The retailer says it does not use customer identity, location, time of day or demand spikes to raise prices. The statement adds to a broader effort by major companies to reassure shoppers about how AI and customer data are being used in pricing.

Target is the latest major retailer to tell customers what it says it is not doing with pricing technology. The company said Thursday that it does not use customer data to raise prices based on an individual shopper’s identity or location, and it does not change prices based on the time of day or during periods of unusually high demand. The clarification comes as retailers face growing scrutiny over dynamic and personalized pricing systems that can use data, algorithms and artificial intelligence to influence what consumers pay.

The company said prices can still vary for reasons that have long been part of retail, including local operating costs, regulation, competition and discounts specific to a store or sales channel. Target’s position is that those differences apply broadly to shoppers using the same platform or shopping in the same location rather than being calculated around the identity of a particular customer. Its Target Circle loyalty program uses shopping history to recommend deals and offers, the company said, but that data is not used to increase prices.

Retailers are trying to define the boundary

Target’s statement follows a similar public push by Walmart, which has rejected suggestions that its digital shelf labels, AI tools or customer data are being used to deploy individualized dynamic pricing. SSC recently examined that response alongside McDonald’s, as both companies tried to draw a public line between technologies used to improve pricing decisions and systems that could make prices feel personally targeted to individual customers.

In that earlier SSC reporting, Walmart and McDonald’s were both trying to reassure consumers that AI would not simply be used to decide how much a particular shopper could be made to pay. The concern is no longer theoretical. Reuters reported in September that McDonald’s has increasingly used AI to guide menu-pricing decisions, including estimates of what customers at particular restaurants may be willing to pay, while the company has pushed back against the characterization that AI itself sets menu prices.

That debate is forcing companies to become more precise about terminology. Dynamic pricing can describe relatively familiar practices, such as changing prices based on market conditions, time or demand. Personalized or surveillance pricing goes further by using information about a specific customer or category of customers to determine what price they see. Regulators have shown particular interest in the second model because it can involve location, browsing history, purchasing behavior and other personal information.

The technology is not the only issue

The anxiety around dynamic pricing reflects something larger than whether a digital shelf label can technically change a price. Consumers are increasingly aware that retailers collect large amounts of information about how they shop, what they buy and where they are, while AI systems make it easier to analyze that information at scale.

That creates a trust problem even when a company says the data is being used only for marketing, recommendations or operational decisions. SSC has previously examined how retailers and brands are competing for consumer trust in a marketplace where technology increasingly mediates purchasing decisions. As brands use more sophisticated tools to personalize recommendations and influence buying behavior, the line between helpful personalization and manipulation becomes harder for consumers to see.

Target’s statement appears designed to make that line more visible. It is telling customers that its loyalty data may influence which offers they receive, but not the underlying price used to charge them more because of who they are.

Price differences are not disappearing

None of this means every Target customer will always see the same price everywhere. The company explicitly left room for differences between locations, platforms and promotions, and those distinctions are important because they show how complicated the pricing debate has become.

A retailer can reject personalized pricing while still operating a highly responsive pricing system. Stores can react to local competition, operating expenses, inventory and channel-specific promotions without calculating an individualized willingness-to-pay score for each shopper.

That separation is similar to another dynamic-pricing issue SSC has followed outside retail. Cities using variable parking rates are also changing prices according to demand and location, but the price generally applies to everyone using the same space at the same time rather than being tailored to the identity of a particular driver. Social Storytellers Collective

The policy and consumer concern grows sharper when the system begins using personal information to determine what a particular person may be willing to pay.

The new retail question is becoming one of proof

Target, Walmart and McDonald’s are now all participating in a broader public argument over what algorithmic pricing is and where companies believe the acceptable boundary should sit. Their statements matter because consumers have limited visibility into the systems operating behind a digital price tag, loyalty offer or menu recommendation.

For retailers, simply saying they use AI is no longer enough information. Consumers and regulators increasingly want to know what data enters the system, whether individual characteristics affect prices, how often prices can change and whether two people shopping under similar conditions are being shown different numbers.

Target’s answer is that customer identity and location are not being used to raise prices and that demand spikes and time of day do not trigger higher prices. The larger issue will be whether retailers can make those assurances sufficiently transparent to rebuild confidence in a pricing environment that is becoming more automated, more responsive and much harder for the customer to see.