Understanding Surveillance Pricing: A Closer Look
In today’s market, prices often reflect not just the product itself but also a company’s perception of its consumers. It’s a growing trend where prices can change based on algorithms that assess personal data about potential buyers.
Surveillance pricing relies on various methods of data collection—tracking personal behavior, purchasing history, location, and even minute details like mouse movements. These bits of information contribute to a comprehensive profile of what an individual might want, including how much they might be willing to pay.
In 2024, the Federal Trade Commission (FTC) launched an investigation into this practice, issuing orders to a number of companies to gather insights about their surveillance pricing methods. The initial findings indicated that companies could personalize prices based on detailed consumer data for a substantial number of clients. The FTC noted that examples provided were purely “hypothetical.”
What sets surveillance pricing apart from standard dynamic pricing—where costs fluctuate based on demand or inventory levels—is its personal nature. Here, specific elements like discounts or financing options may vary based on an individual’s profile. Sometimes these differences are minor, like a small discount being withheld or a pricier product being highlighted first.
A Concept With Roots Preceding the AI Era
This idea is not entirely new. Back in 2012, a Wall Street Journal investigation revealed that Staples.com varied prices based on customers’ estimated locations—those nearer to competitors like OfficeMax saw more discounts, while those farther out faced steeper prices.
Then in 2015, ProPublica discovered that the Princeton Review had different prices for an equivalent online tutoring service based on ZIP codes—a startling finding since residents in areas with substantial Asian populations often faced higher costs, regardless of their income levels.
Despite Princeton Review attributing these pricing strategies to regional market conditions, the example underscores the potential for seemingly impartial data to create significant demographic inequalities.
Orbitz also experimented with preferentially displaying more expensive hotel options to Mac users, based on the assumption that they were likely to spend more.
Most recently, in 2025, a Consumer Reports investigation showed that Instacart’s pricing varied for identical grocery items in the same store, with discrepancies of up to 23%. While it remained uncertain if user profiles dictated pricing, the findings highlighted the ease with which basic necessities can be manipulated.
How Urgency Influences Costs
A particularly concerning aspect of this pricing model is how it exploits consumer urgency. Searching for an emergency plumber at odd hours or seeking last-minute flights could hint that a delay is not an option. Likewise, inquiries about eviction assistance or emergency loans may indicate distress, all without a company needing to see any financial documentation.
Economist Benjamin Shiller highlighted the significance of such insights. In a 2020 study, he found that personalized pricing based on behavioral data could boost profits significantly more than using only demographic data. Though the study was hypothetical, it raised serious questions about potential practices where retailers might capitalize on desperate consumers.
Data brokers have long categorized vulnerable groups. A Senate investigation in 2013 identified segments such as “Credit Crunched” and “Retiring on Empty,” drawing attention to how these categorizations could serve lenders looking to target individuals in urgent need of cash.
Shifting Power Dynamics
Interestingly, two individuals may view different prices or offers on the same website without realizing the discrepancies—the intricacies of algorithms don’t always require obvious demographic data such as race or income to make sensitive inferences. Instead, they can use proxies like ZIP codes or device types.
While targeted pricing can reward loyal customers or provide relief to those in need, the lack of transparency is worrisome. Sellers often have extensive knowledge about buyers, in contrast to the limited insight buyers have regarding their offers.
This imbalance can lead to personal data being abused to extract the highest possible price a consumer might accept. It also complicates the process of comparing prices, as they can fluctuate in ways shoppers might not even be aware of.
Demanding Transparency
Ultimately, it should be a standard for companies to inform consumers when personal data or AI significantly impacts price. Regulators have a part to play, but innovation in tools that help consumers compare prices from various accounts and devices could also help.
With companies monitoring our behaviors, it’s only fair that we have the means to hold them accountable.
When a business knows of a consumer’s desperation, that knowledge ought not to translate into hidden advantages for the company.
