Processing user profiles in online shopping already enables sellers to target ads with considerable accuracy, but at the same time they hope to generate more revenue from sales. U.S. Federal Trade Commission (FTC) warn Online retailers trying to sell the same products at higher prices to wealthy customers could be considered illegal.
Image source: Unsplash, Vitaly Gariev
Regulators are requiring participants in the U.S. online retail market to disclose their use of detailed information about customers’ financial status to determine prices. Sellers must disclose pricing details to the FTC, including the types of information used for disclosure. The agency cannot outright ban sellers from charging higher prices for items of interest to wealthy users, but it is prepared to attract the administrative resources necessary to force companies to disclose such information.
In the United States, personalized pricing for most categories of goods or services has long been a reality when paying online. Collect the most detailed information about website users: purchase and browsing history, geographic location, and even the time the mouse cursor rests on product descriptions. With the help of artificial intelligence, such inputs can be quickly converted into personalized offers that will benefit the seller more than the buyer.
Even under President Joe Biden, the Federal Trade Commission has found that online platforms can charge higher prices to buyers who are less familiar with market conditions. Instacart was discovered to be experimenting with charging customers different prices for the same items in different cities across the United States. After intense customer outrage, the practice had to be curtailed. Food sellers and even taxi aggregators will be required to report details of their pricing algorithms to the Federal Trade Commission. For example, an operator should not charge a higher price for a taxi if it is discovered that competing apps are not available on customers’ mobile devices. Some U.S. states already have requirements for local-level pricing algorithms.
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