Loyalty Pricing or Surveillance Pricing? 

By Robert Levine 

surveillance-pricing-in-hotels_1900

    How hotel and casino resort leaders can tell segment offers from personalized pricing, and explain every rate they publish

    Hotels have always charged different guests different prices. That's not the problem.

    A loyalty member sees a member rate. A corporate traveler books a negotiated rate. A casino player gets a room offer sized to their play. Guests understand these differences, and most of them welcome them.

    What's changing is the technology behind the rate. AI makes it cheap to shrink a segment down to a single person, moving from "the price for this group" to "the price for this guest." When an organization uses individual personal data, online behavior, and algorithms to set bespoke, individual prices, regulators have a name for where that ends up: surveillance pricing.

    So the question for every revenue, loyalty, and marketing leader is simple. Where is your line? And can you show which side of it you're on?

     

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    What Is Surveillance Pricing?

    Surveillance pricing, which the FTC calls personalized pricing, means setting a price from an analysis of one consumer's personal data, such as an estimate of what that person is willing to pay.

    It is not the same as dynamic pricing. Dynamic pricing moves with market conditions like demand, dates, inventory, and competitor rates, and everyone shopping at the same moment sees the same price. At IAPP's fall 2026 conference, one privacy attorney summed it up: the defining trait of dynamic pricing is uniformity. The price follows the market, not the shopper.

    Why Hospitality Is in the Spotlight Now

    This isn't a distant, hypothetical risk. Here's what has happened in 2026 alone:

    • The FTC put a hotel in its examples. On August 19, the Commission voted 2–0 to propose an enforcement policy statement on personalized pricing. It doesn't ban the practice, but it treats undisclosed personalized pricing as a likely violation of Section 5 of the FTC Act. One of its examples: a hotel charging more because data suggests the guest is traveling for a funeral.
    • The FTC kept going. In late October, it ordered nine companies, including Delta, United, and JetBlue, to explain how personal data and AI shape the prices consumers pay.
    • Congress is asking travel sellers directly. On October 22, House Energy and Commerce Ranking Member Frank Pallone sent letters to companies including Booking Holdings and Expedia. His questions include whether loyalty or discount membership programs feed their pricing.
    • States are moving. In January, California's Attorney General launched an investigative sweep that sent inquiry letters to hotels, grocers, and retailers. More than 40 related bills have been introduced in at least 24 states this year.
    • Consumers are paying attention. In a May 2026 poll by Groundwork Collaborative and Data for Progress, groups that favor a ban, 76% of polled Americans said they support ending the practice.

    The hotel industry has noticed. In its September 2026 comment to the FTC, the American Hotel & Lodging Association (AHLA) asked the agency to target deceptive uses of personal data to raise prices, while expressly distinguishing market-based dynamic pricing, loyalty and promotional offers, and genuine personalized discounts.

    AHLA also acknowledged that using loyalty and behavioral data in pricing systems may warrant careful governance. That's the opening for operators. Governance is now part of the revenue job.

    Step 1: Know the Difference

    Three kinds of pricing often get lumped together. They carry very different levels of risk. This isn’t just about room rate, upsell and ancillary revenue have the same reputation and regulatory risk.

    Pricing type

    What sets the price

    Familiar examples

    Risk profile

    Demand-based dynamic pricing

    Market conditions: demand, dates, pace, competitor rates, inventory

    BAR moving by date and booking pace

    Long established; not what regulators are targeting

    Segment and loyalty pricing

    Shared traits of a group, often expected value

    Loyalty tiers, member rates, corporate rates, casino reinvestment offers

    Widely accepted, but the line is still unsettled

    Individualized pricing on personal data

    A prediction about one person

    AI estimating one guest's willingness to pay

    The focus of the FTC, state laws, and media scrutiny

     

    One room, one night, three prices. A king room on a Saturday normally sells for $219.

    Scenario

    Rate shown

    Why

    Passes the headline test?

    A citywide concert fills the market, and the hotel raises its rate for everyone

    $289

    Demand (dynamic pricing)

    Yes: “Saturday is nearly sold out.”

    A logged-in loyalty member sees the published member discount of 10% off

    $260

    Membership in a defined group (loyalty pricing)

    Yes: “That’s our member rate.”

    A model infers that one guest is booking last-minute, is geolocated 60 miles away on the interstate, and is not logged in to a member portal, so it shows that guest a higher rate

    $319

    A prediction about one person (individualized pricing)

    No

    Illustrative example.

    Here's the insight that matters most: AI collapses the middle row into the bottom one. When a "segment" gets small enough to be one guest, segment pricing becomes individualized pricing, whether anyone intended it or not.

    AHLA's comment draws a similar line. It separates three practices: calculating a unique price from one consumer's inferred willingness to pay, using customer data to confirm eligibility for a rate offered to a defined group, and using data to choose which discount or promotion to show. The first is where the risk lives.

    Step 2: Audit Your Inputs

    You can't defend a rate if you don't know what produced it. Start here:

    1. List every input that feeds your pricing, offer, and availability decisions, including the ones inside vendor algorithms.
    2. Sort each one as market-based (demand, dates, competitor rates, inventory) or personal (individual history, browsing behavior, device, location, inferred traits).
    3. Flag the personal inputs for review. Is each use disclosed? Defensible? Consistent with your loyalty program terms?

    Vendors deserve special attention. Speakers at IAPP's fall conference urged companies to know what data their pricing systems use, which vendors are involved, and what contractual rights they have to get answers. Danielle Kays, a partner at Fisher Phillips, gave hoteliers similar advice in CoStar: review third-party contracts to see whether personalized pricing is in play, and add guardrails on the data being used.

    Step 3: Apply the Headline Test

    Every rate should pass one simple test: Could you explain this rate to a guest or a reporter without hesitation?

    If the answer is "it's our member rate" or "Saturday is nearly sold out," you're fine. If the answer starts with "well, the model noticed…," add disclosure or rethink the approach.

    This lines up with where regulators are heading. Under the FTC's proposal, when consumers reasonably expect a price not to vary with their personal data, a business that personalizes should clearly disclose that the price is personalized, the basis for it, and the types of data used.

    Transparency is emerging as the practical middle path. But note that the FTC has declined, for now, to say whether some personalized pricing could be unfair even when fully disclosed. Disclosure helps. It isn't a free pass.

    Why Casino Resorts Are the Early Test Case

    Casino hotels have priced on guest value for decades. Room offers, comp eligibility, and availability often flow from play history or projected worth per stay, the player-based forecasting we explored in Why Casino Revenue Management Isn't RevPAR's Game Anymore.

    That practice is well understood inside gaming, and reinvestment usually lowers what the guest pays. But it is also built, by design, on individual history. As models get sharper and value tiers get narrower, casino operators will be among the first asked to show where reinvestment ends and individualized pricing begins.

    That's not a reason to retreat from player-based strategy. It's a reason to document it: which tiers exist, what qualifies a guest for each, and how every offer can be explained in a sentence.

    Decide Before Someone Decides for You

    The FTC's proposal isn't final, and the state map changes month to month. That uncertainty is exactly why the line should be yours to draw.

    Teams that act now will:

    • Know which of their rates are dynamic, segment-based, or individualized
    • Have an inventory of every pricing input, vendor models included
    • Be able to explain any rate in plain language

    Teams that wait may find the line drawn for them by a regulator, a reporter, or a guest with a screenshot.

    Frequently Asked Questions

    What is the difference between dynamic pricing and surveillance pricing? Dynamic pricing changes with market conditions and applies to everyone shopping at the same time. Surveillance (personalized) pricing sets a price for one person based on their personal data, such as a prediction of what they're willing to pay.

    Are hotel loyalty rates considered surveillance pricing? Not on their own. Published member rates and tier benefits available to everyone who qualifies work like segment pricing, and many state bills exempt loyalty programs. Risk rises when personal data is used to set a unique price for one guest.

    Did the FTC ban personalized pricing? No. The August 2026 proposal is an enforcement policy statement, not a rule, and it isn't final. It signals the FTC will treat undisclosed personalized pricing as a likely Section 5 violation.

    Do casino comp and reinvestment offers count? The line is unsettled. Offers tied to a defined value tier look like segment pricing. Offers computed individually from one player's data move toward individualized pricing. Document how offers are set so each one can be explained.

    What should a hotel or casino resort do first? Inventory every input that feeds pricing, offer, and availability decisions, including vendor algorithms, and flag anything personal for review.

    Let’s Start a Conversation

    If you can't say today which of your rates are market-based and which are personal, now is the time to find out.

    Let's talk about:

    • Mapping the data that feeds your RMS, CRM, casino management system, and vendor tools
    • Separating market-based inputs from personal ones
    • Building reinvestment and loyalty offers you can explain in a sentence
    • Preparing clear, plain-language disclosure where it's needed

    Every property is different. Your pricing governance should be, too.

    Connect with our team to start the conversation.

    This article is for general information and is not legal advice. Consult counsel about your specific practices.

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