny using personal data to set prices, the FTC has signaled that opacity, inconsistent consumer explanations or pricing outcomes that exceed reasonable consumer expectations may independently trigger investigation.

In this environment, the FTC is likely to scrutinize not only how pricing systems operate, but also whether consumer‑facing descriptions are accurate, consistent and sufficiently transparent to reflect underlying pricing mechanics.

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Congressional AI‑driven pricing investigation

In March, the House Oversight Committee formally launched an investigation into the use of surveillance pricing.

The committee sent letters to major travel and platform companies requesting documentation regarding revenue management algorithms, use of consumer data in pricing, testing and experimentation practices and internal communications describing pricing tools and outcomes. The committee has characterized surveillance pricing as a “black box” process in which algorithms infer willingness to pay more and adjust prices accordingly without consumer awareness or meaningful transparency. The investigation reflects a broader shift toward scrutiny of unilateral, data-driven pricing practices, including:

• Whether companies distinguish between market-based dynamic pricing and individualized pricing tied to consumer attributes
• How algorithmic pricing tools are tested, governed and monitored
• Whether pricing varies based on location, device or behavioral signals
• How pricing practices are described to consumers

The inquiry also suggests potential scrutiny of third-party vendors and pricing tools, not just internal systems.

Later, on May 11, the House Energy and Commerce Committee ranking member, Rep. Frank Pallone Jr., who is expected to become chairman if Democrats win control of the House in November, launched a new investigation into the use of surveillance pricing. The ranking member sent an initial round of letters to 25 major grocery and retail companies requesting responses and internal documentation regarding:

• Customer data elements used to inform or set prices.
• The use of AI to inform or set prices.
• Work with third parties to purchase, license or otherwise acquire data for use in informing or setting prices.
• Consumer options to opt-out of data collection.

Even absent immediate legislation, congressional investigations create material risk, including compelled document production, public hearings, reputational exposure and referrals to the FTC, the DOJ or state attorneys general. Given bipartisan interest in the issue, this risk will persist regardless of outcomes in the congressional midterm elections.

In practice, congressional oversight often serves as an early forcing mechanism, requiring companies to explain and defend pricing practices well before formal enforcement begins. For travel, entertainment, housing, e-commerce and other companies, AI‑assisted pricing in consumer‑facing markets is now a priority oversight issue.

Faster-moving state regulation

State enforcement and legislative activity around surveillance pricing is accelerating and converging on consumer data use and transparency.

California is pursuing surveillance pricing through a privacy-enforcement lens, and New York has enacted a law requiring disclosure when personalized algorithmic pricing is used. In addition, Maryland has passed the Protection from Predatory Pricing Act, restricting certain practices and treating violations as deceptive trade practices.

Dozens of additional states are considering similar legislation, underscoring a rapidly expanding and fragmented regulatory environment.

AI’s role in heightening enforcement risk

The FTC has emphasized that machine learning and automated experimentation materially change the enforcement landscape by enabling granular consumer segmentation, rapid A/B price testing and optimization processes that are largely invisible to consumers.

Congress has echoed these concerns, characterizing AI pricing tools as amplifying the potential for unfair, deceptive or discriminatory outcomes where personalization is not transparent.

For companies, AI is now a risk multiplier when used in pricing, merchandising, bundling or fee presentation, particularly where experimentation occurs without consumer disclosure or governance controls.

Practical compliance takeaways for companies

Government activity suggests companies should prioritize:

1. Pricing data mapping. Identify whether consumer or device data influences base prices, fees, bundles, upgrades or recommended offers.
2. Clear separation of pricing models. Distinguish market‑based dynamic pricing from personalized pricing tied to consumer data.
3. Fee and price transparency audits. Ensure total prices and mandatory fees are clearly disclosed at all stages of the consumer journey, consistent with FTC expectations.
4. AI and experimentation governance. Implement appropriate controls for algorithmic pricing tools and A/B testing, including oversight of how models are deployed and evaluated.
5. Inquiry readiness. Ensure pricing practices can be clearly and consistently explained to regulators and Congress, with alignment across legal, business and communications functions regarding data use, pricing logic and consumer disclosures.

Companies should approach these steps with an eye toward regulatory scrutiny and practical defensibility, particularly as FTC enforcement and congressional inquiries continue to evolve.

This article was first published by Holland & Knight. It is adapted here with permission.