Imagine walking into your local grocery store, grabbing a carton of eggs, and heading to the checkout. You might assume the price you see is the price everyone pays. But what if that price is subtly different for you than for your neighbor? In the increasingly data-driven world we inhabit, this isn’t a far-fetched scenario; it’s a reality that is now being brought into the light by a new law in New York.
For years, businesses have been leveraging the vast amounts of data collected about us to personalize experiences, from targeted advertisements to product recommendations. Now, it appears this same data is being used to influence the very prices we pay for everyday essentials. A recent development in New York State is forcing retailers to be more transparent about this practice, marking a significant step towards understanding how our digital footprints can impact our wallets.
The Price of Eggs: A Tale of Two Zip Codes
Let’s consider a simple example: a carton of eggs. On Target’s website, you might see one price for this staple item in Rochester, New York ($1.99), and a slightly higher price in the affluent Tribeca neighborhood of Manhattan ($2.29). While price variations based on location aren’t entirely new – retailers have long adjusted prices for different markets – the reason behind these specific differences is now coming under scrutiny. A new notice on Target’s website offers a clue: “This price was set by an algorithm using your personal data.”
This seemingly small disclosure is the consequence of a newly enacted New York State law. This legislation mandates that businesses employing algorithms to set prices based on customer personal data must inform consumers of this fact. But what exactly constitutes “personal data” in this context? The law defines it broadly as any data that can be “linked or reasonably linked, directly or indirectly, with a specific consumer or device.” This could encompass a wide range of information, from your browsing history and purchase patterns to your device’s location.
Transparency, But Not Much Detail
While the law aims to bring transparency, it’s important to note what it doesn’t require. Businesses are not obligated to reveal precisely what specific pieces of personal information are being used, nor are they required to detail how each data point influences the final price. The law also includes an exception for location data used solely for calculating taxi or rideshare fares based on distance and travel time, but not for broader pricing strategies.
The law does, however, stipulate that these disclosures must be “clear and conspicuous.” This is where things can get a bit murky. Target’s disclosure, for instance, is not immediately obvious. A customer would need to actively look for an “i” icon next to the price, click on it, and then scroll to the bottom of a pop-up window to find the notification. Historically, courts have been hesitant to assume that consumers will consistently click on “more information” links when they are not explicitly prompted to do so.
When approached for comment, Target did not provide details regarding the specific pricing differences or the personal data employed in setting those prices, as per their disclosures.
A History of Algorithmic Pricing
Target’s practice of setting different prices based on location isn’t a new phenomenon. As far back as 2021, reports highlighted how Target’s online prices appeared to fluctuate depending on the user’s associated store location. At the time, a Target spokesperson stated that their online prices “reflect the local market.” More recently, in 2022, the company settled a lawsuit brought by several California county district attorneys who alleged that Target used “geofencing” technology to automatically update prices displayed in their customer apps.
Even today, when you visit Target’s website, it often automatically assigns you to a nearby physical store, though you can manually change this setting. The company has not clarified how it determines which store to automatically associate with a website visitor.
This practice extends beyond just eggs. The price of everyday items like toilet paper has also been observed to vary based on the customer’s associated store. For example, a six-pack of Charmin toilet paper might be priced at $8.69 for a customer linked to a Flushing, Queens store, while a customer associated with Tribeca might see the same product listed for $8.99.
Not an Isolated Incident: The Rise of ‘Surveillance Pricing’
Target’s pricing strategies are far from unique. The concept of dynamically adjusting prices based on various factors, including consumer data, has been around for a while. Back in 2012, The Wall Street Journal reported that office supply retailer Staples was displaying different prices on its website, purportedly based on estimated customer location. Staples acknowledged this practice, citing factors like rent, labor, distribution costs, and other business expenses as reasons for geographical price variations.
In 2015, ProPublica uncovered similar practices with the Princeton Review. Their online SAT tutoring packages sometimes showed price discrepancies of thousands of dollars, seemingly dependent on the customer’s zip code. Like Staples, the Princeton Review attributed these differences to the “costs of running our business and the competitive attributes of the given market.”
This trend has garnered significant attention from regulatory bodies. Last year, the Federal Trade Commission (FTC) launched a market study into what it terms “surveillance pricing.” This encompasses practices where customer location data, among other factors, is used to help set prices. While the FTC released an interim report in January, a final report is still pending. It’s worth noting that Target was not part of this specific FTC market study.
Beyond the Basics: What Else is Being Priced Algorithmically?
While the New York law brings the issue to the forefront, it’s still unclear which other everyday goods are being priced using algorithms, and more importantly, how. The disclosures, while not offering granular details, could potentially shed light on the broader landscape of differential pricing and the variety of products affected. The hope is that these notifications might prompt consumers to be more aware of how their data is being leveraged.
A Rippling Effect: Legislation and Future Trends
New York’s pioneering law may well inspire similar legislation in other states. Pennsylvania, for instance, introduced a comparable bill earlier this year. At the federal level, a bill addressing surveillance pricing was introduced in July, signaling a growing interest in regulating these practices.
The broader regulatory landscape is increasingly focused on the pervasive influence of Artificial Intelligence (AI) and algorithms on consumer pricing. According to JD Supra, over 50 bills related to algorithmic pricing have been introduced at the state level across the United States. These proposals address issues ranging from algorithmic price-fixing to the use of specific consumer characteristics in dynamic pricing algorithms.
The AI Integration: A New Frontier for Retail
Meanwhile, major retailers like Target are actively exploring other innovative uses of technology. Target recently announced its intention to launch an integration with OpenAI’s ChatGPT. This move will allow consumers to utilize the popular chatbot for personalized shopping recommendations, further blurring the lines between data utilization, AI, and the consumer experience. This development highlights a dual approach: on one hand, grappling with the regulatory implications of data-driven pricing, and on the other, embracing advanced AI to enhance customer engagement.
As we navigate this evolving digital economy, understanding the connection between our personal data and the prices we encounter daily is becoming increasingly crucial. New York’s law is a crucial first step, but the conversation around transparency, fairness, and the ethical use of data in pricing is far from over.