The way companies decide what to charge customers has quietly undergone a dramatic transformation over the past ten years, and according to consumer advocate Lindsay Owens, the result is that shoppers are being deliberately overcharged in ways they rarely see coming. Owens, who leads the consumer advocacy group Groundwork Collaborative, argues that what looks like ordinary price fluctuation is often something far more calculated. "What we're seeing is Big Tech reinventing the rip-off," she said, describing the phenomenon as a set of old commercial tricks that have been "supercharged by new technologies."
Owens lays out her findings in a new book published in September titled "Gouged: The End of a Fair Price — And What That Means for Your Wallet," which examines pricing schemes that corporations work hard to conceal and offers readers practical guidance on how to defend themselves. Her central claim is that the shift is not accidental or the natural result of supply and demand. Instead, it reflects a deliberate strategy in which companies gather vast amounts of information about individuals and then use that information to determine the maximum amount each person might be willing to pay.
The most striking example, in Owens' view, is personalized pricing. Retailers, she explains, "collect data on you to better ascertain how much they can charge you." That data can include what websites you browse, what demographic group you belong to, and what you have bought in the past. The information is then fed into algorithms capable of running pricing experiments on consumers in real time, testing different price points to discover which one a particular shopper will accept without walking away.
The travel industry offers some of the clearest illustrations of the practice. Hotels have been known to set room rates based on a customer's geographic location, meaning two people searching for the same stay from different places may see different prices. Airlines, meanwhile, have used browsing data to influence the cost of airfare, so the fare shown to a traveler may depend on their previous searches or online behavior rather than on the actual cost of providing the seat.
Grocery stores have also adopted technology that allows them to test how much customers can be charged for everyday items, a development that raises particular concerns because food shopping is not discretionary for most households. Unlike a vacation or a new pair of shoes, groceries are a recurring necessity, which makes experimentation with their prices especially consequential for family budgets.
What unites these examples, according to Owens, is the use of technology to disguise what is happening. A traveler comparing fares with a friend might simply assume they are seeing the same market price. A shopper at a grocery store has no way of knowing whether the shelf price reflects a test aimed at discovering the upper limit of what customers in that neighborhood will tolerate. The opacity is part of the design.
The good news, Owens says, is that consumers are not powerless. She outlines a series of techniques designed to obscure the digital trail that retailers use to identify and profile shoppers, making it harder for companies to serve up individualized prices. The logic is straightforward: if a company cannot reliably determine who you are and what you might pay, it loses the ability to tailor its pricing to your personal circumstances.
One set of tactics involves limiting the signals you send while shopping online. Because browsing history and search behavior feed directly into the algorithms that set personalized prices, reducing the amount of information a retailer can attach to your identity can blunt the effectiveness of those systems. The goal is not to disappear entirely but to make your profile less valuable and less accurate as a tool for extracting a higher price.
The advice may not be worth the effort for inexpensive purchases. As Owens frames it, few people are likely to go to great lengths to avoid a personalized markup on something as minor as a pair of shoes. But when the stakes are high — as with expensive flights, where a small percentage difference can translate into hundreds of dollars — taking steps to cover your tracks becomes far more worthwhile.
For consumers, the practical takeaway is that price is increasingly a moving target rather than a fixed number printed on a tag. The same product can carry different price tags for different people at the same moment, determined not by cost or availability but by what an algorithm predicts each shopper will pay. Understanding that reality is the first step toward resisting it.
Owens' work also carries a broader message about the balance of power in modern commerce. When companies possess detailed profiles of individual customers and the computing power to act on that information instantly, the traditional assumption that a fair price emerges from an open market begins to erode. The consumer on the other side of the transaction is often unaware that a negotiation is even taking place.
Her book arrives at a moment when scrutiny of corporate pricing practices is intensifying, with advocacy groups and policymakers paying closer attention to how algorithms shape what Americans pay for goods and services. Owens, through Groundwork Collaborative, has been a prominent voice arguing that these developments deserve public attention rather than being treated as an obscure technical matter.
For individual shoppers, the recommendations boil down to a simple principle: make yourself harder to read. By masking the data that retailers rely on, consumers can reintroduce a measure of uncertainty into the pricing process — and uncertainty, in this context, works in the buyer's favor. A company that cannot predict what you will pay has less room to charge you more than the next person.
The stakes extend beyond any single transaction. If personalized pricing becomes the norm across hotels, airlines, and grocery aisles, the cumulative effect could be a marketplace in which the price you see depends less on what something costs to provide and more on what a machine has learned about your habits, your neighborhood, and your willingness to click "buy." Owens' advice is intended to give ordinary shoppers a way to push back against that trend, one obscured trail at a time.
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