How to protect yourself from automated pricing schemes — Terms of Service

[0:02] Welcome back to Terms of Service. I'm CNN tech reporter Claire Duffy. [0:05] I don't think I need to tell anyone that life these days is expensive. Some people are now [0:12] foregoing basic necessities like food, utilities, and gas because of a lack of affordability. [0:19] If you've bought a carton of milk sometime in the past year, for example, you have seen this [0:23] in action. But now imagine that the cost you're charged for a specific carton of milk at a [0:29] store is different from the cost your neighbor pays for the exact same product, maybe because [0:35] you have different budgets or shopping habits. This is not a hypothetical. It's actually happening [0:41] thanks to a practice known as algorithmic pricing. It's also sometimes referred to as [0:46] surveillance pricing or personalized pricing, and it's affecting online shoppers. It's a pricing [0:52] strategy where AI-powered algorithms churn through consumer data like where you live, [0:57] what you make, and what you purchase, and also trends [0:59] in consumer demand for certain products. Corporations then use that information to [1:04] predict what you'd be willing to pay and adjust prices accordingly. To walk me through how this [1:10] works and how it affects consumers, I'm talking to Grace Getty, a policy analyst at Consumer [1:14] Reports. She has some tips on how to look out for automated pricing schemes and what we can [1:20] all do about it. My conversation with Grace after this short break. Hi, Grace. Thank you for being [1:30] here. Thank you so much for having me. So how did you just first become [1:34] familiar with this issue of algorithmic pricing? Sure. Well, I shop around online like everyone [1:40] else, and I've had the experience of, you know, seeing one price in one browser and being like, [1:45] is this the best price? Opening a different browser, having a friend see a different price, [1:49] and, you know, kind of wondering what's going on there. And through my work at Consumer Reports, [1:55] we've been thinking a lot about, you know, how to protect consumers now that everyone's shopping [1:59] online. And a big shift is, you know, everyone's seeing the same price tag in the store, [2:05] to now everyone's seeing prices on their private screens, and also companies having a ton of [2:10] information about each of us individually. Everything from your search history to the [2:15] type of device you're using to your real-time location, battery life, but also demographic [2:20] information, inferences about your income, all these things that can paint a really detailed [2:24] picture of who you are, what you want, and how badly you want it. [2:27] So can you explain what AI-driven algorithmic pricing is in layman's terms for us? [2:35] Algorithmic pricing is kind of a catch-all phrase for a few different, a handful of different [2:40] pricing strategies. The one I've been most focused on is personalized pricing, which, as you said, [2:45] the price varies person to person based on something that company knows about you as [2:50] an individual. So you and your neighbor and your sister might all see different prices for the exact [2:55] same fancy hairdryer at the same store at the same time. There's also dynamic pricing where [3:01] the price is, price is changing over time based on, say, [3:05] predicted demand and a couple other strategies. [3:08] Part of the reason there has been a lot of talk recently about technology enabling [3:14] retailers to run price tests or do personalized pricing is because Consumer Reports published an [3:19] investigation back in December into Instacart's pricing strategy that got a lot of attention. [3:25] Grace walked me through the methodology and how they compiled that report. [3:29] This was a big undertaking. We worked with two partner organizations, Groundwork Collaborative [3:39] and More Perfect Union. And we worked with two partner organizations, Groundwork Collaborative and More Perfect Union. [3:40] And we also worked with over 400 consumer volunteers across the country to do these live price checks. [3:47] And we were looking at Instacart, which is a grocery delivery platform. You know, [3:51] you can log onto the app and have someone pick out groceries from a major grocer on your behalf [3:56] and then deliver them to you. And we were wondering, you know, are people seeing different [4:00] prices for the exact same item at the same store at the same time? And if so, what explains that? [4:07] And at what cost? And so we put together the [4:10] data reporters put together these live Zooms where researchers guided consumers in logging [4:17] into the app, picking out the exact same physical store like I'm based in DC, a Safeway in Washington, [4:22] DC was in the test, and then putting the exact same list of goods into their basket, taking a [4:27] whole bunch of screenshots to record the prices. And then the researchers did a bunch of verification [4:34] of those screenshots, a lot of data analysis, and found out that people were indeed seeing different [4:40] prices for the exact same item at the same time. And why did you choose to focus on Instacart? [4:45] Instacart, it seems, has invested pretty heavily in pricing technology and providing that [4:52] technology to its retail partners. You know, in the case of this investigation, they had purchased [4:58] this company, Eversight AI. And so it seemed like they had a sophisticated operation that was would [5:03] perhaps enable the sort of variable pricing. So what ultimately did you find here? [5:09] So we [5:10] found that for about three quarters of grocery items in our big test, there were different prices [5:16] for the same goods at the same time for different people. And, you know, the highest spread we saw [5:22] was the highest price point was 23% higher than the lowest price point. We also saw some items [5:28] with five different prices at the same time. So, you know, like a dozen eggs from the brand [5:32] Lucerne and a DC Safeway had five different prices. And this all adds up to, you know, [5:39] based on what Instacart says, [5:40] a family of four spends on groceries in a year, a potential cost swing of $1,200 per year. [5:45] So you could be paying $1,200 more for the same products than your neighbor down the street is. [5:52] Yeah, over the course of the year. [5:53] Wow. So how does this work? What kinds of data are they using to determine [5:58] different prices for different consumers? [6:00] So Instacart specifically said that they were doing randomized price testing, [6:05] but there are lots of other examples of companies doing other forms of [6:10] price pricing where, you know, investigative reporters have designed some really clever [6:13] investigations to try and figure out what sort of information they're using. [6:18] So one example, investigative reporters in Minnesota were testing the Target app and [6:24] found that when a customer was on the far side of a Target parking lot, you know, [6:28] say they're looking at a Dyson vacuum, they would see one price, they walk into the store with their [6:33] phone, again, picking up real-time location data, and see the price of that vacuum jump significantly. [6:40] Another example orbits the travel website. The Wall Street Journal found that they were showing [6:45] consumers different hotels at different prices based on whether they were searching from a Mac [6:49] or a PC. People using Macs were being shown higher prices. You know, a third example, [6:55] Princeton Test Prep was found to be charging different prices for virtual tutoring based [6:59] on the customer's zip code. So there are, you know, a whole bunch of types of examples. And, [7:06] you know, as probably people listening to this podcast may well know now, companies [7:10] have [7:10] just a lot of data on each of us. So some of it is based on the device you're using, [7:15] some of it's kind of guesses based on, you know, your search history, your shopping patterns. And [7:20] a lot can be inferred about you based on that information. We should say that these companies, [7:25] including Target and Princeton Review, have previously said their prices are set to reflect [7:30] different competitive factors in different markets. In response to the 2012 Wall Street [7:35] Journal story on orbits that Grace mentioned, the company said at the time its price tests [7:39] were experimental. [7:40] That's also how Instacart framed the pricing strategy highlighted in Grace's report. [7:45] Throughout the report, Instacart's pricing strategy is referred to as experimental. That's [7:52] sort of how the company has talked about this as well. But obviously consumers didn't know [7:56] they were participating in an experiment and they paid for the findings of that experiment [8:00] out of pocket basically with small price differences that, as you said, could add up to [8:06] a big chunk of a family's budget over a year. How should we think about that? Like just the fact that [8:12] companies are experimenting often without our knowledge? [8:15] In the Instacart example, they were actually, you know, bragging on a portion of the website that [8:20] consumers didn't know this was happening. And I think part of the calculus here is when these [8:25] examples have been revealed to the public in the past, they're met with outrage. People really do [8:29] not like it. And so companies have an incentive to not make it clear what's happening. You know, [8:35] there have been lots of good reports. And now a law was recently passed in New York that requires [8:42] to the fore. But I mean, even in the wake of the Instacart investigation, for example, we saw more [8:48] than 12 members of Congress write letters either to Instacart or to the Federal Trade Commission, [8:52] which is a consumer protection government agency. Reuters reported that the FTC was investigating [8:59] Instacart for their pricing tactics, and Instacart walked it back and said they would no longer be [9:03] doing it. And we've seen other kind of instances of something coming to light and a company needing [9:08] to walk it back. Yeah, I wanted to talk a little bit about Instacart's response here. [9:13] They initially responded, as you said, by saying that each retailer's pricing policy was displayed [9:18] on the platform so that customers can see if there are differences between online and in-store [9:22] pricing, and said this was a limited subset of stores that were doing this price experimentation, [9:28] they said, in order to keep prices lower for essential items for customers. But then a few [9:33] weeks later, in the wake of this backlash, it said that it would end these price tests. [9:38] Meaning that going forward, all customers will pay the same thing for the same goods. [9:43] What did you make of that response? I thought it was interesting, [9:47] and it does kind of suggest how strong public sentiment is on this issue. They did say they'd [9:53] still be allowing partner grocers to test different promotions and discounts, which I found [9:58] interesting because I do think the way retailers offer discounts has evolved a lot over the past [10:02] 20 years. And I do think discounts are now more personalized, more targeted, and less kind of, [10:08] I mean, it's not like they're saying, you know, we're going to do this, we're going to do that, [10:08] we're going to do that, we're going to do that, we're going to do that, we're going to do that, [10:08] we're going to do that, we're going to do that, we're going to do that, we're going to do that, [10:08] more complicated and less clear cut when you're getting a good deal. And so that's something that [10:14] I'm kind of increasingly keeping my eye on is when is a discount real? When is it kind of [10:19] approximating personalized pricing? I reached out to Instacart for comment on this episode. [10:25] As Grace and I talked about, an Instacart spokesperson told me that its price tests [10:29] were more random and not, quote, based on personal demographic or user level behavioral [10:35] characteristics. But the spokesperson said Instacart ended its tests after [10:39] realizing that they upset consumers and said, quote, at a time when families are working hard [10:45] to stretch their grocery budgets, customers should never have to question the prices they see on [10:49] Instacart. Still, big picture, companies do have a lot of data on us, while consumers are given [10:56] very little clarity into how prices are determined, meaning it can be hard to tell if they're equal [11:01] and fair. So how can personalized or algorithmic pricing affect your finances? And is there anything [11:09] you can do about it? And if so, what can you do about it? And if so, what can you do about it? [11:09] Grace has some tips. We'll be right back. Do we have a sense of scale in terms of how many [11:19] companies are doing this kind of algorithmically driven pricing? As kind of evidenced by like the [11:25] scale of this Instacart investigation and what it took to pull it off, a tricky thing about it is [11:29] it takes a lot of methodological rigor to really establish definitively that something is happening. [11:35] You know, that's resource intensive. And so most of the examples we have are from investigative [11:39] journalism. That said, the Federal Trade Commission, [11:43] Consumer Protection Agency, launched this study. And, you know, government is in this unique [11:47] position where it can compel companies to say what they are doing. And so it sent a bunch of [11:52] questions to a whole bunch of kind of middlemen type companies that would offer price targeting [11:57] products. So MasterCard, Accenture, JPMorgan Chase were among the companies that received [12:02] these questions. And there were questions about, you know, how are they offering these price [12:06] targeting products to clients, retailers? They learned that these practices were happening in a [12:12] really wide array. And so they were able to do that. And so they were able to do that. And so [12:13] they were able to do that. And so there was a lot of data analysis that was done on the [12:16] sub-stages in a large array of retail segments from from home goods to apparel to, you know, [12:22] home renovation type stores. But it's not clear that that research is ongoing or if it will the [12:28] final results will be revealed to the public. [12:31] You touched on this a little bit, but how does algorithmic pricing differ from other corporate [12:36] pricing tactics that we've seen? Like, obviously, retailers have argued that stores have done price [12:40] testing for years. But how is this different from what we've seen before? [12:43] There are many ways that retailers could test prices in stores 20, 30, 40 years ago. [12:50] They could say, okay, in this store, we're going to vary these prices and see how much [12:53] that changes the volume people buy. [12:55] We're going to mail out coupons and see how that stimulates demand. [12:59] The difference now is they know so much about us, way more than they did previously. [13:06] We did another investigation, an investigation of Kroger's data practices, and under some [13:11] consumer data protection laws, consumers have the right to request data that a company [13:15] has collected about them. [13:16] One of the consumers we worked with got back a 62-page profile with everything from inferences [13:21] about his education level, his income, the likelihood that he had a pet, his likelihood [13:26] to travel. [13:27] This really fine-grained data and predictions that the store can then use or also sell to [13:34] other stores. [13:35] Is personalized pricing happening mainly via online shopping platforms, or are [13:41] in-store shoppers potentially subject to this as well? [13:45] It's certainly easier for a company to pull off online. [13:50] In store, typically, we're all still seeing price tags, although certainly some retailers [13:54] are starting to implement electronic price tags. [13:57] One way that a similar phenomenon starts to happen in stores is these personalized discounts [14:01] I referenced. [14:02] You're being incentivized to use an app, get personalized discounts delivered to you, and [14:07] functionally that can mean that people are paying different prices for the same product. [14:10] Mm-hmm. [14:11] Certainly, the kind of variation on list prices I think we're more likely to see online. [14:18] Is there any potential upside for consumers here, like situations where personalized pricing [14:22] might actually mean that some people are paying lower prices? [14:27] It's definitely possible that some people in some cases might end up paying a lower [14:32] price, right? [14:33] Like, you know, if there's some backpack that you, Claire, really like, you'd be willing [14:38] to pay 90 bucks for it, but the retailer set a price at 50 bucks. [14:42] They're $40 better off. [14:44] And perhaps they know that I'd only be willing to pay $10 less than whatever I just said, [14:49] so maybe I buy it and I'm $10 better off. [14:52] But, you know, I think we can bet on companies not rolling out this practice unless it results [14:57] in them increasing their profits. [15:01] Do consumers have protections against AI-driven algorithmic pricing? [15:07] Well, not as many as they should. [15:10] It's not really clearly prohibited. [15:14] There are a couple of different laws that, like, perhaps intersect with it, right? [15:18] There are consumer data privacy laws, and depending on the type of information about [15:22] you a company is using, if it's particularly sensitive, there might be some restrictions [15:26] there. [15:27] But by and large, privacy laws don't prohibit this sort of thing. [15:31] Some states have price gouging laws, but those are mostly linked to, like, declared emergencies [15:36] like hurricanes or tornadoes, right? [15:39] You don't want water being quintupled in price during a natural disaster. [15:43] And then there are these kinds of things like, you know, like, you don't want water being [15:44] quintupled in price during a natural disaster. [15:45] So, you know, there are kind of bigger, broader consumer protection laws that broadly prohibit [15:47] unfair or deceptive practices. [15:50] And you know, perhaps there is an argument to be made that this is unfair or deceptive, [15:55] but I don't think we've seen a court case that clearly established that yet. [15:59] So, for now, it's not clear that it's illegal, and that's part of the reason that we at Consumer [16:04] Reports and many other advocates think states and the federal government should pass some [16:08] new laws to prohibit it. [16:11] Your investigation came out. [16:12] As we are seeing. [16:13] Some state and federal. [16:14] federal lawmakers attempting to curb these personalized pricing strategies. [16:19] States including New York, Colorado, California, Georgia, Illinois, and Pennsylvania have all [16:24] introduced or advanced legislation to limit individualized or algorithmic-driven pricing. [16:30] And at the federal level, Texas Congressman Greg Kassar's Stop AI Price Gouging and Wage [16:35] Fixing Act would ban the use of personal data for individualized prices. [16:40] What is the status of these policy updates, and do you actually think that they could [16:44] make a difference? [16:45] Yeah. [16:46] So, I mean, this is what I do day in and day out. [16:48] I primarily actually work on state legislation because it tends to move a bit faster than [16:53] Congress and the list has even grown, Minnesota, Hawaii, there are all these states that are [16:58] considering bills that would either require the disclosure of it or prohibit it. [17:03] I think prohibition is probably the right policy approach and so many states are considering [17:08] prohibitions either for all goods and services or just one sector like grocery prices. [17:14] And I think if one of those bills were to pass, that would really start to chip away [17:18] this practice, kind of build political momentum that, you know, policymakers can do something [17:24] about it. [17:25] And so I'm relatively optimistic. [17:27] You know, these political battles are always tricky and the lobbyists for the retailers [17:32] and the tech companies are quite good at their jobs, but I think there's a lot of political [17:36] appetite to do something about this problem. [17:38] Yeah. [17:39] The other thing that was so striking about the timing of your report is it comes at this [17:43] time when food prices overall are outpacing inflation and Americans are reporting that [17:49] the price of groceries is a major cost concern. [17:52] What advice do you have for people shopping for basic necessities who don't want to end [17:57] up paying more than their neighbor? [18:00] I'll share a couple pointers, but I do think the most important takeaway is it's not really [18:05] reasonable for like any individual consumer to try and beat the system here. [18:10] Like there's a major information asymmetry. [18:12] The company has a lot of information about you. [18:14] And you have no information about how they're doing pricing. [18:16] So to a certain extent, the house always wins. [18:19] And I don't really think it's reasonable for individual consumers to be trying to kind of [18:23] game this out on their own. [18:25] That said, a couple of things to think about. [18:27] One is, you know, shopping in person when you can. [18:31] And you know, when you check out, if the store has a big loyalty program that you want to [18:34] make use of, sometimes you can ask the person on the register, does the store have like [18:38] a store card or a store phone number? [18:40] So you want to put in your own card, your own phone number, which is how, you know, [18:43] companies track your purchasing habits. [18:44] If you are shopping online, you could try things like trying a different browser, trying [18:50] a VPN to simulate a different location. [18:54] But again, this is like a kind of sending individual consumers on a treasure hunt to [18:57] figure out if they're being ripped off. [19:00] And I don't think it's really reasonable, right? [19:02] Like we should have baseline legal protections that people can expect that the price they're [19:06] seeing is just the price and they haven't been profiled. [19:09] Well, Grace, thank you so much. [19:11] This is so important. [19:12] And I think just really helpful for people to understand. [19:14] This, even if there's only so much they can do about it as individuals. [19:18] So thank you. [19:19] Thank you, Claire. [19:22] As Grace said, it's not practical to run your own investigation before you buy something [19:27] online. [19:28] But if you can, it may be worth shopping around and taking the extra step to compare prices [19:34] and consider shopping in person rather than online when possible. [19:39] As we often advocate for on this show, it can also help to take basic steps to protect [19:44] your private information online. [19:46] Like declining cookies. [19:47] And avoiding sharing location data with apps and websites whenever possible. [19:52] There are also efforts at the state and federal level to pass legislation that would protect [19:57] consumers from this kind of personalized pricing. [20:00] So keep an eye out for that. [20:02] Before we go, I want to update you on a bit of tech news in case you missed it. [20:06] Back in November, we did an episode with AI expert Henry Ider about the explosion of AI [20:12] slop content across the internet. [20:14] It followed the launch of OpenAI's Sora app for creating and loading and storing data. [20:15] And it was a huge success. [20:16] It was a huge success. [20:17] It was a huge success. [20:17] It was a huge success. [20:17] It was a huge success. [20:17] It was a huge success. [20:17] It was a huge success. [20:18] It was a huge success. [20:19] It was a huge success. [20:20] It was a huge success. [20:21] It was a huge success. [20:22] And with the help of the app, it's now open for everyone enjoy. [20:24] So let's talk about the Railroad site. [20:25] Well, recently, OpenAI announced it is shutting down Sora and moving away from AI video generation [20:26] to focus on other priorities. [20:29] This isn't totally surprising. [20:31] Fewer people were using Sora after the initial hype died down. [20:35] And AI generated video also requires a ton of expensive computing resources that OpenAI [20:41] wants to use on products that could be more profitable. [20:44] The app also suffered from criticisms about the use of intellectual property. [20:47] property and general backlash to AI generated creative works. Now, plenty of other apps still [20:54] offer AI video tools, but this is a pretty significant signal that one of the top players [20:59] in this space has decided AI generated video is not worth investing in. That's it for this [21:05] week's episode of Terms of Service. I'm Claire Duffy. Talk to you next week.