Is your personal data changing what you pay online?

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You've probably encountered dynamic pricing already. Dynamic pricing adjusts prices based on broader conditions such as supply, demand, available inventory, time or location. Rideshare fares may rise when lots of people need cars at once. Airline tickets and hotel rooms can also change as availability and demand shift. That does not mean every shopper will always see the exact same dynamic price. 

Timing, location and other market conditions can change quickly. Personalized pricing is different because information about a particular consumer helps determine the price or offer that person receives. That means two people looking for the same product could potentially receive different offers because of information connected to them.

There's also price steering. A retailer may leave the actual prices alone while changing the order of the products shown to you. FTC research found that pricing tools can use consumer data to give certain products more prominent placement, including potentially showing higher-priced products first. The FTC says consumers generally expect prices to change because of supply and demand. What can come as a surprise is a price influenced by someone's browsing habits, buying history or other personal information.

That information can include:

On some routes, people checking the same trip within minutes of one another received several different prices. Consumer Reports designed the study to reduce the effects of time-based pricing by having volunteers check routes at roughly the same time. However, the investigation could not control for every factor inside Uber's and Lyft's pricing systems, including driver supply, estimated arrival times, traffic, routing differences and network delays.

Uber and Lyft disputed Consumer Reports' conclusions. Both companies said they do not use personal data to personalize base fares and do not engage in behavioral or surveillance pricing. So, the study shows that riders can receive significantly different prices for similar trips checked around the same time. It does not prove those price differences were caused by personal data.

Online groceries have produced another eye-opening example. In December 2025, Consumer Reports, Groundwork Collaborative and More Perfect Union reported that nearly three-quarters of the grocery items they tested on Instacart were offered at different prices to different shoppers. The investigation involved 437 shoppers across four U.S. cities.

Some items showed differences of as much as 23% between the lowest and highest prices. Researchers also found that totals for identical baskets varied by an average of about 7%. Using an Instacart figure for how much a household of four spends on groceries, the researchers estimated that a similar difference over a year could amount to roughly $1,200. 

Instacart strongly disputed that annual extrapolation and said the limited tests should not be treated as though a household would continually pay higher prices throughout an entire year. The company also said the pricing tests were randomized and did not use personal information, demographics, shopping history or individual behavior to decide who received each price.

Researchers were finding customized shopping experiences long before today's AI-powered pricing systems. In 2014, Northeastern University researchers studied 16 major retail and travel websites and found evidence of price discrimination or personalized search results on nine of them. CheapTickets and Orbitz offered reduced hotel prices to members. Expedia and Hotels.com steered some users toward more expensive hotels. Home Depot and Travelocity personalized search results for mobile users.

Priceline personalized the order of hotel search results based on a user's history of clicks and purchases. However, the researchers said those different result orders did not correlate with price, so they did not classify that Priceline example as price steering. The researchers also emphasized that most of their experiments on the 16 sites did not uncover price steering or price discrimination.

Then, in 2015, ProPublica found that The Princeton Review charged different prices for an online SAT tutoring package depending on a customer's ZIP Code. Prices for its Premier package ranged from $6,600 to $8,400. ProPublica found that people living in ZIP Codes with larger Asian populations were 1.8 times as likely to be offered a higher price, regardless of income.

The Princeton Review said its pricing was based on the costs of running its business and competitive conditions in each market. It said its prices were set by geographic region rather than a customer's race. That example was geographic pricing rather than a retailer setting a unique price for each individual shopper. It still shows how data connected to where someone lives can lead different groups of consumers to receive different prices.

There is no guaranteed trick for getting the lowest online price. However, you can reduce some of the information retailers, data brokers and tracking companies can connect to you while giving yourself more ways to compare offers.

Look at a product while signed out before logging into a retailer or loyalty account. Then compare the price or promotion after signing in. A difference does not automatically mean personalized pricing. Membership discounts and other promotions can also explain a change. Still, comparing both views gives you more information before you buy.

If you do not need a retailer account, consider checking out as a guest. That keeps the purchase from automatically becoming another entry in a logged-in shopping history. Guest checkout does not make you anonymous. A retailer may still receive information such as your email address, payment details, shipping address, browser data or device information.

Retailers are only one source of personal information. Data brokers and people ssearch companies collect information from numerous sources and combine it into profiles. FTC research found that pricing systems can incorporate third-party data, including information from data brokers. Reducing what's available through those companies may shrink one part of your broader data footprint. It does not guarantee a lower price or prevent a retailer from using information it collects directly from you. Check out my top picks for data removal services and get a free scan to find out if your personal information is already out on the web by visiting CyberGuy.com.

A private browsing window generally creates a separate browsing session that does not use the normal session history and most existing cookies from your regular browsing window. It does not make you anonymous. Websites may still see information such as your IP address and information you provide directly. The FTC specifically points to private browsing as one step consumers might use when trying to avoid higher personalized prices.

Look for choices such as Do Not Sell or Share My Personal Information, Your Privacy Choices or controls for targeted advertising. The options available depend on the retailer and the privacy laws that apply where you live.

Review which shopping apps can access your precise location. If an app does not need your exact location, consider switching to approximate location or turning location access off. Keep in mind that websites and apps may still estimate your general location from other information, including your IP address.

Cookies can help websites recognize the same browser when you return. Clearing them can remove some of those stored identifiers. However, other tracking methods may still recognize your device, and signing back into an account reconnects your activity to that account.

Before making an expensive purchase, check the retailer's website and app. Then compare the same product with other sellers. Any difference you find may have a simple explanation, but comparison shopping gives you a better picture of the available prices.

For products supported by price-history services, check whether today's deal really is lower than recent prices. A retailer's sale banner only tells you what it wants to advertise. Historical pricing can give you additional context before you buy.

A lower price may require a loyalty membership, subscription, automatic renewal or another commitment. Make sure the offer actually saves you money after those conditions are included.

What gets my attention here is how much information can now go into deciding what we see when we shop online. We do not have evidence that every retailer is quietly setting a different price for each person, and seeing two different prices does not automatically prove personal data caused the change. What we do know is that companies have technology capable of using location, browsing behavior, shopping history and other information to shape prices, discounts and product rankings. 

My advice is to compare prices before you buy, limit tracking you do not need and avoid assuming the first offer on your screen is the best one available. The less unnecessary information you hand over, the less there is to feed into a detailed profile about how you shop.

Would you change where you shop if you learned a retailer was using your personal data to decide what price or offer you see? Let us know by writing to us at CyberGuy.com.

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