Consider this scenario: you walk into your regular grocery store for your weekly shop. As you travel down each aisle, the store knows who you are because you have their app on your phone, and as you approach the items, the electronic price tag changes to the price that the store has calculated to be the most you are willing to pay for the item, based on your shopping habits and other information the store has learned about you such as your zip code. If someone else comes down the aisle later, a different price may be displayed. This isn't science fiction: it is happening today. Often called 'surveillance pricing,' the practice is touted as a way to offer shoppers a 'customized' experience, but consumer protection experts say the real goal is to extract the maximum amount of money that shoppers are willing to spend.

On September 22, Seattle's city council passed an ordinance to ban grocery stores from using artificial intelligence and personal data to set individual prices. The ban "prevents grocers from using personal information like employment status, race, gender and browsing history to manipulate prices." Traditional coupons and discounts for seniors and veterans are not affected by the ban. The ordinance applies to grocers with more than 20 locations, so not all stores will have to follow it - although it does seem less likely that a smaller company would have the resources to implement this type of pricing.

A 2025 Consumer Reports investigation found that surveillance pricing could result in wide variances in how much a customer paid for the same product. In their study, they found that some consumers paid up to 23% more than the lowest-paying consumer for the same items in their Instacart order. While algorithmic pricing is not new - we have long been paying different prices for airline tickets and rideshare services, for example, based on timing and demand - adding in personalized data obtained from in-store data or even from external data brokers is a newer phenomenon.

The idea of a customized experience may be attractive, but there are potential pitfalls to this type of pricing. For instance, one rideshare company tried to get data on customers' smartphone battery level, thinking that those who were nearly out of battery would be more desperate to get a ride and therefore would pay a higher fare. Additionally, "online retailers have been caught showing higher prices to returning customers than to first-time shoppers, or offering better deals only to people arriving from certain websites." Paradoxically, loyalty rewards programs may offer fewer rewards for regular customers than occasional ones. As always, the advice "caveat emptor" remains sound.