Algorithms Set Online Prices Based on Individual Consumer Data

Algorithms are now setting online prices based on individual consumer data rather than market value, a practice that is leading to increased costs for shoppers across various sectors. This phenomenon, termed "surveillance pricing," involves corporations meticulously collecting and analyzing customer data to predict purchasing behavior and tailor prices accordingly. The extent and implications of this practice are detailed in the newly released book "Gouged: The End of a Fair Price — And What That Means For Your Wallet," authored by Lindsay Owens, president and CEO of the Groundwork Collaborative.

Owens, who previously served as an economic policy advisor to Senator Elizabeth Warren, explains in her book how AI shopping assistants and sophisticated algorithms contribute to price discrimination. This means consumers may pay different prices for the same products, from airline tickets to groceries, based on their individual profiles. Companies leverage extensive data, including purchase history, location, and participation in loyalty programs, to create detailed customer insights that inform these dynamic pricing strategies. The lack of transparency in this process makes it difficult for consumers to budget effectively or engage in meaningful price comparisons.

The issue has gained significant attention, prompting a hearing by the Senate Judiciary Subcommittee on Crime and Counterterrorism earlier this year. During this hearing, Owens testified as one of five witnesses, elaborating on how major corporations such as Walmart, Kroger, Instacart, and prominent airlines utilize customer data to implement these advanced pricing tactics. She emphasized that surveillance pricing not only creates an unfair market but also undermines transparency and predictability, making financial planning challenging for households. As more shopping shifts online and to mobile devices, consumers are increasingly unaware if they are being charged more than their neighbors for identical goods.

The practice of personalized pricing, driven by AI and data analytics, represents a significant shift in how goods and services are valued and sold. By understanding individual willingness to pay, companies can potentially maximize revenue, but this comes at the cost of consumer trust and equitable access to goods. The detailed analysis presented by Owens and explored in congressional hearings highlights a growing concern about the pervasive influence of data-driven pricing on the everyday lives and financial well-being of consumers.