Personalized pricing is the practice of charging different customers different prices for the exact same product or service, based on data about who they are and how they shop. Unlike a store-wide sale or a seasonal markdown that applies to everyone, personalized pricing uses your individual data, like browsing history, location, past purchases, and even the device you’re shopping on, to estimate the highest price you’re likely to pay. It’s sometimes called “surveillance pricing,” and it’s far more widespread than most shoppers realize.
How Personalized Pricing Works
The basic idea is straightforward: a retailer collects data about you, feeds it into an algorithm, and the algorithm spits out a price tailored to your estimated willingness to pay. If the system thinks you’re eager to buy, short on alternatives, or historically willing to spend more, you’ll see a higher price. If you look like a bargain hunter who might walk away, you may see a lower one.
Most companies don’t build these systems in-house. They hire intermediary firms that specialize in algorithmic pricing. The FTC studied several of these middlemen, including Mastercard, Accenture, McKinsey, and others, and found that instead of a price being a fixed feature of a product, the same item could carry a different price depending on a range of inputs tied to the individual shopper.
What Data Gets Used
The range of personal data feeding these pricing models is broader than most people expect. An FTC study on surveillance pricing found that details as granular as your precise location or browser history can be used to target you with individualized prices. Staff discovered that even mouse movements on a webpage and the specific products you leave unpurchased in an online shopping cart can be tracked and factored into pricing decisions.
Industry analysts say previous purchase history is the single most important data point for retailers setting individualized prices. Beyond that, the algorithms may draw on demographic characteristics like age, gender, household size, and household income. Behavioral signals matter too. One commonly used metric identifies whether you’re a first-time buyer for a particular brand, since new customers often react differently to price changes than repeat buyers. All of these data points get layered together to build a profile of what you’re likely to spend.
Where You’ll Encounter It
Personalized pricing shows up across a surprising number of industries. Airfare is one of the most familiar examples. Searching for the same flight from two different devices, or at different times, can produce different prices. Grocery delivery platforms have also experimented with AI-driven pricing that adjusts what individual customers see. Ride-sharing apps use surge pricing that responds to demand in real time, and some consumer advocates argue these services layer in individual data on top of the demand signal.
Online retail is where the practice is most pervasive. Any e-commerce site that tracks your browsing behavior has the raw material to test individualized pricing, even if it starts small, such as offering different coupon values to different shoppers rather than changing sticker prices outright.
How It Differs from Dynamic Pricing
Dynamic pricing changes the price of a product based on broad market conditions: supply and demand, inventory levels, time of day, or competition. Everyone shopping at the same moment sees the same price. Hotel rooms that cost more on holiday weekends and concert tickets that rise as seats fill up are classic dynamic pricing.
Personalized pricing goes a step further. It uses data about you specifically to set a price that may differ from what another person sees at the exact same moment for the exact same product. The distinction matters because dynamic pricing responds to market forces, while personalized pricing responds to an estimate of your individual spending behavior.
Who Benefits and Who Pays More
The consumer impact of personalized pricing is genuinely mixed. Research from Yale and the Toulouse School of Economics found that for widely purchased, inexpensive-to-produce goods, personalized pricing tends to benefit consumers on average. In these markets, competition is intense enough that companies use individualized pricing to fight for customers, often by offering targeted discounts to price-sensitive shoppers.
The picture flips for niche products purchased by fewer people, or for goods that are expensive to produce. In those markets, personalized pricing tends to hurt consumers because sellers have more leverage and less competitive pressure to offer deals. The research also highlights a pattern of winners and losers: if you have broad, flexible preferences and could easily switch brands, companies will compete aggressively for your business. But if you strongly prefer one product or brand over others, the company you favor can charge you a premium because it knows you’re unlikely to leave.
Data imbalance makes this worse. A massive online retailer that tracks millions of transactions knows far more about its customers than a small local competitor does. That information asymmetry lets the larger company target prices in ways smaller businesses simply can’t match, which can tilt the market against consumers.
Economists call the extreme version of this “first-degree price discrimination,” meaning the seller charges each customer the absolute maximum they’d be willing to pay. It’s “perfect” from the seller’s perspective because it captures all the value in a transaction. For shoppers, it means a high price doesn’t signal higher quality. It just means the algorithm tagged you as someone willing to spend more.
What Regulators Are Doing
The FTC has taken a direct interest in surveillance pricing. In January 2025, the agency published initial findings from a market study examining how intermediary pricing firms operate. The study confirmed that a wide range of personal data is being used to set individualized consumer prices. Alongside the findings, the FTC issued a request for public comment, asking consumers to share their experiences with surveillance pricing and asking businesses whether these tools give certain competitors an unfair advantage. The agency also asked whether gig workers or employees have been affected by surveillance pricing applied to their compensation.
There is also growing concern about AI pricing tools operating without human oversight. FTC experiments have shown that AI programs can effectively collude among themselves to raise prices without any human directing them to do so. That raises questions about whether existing antitrust frameworks are equipped to handle algorithmic coordination.
How to Limit Personalized Pricing
You can’t fully opt out of personalized pricing, but you can make it harder for algorithms to build an accurate profile of your spending habits. Start by clearing your browser cookies and cache before making a major purchase. Pricing algorithms track return visits, and seeing you come back repeatedly for the same item can signal urgency.
Using a VPN masks your precise location, which removes one of the data points the FTC flagged as frequently used in price targeting. Shopping in a private or incognito browser window prevents sites from pulling in your full browsing history during that session. Comparing prices across different devices, or across a logged-in account versus a guest checkout, can also reveal whether you’re seeing an individualized price.
Price comparison tools and browser extensions that track price history on specific products help you spot whether a “deal” is genuinely discounted or just calibrated to look appealing based on what you’ve been shown before. And when possible, checking the price at a physical store or on a competitor’s site gives you a baseline that isn’t shaped by your personal data.