The FTC Isn’t Buying It at Any Price: Surveillance Pricing, Dynamic Pricing, and Algorithmic Pricing

by: Rosanne Yang and John Allaire

The FTC is very interested in how companies are pricing their goods and services.  In just the last week, the FTC issued orders to 8 intermediary companies asking for more information on how they use artificial intelligence (AI) and other technology, consumer data, and competitor data to help their merchant customers set pricing, including variable pricing, and the FTC announced the first public meeting of a joint strike force with the DOJ, with the aim of the strike force being to root out pricing arising out of anti-competitive, unfair, deceptive, or fraudulent business practices.  Last month, the FTC brought a case to enforce the still-on-the-books-but-long-dormant Robinson Patman Act, which essentially prohibits a seller of goods from offering lower prices or greater promotional allowances to one purchaser than to another competing purchaser.

This enforcement activity is on top of other recent activity regarding algorithmic pricing, including the introduction of the Preventing Algorithmic Collusion Act in the U.S. Senate, and cases brought in Nevada, New Jersey, Tennessee, and Washington regarding the algorithms used in rental property and hotel room rate setting.

In light of these developments, businesses should take a close look at how they are determining their prices, and consider the potential consequences and impacts of such processes.

What’s All the Fuss About?

A growing number of companies are using complicated algorithms – either home grown or through a vendor – that look at competitor pricing, supply and demand in increasingly narrow slivers of time, and/or customer behavior both with the merchant and elsewhere, including a consumer’s location, credit history, demographics, and browsing history. These algorithms may set personalized pricing and offers – also referred to as individualized pricing, dynamic pricing, segmented pricing, differential pricing, and surveillance pricing – or they may just to be used to set the price for everyone.  In any case, there are legal implications that businesses may need to grapple with.

While algorithmic pricing certainly can optimize pricing strategies and may in certain circumstances enhance competitiveness, other considerations are implicated, and this is increasingly becoming a high-risk activity.  The FTC will be examining the potential impact these practices have on competition, privacy, and consumer protection.

The FTC orders are far reaching and request information on the types and products being offered by these intermediary companies, information on the data sources used for each product or service, customer and sales information, and information on the potential impact of these products and services on surveilled customers, including the prices they pay. 

Of course, many companies still do nothing more than add a standard markup to their cost that leaves them some room for promotional activities as the inventory sells through. If that’s your company, then you may be thinking you don’t have anything to worry about on this front.  But before you move on, it would be worth identifying how promotional activities are determined.  If that activity is determined through the use of AI, automated processing, consumer data, and/or competitor data, stay with us as we move through the various considerations.

Potential Anti-Competitive Actions

Regulators are increasingly viewing algorithmic pricing as potentially anti-competitive.  Even if the merchants do not intend to collude with each other to establish their prices, use of common algorithm vendors may allow prices to be autonomously aligned.  The effect, it is argued, resembles traditional collusion/price fixing. The cases noted above were each private civil antitrust complaints, and the FTC and/or DOJ has filed statements of interest in several of them.  The recent orders from the FTC are a further signal of the FTC’s intent to probe this issue further, particularly given the focus on intermediaries who provide such tools to merchants.

Given this surge of interest, businesses that are using an algorithm to set their prices (even if not dynamic or personalized prices) should evaluate what data is going into the algorithm and where it comes from, and determine the level of risk that algorithm presents.

Privacy Implications

There are a variety of state privacy laws that regulate the use of consumer data, including laws that either require opt-in consent from consumers or provide for the right to opt-out of certain types of data uses.  Whether an opt-in or opt-out regime is applicable depends on where the consumer is, who the consumer is (the law is more protective of children), what kind of data is being used, and how it is being used.  The merchant’s privacy policy should be clear about what kind of data is being collected, used, and shared and how and why, and make it clear how consumers can exercise their rights.

It is important to take into account whether current disclosures and rights fulfillment processes account for the company’s price-setting tools, and make any adjustments that may be needed. 

In addition, these privacy laws tend to include non-discrimination provisions that may be implicated by these algorithmic pricing determinations.  These provisions generally prohibit merchants from charging higher prices to those who exercise their privacy rights, so if the result to the consumer is that they are charged higher prices because their data is not available to the merchant to provide individualized pricing, this may trigger concerns under the non-discrimination provisions.  While there can be exceptions that permit differential pricing, the impact and risk of individualized pricing strategies should be vetted.  

Consumer Protection

The FTC may have various takes on “unfair” pricing in regard to consumer protection.  However, one salient concern with individualized or surveillance pricing is the effect on a merchant’s ability to establish a regular price and the advertising of discounts.  Proper establishment of a regular price is critical if one is to offer sales that refer to those regular prices either explicitly (was/now or strike-through pricing) or slightly more subtly (% or $ off). 

Under federal law (Section 5 of the FTC Act) and corollary state laws and regulations in all 50 states, companies are obligated to substantiate any claims about the discounting of their products or services.  In general, substantiation requires at least offering the goods or services for a reasonably substantial period of time at the advertised regular price.  Some states set specific time frames for the period (e.g., 28 days, 40% of the time, or 50% of the time, often measured over the 90 days immediately preceding the sale).   If the advertised regular price (the reference price) cannot be substantiated as a real price under these laws, then the merchant’s claim that the item is discounted can be viewed as false or deceptive. 

False reference price and deceptive pricing class actions have been a significant concern for retailers since the mid-2010s, with hundreds of lawsuits having been filed and settlements that have gone as high as $197M.

When prices are individualized, establishing a “regular” or “original” price in accordance with these laws becomes much more difficult, and the question merchants will increasingly need to answer is whether the benefits of individualized pricing to the company are outweighed by the risks.

In summary, the FTC and others are zeroing in on a variety of issues related to algorithmic pricing.  Merchants engaged in the practice should review their practices closely and mitigate risks where warranted.

You can read the FTC’s Surveillance Pricing announcement here and its accompanying blog post here.  The FTC and DOJ’s press release on the strike force is here.

Originally published by InfoLawGroup LLP. If you would like to receive regular emails from us, in which we share updates and our take on current legal news, please subscribe to InfoLawGroup’s Insights HERE.