Article: A Market of One—AI Personalized Pricing

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In the past several years, artificial intelligence has radically improved the opportunity for sellers of goods and services to tailor prices to individuals. So-called personalized pricing refers to adjusting price not to what the market will bear but what that individual will bear. In other words, companies can now use AI to find patterns in an individual’s behavior combined with trends in pricing from competitors, supply chain status, and market seasonality to guess the highest price an individual will pay and even respond to the potential loss of a sale with instant coupons delivered to a smartphone via a mobile app. 

Companies collect vast amounts of data on individuals, such as where a person goes, how long they stay there, what they buy, what websites they visit, what time of day they exercise, what they do on social media, their search history, and more. AI can keep track of other people visiting the same place, such as a restaurant, bar, library, or store, to build a profile of a customer and others like them. Each customer profile goes into the calculation to predict if a shopper will buy any given product at that time. Technically, every person in a store could pay a different price for the same carton of milk with a fully enabled personalized pricing system. The US Federal Trade Commission (FTC) calls this price discrimination and does not consider it illegal. In a submission to the Organization for Economic Co-Operation and Development, the FTC wrote:

“Price discrimination is common in many markets. In many instances, price discrimination enhances market competition. In the United States, price discrimination is often viewed as efficient. In certain limited circumstances, price discrimination might feature as an aspect of an exclusionary strategy meant to enhance or protect market power. Intervention should be limited to preventing these exclusionary abuses.” (ftc.gov)

The ethics of price discrimination or personalized pricing must be a feature in the move to such a system. Although not the same as personalized pricing, dynamic pricing does shed light on the possible inequities of such variable pricing schemes. Dynamic pricing alters the price of a good or service depending on external conditions such as demand due to extreme circumstances such as weather conditions or social unrest. For example, in the winter of 2021, millions of Texans experienced power outages following a strong storm and colder-than-average temperatures. The spike in demand for electricity to heat homes taxed the power grid, leading to blackouts and triggering dynamic price hikes for electricity 300-fold. (reuters.com) Consumers received electricity bills in the thousands of dollars, resulting in anger and outrage. 

The Texas experience stands in contrast to the New York law that prevents price gouging before and in the aftermath of major storms in the state. “New York law prohibits businesses from taking unfair advantage of consumers by selling goods or services that are vital to health, safety, or welfare for an unconscionably excessive price during emergencies.” (ag.ny.gov) The law followed a heating oil shortage and dynamic pricing during the winter of 1978-1979. (Proskauer.com)

It follows that dynamic pricing, a more general form of discriminatory pricing than personal pricing, has generated public outcry and legislation, especially when it follows dangerous weather events or epidemics that threaten people’s safety. AI, combined with ever-growing amounts of data, now makes personalized pricing a growing reality. It would not go beyond the realm of imagination that personalized pricing, primarily online, could significantly vary from person to person. Companies may charge less for frequent customers to build customer loyalty, which would benefit more affluent clients of hotels or restaurants more than the infrequent visitors. It may take a trial like a natural disaster to surface ethical inequities in personal pricing to see if this type of marketing holds up for consumers in the long run.

Dr. Smith’s career in scientific and information research spans the areas of bioinformatics, artificial intelligence, toxicology, and chemistry. He has published a number of peer-reviewed scientific papers. He has worked over the past seventeen years developing advanced analytics, machine learning, and knowledge management tools to enable research and support high-level decision making. Tim completed his Ph.D. in Toxicology at Cornell University and a Bachelor of Science in chemistry from the University of Washington.

You can buy his book on Amazon in paperback and in kindle format here.