Businesses are increasingly turning to artificial intelligence and other advanced technologies to gain an edge in setting prices. AI-driven pricing models, which leverage sophisticated algorithms to set prices for products and services in real time, promise increased revenue but also raise significant concerns about privacy and market fairness. This shift from traditional pricing methods to more dynamic and personalized approaches presents a host of legal considerations that companies must navigate carefully.

Understanding Algorithmic Pricing

Algorithmic pricing is the practice of setting prices for products or services using computer algorithms that analyze vast amounts of data in real-time. These algorithms take into account various factors such as supply and demand, competitor pricing, market trends, and even customer behavior to determine an “optimal” price point for products or services. By leveraging artificial intelligence and machine learning, algorithmic pricing enables businesses to adjust prices dynamically to maximize revenue (at a much lower cost).

“Dynamic pricing,” a subset of algorithmic pricing specifically focuses on the real-time adjustment of prices based on immediate supply and demand conditions. Most consumers are familiar with dynamic pricing through its prevalent use in the airline and hospitality industries. For example, booking a hotel room during peak travel seasons often incurs higher rates due to increased demand. Dynamic pricing is also utilized in various other contexts, such as surge pricing during peak hours. Companies like Airbnb, Uber, and Booking.com all legally utilize dynamic pricing to change prices based on demand.

People became more aware of the spread of “dynamic pricing” to other industries when Wendy’s announced its plan in February of 2024 to change its menu prices throughout the day depending on customer flow. Its announcement generated significant backlash amongst customers who were accustomed to stable, “stickier” prices in this sector. In response, Wendy’s issued a statement “clarifying” it would not raise prices during busy parts of the day but would rather discount products during slower periods.[1]

Surveillance Pricing: Personalization or Privacy Invasion?

Taking a step beyond dynamic pricing is the concept of “personalized pricing,” which some term, “surveillance pricing.” This approach uses advanced algorithms, AI, and extensive datasets containing consumers’ personal information – such as location, demographics, credit history, and browsing or shopping history – to set targeted prices for individuals. Surveillance pricing raises significant concerns not

only about privacy but also antitrust violations and market manipulation.

In July 2024, the FTC ordered several companies to reveal what pricing services they offer, what data they collect to power these services, and who is using their services and what effect that’s having on consumer prices.[2]

Personalized pricing has often been discussed as a form of price discrimination whereby the price is set according to what a consumer is willing and able to pay. It differs from dynamic pricing in that, unlike just the location or number of customers willing to pay, surveillance pricing is set by an individual person’s characteristics and behavior, which implicates privacy and discrimination concerns more directly and immediately.

Recent Algorithm Litigation

Amazon and “Project Nessie”: In September of 2023, the FTC and more than a dozen state attorney generals sued Amazon, alleging that the company operates an illegal monopoly through a program called “Project Nessie.”[3] According to the complaint, Amazon used this secret algorithm to identify products for which price increases would be matched by competitors. When Amazon raised prices, other stores would follow suit, allowing Amazon to maintain higher prices without losing sales. This practice purportedly enabled Amazon to generate an additional $1.4 billion in profits over five years, allegedly violating 15 U.S.C. § 45(a) (which prohibits unfair or deceptive acts or practices in or affecting commerce) and Section 2 of the Sherman Act, 15 U.S.C. § 2 (which prohibits monopolization or attempts at monopolizing any aspect of interstate trade or commerce).

The FTC claims Project Nessie manipulated competitors into matching Amazon’s price increases, thereby creating an artificial price floor and stifling competition. Additionally, Amazon allegedly coerced Amazon sellers to use Fulfillment by Amazon if they wanted their products to be eligible for Amazon Prime, even though many sellers would rather use an alternative fulfillment method (as Amazon has allegedly increased its fulfillment fees by approximately 30% between 2020 and 2022). Because of these processes, the FTC argues that: “Although each of these acts is anticompetitive in its own right, these interrelated and independent actions have had a cumulative and synergistic effect that has harmed competition and the competitive process.” Amazon recently filed a motion for judgment on the pleadings, which remains pending.

Price Fixing in Rents: In recent years, numerous lawsuits have been filed alleging that companies are licensing software to property managers that allow them to collude on increasing rent prices.

In Duffy v. Yardi Systems, a class of plaintiffs allege that Yardi Systems Inc. (“Yardi”) and certain property management companies violated Section 1 of the Sherman Act (15 U.S.C. § 1) by unlawfully agreeing to use Yardi’s pricing algorithms to artificially inflate multifamily rental prices. Yardi is a property management software company which provides a system called “RENTmaximizer,” a revenue management software. RENTmaximizer is an “algorithmic pricing tool” marketed to landlords that is intended to automate the rental pricing process and thus help “multifamily property managers maximize rental income.”[4]

How does the system work? Allegedly, Yardi asks landlords to input their data, such as rental rates and occupancy. The system also automatically incorporates market-specific information on “comparative rent” in order to give landlords “accurate and timely information regarding your market.” RENTmaximizer then calculates a “rent recommendation” that users can automatically adopt. These prices then update daily. The plaintiffs alleged that: “The result of this scheme is that [landlords] outsource their once-independent pricing and supply decisions to a single decisionmaker, RENTmaximizer, and need not directly disclose their pricing strategies to each other in order to fix rent prices. Instead, [landlords] collectively adopt a coordinated pricing strategy implemented and enforced by Yardi’s RENTmaximizer product.

On December 4, 2024, the Yardi court denied the defendants’ motion to dismiss on the grounds of failure to state a claim.[5] The Court held that “plaintiffs have adequately alleged ‘a conscious commitment to a common scheme designed to achieve an unlawful objective’ on the part of the [defendants].”The Court deemed that plaintiffs had reached the “plausibility” standard required in federal court as the plaintiffs had successfully pleaded “[t]he who, what, with whom, where, and when of the scheme.” Specifically, (1) the landlords entered into agreements with Yardi to provide commercial sensitive data, (2) knowing Yardi as collecting similar data from other landlords which it would use to (3) generate rental rate recommendations that would result in supracompetitive returns.

The case is now scheduled for trial and stands in direct comparison to a variety of other artificial intelligence cases dismissed from federal court for failure to state a claim, some of which are discussed below.

Similarly, in In re RealPage, Inc., Rental Software Antitrust Litigation (No. II), currently in multi-district litigation, class action complaints allege an unlawful agreement to use RealPage software recommendations to “raise multifamily rental housing prices” and “artificially inflate the prices of student housing.” The allegations also center around rental price suggestions based on landlords’ information submitted to their system.[6]

Dismissal on the Vegas Strip: In Gibson v. Cendyn Group, the District of Nevada dismissed a class action, a proposed class of all persons who rented hotel rooms on the Las Vegas Strip, alleging that defendant hotel operators engaged in a price-fixing conspiracy by collectively using a software provider’s algorithm (also a defendant) for hotel room pricing recommendations.[7] The court dismissed the case because the hotels signed up for the pricing software service at different times and the allegations insufficiently pled that the hotel defendants exchanged confidential information or agreed to be bound by the software’s pricing recommendations.

The Gibson court distinguished Yardi and RealPage cases because the Gibson plaintiffs could not sufficiently allege that non-public information was fed into the algorithm. Instead, information may have been received from hotel websites and then “shopped” back to the client after being processed through the algorithm. The court granted leave to file an amended complaint, but the court dismissed the amended complaint (this time with prejudice) in May 2024, finding that the plaintiffs still could not allege that the defendants had plausibly alleged that defendants ever agreed to fix prices, agreed to accept pricing recommendations, or exchanged nonpublic information.[8]

A similar case was filed in the District of New Jersey, also alleging hotel algorithmic price fixing, in Cornish-Adebiyi v. Caesars Entertainment, Inc. The court dismissed this case, relying in large part on the Gibson decisions.[9]

Conclusion

AI-driven pricing, whether through algorithmic, dynamic, or personalized methods, offers benefits in terms of efficiency and revenue optimization. However, these practices also pose significant challenges related to transparency, fairness, privacy, and antitrust concerns. Companies must stay informed about the legal risks associated with these strategies to ensure that they do not violate existing laws and rules, as well as any new and pending litigation in numerous states.

Footnotes

[1] See Liz Calvario, Wendy’s New ‘Dynamic Pricing’ System Won't Raise Prices at Peak Times, Spokesperson Says, TODAY (last updated Feb. 29, 2024), https://www.today.com/food/restaurants/wendys-surge-pricing-rcna140601.

[2] See Behind the FTC’s Inquiry into Surveillance Pricing Practices, Fed. Trade Comm’n: Blog (July 23, 2024), https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2024/07/behind-ftcs-inquiry-surveillance-pricingpractices.

[3] See Amended Complaint at 5-6, FTC v. Amazon.com, Inc., No. 2:23-cv-01495-JHC (W.D. Wash. Mar. 14, 2024),https://www.ftc.gov/system/files/ftc_gov/pdf/01712024.03.14RedactedAmendedComplaint%20%28002%29.pdf.

[4] All such information about Yardi’s system was available in public promotional materials and formed the basis of the class action complaint. See Amended Complaint ¶¶ 5, 71, 75, Duffy v. Yardi Sys., Inc., No. 2:23-cv-01391-RSL (W.D. Wash. Nov. 3, 2023).

[5] See Duffy v. Yardi Sys., Inc., No. 2:23-cv-01391-RSL, 2024 WL 5049074 (W.D. Wash. Dec. 5, 2024) (order denying motion to dismiss).

[6]In re RealPage, Inc., Rental Software Antitrust Litigation (No. II), No. 3:23-md-03071, 2023 WL 9004806 (M.D. Tenn. Dec. 28, 2023).

[7] Gibson v. MGM Resorts Int’l, No. 2:23-cv-00140-MMD-DJA, 2023 WL 7025996, at *1 (D. Nev. Oct. 24, 2023).

[8] Gibson v. Cendyn Group, LLC, No. 2:23-cv-00140-MMD-DJA, 2024 WL 2060260 (D. Nev. May 8, 2024).

[9] Cornish-Adebiyi v. Caesars Entertainment, Inc., 1:23-cv-02536-KMW-EAP, 2024 WL 4356188 (D.N.J. Sept. 30, 2024) (order granting motion to dismiss).