Retailers are increasingly relying on consumer data, predictive analytics, and artificial intelligence to personalize offers and pricing – a practice commonly referred to as “surveillance pricing.” In the absence of comprehensive federal legislation, individual states have begun regulating the practice.
New Jersey’s Fair Price Protection Act
Surveillance Pricing
On July 23, New Jersey Gov. Mikie Sherrill signed the Fair Price Protection Act (the Act) into law, making it a deceptive and unfair practice for retailers to use a consumer’s online activity, location, purchasing history, or other collected data to charge different prices for identical grocery and household essential products based on what an algorithm predicts a consumer is willing (or able) to pay.
The Act amends New Jersey’s Consumer Fraud Act to prohibit the use of surveillance pricing (or any other pricing strategy in which an algorithm or automated system uses consumer personal data) to determine or vary the price of covered products. Under the Act, covered products are limited to “groceries and other foodstuffs,” which are defined broadly to include groceries, beverages, paper products, household cleaning items, health and beauty products, pet food and supplies, and similar household necessities. Prepared foods sold for immediate consumption are excluded.
In-scope retailers will have until August 1, 2027 to comply with the surveillance pricing restrictions.
Electronic Shelf Labels (ESLs)
The Act also establishes a one-year moratorium, effective February 1, 2027, on the deployment of new ESLs while the state studies their potential relationship to surveillance pricing. Following the expiration of the moratorium, any new ESL use must comply with any rules or regulations promulgated based on the resulting study. In the meantime, any current use of such labels may continue.
Exclusions and Exemptions
The Act exempts certain pricing practices, such as:
- Reasonable costs associated with providing covered items to different consumers, provided that the price of the item may not be adjusted more than once in a 24-hour period
- Bona fide discounts to consumers, provided that the eligibility criteria for such discounts is (a) publicly and conspicuously disclosed and (b) uniformly applied to any member of a broadly defined group (e.g., a 20% discount to students)
- Discounts offered as part of a consumer loyalty program, provided that the loyalty program meets certain outlined criteria, including voluntary opt-in and publicly disclosed terms and conditions that detail the retailer’s data practices
Additionally, the Act imposes certain restrictions on the subsequent use of data subject to an exemption.
Penalties and Private Rights
Violations of the Act may result in civil penalties of up to $10,000 for a first offense and $20,000 for subsequent offenses, as well as injunctive relief and restitution.
One important open question concerns private litigation. Earlier versions of the legislation included an express private right of action that does not appear in the enacted statute. Nevertheless, because the Act amends the New Jersey Consumer Fraud Act, plaintiffs are likely to argue that existing Consumer Fraud Act remedies remain available for alleged violations. Whether courts ultimately adopt that position remains to be seen.
A Growing Patchwork of State Laws
With the passage of the Act, New Jersey joins Maryland and Connecticut in regulating surveillance pricing, while New York (which had already implemented an algorithmic-pricing disclosure regime) is poised to implement broader restrictions if Gov. Hochul signs the pending One Fair Price Act.
This patchwork of laws reflects a shared concern regarding the use of consumer data in pricing decisions; however, each state takes a different approach with respect to covered entities, prohibited conduct, and loyalty-program exceptions.
Connecticut’s Broad Approach
Earlier this year, Connecticut passed SB4/HB5563, amending the Connecticut Data Privacy Act to prohibit the use of surveillance pricing by any retail seller operating in the state, including food establishments and third-party delivery services, across a wide range of industries. Effective October 1, 2026, the law also requires a mandatory disclosure in the event a consumer’s personal data was used to increase that price.
As with New Jersey, certain common pricing practices are exempt from the broad sweep of the statute, including (i) pricing for customer retention, (ii) justifiable costs incurred in providing a good or service, and (iii) group discounts and loyalty programs.
Maryland’s Narrower Scope
Effective October 1, 2026, Maryland’s Protection from Predatory Pricing Act prohibits the use of surveillance pricing within the food industry to set higher prices using consumer data, specifically targeting large food retailers and third-party delivery services.
As with New Jersey and Connecticut, Maryland’s law exempts certain pricing practices, including customer retention, justifiable costs incurred, group discounts, and loyalty programs.
New York: One to Watch
New York’s Algorithmic Pricing Disclosure Law is already in effect and requires disclosures when certain personalized pricing practices rely on consumer data and algorithms. With the One Fair Price Act (S.8623B/A.9349B), New York lawmakers have approved broader legislation that would move the state beyond disclosure requirements and toward substantive restrictions on surveillance pricing in line with Connecticut and New Jersey.
Key Compliance Considerations
Surveillance pricing is a rapidly evolving area, with new laws pending or being enacted in multiple jurisdictions. Retailers should take the time to review their current consumer data and pricing practices, along with the terms of any loyalty or other discount programs, to understand (and potentially amend) how consumer data is obtained and utilized with respect to the pricing of covered items.
Retailers should consider:
- What consumer data feeds pricing systems?
- Are discounts offered on standardized terms?
- Does an algorithm determine actual prices or merely identify promotions?
- Are protected characteristics, or proxies for protected characteristics, influencing pricing outcomes?
- Do existing loyalty programs adequately disclose how consumer data is collected and used?
- Do similarly situated loyalty-program participants receive the same benefits?