WEBVTT - Corporations Learned The Maximum Amount They Can Charge For a Product 0:00:02.520 --> 0:00:17.520 Bloomberg Audio Studios, Podcasts, Radio News. 0:00:18.320 --> 0:00:22.520 Hello and welcome to another episode of The Odd Lots Podcast. 0:00:22.600 --> 0:00:24.919 I'm Joe Wisenthal and I'm Tracy Alloway. 0:00:25.000 --> 0:00:28.280 Tracy, you know what I feel is become a common 0:00:28.520 --> 0:00:31.520 Twitter conversation that I've seen happen a bunch of times. 0:00:32.520 --> 0:00:35.800 Uh, this could be anything, but go on. 0:00:36.600 --> 0:00:39.320 Someone tweets like, oh my god, I just paid, like, 0:00:39.400 --> 0:00:43.279 you know, fourteen dollars for a hamburger and fries at 0:00:43.320 --> 0:00:47.640 the McDonald's and then someone else goes, well, actually, you 0:00:47.680 --> 0:00:49.760 can get it for three ninety nine right now if 0:00:49.760 --> 0:00:53.040 you just use the app. Yes, I've seen this many times. 0:00:53.120 --> 0:00:56.400 Both of them are not wrong, but it is crazy. 0:00:57.240 --> 0:00:59.520 First of all, I'm so thrilled that we're finally going 0:00:59.600 --> 0:01:04.000 to do price Pack Architecture episode. That's basically what this is, right, 0:01:04.240 --> 0:01:07.440 all these different strategies when it comes to how companies 0:01:07.480 --> 0:01:10.800 are actually pricing their goods. But I feel like McDonald's 0:01:11.000 --> 0:01:15.000 has become a very very good example of this particular behavior. 0:01:15.040 --> 0:01:17.520 And at this point, as you pointed out, it is 0:01:18.120 --> 0:01:21.120 well known that if you just roll up to a 0:01:21.200 --> 0:01:23.800 McDonald's and you know, order at the drive through or 0:01:23.840 --> 0:01:25.959 in the store, you are going to be paying a 0:01:26.040 --> 0:01:29.520 higher price than if you used the app and ordered 0:01:29.600 --> 0:01:32.520 on there, And they have tons of discounts. The discounts 0:01:32.560 --> 0:01:36.080 are almost gamified at this point, like you know, you 0:01:36.160 --> 0:01:38.240 check in on different days and you can get different 0:01:38.280 --> 0:01:40.960 things and they're constantly changing. Oh and also they have 0:01:41.040 --> 0:01:44.920 an actual game that if you play, you get loyalty 0:01:45.000 --> 0:01:48.120 points that turn into discounts. But the thing that I 0:01:48.160 --> 0:01:50.680 think is so fascinating about all of this is it 0:01:50.720 --> 0:01:54.960 throws up really interesting questions around fairness. So is it 0:01:55.080 --> 0:01:58.760 fair that people are paying two different prices depending on 0:01:58.800 --> 0:02:01.240 the way that they are actually buying the thing. I 0:02:01.240 --> 0:02:03.360 think the other thing that's remarkable in all the price 0:02:03.440 --> 0:02:06.880 conversations is people seem to think that one person paying 0:02:06.880 --> 0:02:09.200 a higher price is really unfair. But on the other hand, 0:02:09.240 --> 0:02:12.280 everyone likes discounts, y Like if the lower price comes 0:02:12.360 --> 0:02:15.040 in the form of a coupon, people get really excited. 0:02:15.440 --> 0:02:19.120 It also throws up interesting questions about data privacy. So 0:02:19.280 --> 0:02:22.520 the reason McDonald's wants you on the app is so 0:02:22.639 --> 0:02:25.240 that it can collect your data and it gives you 0:02:25.320 --> 0:02:28.680 a lower price in return for that. And then thirdly, 0:02:28.840 --> 0:02:33.960 it raises all sorts of interesting macroeconomic questions. Right, if 0:02:34.120 --> 0:02:39.120 companies are becoming more strategic, more differentiated in the way 0:02:39.200 --> 0:02:42.920 they're pricing their goods, what does that mean for things 0:02:43.160 --> 0:02:46.960 like inflation? What does it mean for traditional interpretations of 0:02:47.000 --> 0:02:50.200 the way inflation works? Is it just you know, unemployment, 0:02:50.360 --> 0:02:53.120 supply demand, that sort of thing, Right. 0:02:53.040 --> 0:02:56.600 Like companies basically just getting better at figuring out the 0:02:56.639 --> 0:02:58.880 maximum price they can charge for something. Wait, I have 0:02:58.880 --> 0:03:01.640 a personal question for your Tracy. I've never asked you 0:03:01.639 --> 0:03:04.280 this before. Are you like a points person, like when 0:03:04.280 --> 0:03:06.680 it comes to hotels and airlines and stuff like that. 0:03:06.800 --> 0:03:10.000 No, I'm not, and I feel like I'm basically too 0:03:10.080 --> 0:03:12.080 lazy to sign up for a lot of things. But 0:03:12.120 --> 0:03:15.200 I will say McDonald's got me, I do have. I 0:03:15.240 --> 0:03:17.880 do have the app, and I have, as a result 0:03:18.000 --> 0:03:21.480 of the app, ended up ordering like insane amounts of 0:03:21.600 --> 0:03:24.040 junk food because I'm just like, Oh, I can buy 0:03:24.240 --> 0:03:26.960 two things of French fries instead of one, So why 0:03:26.960 --> 0:03:27.960 don't I go ahead and do that? 0:03:28.080 --> 0:03:30.639 Yeah, I'm so lazy. I am not a points person. 0:03:30.800 --> 0:03:34.080 I'm not an app person. I've never been a Miles person. 0:03:35.040 --> 0:03:37.720 It seems like I probably should. I don't trouble that much, 0:03:37.760 --> 0:03:40.240 but probably enough that I should like track this stuff 0:03:40.240 --> 0:03:42.560 and have a favorite hotel that I go to in 0:03:42.600 --> 0:03:45.400 every town, or have a favorite airline. All airlines seem 0:03:45.440 --> 0:03:47.280 the same to me. They all seem sort of various 0:03:47.480 --> 0:03:51.200 versions of kind of unpleasant. But I'm not like optimized 0:03:51.240 --> 0:03:53.360 for that at all, But it feels like to some extent, 0:03:53.360 --> 0:03:58.000 what we're talking about is this sort of widespreadness across 0:03:58.080 --> 0:04:01.440 many industries of what the airlines have figured out for decades. 0:04:01.800 --> 0:04:05.640 Absolutely, and also Uber is the classic example with dynamic 0:04:05.800 --> 0:04:09.200 surge pricing. And you can remember earlier this year when 0:04:09.240 --> 0:04:13.520 Wendy's mentioned dynamic pricing in its earnings call, the world 0:04:13.600 --> 0:04:16.840 absolutely went nuts, and then they kind of backwalked on it. 0:04:17.160 --> 0:04:20.159 But I mean my argument is like surge pricing in 0:04:20.240 --> 0:04:23.000 fast food is kind of already there, right, you know, 0:04:23.160 --> 0:04:26.200 the difference in how you're ordering at McDonald's is a 0:04:26.320 --> 0:04:30.240 variable of how much value you place on your time 0:04:30.560 --> 0:04:35.120 and your convenience, and so it's kind of already happening. 0:04:35.200 --> 0:04:37.640 And I think this is such a fascinating topic for 0:04:37.720 --> 0:04:40.560 many many reasons. But I am so so happy that 0:04:40.600 --> 0:04:42.000 we are finally doing this one. 0:04:42.240 --> 0:04:44.239 I am too. I'm going to just lay my cards 0:04:44.279 --> 0:04:46.839 out on the table right here. It's like, I don't know. 0:04:46.880 --> 0:04:48.880 I kind of get surge pricing for food. If a 0:04:48.920 --> 0:04:50.880 bunch of people all jam up at the same time, 0:04:50.960 --> 0:04:53.520 maybe like raise the prices so people spread it out 0:04:53.520 --> 0:04:56.680 a little bit. In this conversation, I will play the 0:04:56.760 --> 0:04:59.280 role of the devil's advocate, who is like, yeah, I'm 0:04:59.279 --> 0:05:02.040 okay with like, you know, differentiating prices. 0:05:01.720 --> 0:05:04.400 Joe, this is stupid. It's stupid. I'll tell you why. 0:05:04.440 --> 0:05:08.799 Because surge pricing was supposed to invite more supply into 0:05:08.800 --> 0:05:11.280 the market. So the idea is that you incentivize more 0:05:11.400 --> 0:05:14.080 drivers to get out on the street if they can 0:05:14.120 --> 0:05:16.760 earn more money. You're not going to get that with fastest. 0:05:16.800 --> 0:05:18.080 Do you think there's going to be in a meaningul 0:05:18.360 --> 0:05:19.960 supply response in Hamburgers? 0:05:20.000 --> 0:05:22.160 But there could be demand destruction, which I do think 0:05:22.200 --> 0:05:24.000 is part of the uber thing, which is that, yeah, 0:05:24.040 --> 0:05:26.679 you can't really have enough cards if everyone all wants 0:05:26.720 --> 0:05:30.680 to take a Uber at twelve oh one New Year's like, 0:05:30.720 --> 0:05:33.239 you have to raise the price such that some people 0:05:33.279 --> 0:05:36.120 like I'll take the subway or whatever. Anyway, enough, what. 0:05:36.120 --> 0:05:37.839 I think we don't have to debate. 0:05:37.640 --> 0:05:40.159 We don't have to debate this. We really do have 0:05:40.360 --> 0:05:44.560 two perfect guests to talk about this topic about how 0:05:44.680 --> 0:05:48.480 companies are getting better and better at personalized pricing, finding 0:05:48.520 --> 0:05:50.960 the absolute most they can charge for something at any 0:05:51.040 --> 0:05:53.560 given moment. We're going to be speaking with Lindsay Owen. 0:05:53.640 --> 0:05:57.159 She is the executive director of the Groundwork Collaborative and 0:05:57.240 --> 0:06:00.320 the author of a forthcoming book called Gouge that will 0:06:00.320 --> 0:06:02.600 be some time out in the future. And we're going 0:06:02.640 --> 0:06:05.680 to be speaking with David Dayan. He's the executive editor 0:06:05.760 --> 0:06:09.680 of The American Prospect magazine, and The American Prospect has 0:06:09.720 --> 0:06:12.839 a full edition of the magazine coming out on June 0:06:12.920 --> 0:06:16.960 third that is entirely devoted to the world of pricing 0:06:17.080 --> 0:06:19.600 and how companies do this in the history, and both 0:06:19.600 --> 0:06:21.479 of us have read the whole edition in the magazine. 0:06:21.760 --> 0:06:24.680 Is fantastic. They've worked together on this. It is a 0:06:24.800 --> 0:06:27.240 really interesting body of work. I think it will be 0:06:27.240 --> 0:06:29.720 important thing that a lot of people read. So excited 0:06:29.960 --> 0:06:32.680 to have Lindsay and David on the show, So thank 0:06:32.720 --> 0:06:35.159 you so much for coming on Outlaws, thanks for having me. 0:06:35.160 --> 0:06:36.080 Thanks for having us. 0:06:36.480 --> 0:06:39.680 Maybe David, I'll start with you, as the editor at 0:06:39.680 --> 0:06:42.960 the American Prospect doing this whole edition of the magazine 0:06:43.000 --> 0:06:45.520 on this topic. But both of you come in, why 0:06:45.680 --> 0:06:47.680 is this something I mean, you know, Tracy and I 0:06:47.720 --> 0:06:50.320 are both interested in this, but why is this something 0:06:50.680 --> 0:06:52.640 that is worth an entire magazine. 0:06:52.839 --> 0:06:56.560 Well, if you look at any poll that is talking 0:06:56.600 --> 0:07:00.200 to voters coming up in this election, inflation is the 0:07:00.320 --> 0:07:04.200 number one or right near the number one issue. So 0:07:04.920 --> 0:07:08.400 we have looked at this for a while. Lindsay obviously 0:07:08.560 --> 0:07:10.720 and her team at Groundwork has done a great job, 0:07:11.280 --> 0:07:12.960 and they came to me and said, you know, we 0:07:13.000 --> 0:07:16.720 really want to put something together that looks at pricing 0:07:16.880 --> 0:07:19.440 kind of in a holistic way. What we know has 0:07:19.520 --> 0:07:24.040 happened is that after the pandemic, there was this inflationary 0:07:24.120 --> 0:07:29.800 episode and markups and margins for companies went up, and 0:07:30.160 --> 0:07:33.160 they kind of stayed there even as inflation has eased. 0:07:33.800 --> 0:07:37.800 So we wanted to try to interrogate why this is 0:07:37.840 --> 0:07:40.440 happening and whether we've hit sort of a new era 0:07:41.240 --> 0:07:45.840 where these pricing strategies for a variety of reasons have 0:07:46.120 --> 0:07:53.280 become more widespread and companies have become more experimental, let's say, 0:07:53.760 --> 0:08:00.360 in trying to engage in this process of maximizing williness 0:08:00.360 --> 0:08:04.520 to pay among their customers, and so we think we 0:08:04.560 --> 0:08:06.680 can come up with kind of a thesis for this, 0:08:07.080 --> 0:08:10.400 and then the issue lays out that framing of why 0:08:10.440 --> 0:08:12.920 this is happening, and then looks at all of the 0:08:12.960 --> 0:08:16.640 strategies that are really being put to bear. You've mentioned 0:08:16.640 --> 0:08:19.280 some of them in the intro, whether it's surge pricing 0:08:19.360 --> 0:08:25.280 or dynamic pricing, or junk fees or using subscriptions to 0:08:25.400 --> 0:08:29.119 kind of we call it the inattention economy, get people 0:08:29.160 --> 0:08:32.240 to sign up to enough subscriptions so that they forget 0:08:32.559 --> 0:08:36.439 that they have them. You know, there's credit pricing, there's 0:08:36.679 --> 0:08:40.160 price fixing through algorithm that we're seeing more and more, 0:08:40.679 --> 0:08:44.680 and then there's this whole kind of next frontier of 0:08:45.080 --> 0:08:49.760 using digital surveillance and isolating customers enough so that you 0:08:49.800 --> 0:08:54.000 can personalize prices, which is really kind of where I 0:08:54.040 --> 0:08:56.880 think a lot of businesses see a lot of opportunity, 0:08:57.400 --> 0:09:00.160 the idea of that my price isn't the same is 0:09:00.200 --> 0:09:03.960 your price. So, you know, we lay out these strategies, 0:09:04.360 --> 0:09:07.000 I think it's important to see, you know, what companies 0:09:07.040 --> 0:09:11.560 are up to, and if it is deemed unfair or deceptive, 0:09:12.120 --> 0:09:15.280 what government, what role they have to play in maybe 0:09:15.520 --> 0:09:16.480 doing something about it. 0:09:17.760 --> 0:09:21.200 So I want to get into everything that you just mentioned, 0:09:21.400 --> 0:09:25.600 especially the sort of data privacy and algorithmic pricing points. 0:09:25.840 --> 0:09:28.560 But before we do, I think there's a tendency on 0:09:28.600 --> 0:09:32.320 this topic when you're talking about the idea of companies 0:09:32.600 --> 0:09:36.960 maybe driving up their prices, maybe that feeding into inflation. 0:09:37.280 --> 0:09:40.800 Lots of people use the word greed inflation here. I 0:09:41.120 --> 0:09:44.240 tend not to do that because the immediate reaction you 0:09:44.280 --> 0:09:48.000 will get, Joe, you mentioned well worn Twitter debates, But 0:09:48.080 --> 0:09:51.000 the immediate thing that happens is, oh, companies didn't get 0:09:51.320 --> 0:09:53.840 more greedy all of a sudden. They were always greedy, 0:09:53.880 --> 0:09:58.560 And so people tend to waive this theory away. But 0:09:58.600 --> 0:10:02.559 could you maybe talk about concrete evidence we have that 0:10:02.679 --> 0:10:07.560 companies are becoming more sophisticated when it comes to pricing 0:10:07.720 --> 0:10:13.720 or more willing to experiment with demand elasticity in recent years. 0:10:13.840 --> 0:10:16.000 Is there concrete numbers that back that up? 0:10:16.600 --> 0:10:19.520 Sure? So, I think one of the most interesting places 0:10:19.559 --> 0:10:22.679 to look here to answer your question is actually the 0:10:22.720 --> 0:10:28.520 burgeoning industry of algorithmic pricing companies and specialists. Right. So, 0:10:28.880 --> 0:10:31.400 you know, there was just this really interesting report that 0:10:31.520 --> 0:10:34.280 dropped last month from the Boston consulting Group, and the 0:10:34.320 --> 0:10:36.880 first sense of the report is retailers are in a 0:10:36.880 --> 0:10:39.400 new age of pricing and they need a new set 0:10:39.440 --> 0:10:41.800 of tools. And when you look through the report, what 0:10:41.840 --> 0:10:45.640 you see is really the consulting group outlining this new 0:10:45.679 --> 0:10:49.160 era of pricing and how companies need to increasingly be 0:10:49.240 --> 0:10:54.880 working with algorithmic data specialists and data service providers to compete. 0:10:54.960 --> 0:10:58.719 And so there's just this flourishing cottage industry of companies 0:10:58.760 --> 0:11:03.600 like Ravionics and Mantec and others who are basically bundling 0:11:03.679 --> 0:11:07.880 up competitors' data using sort of surveillance, targeting and geoanalytics 0:11:07.920 --> 0:11:11.000 to take in competitors' data and then spitting out for 0:11:11.040 --> 0:11:15.240 the retailer's advice and recommendations on you know, how to 0:11:15.320 --> 0:11:18.880 keep prices higher for faster and longer. And so when 0:11:18.920 --> 0:11:20.560 I get a question like this, I really just like 0:11:20.600 --> 0:11:23.920 to go to the quotes from the companies themselves, right, 0:11:24.000 --> 0:11:26.880 So what are they saying that they're selling, what are 0:11:26.920 --> 0:11:30.160 they recommending to these companies? And you know, some of 0:11:30.160 --> 0:11:33.040 the examples that we have I think are quite stark. 0:11:33.280 --> 0:11:36.040 You can go through just a couple of them. You know, 0:11:36.160 --> 0:11:42.079 companies recommending quote faster lasting implementation of price increases, recommending 0:11:42.120 --> 0:11:45.720 that they can help companies ferret out when they inadvertently 0:11:45.880 --> 0:11:49.560 keep prices quote too low for too long, help folks 0:11:49.640 --> 0:11:53.640 quote more quickly react to competitors pricing, and also ensure 0:11:53.720 --> 0:11:56.880 that their price hikes quote stick right. And so what 0:11:56.920 --> 0:11:59.640 you're seeing is a sort of cottage industry of companies 0:11:59.640 --> 0:12:03.800 who's really pushing retailers to go higher, faster, and for 0:12:03.920 --> 0:12:06.880 longer on prices. And I think that really matches what 0:12:07.040 --> 0:12:09.920 Dave mentioned up top, which is that you know, the 0:12:10.000 --> 0:12:13.000 sort of age of cost cutting has maybe hit bone, 0:12:13.120 --> 0:12:15.199 and now we're in this sort of age of recruitment 0:12:15.400 --> 0:12:19.400 and where revenue maximization and pricing is really critical to 0:12:19.480 --> 0:12:22.640 the game. And big data and new technologies has really 0:12:22.679 --> 0:12:25.640 allowed this pricing to go high tech and these new 0:12:25.679 --> 0:12:28.560 strategies to really flourish. And so I don't like to 0:12:28.559 --> 0:12:31.160 make too many predictions, but you know, my instinct here 0:12:31.240 --> 0:12:33.960 is that this is really the very very beginning of 0:12:34.000 --> 0:12:37.040 this new era of pricing. And I think you know, 0:12:37.080 --> 0:12:39.880 the amount of online shopping that folks did during COVID 0:12:39.920 --> 0:12:43.400 nineteen has obviously allowed folks to collect more and more 0:12:43.480 --> 0:12:47.040 data on consumers and I think we're just really at 0:12:47.080 --> 0:12:48.800 the tip of the iceberg here. We're just sort of 0:12:48.840 --> 0:12:52.719 starting to see these strategies unleashed across industries. 0:13:08.400 --> 0:13:11.440 As a journalist, I really like data, and I like 0:13:11.920 --> 0:13:16.120 companies that gather data and publish data on their corporate 0:13:16.160 --> 0:13:18.560 blogs about what's happening with this, and it's been certainly 0:13:18.640 --> 0:13:21.120 nice over the last several years to see more of 0:13:21.120 --> 0:13:26.000 them talk about though, like how this data is actually used, 0:13:26.040 --> 0:13:29.079 because one of the themes that comes up in this edition 0:13:29.160 --> 0:13:31.760 of the magazine is that when the data is out 0:13:31.800 --> 0:13:35.640 there in public, then companies can see more quickly, oh, 0:13:35.840 --> 0:13:39.240 we're actually underpricing or actually everyone else is charging more, 0:13:39.280 --> 0:13:42.560 and we can see this more easily than perhaps in 0:13:42.600 --> 0:13:45.640 the past when companies are trying to get data. Talk 0:13:45.679 --> 0:13:49.000 about this sort of like the role that data aggregators 0:13:49.000 --> 0:13:52.520 have and maybe the specific industries that use this data 0:13:52.559 --> 0:13:53.959 to get better at pushing price. 0:13:54.440 --> 0:13:56.160 Yeah, I mean, I think we can talk about it 0:13:56.240 --> 0:13:58.760 in a couple different ways. The first is this use 0:13:58.840 --> 0:14:02.320 of what has been called called algorithmic price fixing. So 0:14:03.120 --> 0:14:07.160 we see these aggregators that have arisen and it's not 0:14:07.240 --> 0:14:11.040 a very new thing. Actually, the airline industry has this 0:14:11.120 --> 0:14:15.400 thing called atp CO, the Airline Tariff Publishing Company, and 0:14:15.440 --> 0:14:18.760 it's been around since the I believe, the since deregulation 0:14:18.840 --> 0:14:23.600 in the nineteen eighties. And they collect real time data 0:14:23.680 --> 0:14:27.800 on every fare that's been published in the US and 0:14:27.840 --> 0:14:32.400 around the world, and all the companies who subscribe to 0:14:32.440 --> 0:14:35.240 atp CO can look at that and know when to 0:14:35.320 --> 0:14:38.040 adjust their prices in real time. The Justice Department actually 0:14:38.040 --> 0:14:42.800 looked at this as a collusion operation, but they allowed 0:14:42.840 --> 0:14:45.920 it to go forward in the nineteen nineties. Some of 0:14:45.960 --> 0:14:49.880 this data is proprietary. There's a lawsuit right now active 0:14:50.320 --> 0:14:53.840 between the Justice Department and a company called Agristats, which 0:14:53.840 --> 0:14:56.520 has also been around for quite a while. And this 0:14:56.600 --> 0:15:01.520 company collects real time proprietary data from all of the 0:15:01.920 --> 0:15:05.560 meat packing producers in a given market, whether it's pork 0:15:05.680 --> 0:15:10.560 or poultry, or chicken or turkey, and they put all 0:15:10.600 --> 0:15:13.360 this data in these giant books and they give them 0:15:13.440 --> 0:15:18.160 out to these various competitors, which now have basically a 0:15:18.240 --> 0:15:22.720 setup of everything that their competitors are doing, including their price, 0:15:22.760 --> 0:15:27.400 including their supply, including every single thing part of their market. 0:15:27.920 --> 0:15:31.400 And now they can know that, oh, I can probably 0:15:31.520 --> 0:15:35.880 raise my price because I'm under price relative to my competitor, 0:15:36.160 --> 0:15:38.680 but I won't lose market share because my competitor is 0:15:38.880 --> 0:15:42.600 charging more for this product and it has the tendency 0:15:42.640 --> 0:15:45.760 to ratchet prices upward. We've also seen this in rental 0:15:45.800 --> 0:15:50.080 markets with a company like real Page, which again goes 0:15:50.120 --> 0:15:53.760 out to landlords in a particular area, collects all of 0:15:53.800 --> 0:15:57.880 their pricing data, all of their supply data, distributes it 0:15:58.000 --> 0:16:03.600 broadly among these competitors, and allows them to raise their 0:16:03.640 --> 0:16:08.840 prices in tandem throughout the market. We know that price 0:16:08.880 --> 0:16:12.200 fixing has been kind of a bedrock of antitrust legislation. 0:16:12.320 --> 0:16:16.800 If you have evidence that three people executives have gone 0:16:16.840 --> 0:16:19.720 into a room and said we're going to raise our 0:16:19.760 --> 0:16:23.600 price by X amount of dollars, then the Justice Department 0:16:23.600 --> 0:16:26.040 will step in and they will put a lawsuit on 0:16:26.200 --> 0:16:29.480 those various people and put them in jail. Potentially, if 0:16:29.520 --> 0:16:31.920 you do it through an algorithm, which is the way 0:16:31.960 --> 0:16:35.760 that real Page and some of these other organizations operate, 0:16:36.280 --> 0:16:39.280 it's sort of more of an open question as to 0:16:39.960 --> 0:16:44.680 what the legal system will take from that and actually 0:16:44.760 --> 0:16:48.840 look at prosecuting it. But there's no real difference between 0:16:48.880 --> 0:16:53.000 algorithmic price collusion and in person price collusion, and so 0:16:53.120 --> 0:16:56.480 that is one of the ways by distributing aggregating that 0:16:56.600 --> 0:17:01.000 data across an entire industry and allowing those companies to 0:17:01.040 --> 0:17:04.000 have a window into that pricing. That's one way that 0:17:04.040 --> 0:17:06.400 this gets done. We can talk about the other way, 0:17:06.400 --> 0:17:09.520 which actually interacts with the McDonald's app, which we wrote 0:17:09.520 --> 0:17:12.040 about pretty extensively in this series. 0:17:11.760 --> 0:17:12.360 Go for It. 0:17:13.720 --> 0:17:16.720 So the McDonald's app is put together by a company 0:17:16.760 --> 0:17:20.359 called Plexure, and Plexure works with Ikea, they work with 0:17:20.400 --> 0:17:24.480 seven to eleven, they work with White Castle. And the reason, 0:17:24.680 --> 0:17:28.720 as you correctly said, Tracy, that McDonald's gives discounts on 0:17:28.760 --> 0:17:31.640 the app is because they want to get on your phone. 0:17:31.680 --> 0:17:33.720 They want to get on your phone and be able 0:17:34.240 --> 0:17:38.879 to figure out what you're doing on that phone, where 0:17:38.920 --> 0:17:42.280 you are at particular times of day, what your food 0:17:42.320 --> 0:17:46.840 preferences are, what you're ordering habits are, potentially, what you're 0:17:46.920 --> 0:17:50.440 using to pay for those things, and your financial behaviors. 0:17:50.480 --> 0:17:54.080 Through that, they're aggregating a bunch of data about you. 0:17:54.800 --> 0:17:58.080 And we had one of the slides from this presentation 0:17:58.760 --> 0:18:01.520 that Plexure put together the other that shows how they 0:18:01.720 --> 0:18:05.320 are using this data. And one of the things that 0:18:05.359 --> 0:18:09.480 they were using to make predictions about what people would 0:18:09.480 --> 0:18:13.080 be willing to pay was their payday. So you can 0:18:13.160 --> 0:18:16.360 imagine how you can use this. If the app knows 0:18:16.480 --> 0:18:20.200 that you get paid every other Friday, it might give 0:18:20.280 --> 0:18:24.360 you a three dollars McMuffin on Thursday, but when Friday 0:18:24.400 --> 0:18:26.280 you have some money in your pocket, it might raise 0:18:26.280 --> 0:18:27.240 it to four dollars. 0:18:27.440 --> 0:18:27.640 Right. 0:18:28.200 --> 0:18:31.840 If it knows that it's cold out, it might raise 0:18:31.880 --> 0:18:35.000 the price of hot coffee. If it knows it's hot out, 0:18:35.040 --> 0:18:39.200 it might raise the price of a mcflurry. Often, plecture 0:18:39.240 --> 0:18:44.160 combines this data that's within the app, like what they 0:18:44.160 --> 0:18:49.080 call first party data, with additional data about you through 0:18:49.320 --> 0:18:54.399 what is called an identity graph that aggregates both you know, 0:18:54.480 --> 0:18:57.560 stuff you're doing on the app, with your email, with 0:18:57.600 --> 0:19:01.119 your social media, with your browser, with your subscriptions, with 0:19:01.160 --> 0:19:04.240 your other app downloads, with your travel history, with your 0:19:04.320 --> 0:19:09.200 retail history, all of these other things. And the predictive 0:19:09.440 --> 0:19:15.400 power of that is such that you can pinpoint what 0:19:15.440 --> 0:19:18.200 you're going to buy, maybe before you even know, and 0:19:18.359 --> 0:19:23.040 therefore you can target prices accordingly. So I think we're 0:19:23.080 --> 0:19:25.400 at the beginning of this where they're trying to discount 0:19:25.400 --> 0:19:28.159 things and get people on the app and get people 0:19:28.280 --> 0:19:30.320 used to ordering on the app. But what that has 0:19:30.359 --> 0:19:34.400 the effect of doing is isolating the consumer. If you're 0:19:34.520 --> 0:19:37.600 buying through an app, there is no public price, there's 0:19:37.760 --> 0:19:41.040 just a price for you. And there are other ways 0:19:41.119 --> 0:19:44.760 that you know, through online commerce or through deals that 0:19:44.840 --> 0:19:49.040 are done through a smart TV, where the customer is 0:19:49.040 --> 0:19:52.800 isolated and doesn't really know what other people are paying 0:19:52.880 --> 0:19:56.399 for the same product. Because what personalized pricing is always 0:19:56.440 --> 0:20:01.160 run into is this sense of unfairness. And if it's 0:20:01.520 --> 0:20:04.960 very apparent that I paid three dollars and the guy 0:20:05.040 --> 0:20:07.760 behind me in line paid four dollars, I'm going to 0:20:07.800 --> 0:20:10.520 be mad about that. If I'm the guy paying four dollars, 0:20:10.520 --> 0:20:13.000 why did I pay more than the other guy? But 0:20:13.080 --> 0:20:15.600 if you don't know, if it's through your television, if 0:20:15.600 --> 0:20:18.320 it's through your phone, if it's through your web browser, 0:20:18.680 --> 0:20:21.280 and you don't have any idea what the other person paid, 0:20:21.840 --> 0:20:24.280 you're just not going to know to be upset, right. 0:20:24.960 --> 0:20:28.920 So I think that is the frontier that we are 0:20:29.080 --> 0:20:33.119 in many ways moving toward. And it's a fascinating and 0:20:33.600 --> 0:20:36.840 maybe you know, to some people, dystopian reality. 0:20:37.119 --> 0:20:39.320 I was literally about to use that word. 0:20:39.560 --> 0:20:41.600 Sorry, I just wanted to add I think it's just 0:20:41.640 --> 0:20:45.200 this really interesting period in history as well, because of course, 0:20:45.200 --> 0:20:47.040 this is sort of where we started, right. You know, 0:20:47.080 --> 0:20:49.800 people haggled. There was no set price for a good. 0:20:50.240 --> 0:20:52.040 You went to the bazaar, you went to the market, 0:20:52.080 --> 0:20:53.480 and you know, they took a look at you and 0:20:53.520 --> 0:20:55.879 maybe looked at your shoes, and depending on what they 0:20:56.000 --> 0:20:58.399 ate for breakfast that morning, they decided what to charge you. 0:20:58.600 --> 0:21:01.120 And in the United States context, you know, there were 0:21:01.119 --> 0:21:03.760 a few people who didn't think that was right. You know, 0:21:03.760 --> 0:21:06.720 the Quakers in Philadelphia felt that this type of price 0:21:06.800 --> 0:21:11.080 discrimination violated their religious principles that sort of every man 0:21:11.320 --> 0:21:16.000 was equal under God. And John Wannamaker, the Philadelphia department 0:21:16.040 --> 0:21:19.600 store owner, similarly had concerns about this. And by the way, 0:21:19.600 --> 0:21:22.439 a business case in a large department store, you know, 0:21:22.520 --> 0:21:25.000 haggling takes a little time, right, Like you want to 0:21:25.000 --> 0:21:27.159 move people through, like pick up your scarf, pick up 0:21:27.160 --> 0:21:29.760 your lipstick, get in line and check out. And he 0:21:29.960 --> 0:21:32.679 started the price tag, right. His sort of credo was 0:21:33.240 --> 0:21:36.680 one price and goods returnable. He also sort of invented 0:21:36.720 --> 0:21:39.800 the money back guarantee and allowed folks to start returning 0:21:39.800 --> 0:21:42.439 goods that they weren't satisfied with. And so, you know, 0:21:42.520 --> 0:21:45.800 for a long time, we've lived in a world throughout 0:21:45.840 --> 0:21:48.159 all of the twentieth century where there was by and 0:21:48.280 --> 0:21:51.600 large one price for goods. You know that was sometimes discounted, 0:21:51.640 --> 0:21:53.960 sometimes marked up. But you know, you went into the 0:21:54.000 --> 0:21:57.480 supermarket or the department store, and you know, unless you 0:21:57.640 --> 0:21:59.560 got there on the wrong day before the sale, like 0:21:59.800 --> 0:22:01.879 you the same amount as your friend did for the 0:22:01.920 --> 0:22:04.560 same good. And we're really in some ways returning to 0:22:04.640 --> 0:22:08.040 the bizarre the marketplace because of new technologies that are 0:22:08.119 --> 0:22:12.639 enabling companies to more aggressively tailor price discrimination. 0:22:13.600 --> 0:22:19.440 So this raises points about fairness and also privacy data 0:22:19.440 --> 0:22:24.359 privacy specifically, And David, you mentioned the word dystopian there, 0:22:24.480 --> 0:22:26.800 and I was thinking back to I used to cover 0:22:26.880 --> 0:22:29.359 the banks at the Ft and I wrote a piece 0:22:29.400 --> 0:22:34.199 back in twenty fifteen about exactly this theme. So the 0:22:34.280 --> 0:22:38.400 idea of financial companies using new types of data, new 0:22:38.440 --> 0:22:43.560 technology to basically build proxy profiles of their customers. And 0:22:43.640 --> 0:22:45.600 I remember I was out in San Francisco. I was 0:22:45.720 --> 0:22:49.760 talking to this new startup lender. They don't exist anymore, 0:22:49.760 --> 0:22:51.960 so I think I can tell the story, But they 0:22:52.000 --> 0:22:55.280 were talking about the types of data that they could 0:22:55.280 --> 0:22:58.200 collect from their customers, and I really think people don't 0:22:58.280 --> 0:23:02.720 understand the extent of what is available to companies. But 0:23:02.800 --> 0:23:05.359 they were talking about how if someone was applying for 0:23:05.440 --> 0:23:09.160