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I gathered six colleagues to pull up a ticket on StubHub for a New York Knicks game, the exact same seat at the exact same time. And we got vastly different prices. On my laptop and my phone, I see $1,420. The total price on my phone was $1,491. I'm seeing on my laptop $1,385. The discount. On my phone, I got $1,538. That's crazy. That is so weird. There was a $153 gap between my price and Nikita's for this seat at a New York Knicks game on StubHub. It was suspicious, so I took a closer look at our receipts.
The base price of both tickets was exactly the same, but there was an unexplainable difference in the fees, which are the part of the price StubHub sets. It's why it feels like it's exponentially more expensive than everyone else's. Yeah, I'm kind of like, what did I do wrong? This didn't look like the kind of dynamic pricing we're used to seeing with tickets. When a price suddenly jumps, it's easy to blame dynamic pricing.
Prices that shift with demand or availability. You're doing dynamic pricing on all of us. Dynamic pricing. My god, that pisses me off. But there's another kind of pricing trying to screw you. Critics call it surveillance pricing, where companies can charge you the highest price they think you're willing to pay. Which at that point I was like, does the list price ever actually exist? Your frustration is really valid. Something has shifted. Companies have gotten a lot better at overcharging you. After running more experiments and digging through patents, I'll show you how companies create prices just for you. And if could mean you're paying
more. Okay. So it's October 29th and I'm going to check this round trip plane ticket from New York to LA in November. So it's $976.80 per person. And now it's October 30th and that same flight is $891.80. Before this became what buying a flight looked like, everyday shopping felt a lot more straightforward. You looked at the price tag. But before price tags came along, shoppers often had to haggle. In the US context, it was actually the Quakers who started to feel like haggling was unfair and so they stopped using haggling and instituted fixed prices. In the mid 1800s, American department stores brought that principle to mass retail. For, you know, nearly 150 years, American consumers have been able to
rely on a posted price. Posted prices allow for transparency. You can walk across the street to another department store and compare prices. Lindsey Owens studies how companies set prices. The title of her book, Gouged, offers a hint about what she concluded. With the posted price, shoppers generally saw the same price for the same product. Airlines and live events worked a little differently. They sold seats and tiers. The tradeoff was pretty clear. Pay more, get a better seat. By the late 1980s, airlines pioneered an early form of dynamic pricing using seat availability and expected demand to determine which fares remained available.
Today, versions of that same logic show up far beyond airlines. Take a ticket to Disneyland, for example. I'm checking for a one day adult ticket to Disneyland for October 30th. It costs $174. And now I'm going to check that same ticket on Christmas Day. And let's see what we get. Okay, it's $224. That's date-based pricing. So maybe it doesn't feel great knowing that you have to pay more for essentially the same experience, but it's pretty transparent. Uber took dynamic pricing a step further by popularizing surge pricing. The price can move in real time as rider demand and driver availability change.
Like I don't have a problem with surge pricing. To me, that's something that's pretty clearly defined. Even if it costs 100 bucks, sometimes I might be willing to pay 100 bucks for that ride if it means I can truly get one. Concert ticket prices can move quickly, too. On primary and resale sites, artists and sellers can raise prices as demand grows. In retrospect, it was really a tipping point for what has come next, which is dynamic pricing seeping into so many areas of our lives where more transparent fixed pricing used to be the norm. By 2012, dynamic pricing was no longer confined to flights, hotels, and event tickets. Companies were using algorithms to change prices of all sorts of ordinary products online like video
games, refrigerators, and even toilet paper. In 2012, a computer scientist told the Wall Street Journal, in the age of the internet, fixed prices are a thing of the past. I wanted to run a series of experiments to really figure out how far dynamic pricing has gone. Before I did that, I hopped on a call with my colleague Juliana, who conducted her own experiment a few ago. All right. It is recording. Yay. I really like Old Navy. That's why I was doing this. This shirt is actually from Old Navy.
First, she tracked a cart full of basics, socks, leggings, a t-shirt, and jeans every day for two weeks on the Old Navy app. Over those weeks, her total changed five times. I think the thing that was sort of like crazy making is that the prices would not move in concert or like in tandem. For instance, Juliana found t-shirt prices would go up, but jean prices would go down. In another example, jean prices would go up, but legging prices would go down. Then she took that same cart into two Old Navy stores.
Some of them are almost twice as much in the store. Like the store really did default to the list price, the price that was actually on the price tag. Granted, the Old Navy Juliana went to says it will match the online prices. Old Navy's price tags even tell you to download the app, but I noticed a lot of older folks in there too and less likely to be tech savvy. And to me, I was like, well, technically this information would be available to them if they knew to seek it out, but who's going to tell them to seek it out?
Okay, so I added similar items on my digital cart as Juliana's, starting with these ankle jeans I found in the store. And, you know, taking a look at the price here, it's 50% off. And it's $22. It's almost a $5 difference online versus in store. Old Navy didn't respond to either of our requests for comment. Airline fares are pretty front facing of fluctuating prices, but brick and mortar like retail stores aren't sticker above my head. We're also doing fluctuating prices. It's something that you kind of notice by yourself. Which at that point I was like does the list price ever actually exist if there's always some sort of price fluctuation on it? That's when I realized dynamic pricing
isn't the whole story. This isn't just about supply and demand moving the price. It points to another question. Who gets what price? The idea of giving different people different prices isn't new. Economist call it price discrimination. The basic idea is companies will set different prices for different groups. That's price discrimination. One form of price discrimination is a student discount, right? Where students are offered a better price, you know, at a restaurant or at a movie theater. Companies use customer groups as clues for what people might pay. And sometimes we do the sorting ourselves.
You can see this at play during happy hour. Same drink, same bar, just cheaper for the group of customers willing to come when demand is low. In these examples you can see what group you're a part of plus there are advertised discounts. And who doesn't love a good deal? But the internet and shopping online changes that. The group you're in can be much harder to see. I asked our senior retail reporter about these invisible groups when I reported on Uber earlier this year. They have all this data on a bunch of people like you or people who have certain similar habits. And so, you know, if we send that group of people a specific price, you're likely to respond to that in a certain way.
Essentially Alex is saying companies like Uber can sort you into these invisible pricing groups. He also mentioned something interesting about how they do it. They have all this data. I got a glimpse of this when I asked McDonald's to send all the data it had collected about me. I got 431 pages back. McDonald's sorted me into a customer group called CV 1 and scored me zero on its measure of how likely I am to stop visiting. I have to admit that one called me out. The file doesn't show whether any of those predictions changes the prices I paid, but it does
illustrate how companies can sort customers into groups you'd never know about. The internet made pricing less transparent. Companies having access to consumer data has taken it to the next level. And part of what fueled that growth is sitting right in our pockets. Our phones can generate a detailed trail of how we browse and shop. You have mobile devices in the 2010s which really increased the level of location granularity. That's Stephanie Winn, former chief technologist for the Federal Trade Commission.
She explained that personal data collection goes beyond tracking your taps and building a profile about you. The rise of algorithms and AI made it easier to connect the dots and make predictions about us. You also have now the ability for companies to collect that data in near real time. Now, of course, with AI, increasingly companies can also track sort of conversational data in addition to behavioral data. They can collect and store your confessions, what you tell your chatbot. All right. So I'm going to try chatting with Amazon's Alexa for shopping, which is Amazon's AI shopping assistant, and see what AI answer we get.
Can't deny that these are pretty accurate. Amazon describes Alexa for shopping as helping customers make shopping decisions and understand product prices. Just from purchase history alone, it paints a pretty, as it says, vivid picture of what I like to buy and what I need. The thing is companies tell us very little about what happens between the data they collect and the price we see. But their patents can show us what they may be trying to build. A researcher out of MIT tracked more than 3,000 US patent applications filed by 23 major companies from 2015 to 2025.
This was the most common type of patent, consumer data collection and market modeling. The patents themselves reveal a lot, like this one assigned to DoorDash in 2021. It describes tracking a shopper's internet traffic to figure out what they know about a product's price. A 2018 Amazon patent describes using location data to predict where people gather then picking the timing and discount for a deal. Lindsey's team specifically looked at Walmart's patents. She found one from 2021 that describes changing the online prices you see based partly on who you are, how you shopped, and how close you are to a store.
Now the patents the MIT researcher and Lindsey's team cite don't prove that these technologies are being used. DoorDash told me that the patent referenced was never used and has been withdrawn and that the company does not use customers' emotional state, stress level, or biometric data to charge customers more. Amazon also shared with me that the patent is unrelated to its online store and it does not use personal information to charge customers higher prices. Meanwhile, Walmart said the patent has been issued, but we will not use it or renew it. Together, their patent reviews show where technology may be headed.
Pricing built around shopper data. Data is the bedrock foundation for commercial surveillance and for AI models. And consent at this point effectively looks like a tiny little pop-up window with legalese. The ability for someone to know that any of this is going on is effectively opaque. Your data can make pricing much more precise. Rather than stopping at what a group might pay, experts worry companies will try to estimate what you might pay. That's personalized pricing. And when personal data helps determine the price, critics call it surveillance pricing. Surveillance pricing is not the same as dynamic pricing. This is pricing that is based on the company's assessment of your willingness to pay.
These days, talk of surveillance and personalized pricing is hard to avoid. It's in headlines and people online are putting it to the test. Go to Target and buy spam. Okay. How much is it? $4.99. My price is $4.59. That's dude. The FTC is now examining this, too. It's ordered nine companies from airlines and ride-hailing apps to retailers to explain how they set prices and whether personal data plays a role.
Instead of student and adult, there's a million different bins and you're you're getting very At that point, you're getting very close to personalized pricing where they're giving basically individual prices, right? That brings us back to our StubHub experiment from the beginning of the video. Nikita and I were shown totals $153 apart for the same next seat, even though the base price of the ticket itself was exactly the same. So we tried again. This time with a different event. Olivia Rodrigo at Barclays Center on February 23rd. We ended up having very similar results. My phone, the ticket was also $252, but the fees were $68 for a total price of $321.
Um similarly to everyone, the base ticket is 252, um but the fees on my computer were $66. And $51 on my laptop. And on my phone, the fees were $78 which brought my total to 331. For the second time, Nikita's phone showed the highest fees in our group. My laptop showed the lowest. This time the gap was $27. All of it still coming from the fee. So it almost feels like you just have to hope that you're lucky enough to get a fair ticket price. And we wouldn't have known if we didn't all do it at the same time. To help interpret our results, I spoke to Mark Tremblay, an economics professor who studies digital platforms and fee transparency.
My initial reaction to this is like, oh this could be the mechanism where they implement personalization, right? now because everything's all inclusive, you can also kind of maybe hide where price variations are coming from. When I asked StubHub about results, they said these are randomized anonymous tests to find which discounts best balance demand and access, and that these tests don't use data like demographics, location, or purchase history. StubHub shared a similar response to my colleague Emily after she documented the same kind of fee variation in June. I invited her to join our experiment. With me, they're just like, it's experiments. And they do point out that
like other platforms do this as well. I mean, at least when I looked at it like Dove Hub had more like variation, wider variation. But it's like it just kind of feels like sucky and unfair that you don't get to know. This is where the economic theory kind of butts heads a bit with some consumer groups. So let's say they were personalized pricing or even adding randomness to the prices. You could ban this and then everyone would get the same price. And that price is going to be in the middle between the high and the low. So the winner is the person that had the higher price and the loser is the person that had the lower price which now has
to pay a higher price. Can a pricing test or experiment or you know still create fairness or transparency concerns even if it's random or if it is you know even if it's not personalized? Uh I mean I could talk for hours on this. But you're right. I mean I don't think they're disclosing this to the consumers that hey you're a part of a pricing experiment. Our experiment was limited to a few data points. But Groundwork Collaborative, Consumer Reports, and More Perfect Union asked a similar question on a much larger scale to 437 Instacart shoppers across four cities.
Lindsay's team was at the forefront of the study. For almost 75% of the items in the cart, different shoppers were being charged different amounts. Instacart called them short-term randomized tests, not prices based on personal, demographic, or behavioral data. It later shut down all item price testing on the platform. In the e-commerce setting, we're all just guinea pigs in this big pricing lab. It's the best excuse, right? Like if you don't want to say personalization, you say randomization. If they do, give me a call. I'll stop doing a lot of my research of trying to personalization if they're just going to start admitting it.
One place companies openly acknowledge using personal or behavioral data is for personalized discounts and promotions. We know this because Uber says so. Instacart, Target, and Starbucks all disclose versions of this practice. These deals aren't always as great as they seem. In an Uber experiment we did comparing prices for the same ride at the exact same time, we found that discounts didn't always lead to lower prices. Okay, my Uber X is 88.05. Yeah, 88.10. Oh, they're all Mine's 88 But I got a promotion.
Mine's 87. Mine's 90 17.6 90 Who has a promotion? Everyone has a promotion. Okay, that was a lot. My friends like to talk over each other. Essentially, Hamza's reference price was $90.17. With his promotion, his price went down to $89.29. That discounted price was still higher than what others in the group without a discount were quoted for the exact same ride. So Uber says it may use personal data to dole out discounts, and that discounted price isn't guaranteed to be cheaper. Consumer Reports found a similar pattern with Uber and Lyft with 174 participants. In 12.4% of advertised
discounts, the final fare was actually higher than the median undiscounted quote for the same route. Both companies disputed the findings and denied using fictitious discounts or surveillance pricing. When we asked Uber about this, it said that each participant had slightly different pickup routes and that different pickup estimates can lead to small variations in price. If you and I are shopping in an app and both of us are looking for a ride and you're offered a discount, and I'm not. I'm charged more than you. That's personalized pricing, whether it was done by increasing the base price or whether it was done by offering one of us a discount.
Maybe the difference is random, as companies say. Maybe it's personal, as Lindsay suspects. Either way, consumers see different prices without knowing why. It felt like almost a loss of agency. As pricing becomes more and more of a black box, I realized no matter how long I gamed it, I would never win. So this is kind of the challenge with personalized pricing that I think economists like myself want to push back a little bit on is it can be used to do good things, right? You can offer discounts and kind of expand the market. That being said, I think seeing all of the pushback we've seen around this issue, I've been somewhat surprised.
It seems like a lot of consumers, even if they're getting the lower price, don't necessarily feel good about that. Okay. So shopping can feel like a losing game. Is there anything you can do? One obvious solution would be to join a company's loyalty program. Sign up, get the deals. Well, critics say it's yet another way companies can get more of your data. Washington Post reporter Jeffrey Fowler shared his Starbucks rewards records with Stephanie. She found that the months Fowler bought more coffee, Starbucks sent him fewer promotions. Stephanie says it suggests that Starbucks used the data they had on
a customer to limit their discounts. This American deal is being degraded. The benefits that we used to receive are not as good. Starbucks confirmed that it does use purchase history to choose discounts. Stephanie points out that shrinking rewards are part of a broader pattern she calls the hike. The hike is what happens when they have all this data, they can be able to you know understand who you are and prices might go up. Perhaps the benefits that you get as a loyalty user goes down. As Stephanie describes in her paper,
companies learn more and more about their customers through loyalty programs while using that information to restrict what members get in return. I wanted to find out how much data companies are gathering through loyalty programs. So I checked for myself. That brings us back to the 431 pages McDonald's sent about me. There were hundreds of pages logging my interactions on the app over my year of being a member. Near the end came its reading on my eating habits. It ranked 20 piece McNuggets as my top product, calling my visits dinner on the go and late night, which I can't really argue with. Then it predicted that I'll visit 3.56 times and spend $70.08
over the next 6 weeks. The file doesn't show whether any of those predictions influenced the prices I paid or the deals I got. McDonald's also shared with me that it doesn't use any personal information such as purchase history to set individual prices or menu prices. Given all of the commercial surveillance and targeting infrastructure that has developed over time is that loyalty programs have become a Trojan horse for that surveillance.
Some worry that even in-store prices could soon be under attack. At this Walmart in New Jersey, they've installed digital price labels. Walmart CEO said that they price the products, not the person. The company also told me that a digital shelf label isn't a pricing strategy. Lindsay's team found a patent that describes changing a shelf price based on what a shopper scans into their cart. Scan tuna, for example, in May it could be a different price.
Walmart shared with me that a patent isn't a plan and that they will not use or renew this specific patent. Is there something consumers can realistically do to you know mitigate this while it's it's happening? I think the burden should not be on consumers to try and navigate this complex infrastructure. It is taken hundreds of reporters and researchers and people whose job it is to actually hunt down pricing abuses to unveil what these things are. Stephanie says it's lawmakers who should intervene and they are starting to take notice. In August, surveillance pricing was the subject of a hearing on Capitol Hill.
Members of both parties expressed concern. AI surveillance pricing is the unholy trinity of everything Americans hate, spying on people, ripping them off, and taking away jobs. Meanwhile, four states have enacted laws aimed directly at the practice. New York requires businesses to disclose when personal data is used to help set prices. Maryland now restricts personal data pricing for groceries. Connecticut's broader retail ban begins in July 2027 and New Jersey's ban on groceries starts in August 2027. But these laws still exempt certain discounts and loyalty programs.
All encompassing policies are really important here. It's not just prices have many components of you know booking fees, base prices, all these things. It's also promotions. I think we need to return to ensuring that we actually see real prices and that we can compare real prices in the marketplace. Without that, it we have no functioning marketplace. Yeah, we can put a stop to this. We can fight back. We can restore a fair price in this country.
