California Bill S.B. 2564 to Ban Surveillance Pricing

On June 11, 2026, the Electronic Frontier Foundation (EFF) issued a statement supporting California’s bill S.B. 2564, which seeks to ban “surveillance pricing”—the practice of varying the price of the same product based on personal data. The EFF praised the bill as “an essential regulation from the perspectives of privacy, fairness, and price transparency.”

The EFF’s statement strongly criticizes how companies collect and misuse personal data. “Companies collect and monetize our personal data. The toxic fruit of that tree is surveillance pricing,” the foundation stated.

How Surveillance Pricing Works

Surveillance pricing refers to the practice where companies collect and analyze personal data such as browsing history, physical location, and purchase history to offer different prices for the same product or service.

A 2025 report released by the U.S. Federal Trade Commission (FTC) detailed the practices of six companies providing surveillance pricing services. These services are used by hundreds of businesses, including grocery stores and apparel retailers.

According to the FTC report, data is obtained from the selling company itself, the surveillance pricing service provider, or third-party data brokers. Customers are grouped based on personal data, and prices fluctuate using mechanisms similar to targeted advertising.

Former FTC Chair Lina Khan explained, “Retailers frequently use consumers’ personal information to set targeted prices for goods and services, ranging from a consumer’s location and demographics to their mouse movements on a webpage.”

However, the current FTC Chair has closed the public comment portal on surveillance pricing, signaling a retreat from federal regulation. Meanwhile, the California Attorney General has launched an independent investigation.

Documented Discriminatory Cases

Researchers have identified numerous cases of surveillance pricing. These cases highlight how price discrimination based on personal data disadvantages certain groups.

Princeton Review: Charged higher prices for test prep services to people living in specific zip codes. Asian respondents were twice as likely to be quoted a higher price than non-Asian respondents.

Uber and Lyft: A one-year study of tens of millions of rides in Chicago found that trips to areas with predominantly non-white populations were charged higher fares.

Tinder: Users aged 30–49 were shown higher prices for Tinder Plus than users aged 18–29.

Orbitz: Users on Apple computers were shown higher hotel prices than those using other computers.

Hotel booking sites: Residents of San Francisco were shown higher hotel prices than residents of other cities.

Target: Customers physically in the store were shown higher prices than those elsewhere.

Staples: Customers living far from competitors were shown higher prices than those living nearby.

Three Harms Highlighted by the EFF

The EFF identifies three harms from surveillance pricing: privacy infringement, erosion of fairness, and lack of price transparency.

From a privacy standpoint, the problem is that consumers unintentionally have their personal data used for pricing decisions. Browsing history and location data are sensitive data that should not be used to set prices.

From a fairness perspective, a major issue is that discriminatory pricing is based on attributes consumers cannot change, such as ethnicity, age, or place of residence. As the cases above show, certain groups are disproportionately burdened with higher prices.

From a price transparency perspective, consumers have no way to determine whether the price offered to them is fair. They have no means of knowing that the same product is being sold to others at different prices, which undermines market mechanisms.

Prospects for the Bill’s Passage

California is the largest economy in the U.S., and laws passed there often influence national regulatory trends. The California Consumer Privacy Act (CCPA) and its amendment (CPRA) set precedents for privacy laws across the country.

If S.B. 2564 passes, companies operating in California may face a complete overhaul of their pricing algorithms. The impact would be especially significant for platforms and retailers that use dynamic pricing based on personal data.

However, the detailed provisions and timeline for the bill’s passage have not been disclosed in the EFF’s statement at this point. The future of legislative deliberations and lobbying activities by relevant stakeholders will be closely watched.

Editorial Opinion

In the short term, if S.B. 2564 passes, retail and service companies operating in California will likely be forced to significantly revise their pricing algorithms. In particular, companies that rely on data brokers and third-party data for price optimization may face substantial compliance costs. The EFF’s support signals a strong pro-privacy stance and could prompt similar bills in other states.

In the long term, banning surveillance pricing could spur new directions for AI-driven price optimization. We may see a shift toward pricing models that do not rely on personal data, such as those based solely on time of day or inventory levels. This movement could also be seen as a turning point from the era when “data is the new oil.” Companies will be forced to reassess the value and risks of data collection and reconsider the scope of data they gather.

The editorial team asks: While banning surveillance pricing is appropriate for consumer protection, not all forms of price discrimination in a competitive market are harmful. For example, student discounts or regional promotions are forms of price discrimination based on attributes but are socially accepted. If S.B. 2564 passes, its implementing regulations and case law will clarify the boundary between legal and illegal price discrimination.

References

• Yes to California’s Bill to Ban Surveillance Pricing | EFF — Published June 11, 2026
• Related: OpenAI Files Confidential IPO, Following Competitor Anthropic — Referenced for the relationship between market regulation and corporate behavior.