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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of The Odd Lots Podcast.

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Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.

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Speaker 2: Tracy, you know what I feel is become a common

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Speaker 2: Twitter conversation that I've seen happen a bunch of times.

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Speaker 3: Uh, this could be anything, but go on.

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Speaker 2: Someone tweets like, oh my god, I just paid, like,

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Speaker 2: you know, fourteen dollars for a hamburger and fries at

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Speaker 2: the McDonald's and then someone else goes, well, actually, you

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Speaker 2: can get it for three ninety nine right now if

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Speaker 2: you just use the app. Yes, I've seen this many times.

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Speaker 3: Both of them are not wrong, but it is crazy.

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Speaker 3: First of all, I'm so thrilled that we're finally going

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Speaker 3: to do price Pack Architecture episode. That's basically what this is, right,

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Speaker 3: all these different strategies when it comes to how companies

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Speaker 3: are actually pricing their goods. But I feel like McDonald's

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Speaker 3: has become a very very good example of this particular behavior.

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Speaker 3: And at this point, as you pointed out, it is

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Speaker 3: well known that if you just roll up to a

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Speaker 3: McDonald's and you know, order at the drive through or

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Speaker 3: in the store, you are going to be paying a

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Speaker 3: higher price than if you used the app and ordered

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Speaker 3: on there, And they have tons of discounts. The discounts

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Speaker 3: are almost gamified at this point, like you know, you

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Speaker 3: check in on different days and you can get different

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Speaker 3: things and they're constantly changing. Oh and also they have

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Speaker 3: an actual game that if you play, you get loyalty

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Speaker 3: points that turn into discounts. But the thing that I

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Speaker 3: think is so fascinating about all of this is it

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Speaker 3: throws up really interesting questions around fairness. So is it

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Speaker 3: fair that people are paying two different prices depending on

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Speaker 3: the way that they are actually buying the thing. I

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Speaker 3: think the other thing that's remarkable in all the price

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Speaker 3: conversations is people seem to think that one person paying

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Speaker 3: a higher price is really unfair. But on the other hand,

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Speaker 3: everyone likes discounts, y Like if the lower price comes

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Speaker 3: in the form of a coupon, people get really excited.

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Speaker 3: It also throws up interesting questions about data privacy. So

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Speaker 3: the reason McDonald's wants you on the app is so

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Speaker 3: that it can collect your data and it gives you

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Speaker 3: a lower price in return for that. And then thirdly,

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Speaker 3: it raises all sorts of interesting macroeconomic questions. Right, if

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Speaker 3: companies are becoming more strategic, more differentiated in the way

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Speaker 3: they're pricing their goods, what does that mean for things

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Speaker 3: like inflation? What does it mean for traditional interpretations of

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Speaker 3: the way inflation works? Is it just you know, unemployment,

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Speaker 3: supply demand, that sort of thing, Right.

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Speaker 2: Like companies basically just getting better at figuring out the

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Speaker 2: maximum price they can charge for something. Wait, I have

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Speaker 2: a personal question for your Tracy. I've never asked you

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Speaker 2: this before. Are you like a points person, like when

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Speaker 2: it comes to hotels and airlines and stuff like that.

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Speaker 3: No, I'm not, and I feel like I'm basically too

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Speaker 3: lazy to sign up for a lot of things. But

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Speaker 3: I will say McDonald's got me, I do have. I

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Speaker 3: do have the app, and I have, as a result

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Speaker 3: of the app, ended up ordering like insane amounts of

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Speaker 3: junk food because I'm just like, Oh, I can buy

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Speaker 3: two things of French fries instead of one, So why

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Speaker 3: don't I go ahead and do that?

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Speaker 2: Yeah, I'm so lazy. I am not a points person.

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Speaker 2: I'm not an app person. I've never been a Miles person.

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Speaker 2: It seems like I probably should. I don't trouble that much,

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Speaker 2: but probably enough that I should like track this stuff

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Speaker 2: and have a favorite hotel that I go to in

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Speaker 2: every town, or have a favorite airline. All airlines seem

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Speaker 2: the same to me. They all seem sort of various

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Speaker 2: versions of kind of unpleasant. But I'm not like optimized

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Speaker 2: for that at all, But it feels like to some extent,

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Speaker 2: what we're talking about is this sort of widespreadness across

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Speaker 2: many industries of what the airlines have figured out for decades.

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Speaker 3: Absolutely, and also Uber is the classic example with dynamic

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Speaker 3: surge pricing. And you can remember earlier this year when

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Speaker 3: Wendy's mentioned dynamic pricing in its earnings call, the world

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Speaker 3: absolutely went nuts, and then they kind of backwalked on it.

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Speaker 3: But I mean my argument is like surge pricing in

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Speaker 3: fast food is kind of already there, right, you know,

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Speaker 3: the difference in how you're ordering at McDonald's is a

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Speaker 3: variable of how much value you place on your time

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Speaker 3: and your convenience, and so it's kind of already happening.

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Speaker 3: And I think this is such a fascinating topic for

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Speaker 3: many many reasons. But I am so so happy that

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Speaker 3: we are finally doing this one.

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Speaker 2: I am too. I'm going to just lay my cards

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Speaker 2: out on the table right here. It's like, I don't know.

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Speaker 2: I kind of get surge pricing for food. If a

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Speaker 2: bunch of people all jam up at the same time,

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Speaker 2: maybe like raise the prices so people spread it out

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Speaker 2: a little bit. In this conversation, I will play the

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Speaker 2: role of the devil's advocate, who is like, yeah, I'm

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Speaker 2: okay with like, you know, differentiating prices.

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Speaker 3: Joe, this is stupid. It's stupid. I'll tell you why.

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Speaker 3: Because surge pricing was supposed to invite more supply into

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Speaker 3: the market. So the idea is that you incentivize more

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Speaker 3: drivers to get out on the street if they can

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Speaker 3: earn more money. You're not going to get that with fastest.

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Speaker 3: Do you think there's going to be in a meaningul

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Speaker 3: supply response in Hamburgers?

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Speaker 2: But there could be demand destruction, which I do think

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Speaker 2: is part of the uber thing, which is that, yeah,

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Speaker 2: you can't really have enough cards if everyone all wants

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Speaker 2: to take a Uber at twelve oh one New Year's like,

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Speaker 2: you have to raise the price such that some people

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Speaker 2: like I'll take the subway or whatever. Anyway, enough, what.

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Speaker 3: I think we don't have to debate.

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Speaker 2: We don't have to debate this. We really do have

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Speaker 2: two perfect guests to talk about this topic about how

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Speaker 2: companies are getting better and better at personalized pricing, finding

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Speaker 2: the absolute most they can charge for something at any

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Speaker 2: given moment. We're going to be speaking with Lindsay Owen.

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Speaker 2: She is the executive director of the Groundwork Collaborative and

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Speaker 2: the author of a forthcoming book called Gouge that will

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Speaker 2: be some time out in the future. And we're going

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Speaker 2: to be speaking with David Dayan. He's the executive editor

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Speaker 2: of The American Prospect magazine, and The American Prospect has

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Speaker 2: a full edition of the magazine coming out on June

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Speaker 2: third that is entirely devoted to the world of pricing

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Speaker 2: and how companies do this in the history, and both

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Speaker 2: of us have read the whole edition in the magazine.

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Speaker 2: Is fantastic. They've worked together on this. It is a

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Speaker 2: really interesting body of work. I think it will be

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Speaker 2: important thing that a lot of people read. So excited

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Speaker 2: to have Lindsay and David on the show, So thank

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Speaker 2: you so much for coming on Outlaws, thanks for having me.

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Speaker 4: Thanks for having us.

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Speaker 2: Maybe David, I'll start with you, as the editor at

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Speaker 2: the American Prospect doing this whole edition of the magazine

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Speaker 2: on this topic. But both of you come in, why

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Speaker 2: is this something I mean, you know, Tracy and I

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Speaker 2: are both interested in this, but why is this something

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Speaker 2: that is worth an entire magazine.

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Speaker 5: Well, if you look at any poll that is talking

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Speaker 5: to voters coming up in this election, inflation is the

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Speaker 5: number one or right near the number one issue. So

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Speaker 5: we have looked at this for a while. Lindsay obviously

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Speaker 5: and her team at Groundwork has done a great job,

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Speaker 5: and they came to me and said, you know, we

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Speaker 5: really want to put something together that looks at pricing

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Speaker 5: kind of in a holistic way. What we know has

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Speaker 5: happened is that after the pandemic, there was this inflationary

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Speaker 5: episode and markups and margins for companies went up, and

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Speaker 5: they kind of stayed there even as inflation has eased.

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Speaker 5: So we wanted to try to interrogate why this is

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Speaker 5: happening and whether we've hit sort of a new era

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Speaker 5: where these pricing strategies for a variety of reasons have

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Speaker 5: become more widespread and companies have become more experimental, let's say,

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Speaker 5: in trying to engage in this process of maximizing williness

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Speaker 5: to pay among their customers, and so we think we

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Speaker 5: can come up with kind of a thesis for this,

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Speaker 5: and then the issue lays out that framing of why

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Speaker 5: this is happening, and then looks at all of the

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Speaker 5: strategies that are really being put to bear. You've mentioned

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Speaker 5: some of them in the intro, whether it's surge pricing

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Speaker 5: or dynamic pricing, or junk fees or using subscriptions to

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Speaker 5: kind of we call it the inattention economy, get people

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Speaker 5: to sign up to enough subscriptions so that they forget

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Speaker 5: that they have them. You know, there's credit pricing, there's

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Speaker 5: price fixing through algorithm that we're seeing more and more,

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Speaker 5: and then there's this whole kind of next frontier of

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Speaker 5: using digital surveillance and isolating customers enough so that you

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Speaker 5: can personalize prices, which is really kind of where I

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Speaker 5: think a lot of businesses see a lot of opportunity,

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Speaker 5: the idea of that my price isn't the same is

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Speaker 5: your price. So, you know, we lay out these strategies,

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Speaker 5: I think it's important to see, you know, what companies

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Speaker 5: are up to, and if it is deemed unfair or deceptive,

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Speaker 5: what government, what role they have to play in maybe

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Speaker 5: doing something about it.

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Speaker 3: So I want to get into everything that you just mentioned,

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Speaker 3: especially the sort of data privacy and algorithmic pricing points.

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Speaker 3: But before we do, I think there's a tendency on

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Speaker 3: this topic when you're talking about the idea of companies

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Speaker 3: maybe driving up their prices, maybe that feeding into inflation.

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Speaker 3: Lots of people use the word greed inflation here. I

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Speaker 3: tend not to do that because the immediate reaction you

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Speaker 3: will get, Joe, you mentioned well worn Twitter debates, But

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Speaker 3: the immediate thing that happens is, oh, companies didn't get

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Speaker 3: more greedy all of a sudden. They were always greedy,

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Speaker 3: And so people tend to waive this theory away. But

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Speaker 3: could you maybe talk about concrete evidence we have that

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Speaker 3: companies are becoming more sophisticated when it comes to pricing

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Speaker 3: or more willing to experiment with demand elasticity in recent years.

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Speaker 3: Is there concrete numbers that back that up?

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Speaker 4: Sure? So, I think one of the most interesting places

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Speaker 4: to look here to answer your question is actually the

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Speaker 4: burgeoning industry of algorithmic pricing companies and specialists. Right. So,

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Speaker 4: you know, there was just this really interesting report that

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Speaker 4: dropped last month from the Boston consulting Group, and the

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Speaker 4: first sense of the report is retailers are in a

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Speaker 4: new age of pricing and they need a new set

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Speaker 4: of tools. And when you look through the report, what

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Speaker 4: you see is really the consulting group outlining this new

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Speaker 4: era of pricing and how companies need to increasingly be

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Speaker 4: working with algorithmic data specialists and data service providers to compete.

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Speaker 4: And so there's just this flourishing cottage industry of companies

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Speaker 4: like Ravionics and Mantec and others who are basically bundling

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Speaker 4: up competitors' data using sort of surveillance, targeting and geoanalytics

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Speaker 4: to take in competitors' data and then spitting out for

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Speaker 4: the retailer's advice and recommendations on you know, how to

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Speaker 4: keep prices higher for faster and longer. And so when

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Speaker 4: I get a question like this, I really just like

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Speaker 4: to go to the quotes from the companies themselves, right,

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Speaker 4: So what are they saying that they're selling, what are

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Speaker 4: they recommending to these companies? And you know, some of

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Speaker 4: the examples that we have I think are quite stark.

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Speaker 4: You can go through just a couple of them. You know,

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Speaker 4: companies recommending quote faster lasting implementation of price increases, recommending

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Speaker 4: that they can help companies ferret out when they inadvertently

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Speaker 4: keep prices quote too low for too long, help folks

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Speaker 4: quote more quickly react to competitors pricing, and also ensure

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Speaker 4: that their price hikes quote stick right. And so what

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Speaker 4: you're seeing is a sort of cottage industry of companies

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Speaker 4: who's really pushing retailers to go higher, faster, and for

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Speaker 4: longer on prices. And I think that really matches what

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Speaker 4: Dave mentioned up top, which is that you know, the

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Speaker 4: sort of age of cost cutting has maybe hit bone,

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Speaker 4: and now we're in this sort of age of recruitment

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Speaker 4: and where revenue maximization and pricing is really critical to

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Speaker 4: the game. And big data and new technologies has really

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Speaker 4: allowed this pricing to go high tech and these new

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Speaker 4: strategies to really flourish. And so I don't like to

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Speaker 4: make too many predictions, but you know, my instinct here

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Speaker 4: is that this is really the very very beginning of

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Speaker 4: this new era of pricing. And I think you know,

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Speaker 4: the amount of online shopping that folks did during COVID

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Speaker 4: nineteen has obviously allowed folks to collect more and more

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Speaker 4: data on consumers and I think we're just really at

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Speaker 4: the tip of the iceberg here. We're just sort of

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Speaker 4: starting to see these strategies unleashed across industries.

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Speaker 2: As a journalist, I really like data, and I like

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Speaker 2: companies that gather data and publish data on their corporate

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Speaker 2: blogs about what's happening with this, and it's been certainly

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Speaker 2: nice over the last several years to see more of

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Speaker 2: them talk about though, like how this data is actually used,

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Speaker 2: because one of the themes that comes up in this edition

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Speaker 2: of the magazine is that when the data is out

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Speaker 2: there in public, then companies can see more quickly, oh,

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Speaker 2: we're actually underpricing or actually everyone else is charging more,

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Speaker 2: and we can see this more easily than perhaps in

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Speaker 2: the past when companies are trying to get data. Talk

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Speaker 2: about this sort of like the role that data aggregators

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Speaker 2: have and maybe the specific industries that use this data

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Speaker 2: to get better at pushing price.

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Speaker 5: Yeah, I mean, I think we can talk about it

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Speaker 5: in a couple different ways. The first is this use

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Speaker 5: of what has been called called algorithmic price fixing. So

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Speaker 5: we see these aggregators that have arisen and it's not

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Speaker 5: a very new thing. Actually, the airline industry has this

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Speaker 5: thing called atp CO, the Airline Tariff Publishing Company, and

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Speaker 5: it's been around since the I believe, the since deregulation

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Speaker 5: in the nineteen eighties. And they collect real time data

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Speaker 5: on every fare that's been published in the US and

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Speaker 5: around the world, and all the companies who subscribe to

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Speaker 5: atp CO can look at that and know when to

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Speaker 5: adjust their prices in real time. The Justice Department actually

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Speaker 5: looked at this as a collusion operation, but they allowed

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Speaker 5: it to go forward in the nineteen nineties. Some of

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Speaker 5: this data is proprietary. There's a lawsuit right now active

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Speaker 5: between the Justice Department and a company called Agristats, which

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Speaker 5: has also been around for quite a while. And this

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Speaker 5: company collects real time proprietary data from all of the

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Speaker 5: meat packing producers in a given market, whether it's pork

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Speaker 5: or poultry, or chicken or turkey, and they put all

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Speaker 5: this data in these giant books and they give them

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Speaker 5: out to these various competitors, which now have basically a

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Speaker 5: setup of everything that their competitors are doing, including their price,

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Speaker 5: including their supply, including every single thing part of their market.

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Speaker 5: And now they can know that, oh, I can probably

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Speaker 5: raise my price because I'm under price relative to my competitor,

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Speaker 5: but I won't lose market share because my competitor is

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Speaker 5: charging more for this product and it has the tendency

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Speaker 5: to ratchet prices upward. We've also seen this in rental

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Speaker 5: markets with a company like real Page, which again goes

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Speaker 5: out to landlords in a particular area, collects all of

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Speaker 5: their pricing data, all of their supply data, distributes it

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Speaker 5: broadly among these competitors, and allows them to raise their

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Speaker 5: prices in tandem throughout the market. We know that price

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Speaker 5: fixing has been kind of a bedrock of antitrust legislation.

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Speaker 5: If you have evidence that three people executives have gone

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Speaker 5: into a room and said we're going to raise our

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Speaker 5: price by X amount of dollars, then the Justice Department

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Speaker 5: will step in and they will put a lawsuit on

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Speaker 5: those various people and put them in jail. Potentially, if

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Speaker 5: you do it through an algorithm, which is the way

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Speaker 5: that real Page and some of these other organizations operate,

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Speaker 5: it's sort of more of an open question as to

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Speaker 5: what the legal system will take from that and actually

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Speaker 5: look at prosecuting it. But there's no real difference between

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Speaker 5: algorithmic price collusion and in person price collusion, and so

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Speaker 5: that is one of the ways by distributing aggregating that

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Speaker 5: data across an entire industry and allowing those companies to

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Speaker 5: have a window into that pricing. That's one way that

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Speaker 5: this gets done. We can talk about the other way,

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Speaker 5: which actually interacts with the McDonald's app, which we wrote

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Speaker 5: about pretty extensively in this series.

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Speaker 3: Go for It.

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Speaker 5: So the McDonald's app is put together by a company

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Speaker 5: called Plexure, and Plexure works with Ikea, they work with

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Speaker 5: seven to eleven, they work with White Castle. And the reason,

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Speaker 5: as you correctly said, Tracy, that McDonald's gives discounts on

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Speaker 5: the app is because they want to get on your phone.

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Speaker 5: They want to get on your phone and be able

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Speaker 5: to figure out what you're doing on that phone, where

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Speaker 5: you are at particular times of day, what your food

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Speaker 5: preferences are, what you're ordering habits are, potentially, what you're

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Speaker 5: using to pay for those things, and your financial behaviors.

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Speaker 5: Through that, they're aggregating a bunch of data about you.

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Speaker 5: And we had one of the slides from this presentation

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Speaker 5: that Plexure put together the other that shows how they

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Speaker 5: are using this data. And one of the things that

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Speaker 5: they were using to make predictions about what people would

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Speaker 5: be willing to pay was their payday. So you can

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Speaker 5: imagine how you can use this. If the app knows

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Speaker 5: that you get paid every other Friday, it might give

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Speaker 5: you a three dollars McMuffin on Thursday, but when Friday

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Speaker 5: you have some money in your pocket, it might raise

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Speaker 5: it to four dollars.

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Speaker 3: Right.

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Speaker 5: If it knows that it's cold out, it might raise

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Speaker 5: the price of hot coffee. If it knows it's hot out,

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Speaker 5: it might raise the price of a mcflurry. Often, plecture

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Speaker 5: combines this data that's within the app, like what they

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Speaker 5: call first party data, with additional data about you through

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Speaker 5: what is called an identity graph that aggregates both you know,

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Speaker 5: stuff you're doing on the app, with your email, with

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Speaker 5: your social media, with your browser, with your subscriptions, with

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Speaker 5: your other app downloads, with your travel history, with your

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Speaker 5: retail history, all of these other things. And the predictive

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Speaker 5: power of that is such that you can pinpoint what

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Speaker 5: you're going to buy, maybe before you even know, and

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Speaker 5: therefore you can target prices accordingly. So I think we're

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Speaker 5: at the beginning of this where they're trying to discount

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Speaker 5: things and get people on the app and get people

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Speaker 5: used to ordering on the app. But what that has

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Speaker 5: the effect of doing is isolating the consumer. If you're

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Speaker 5: buying through an app, there is no public price, there's

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Speaker 5: just a price for you. And there are other ways

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Speaker 5: that you know, through online commerce or through deals that

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Speaker 5: are done through a smart TV, where the customer is

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Speaker 5: isolated and doesn't really know what other people are paying

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Speaker 5: for the same product. Because what personalized pricing is always

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Speaker 5: run into is this sense of unfairness. And if it's

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Speaker 5: very apparent that I paid three dollars and the guy

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Speaker 5: behind me in line paid four dollars, I'm going to

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Speaker 5: be mad about that. If I'm the guy paying four dollars,

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Speaker 5: why did I pay more than the other guy? But

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Speaker 5: if you don't know, if it's through your television, if

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Speaker 5: it's through your phone, if it's through your web browser,

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Speaker 5: and you don't have any idea what the other person paid,

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Speaker 5: you're just not going to know to be upset, right.

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Speaker 5: So I think that is the frontier that we are

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Speaker 5: in many ways moving toward. And it's a fascinating and

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Speaker 5: maybe you know, to some people, dystopian reality.

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Speaker 3: I was literally about to use that word.

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Speaker 4: Sorry, I just wanted to add I think it's just

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Speaker 4: this really interesting period in history as well, because of course,

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Speaker 4: this is sort of where we started, right. You know,

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Speaker 4: people haggled. There was no set price for a good.

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Speaker 4: You went to the bazaar, you went to the market,

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Speaker 4: and you know, they took a look at you and

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Speaker 4: maybe looked at your shoes, and depending on what they

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Speaker 4: ate for breakfast that morning, they decided what to charge you.

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Speaker 4: And in the United States context, you know, there were

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Speaker 4: a few people who didn't think that was right. You know,

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Speaker 4: the Quakers in Philadelphia felt that this type of price

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Speaker 4: discrimination violated their religious principles that sort of every man

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Speaker 4: was equal under God. And John Wannamaker, the Philadelphia department

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Speaker 4: store owner, similarly had concerns about this. And by the way,

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Speaker 4: a business case in a large department store, you know,

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Speaker 4: haggling takes a little time, right, Like you want to

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Speaker 4: move people through, like pick up your scarf, pick up

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Speaker 4: your lipstick, get in line and check out. And he

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Speaker 4: started the price tag, right. His sort of credo was

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Speaker 4: one price and goods returnable. He also sort of invented

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Speaker 4: the money back guarantee and allowed folks to start returning

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Speaker 4: goods that they weren't satisfied with. And so, you know,

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Speaker 4: for a long time, we've lived in a world throughout

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Speaker 4: all of the twentieth century where there was by and

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Speaker 4: large one price for goods. You know that was sometimes discounted,

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Speaker 4: sometimes marked up. But you know, you went into the

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Speaker 4: supermarket or the department store, and you know, unless you

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Speaker 4: got there on the wrong day before the sale, like

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Speaker 4: you the same amount as your friend did for the

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Speaker 4: same good. And we're really in some ways returning to

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Speaker 4: the bizarre the marketplace because of new technologies that are

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Speaker 4: enabling companies to more aggressively tailor price discrimination.

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Speaker 3: So this raises points about fairness and also privacy data

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Speaker 3: privacy specifically, And David, you mentioned the word dystopian there,

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Speaker 3: and I was thinking back to I used to cover

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Speaker 3: the banks at the Ft and I wrote a piece

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Speaker 3: back in twenty fifteen about exactly this theme. So the

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Speaker 3: idea of financial companies using new types of data, new

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Speaker 3: technology to basically build proxy profiles of their customers. And

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Speaker 3: I remember I was out in San Francisco. I was

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Speaker 3: talking to this new startup lender. They don't exist anymore,

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Speaker 3: so I think I can tell the story, But they

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Speaker 3: were talking about the types of data that they could

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Speaker 3: collect from their customers, and I really think people don't

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Speaker 3: understand the extent of what is available to companies. But

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Speaker 3: they were talking about how if someone was applying for

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Speaker 3: a loan on their website, they could use a sort

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Speaker 3: of slider to decide what amount of money they were

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Speaker 3: asking for, so anything from I don't know, one hundred

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Speaker 3: dollars to like ten thousand dollars something like that, and

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Speaker 3: the company could track how fast they were moving that slider,

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Speaker 3: and it was supposed to be an indication of how

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Speaker 3: sort of what's the word impulsive the customer was. So

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Speaker 3: if you move the slider really fast, you're probably not

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Speaker 3: a very good credit risk. But if you're sort of

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Speaker 3: like considerate, or you immediately move it to one point

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Speaker 3: and leave it there, maybe you're a better risk. And

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Speaker 3: then in addition to that, when it comes to finances

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Speaker 3: and extending credit, there are obviously protected classes out there,

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Speaker 3: so you know, race, gender, I think age as well,

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Speaker 3: that companies are not allowed to discriminate against. But when

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Speaker 3: you have all this data, you can basically build proxy

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Speaker 3: profiles of people, and there are certain you know, indicators

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Speaker 3: of whether or not someone is white or black, depending

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Speaker 3: on like what type of browser they're using, what type

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Speaker 3: of phone, where they are, et cetera, et cetera. How

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Speaker 3: does our current legal system view some of this personalized pricing?

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Speaker 3: What's that discussion like at the moment?

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Speaker 5: Yeah, I mean I talked to Lena Khan for this issue.

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Speaker 5: She's the chair of the Federal Trade Commission, and you know,

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Speaker 5: she said that there was one point in which this

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Speaker 5: idea of personalized pricing or what you know some people

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Speaker 5: that I talked to called surveillance pricing, that it was

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Speaker 5: just sort of a theoretical exercise. It was something that

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Speaker 5: economists liked to take a look at to see whether

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Speaker 5: it created surplus value or not. And now we're reaching

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Speaker 5: this kind of terrifying reality where actually you collect enough

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Speaker 5: data that you can do it. One of the more

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Speaker 5: disturbing things that we saw in this in going through

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Speaker 5: the research for this issue, was to study out in

435
00:25:06,240 --> 00:25:12,520
Speaker 5: Belgium where they looked at uber prices and they took

436
00:25:12,680 --> 00:25:16,639
Speaker 5: two people in the same place going to the same destination,

437
00:25:17,480 --> 00:25:21,840
Speaker 5: and it noticed that it charged more if the individual's

438
00:25:21,920 --> 00:25:27,399
Speaker 5: phone battery was low. And what the surmise is is

439
00:25:27,440 --> 00:25:31,639
Speaker 5: that that's a proxy for you're desperate, you need a

440
00:25:31,720 --> 00:25:34,240
Speaker 5: ride pretty much right now because your battery is going

441
00:25:34,280 --> 00:25:37,639
Speaker 5: to run out, and so we can charge you more

442
00:25:37,840 --> 00:25:40,000
Speaker 5: on that point. And you know, I've talked to a

443
00:25:40,200 --> 00:25:42,520
Speaker 5: University of Chicago economists that said, well, that might be

444
00:25:42,520 --> 00:25:45,080
Speaker 5: a proxy for it's late in the night, but that's

445
00:25:45,119 --> 00:25:47,560
Speaker 5: not the way that they designed the experiment. It was

446
00:25:47,640 --> 00:25:50,520
Speaker 5: two people at the very same time. One had eighty

447
00:25:50,560 --> 00:25:53,040
Speaker 5: four percent on their battery and one had twelve percent,

448
00:25:53,320 --> 00:25:56,040
Speaker 5: and the twelve percent person was charged more from the

449
00:25:56,080 --> 00:25:59,600
Speaker 5: same location going to the same place. So this kind

450
00:25:59,640 --> 00:26:03,040
Speaker 5: of stuff just wasn't available a while ago. And one

451
00:26:03,160 --> 00:26:06,159
Speaker 5: question is what the legal system is going to do

452
00:26:06,280 --> 00:26:09,560
Speaker 5: about this In terms of court cases. Talking about the

453
00:26:09,640 --> 00:26:12,360
Speaker 5: algorithmic surge pricing that I mentioned, there was a quirk

454
00:26:12,440 --> 00:26:17,320
Speaker 5: case over a company called rain Maker, which was working

455
00:26:17,600 --> 00:26:22,240
Speaker 5: with Las Vegas hotels and once again aggregating prices, showing

456
00:26:22,440 --> 00:26:25,800
Speaker 5: these particular casino hotels a picture of the market so

457
00:26:25,840 --> 00:26:28,560
Speaker 5: that they could raise their prices, and the judge throw

458
00:26:28,600 --> 00:26:31,880
Speaker 5: out the case because he said, well, they were only

459
00:26:31,960 --> 00:26:36,080
Speaker 5: recommending certain prices, they weren't mandating it. Even though the

460
00:26:36,160 --> 00:26:41,119
Speaker 5: statistics that rain Maker even submitted say that ninety percent

461
00:26:41,160 --> 00:26:43,760
Speaker 5: of the time the recommendation has taken and that they

462
00:26:44,200 --> 00:26:48,480
Speaker 5: strongly encourage people to take the recommendation otherwise they cut

463
00:26:48,480 --> 00:26:51,439
Speaker 5: them off the service. So how the legal system is

464
00:26:51,440 --> 00:26:54,840
Speaker 5: going to react here as an open question. But lawmakers

465
00:26:54,920 --> 00:26:59,800
Speaker 5: and policymakers do have tools here. There are tools against

466
00:27:00,119 --> 00:27:04,480
Speaker 5: unfair and deceptive practices that the FTC has and also

467
00:27:04,760 --> 00:27:08,359
Speaker 5: you know agencies like the Department of Transportation has with

468
00:27:08,400 --> 00:27:13,159
Speaker 5: respect to the airlines. There are other various anti price

469
00:27:13,200 --> 00:27:16,040
Speaker 5: gouging tools and things of that nature, and there are

470
00:27:16,080 --> 00:27:19,400
Speaker 5: also anti trust tools. Because the one secret sauce here

471
00:27:19,520 --> 00:27:24,040
Speaker 5: is market power. The idea that you can just sort

472
00:27:24,080 --> 00:27:27,720
Speaker 5: of willy nearly raise your prices in a competitive market.

473
00:27:27,760 --> 00:27:30,480
Speaker 5: That's going to create a situation where a competitor is

474
00:27:30,520 --> 00:27:33,679
Speaker 5: going to undercut you because they know that you're charging

475
00:27:33,680 --> 00:27:36,600
Speaker 5: too much. In the market will sort of rebalance itself.

476
00:27:37,000 --> 00:27:39,359
Speaker 5: If you have a tremendous amount of market power and

477
00:27:39,400 --> 00:27:42,919
Speaker 5: therefore pricing power, you have the ability to continue this

478
00:27:43,119 --> 00:27:47,399
Speaker 5: without kind of worrying about whether your customers will go away.

479
00:27:47,440 --> 00:27:51,280
Speaker 5: You've created a moat around your business. So that's a

480
00:27:51,359 --> 00:27:54,000
Speaker 5: key facet of this as well. If you know, competition

481
00:27:54,119 --> 00:27:57,760
Speaker 5: policy moves towards a place where these markets suddenly have

482
00:27:58,160 --> 00:28:02,199
Speaker 5: more choices for customers, then these pricing strategies lose a

483
00:28:02,240 --> 00:28:03,200
Speaker 5: little bit of their power.

484
00:28:03,640 --> 00:28:05,440
Speaker 4: One thing I would just add is, I think we're

485
00:28:05,520 --> 00:28:09,560
Speaker 4: really in a new legal frontier when it comes to

486
00:28:09,800 --> 00:28:15,560
Speaker 4: personalized pricing and price discrimination and protection of protected classes.

487
00:28:15,800 --> 00:28:19,240
Speaker 4: You know, as you point out, any set of pricing

488
00:28:19,400 --> 00:28:24,000
Speaker 4: that relies in whole or in part on geography in

489
00:28:24,040 --> 00:28:28,760
Speaker 4: the United States, given the extraordinary segregation in the United

490
00:28:28,760 --> 00:28:33,000
Speaker 4: States by geography, is ultimately going to have a racial bias,

491
00:28:33,200 --> 00:28:36,159
Speaker 4: intended or unintended, right, And so you know, there have

492
00:28:36,160 --> 00:28:39,080
Speaker 4: been some really interesting studies. There was a study of

493
00:28:39,240 --> 00:28:42,720
Speaker 4: uber and lyft rides in Chicago and they looked at

494
00:28:42,720 --> 00:28:45,520
Speaker 4: like over one hundred million rides, I believe, and what

495
00:28:45,560 --> 00:28:49,360
Speaker 4: they showed is that, you know, if either the destination

496
00:28:49,520 --> 00:28:52,040
Speaker 4: or the pickup point had a higher percentage of non

497
00:28:52,080 --> 00:28:56,360
Speaker 4: white residents, low income residents, or low income residents, you

498
00:28:56,480 --> 00:29:00,280
Speaker 4: saw higher fares. Now, of course, supply and demand can

499
00:29:00,280 --> 00:29:03,680
Speaker 4: play a large role in that, but these overlays around

500
00:29:03,840 --> 00:29:07,000
Speaker 4: geography are going to be interesting to consider. And the

501
00:29:07,040 --> 00:29:08,720
Speaker 4: next thing I would just say on this point is,

502
00:29:09,200 --> 00:29:11,520
Speaker 4: you know, when you think about surge pricing, right and

503
00:29:11,560 --> 00:29:14,440
Speaker 4: you think, okay, well, in an area, you know where

504
00:29:14,480 --> 00:29:17,320
Speaker 4: there's sort of less supply, you might want to ration

505
00:29:17,480 --> 00:29:20,360
Speaker 4: by price. If you're in a low income area where

506
00:29:20,360 --> 00:29:23,800
Speaker 4: there's only one store and there's not a lot of competition,

507
00:29:24,280 --> 00:29:27,120
Speaker 4: surge pricing is going to hit that space harder because

508
00:29:27,120 --> 00:29:30,160
Speaker 4: there's just going to be low supply and that's likely

509
00:29:30,200 --> 00:29:33,080
Speaker 4: to be a low income area, a minority or a

510
00:29:33,120 --> 00:29:35,280
Speaker 4: black or brown area as well. And so I think

511
00:29:35,440 --> 00:29:38,800
Speaker 4: the overlay of sort of the geography of concentration in

512
00:29:38,800 --> 00:29:41,840
Speaker 4: the United States, the geography of segregation in the United States,

513
00:29:41,920 --> 00:29:45,600
Speaker 4: and personalized pricing is absolutely going to create some winners

514
00:29:45,600 --> 00:29:47,920
Speaker 4: and losers. And I think the question is whether or

515
00:29:47,960 --> 00:29:50,400
Speaker 4: not existing law is up to the task, or whether

516
00:29:50,480 --> 00:29:53,240
Speaker 4: or not new laws will be required to protect consumers

517
00:29:53,320 --> 00:29:55,240
Speaker 4: from discriminatory practices in tracing.

518
00:30:10,840 --> 00:30:12,880
Speaker 2: You know, I mentioned by the way that I'll play

519
00:30:12,920 --> 00:30:15,880
Speaker 2: Devil's advocate here and I'll just say, if my battery

520
00:30:16,000 --> 00:30:18,320
Speaker 2: on my phone was about to die, I'm fine with

521
00:30:18,360 --> 00:30:20,680
Speaker 2: paying a few extra dollars to get the car over

522
00:30:20,840 --> 00:30:23,000
Speaker 2: the other guy. I'm I'm just gonna throw that out there.

523
00:30:23,160 --> 00:30:25,360
Speaker 2: But actually, lindsay, I want to follow up on this

524
00:30:25,520 --> 00:30:28,520
Speaker 2: point because you're leading to something that I was going

525
00:30:28,520 --> 00:30:31,080
Speaker 2: to ask about, which is that you know, one of

526
00:30:31,120 --> 00:30:35,280
Speaker 2: the things we're sort of talking about is a time tax, right,

527
00:30:35,360 --> 00:30:37,040
Speaker 2: Like some people are going to just roll up to

528
00:30:37,040 --> 00:30:39,400
Speaker 2: the McDonald's, and some people are going to take the

529
00:30:39,440 --> 00:30:42,480
Speaker 2: time to download an app and put in their data.

530
00:30:42,600 --> 00:30:44,240
Speaker 2: I am not one of those people. I'm not very

531
00:30:44,280 --> 00:30:47,240
Speaker 2: well organized, et cetera. But I probably in theory if

532
00:30:47,240 --> 00:30:49,520
Speaker 2: I really cared, like, would have you know, the time

533
00:30:49,560 --> 00:30:51,120
Speaker 2: to like set all these things up and do the

534
00:30:51,160 --> 00:30:53,800
Speaker 2: miles and everything. Talk to us about like the disparate

535
00:30:53,920 --> 00:30:57,760
Speaker 2: impact of basically, yes, there are better prices out there

536
00:30:58,280 --> 00:31:01,160
Speaker 2: if you're willing to jump over these hurdles and take

537
00:31:01,200 --> 00:31:03,760
Speaker 2: that time and be fully just like aware of all

538
00:31:03,760 --> 00:31:06,360
Speaker 2: of the different availability. It seems like difficult to me

539
00:31:06,400 --> 00:31:10,320
Speaker 2: because I'm disorganized, but basically like targeting different sets of

540
00:31:10,360 --> 00:31:14,240
Speaker 2: populations based on how informed they are and the capacity

541
00:31:14,520 --> 00:31:16,600
Speaker 2: that they have to deal with all of these different

542
00:31:16,640 --> 00:31:18,480
Speaker 2: rewards programs and things like that.

543
00:31:19,080 --> 00:31:22,440
Speaker 4: Yeah, I don't even have airline points because I'm too

544
00:31:22,440 --> 00:31:25,840
Speaker 4: disorganized to keep up with accounts for Delta and America

545
00:31:25,920 --> 00:31:28,640
Speaker 4: and things like that. So I hear you one hundred

546
00:31:28,680 --> 00:31:31,520
Speaker 4: percent on that point. Look, I think it's a really

547
00:31:31,520 --> 00:31:34,560
Speaker 4: interesting question, right. There is this temptation to sort of

548
00:31:34,560 --> 00:31:37,719
Speaker 4: figure out how you can hack personalized pricing, or use

549
00:31:37,760 --> 00:31:41,040
Speaker 4: a VPN to get around dark patterns, or how can

550
00:31:41,080 --> 00:31:44,120
Speaker 4: I beat AI and get a good discount. But I

551
00:31:44,160 --> 00:31:46,680
Speaker 4: think really what the issue that we put out of

552
00:31:46,720 --> 00:31:51,160
Speaker 4: the prospect shows is that increasingly in almost every area

553
00:31:51,240 --> 00:31:53,040
Speaker 4: of your life, right, if you look at your household

554
00:31:53,040 --> 00:31:55,920
Speaker 4: budget and the rental market, where Real Page is helping

555
00:31:55,960 --> 00:31:59,880
Speaker 4: landlords fixed prices, in the grocery store for your family vacation,

556
00:32:00,160 --> 00:32:03,280
Speaker 4: where you're having to deal with algorithmic price fixing in

557
00:32:03,400 --> 00:32:07,880
Speaker 4: both airline class as well as hotels, you're up against

558
00:32:07,920 --> 00:32:11,320
Speaker 4: the machine here, right, And I honestly don't know that

559
00:32:11,400 --> 00:32:14,960
Speaker 4: even consumers with considerable time are able to coop on

560
00:32:15,000 --> 00:32:16,920
Speaker 4: clip their way out of this one, right. I mean,

561
00:32:17,000 --> 00:32:19,720
Speaker 4: imagine a world in which you hear from your friend

562
00:32:19,800 --> 00:32:23,040
Speaker 4: that there's a discount on I don't know, cheerios. I'm

563
00:32:23,040 --> 00:32:25,200
Speaker 4: buying a lot of those from my toddler right now

564
00:32:25,680 --> 00:32:28,200
Speaker 4: at the Kroger down the street. But you know they've

565
00:32:28,240 --> 00:32:31,200
Speaker 4: installed electronic price tags on the shelves. You know, by

566
00:32:31,200 --> 00:32:32,959
Speaker 4: the time you get in your car and drive up

567
00:32:32,960 --> 00:32:36,280
Speaker 4: to the Kroger, like, the price of ceios has already changed, right,

568
00:32:36,680 --> 00:32:39,200
Speaker 4: And so I think this is not a space where

569
00:32:39,640 --> 00:32:41,520
Speaker 4: even folks with sort of like a lot of time,

570
00:32:41,840 --> 00:32:43,360
Speaker 4: you know, who used to sit down and get the

571
00:32:43,440 --> 00:32:46,440
Speaker 4: Sunday papers and pull together three sets of coupons and

572
00:32:46,680 --> 00:32:49,000
Speaker 4: organize them in a book and go to three stores

573
00:32:49,040 --> 00:32:51,720
Speaker 4: to get three different deals. You know, even that is

574
00:32:52,000 --> 00:32:54,840
Speaker 4: starting to look a little quaint and antiquated in a

575
00:32:54,920 --> 00:32:58,280
Speaker 4: space with real time pricing, and in a space where

576
00:32:58,320 --> 00:33:02,520
Speaker 4: there are companies using you know, predictive AI to move

577
00:33:02,640 --> 00:33:06,880
Speaker 4: prices you know, instantaneously, right, I just don't know that

578
00:33:06,960 --> 00:33:09,160
Speaker 4: the consumer is going to win this one. I think

579
00:33:09,160 --> 00:33:12,160
Speaker 4: we ultimately have to decide which pieces of this we're

580
00:33:12,160 --> 00:33:14,160
Speaker 4: not happy to deal with but we think they're legal,

581
00:33:14,680 --> 00:33:16,480
Speaker 4: which pieces of this are illegal and we should go

582
00:33:16,480 --> 00:33:19,280
Speaker 4: ahead and enforce the law, And then honestly, which pieces

583
00:33:19,320 --> 00:33:23,160
Speaker 4: of these items are unfair and we just don't like it.

584
00:33:23,240 --> 00:33:26,200
Speaker 4: And maybe if enough of us are focused on how

585
00:33:26,280 --> 00:33:29,240
Speaker 4: unfair they are, we'll see the next Wanta maker coming

586
00:33:29,280 --> 00:33:32,240
Speaker 4: back in and saying, hey, guys like I have the

587
00:33:32,280 --> 00:33:36,160
Speaker 4: ability to use dynamic pricing, but like you know, what

588
00:33:36,200 --> 00:33:38,480
Speaker 4: you get when you come to Lindsay's store is like

589
00:33:38,600 --> 00:33:41,880
Speaker 4: one frickin' price. It may not be the lowest price,

590
00:33:42,040 --> 00:33:44,200
Speaker 4: but like I promised you, you and your neighbor will

591
00:33:44,240 --> 00:33:46,400
Speaker 4: pay the same price. Right. So I think there are

592
00:33:46,440 --> 00:33:50,040
Speaker 4: a number of ways that this unfolds. But you know,

593
00:33:50,080 --> 00:33:52,360
Speaker 4: I think that some of it is absolutely already illegal,

594
00:33:52,680 --> 00:33:55,320
Speaker 4: some of it probably should be illegal, and some of

595
00:33:55,360 --> 00:33:58,400
Speaker 4: it is just maybe unfair and uncomfortable. And I think

596
00:33:58,400 --> 00:34:01,440
Speaker 4: it's okay for consumers to to think things are unfair

597
00:34:01,480 --> 00:34:04,440
Speaker 4: that are legal. That's an opinion and a belief and

598
00:34:04,480 --> 00:34:06,720
Speaker 4: a value we can all hold, and we can try

599
00:34:06,720 --> 00:34:08,320
Speaker 4: to push for shopping to look different.

600
00:34:08,719 --> 00:34:11,960
Speaker 3: Right. And also, I mean it's pretty obvious to me

601
00:34:12,280 --> 00:34:16,640
Speaker 3: that if you are a poor single mother working two jobs,

602
00:34:16,840 --> 00:34:19,480
Speaker 3: you are going to have less time to try to

603
00:34:19,760 --> 00:34:22,040
Speaker 3: game the system, and so you're not going to be

604
00:34:22,080 --> 00:34:24,560
Speaker 3: able to find the types of deals that maybe other

605
00:34:24,600 --> 00:34:28,080
Speaker 3: people with oodles of spare time can find. But there's

606
00:34:28,080 --> 00:34:33,080
Speaker 3: another aspect of unfairness here which we haven't really discussed

607
00:34:33,200 --> 00:34:36,600
Speaker 3: just yet, which is in addition to seeing different prices.

608
00:34:37,080 --> 00:34:39,360
Speaker 3: And actually I would love to know why. It seems

609
00:34:39,440 --> 00:34:43,239
Speaker 3: that like people that are coded as poor by algorithms

610
00:34:43,280 --> 00:34:46,239
Speaker 3: often end up being charged higher prices. So I'd love

611
00:34:46,280 --> 00:34:48,600
Speaker 3: to ask you that, first of all. But then secondly,

612
00:34:49,000 --> 00:34:53,080
Speaker 3: it feels like all these proxy profiles of customers where

613
00:34:53,120 --> 00:34:56,120
Speaker 3: you can see their past behavior, you can see certain

614
00:34:56,160 --> 00:35:00,799
Speaker 3: demographic info that also feeds into advertising. Right, So the

615
00:35:00,880 --> 00:35:04,560
Speaker 3: world that a poor person might live in, based on

616
00:35:04,640 --> 00:35:07,359
Speaker 3: the ads that they are seeing around them, is very

617
00:35:07,400 --> 00:35:10,680
Speaker 3: different to the world that a wealthier person is seeing.

618
00:35:10,719 --> 00:35:12,680
Speaker 3: So the poor person is probably going to see things

619
00:35:12,680 --> 00:35:16,200
Speaker 3: for payday lenders or you know, buy now, pay later

620
00:35:16,320 --> 00:35:18,399
Speaker 3: type stuff, and the wealthy person is going to see

621
00:35:18,440 --> 00:35:22,920
Speaker 3: ads for I don't know, brokerage accounts or luxury waterfront property,

622
00:35:23,320 --> 00:35:26,520
Speaker 3: and that ends up feeling very unfair to me as

623
00:35:26,600 --> 00:35:30,960
Speaker 3: well and perhaps exacerbating inequality problem that we currently have.

624
00:35:31,719 --> 00:35:36,600
Speaker 5: Yeah, I mean, the first really comprehensive study on why

625
00:35:37,160 --> 00:35:42,480
Speaker 5: this phenomenon of poorer Americans paying more happens was published

626
00:35:42,480 --> 00:35:47,200
Speaker 5: in nineteen sixty three. This is nothing really new, and

627
00:35:47,280 --> 00:35:50,000
Speaker 5: we see it in some of these personalized attitudes. There

628
00:35:50,040 --> 00:35:54,080
Speaker 5: was a story several years ago about staples on their

629
00:35:54,160 --> 00:35:59,360
Speaker 5: online products offering different prices in different geolocations based on

630
00:35:59,400 --> 00:36:03,239
Speaker 5: the IP address and the areas that saw that discount

631
00:36:03,239 --> 00:36:07,200
Speaker 5: prices had higher average income. And you know, ability to

632
00:36:07,280 --> 00:36:11,560
Speaker 5: pay and willingness to pay are two different things, and

633
00:36:11,640 --> 00:36:14,600
Speaker 5: I think that's an important concept to know here because

634
00:36:14,600 --> 00:36:20,000
Speaker 5: sometimes they get conflated. Sometimes economists say, well, actually, personalized

635
00:36:20,040 --> 00:36:23,160
Speaker 5: pricing is a great thing because poor people will be

636
00:36:23,200 --> 00:36:26,359
Speaker 5: able to access goods that if there was one fixed price,

637
00:36:26,400 --> 00:36:28,960
Speaker 5: they wouldn't be able to access. And they're making an

638
00:36:28,960 --> 00:36:32,839
Speaker 5: assumption that it's all based on ability to pay. That

639
00:36:33,320 --> 00:36:36,439
Speaker 5: the way that a personalized price will go is that

640
00:36:36,520 --> 00:36:39,520
Speaker 5: you'll be charged more as you go up the income ladder.

641
00:36:39,920 --> 00:36:42,040
Speaker 5: But that's not really how it works.

642
00:36:42,239 --> 00:36:42,480
Speaker 3: You know.

643
00:36:42,600 --> 00:36:46,560
Speaker 5: It could be desperation, as Joe just assented to, that

644
00:36:46,760 --> 00:36:50,520
Speaker 5: causes your higher price. It could be other factors like

645
00:36:50,800 --> 00:36:54,680
Speaker 5: this being a basic necessity that determines the higher price,

646
00:36:54,760 --> 00:36:59,400
Speaker 5: and so the willingness to pay is calculated under a

647
00:36:59,480 --> 00:37:03,000
Speaker 5: number of different factors. It could be that the algorithm

648
00:37:03,120 --> 00:37:07,680
Speaker 5: knows that you only have an hour between jobs or

649
00:37:07,719 --> 00:37:11,520
Speaker 5: while you're going to school to grab some lunch, and

650
00:37:11,640 --> 00:37:14,440
Speaker 5: so they're going to send you or serve you and

651
00:37:14,600 --> 00:37:18,000
Speaker 5: offer that is more in that time of day when

652
00:37:18,040 --> 00:37:20,919
Speaker 5: they know that you have to eat and you're out

653
00:37:20,920 --> 00:37:24,640
Speaker 5: and about and that's where you're going to spend your dollars.

654
00:37:24,840 --> 00:37:28,560
Speaker 5: So there are a whole number of ways where this

655
00:37:28,680 --> 00:37:32,160
Speaker 5: does not look like you just pay more if you

656
00:37:32,320 --> 00:37:37,520
Speaker 5: have more resources. Willingness to pay is a very different concept.

657
00:37:37,760 --> 00:37:40,640
Speaker 4: One interesting thing about the Staple study that Dave mentioned

658
00:37:40,640 --> 00:37:44,160
Speaker 4: that I think raises an important sort of macro point

659
00:37:44,200 --> 00:37:48,239
Speaker 4: about this entire world of pricing strategies and tactics is that,

660
00:37:48,560 --> 00:37:53,080
Speaker 4: you know, corporate concentration and consolidation undergirds at all and

661
00:37:53,239 --> 00:37:56,400
Speaker 4: facilitates and accelerates it all. And so the reason that

662
00:37:56,560 --> 00:38:00,760
Speaker 4: rich people who could afford to pay more for things

663
00:38:00,800 --> 00:38:03,399
Speaker 4: at Staples right, I mean, as a percentage of your

664
00:38:03,440 --> 00:38:07,319
Speaker 4: budget office supplies is not large if you're wealthy, the

665
00:38:07,360 --> 00:38:10,279
Speaker 4: reason they were getting better deals is because there were

666
00:38:10,320 --> 00:38:15,760
Speaker 4: more competitors to Staples in wealthier geographies, right, Whereas lower

667
00:38:15,800 --> 00:38:19,080
Speaker 4: income folks, we're paying more at Staples because Staples knew

668
00:38:19,080 --> 00:38:21,560
Speaker 4: they had them over a barrel, right. And so the

669
00:38:21,640 --> 00:38:25,439
Speaker 4: corporate concentration overlay is key here, and it is key

670
00:38:25,480 --> 00:38:28,880
Speaker 4: in one other way as well, which is really featured

671
00:38:29,040 --> 00:38:33,440
Speaker 4: prominently in the issue, which is that increasingly the business

672
00:38:33,440 --> 00:38:38,640
Speaker 4: case for mergers is data. So, you know, we highlight

673
00:38:38,719 --> 00:38:42,040
Speaker 4: the example of Walmart buying Visio. Why is Walmart buying

674
00:38:42,080 --> 00:38:44,800
Speaker 4: a TV company? Well, they're not buying a TV company,

675
00:38:44,840 --> 00:38:48,480
Speaker 4: it's a smart TV manufacturer masquerading as a media company. Right.

676
00:38:48,840 --> 00:38:53,000
Speaker 4: They're buying streaming data so that they can type Walmart

677
00:38:53,000 --> 00:38:56,160
Speaker 4: advertisements into your home, and so they also can collect

678
00:38:56,560 --> 00:38:58,680
Speaker 4: data on sort of what you're watching and what you're

679
00:38:58,719 --> 00:39:03,160
Speaker 4: clicking on. Smilarly in the piece, you know, there's considerable

680
00:39:03,160 --> 00:39:06,240
Speaker 4: speculation that one of the major motivations for the Kroger

681
00:39:06,280 --> 00:39:10,200
Speaker 4: Albertson's merger is the consumer data. And you know, the

682
00:39:10,360 --> 00:39:13,919
Speaker 4: grocers are making just as much money selling your data

683
00:39:13,920 --> 00:39:16,799
Speaker 4: at the highest bidder as they are on selling you cerios, right,

684
00:39:16,920 --> 00:39:19,360
Speaker 4: And so I think the data, the value of the

685
00:39:19,440 --> 00:39:23,799
Speaker 4: data for companies, and the interlay with consolidation, both as

686
00:39:23,880 --> 00:39:27,520
Speaker 4: a motivator for consolidation but also as something that you

687
00:39:27,560 --> 00:39:31,360
Speaker 4: can just do more aggressively if you aren't worried about competition,

688
00:39:31,840 --> 00:39:34,560
Speaker 4: is a key piece of why pricing looks different today.

689
00:39:35,160 --> 00:39:37,839
Speaker 2: That's really interesting about the Kroger Albertsons. It's come up

690
00:39:37,880 --> 00:39:41,200
Speaker 2: a few times because now, of course with AI, like

691
00:39:41,239 --> 00:39:44,640
Speaker 2: all these companies are just desperate to get any fresh data,

692
00:39:44,719 --> 00:39:48,279
Speaker 2: and people have legitimately made the case actually Kroger's is

693
00:39:48,320 --> 00:39:51,280
Speaker 2: an AI play because it just has so much unique

694
00:39:51,360 --> 00:39:53,839
Speaker 2: data that no one else has, so that that makes

695
00:39:53,840 --> 00:39:56,320
Speaker 2: a lot of sense. I have one more big question,

696
00:39:56,520 --> 00:39:58,640
Speaker 2: which is you know, I started we mentioned in the

697
00:39:58,640 --> 00:40:01,360
Speaker 2: intro the one industry it has been doing this forever,

698
00:40:01,560 --> 00:40:04,359
Speaker 2: or it seems like, is the airline industry. And both

699
00:40:04,360 --> 00:40:07,279
Speaker 2: of you mentioned some of these third party consultants that

700
00:40:07,320 --> 00:40:10,320
Speaker 2: are sort of bringing some of those practices to other industries.

701
00:40:10,480 --> 00:40:13,319
Speaker 2: Can you talk a little bit about that further? How

702
00:40:13,480 --> 00:40:16,560
Speaker 2: direct or how bright is the line between what the

703
00:40:16,600 --> 00:40:20,320
Speaker 2: airlines have been doing with frequent fire miles for decades

704
00:40:20,800 --> 00:40:24,680
Speaker 2: and then that sort of migrating over through consultants, etc.

705
00:40:25,080 --> 00:40:28,479
Speaker 2: Into other industries realizing that they can more or less

706
00:40:28,640 --> 00:40:29,319
Speaker 2: do the same thing.

707
00:40:29,800 --> 00:40:33,160
Speaker 5: Well, it's really interesting because we had you know, a

708
00:40:33,239 --> 00:40:37,560
Speaker 5: number of different authors right these different pieces, and you know,

709
00:40:37,640 --> 00:40:40,600
Speaker 5: I was the editor, and they all came in and

710
00:40:40,920 --> 00:40:43,480
Speaker 5: it seemed like every single piece went back to the

711
00:40:43,520 --> 00:40:47,200
Speaker 5: airlines initially as kind of the originator of a lot

712
00:40:47,239 --> 00:40:51,000
Speaker 5: of these strategies. There is a consultant called Idea Works

713
00:40:51,120 --> 00:40:54,200
Speaker 5: Company and they've been around for a while. The guy

714
00:40:54,280 --> 00:40:57,759
Speaker 5: who runs it is named Jay Sorenson, and for one

715
00:40:57,760 --> 00:41:00,959
Speaker 5: of these articles we actually talked to them. And it's

716
00:41:01,000 --> 00:41:04,840
Speaker 5: not only that Idea Works Company presents these reports and

717
00:41:04,960 --> 00:41:09,480
Speaker 5: research mostly about junk fees or about ancillary revenue is

718
00:41:09,520 --> 00:41:13,000
Speaker 5: what they call it. They even pulled this thing called

719
00:41:13,040 --> 00:41:18,520
Speaker 5: an ancillary Revenue Masterclass, which literally is a junk fee

720
00:41:18,600 --> 00:41:23,600
Speaker 5: boot camp that explains they bring in executives and they

721
00:41:23,680 --> 00:41:27,160
Speaker 5: tell them, here is how you can raise money by

722
00:41:27,360 --> 00:41:31,840
Speaker 5: adding different various fees onto things that used to be

723
00:41:31,920 --> 00:41:34,719
Speaker 5: bundled with the ticket fare. And so now we have

724
00:41:34,800 --> 00:41:37,879
Speaker 5: baggage fees, and we have change fees, and we have

725
00:41:38,320 --> 00:41:41,439
Speaker 5: fees if you want a better seat with more leg room.

726
00:41:41,960 --> 00:41:44,960
Speaker 5: And all of this comes from sort of the brainchild

727
00:41:45,080 --> 00:41:49,560
Speaker 5: of Idea Works Company, which sends these reports that cheerlead.

728
00:41:50,000 --> 00:41:54,680
Speaker 5: When ancillary revenue numbers go up, it's become a huge

729
00:41:54,719 --> 00:41:58,000
Speaker 5: business for the airlines to unbundle their tickets and add

730
00:41:58,040 --> 00:42:01,799
Speaker 5: all of these extra fees, basically making your situation in

731
00:42:01,880 --> 00:42:04,880
Speaker 5: air travel miserable unless you pay your way out of it.

732
00:42:05,400 --> 00:42:07,840
Speaker 5: And so we've seen that there. We see it an

733
00:42:07,880 --> 00:42:12,239
Speaker 5: algorithmic price fixing. And all of these strategies started with

734
00:42:12,280 --> 00:42:15,080
Speaker 5: the airlines, or at least some of them, but they've migrated,

735
00:42:15,120 --> 00:42:17,759
Speaker 5: they've moved on, like in the junk fee example. One

736
00:42:17,800 --> 00:42:20,720
Speaker 5: of my favorite things in the issue, there's this company

737
00:42:20,719 --> 00:42:24,440
Speaker 5: called Suburban pro Pain obviously, as they sell propaine to

738
00:42:24,560 --> 00:42:27,720
Speaker 5: various people, whether they use it in camping or whatever

739
00:42:27,760 --> 00:42:30,600
Speaker 5: they use it in, and they have a fee schedule

740
00:42:30,680 --> 00:42:33,239
Speaker 5: on their website, and I'm just going to read what

741
00:42:33,320 --> 00:42:37,280
Speaker 5: the fees are. They have a safety practices and training fee,

742
00:42:37,600 --> 00:42:42,360
Speaker 5: a tank rental fee, a transportation fuel fee, a restocking fee,

743
00:42:42,440 --> 00:42:46,160
Speaker 5: a tank pickup fee, a minimum monthly purchase fee, a

744
00:42:46,239 --> 00:42:50,400
Speaker 5: system leak test fee, a reconnect fee, a wheel call fee,

745
00:42:50,760 --> 00:42:56,360
Speaker 5: a forklift minimum delivery fee, a diagnostic fee, an installation fee,

746
00:42:56,560 --> 00:43:01,239
Speaker 5: an early termination fee, an emergency special livery fee, a

747
00:43:01,320 --> 00:43:05,120
Speaker 5: late fee, a return check fee, and a meter account

748
00:43:05,280 --> 00:43:09,560
Speaker 5: maintenance fee. And I'd like to say that was an outlier,

749
00:43:09,960 --> 00:43:12,680
Speaker 5: but I'm not sure it is. Like we are seeing

750
00:43:12,760 --> 00:43:17,920
Speaker 5: these add on fees in all sorts of industries. It

751
00:43:18,000 --> 00:43:22,440
Speaker 5: originated in the airlines and now it's gone everywhere. And

752
00:43:23,000 --> 00:43:26,040
Speaker 5: you see the Biden administration actually taking this up as

753
00:43:26,080 --> 00:43:30,440
Speaker 5: a cause. The term junk fee was kind of invented

754
00:43:30,560 --> 00:43:33,960
Speaker 5: or coined by ro Hit Chopra, who's the director of

755
00:43:34,000 --> 00:43:37,640
Speaker 5: the Consumer Financial Protection Bureau, and they're trying to attack

756
00:43:37,760 --> 00:43:40,359
Speaker 5: this issue. The Federal Trade Commission has put out a

757
00:43:40,480 --> 00:43:42,360
Speaker 5: kind of ban on junk fees, which is more of

758
00:43:42,400 --> 00:43:46,560
Speaker 5: a disclosure rule saying you have to do all upfront pricing,

759
00:43:47,200 --> 00:43:51,200
Speaker 5: and the CFPB has tried to ban or cap credit

760
00:43:51,200 --> 00:43:55,319
Speaker 5: card late fees, for example. We're seeing now kind of

761
00:43:55,320 --> 00:43:59,520
Speaker 5: a politics being created out of these different pricing strategies

762
00:43:59,560 --> 00:44:01,960
Speaker 5: and attempted pushback on them.

763
00:44:02,560 --> 00:44:04,800
Speaker 3: I have just one more question, which is going back

764
00:44:05,000 --> 00:44:09,240
Speaker 3: to the introduction and the conversation between myself and Joe

765
00:44:09,400 --> 00:44:14,640
Speaker 3: and the implications that this has for macro economics. If

766
00:44:14,680 --> 00:44:18,920
Speaker 3: we think that companies are becoming more sophisticated in their pricing,

767
00:44:19,080 --> 00:44:22,080
Speaker 3: if we think that we're seeing I guess late stage

768
00:44:22,120 --> 00:44:27,759
Speaker 3: capitalism meet a technological revolution that creates the ability to

769
00:44:27,880 --> 00:44:32,320
Speaker 3: have more sophisticated pricing. What does that mean for inflation?

770
00:44:32,719 --> 00:44:38,359
Speaker 3: If maybe prices become more about data and algorithms rather

771
00:44:38,440 --> 00:44:42,439
Speaker 3: than a function of supply demand or the Phillips curve.

772
00:44:42,520 --> 00:44:47,280
Speaker 3: How do economists and central bankers actually handle that particular problem.

773
00:44:47,560 --> 00:44:50,160
Speaker 5: I think it's a terrific question, and I'm not sure

774
00:44:50,200 --> 00:44:53,680
Speaker 5: it's one that the central Bank really is willing to

775
00:44:53,760 --> 00:44:56,239
Speaker 5: handle just yet. You know, one of the things we

776
00:44:56,320 --> 00:45:02,000
Speaker 5: put in our introduction is this colic between Sharet Brown,

777
00:45:02,080 --> 00:45:06,600
Speaker 5: who's the chair of the Senate Banking Committee, and j

778
00:45:06,800 --> 00:45:10,160
Speaker 5: Powell when he was doing a semi annual report and

779
00:45:10,960 --> 00:45:15,640
Speaker 5: Sharet Brown was asking Powell about these pricing strategies, and

780
00:45:16,160 --> 00:45:19,799
Speaker 5: Howell seemed very very uncomfortable. He didn't really want to

781
00:45:19,840 --> 00:45:23,000
Speaker 5: talk about it. He said, well, you know, search pricing,

782
00:45:23,040 --> 00:45:25,720
Speaker 5: maybe it works out even for the consumer. It doesn't

783
00:45:25,719 --> 00:45:29,399
Speaker 5: have an inflation impact because if they're not that many

784
00:45:29,400 --> 00:45:31,680
Speaker 5: people in the story, you get lower priced, and if

785
00:45:31,680 --> 00:45:33,520
Speaker 5: there are people in the story, you get a higher price.

786
00:45:33,960 --> 00:45:38,600
Speaker 5: But what he ended up on was saying that pricing

787
00:45:38,800 --> 00:45:42,440
Speaker 5: is incredibly important and we have to give companies the

788
00:45:42,480 --> 00:45:46,600
Speaker 5: freedom to do it. So he really sort of disassociated

789
00:45:46,680 --> 00:45:49,839
Speaker 5: himself from this issue. And I think it's a fascinating

790
00:45:49,920 --> 00:45:54,920
Speaker 5: question that you raised, Tracy, that if we see supply

791
00:45:55,000 --> 00:45:58,840
Speaker 5: and demand and the usual kind of reasons for pricing

792
00:45:59,320 --> 00:46:01,640
Speaker 5: become a little bit less. I'm not saying it's going

793
00:46:01,680 --> 00:46:04,759
Speaker 5: to be completely less, but a little bit less of

794
00:46:04,800 --> 00:46:08,840
Speaker 5: a factor. And we see sort of pricing get a

795
00:46:08,880 --> 00:46:14,760
Speaker 5: little bit unmoored from those traditional factors, Then what does

796
00:46:14,800 --> 00:46:17,960
Speaker 5: that mean for how the central bank operates? And I

797
00:46:18,000 --> 00:46:20,960
Speaker 5: think our answer, and Lindsey can speak to this more,

798
00:46:21,480 --> 00:46:25,000
Speaker 5: is that it has to mean that we need more

799
00:46:25,000 --> 00:46:28,680
Speaker 5: of a whole of government approach to these particular issues.

800
00:46:28,920 --> 00:46:33,440
Speaker 5: And for many years we've kind of outsourced any question

801
00:46:33,520 --> 00:46:36,719
Speaker 5: about inflation to the central bank and to monetary policy,

802
00:46:37,040 --> 00:46:39,719
Speaker 5: and I think policymakers have to understand that that might

803
00:46:39,760 --> 00:46:42,680
Speaker 5: not do the whole job anymore, and that there are

804
00:46:42,760 --> 00:46:45,839
Speaker 5: other factors and there are other agencies that can be

805
00:46:45,840 --> 00:46:46,759
Speaker 5: brought to bear here.

806
00:46:47,200 --> 00:46:50,520
Speaker 4: Yeah, policymakers are going to have to actually study individual

807
00:46:50,600 --> 00:46:55,480
Speaker 4: firm behavior, industry level behavior, really start to get up

808
00:46:55,480 --> 00:46:58,600
Speaker 4: to speed on new pricing strategies and tactics if they

809
00:46:58,680 --> 00:47:01,480
Speaker 4: really want to understand what's going on in the economy.

810
00:47:01,719 --> 00:47:04,360
Speaker 4: I think for many Americans, part of the reason why

811
00:47:04,800 --> 00:47:08,960
Speaker 4: you know, we haven't seen folk supplotting inflation headed back

812
00:47:09,000 --> 00:47:11,959
Speaker 4: to two percent is because the word inflation doesn't really

813
00:47:12,000 --> 00:47:16,200
Speaker 4: capture everything. People are experiencing in this economy. Right, Sure,

814
00:47:16,239 --> 00:47:18,759
Speaker 4: inflation is a piece of it, but there's also just

815
00:47:19,120 --> 00:47:22,080
Speaker 4: like plain old price gouging, there's also junk fees, there's

816
00:47:22,120 --> 00:47:26,080
Speaker 4: also dynamic pricing. All of these different ways people are

817
00:47:26,120 --> 00:47:29,120
Speaker 4: experiencing the economy when it comes to pricing sort of

818
00:47:29,200 --> 00:47:32,120
Speaker 4: isn't captured, I think fully with the word inflation. I

819
00:47:32,120 --> 00:47:35,399
Speaker 4: think it's why people are so unhappy with the economy today.

820
00:47:35,440 --> 00:47:38,720
Speaker 4: There's a lot underlying this shift. You know, of course

821
00:47:38,760 --> 00:47:42,440
Speaker 4: these techniques proceeded inflation, but they do seem to have

822
00:47:42,520 --> 00:47:46,080
Speaker 4: been unleashed and hypercharged during this period of high inflation.

823
00:47:46,280 --> 00:47:48,720
Speaker 4: And it'll be interesting to see sort of what happens

824
00:47:48,760 --> 00:47:51,000
Speaker 4: in the future. But it sure seems like the genies

825
00:47:51,000 --> 00:47:52,880
Speaker 4: out of the bottle here. And I think we're just

826
00:47:52,920 --> 00:47:55,479
Speaker 4: going to see more of this type of activity rather

827
00:47:55,520 --> 00:47:57,520
Speaker 4: than less. And I think that's why you're seeing this

828
00:47:57,600 --> 00:48:01,239
Speaker 4: burgeoning sort of cottage industry of racing data firms, right.

829
00:48:01,320 --> 00:48:04,120
Speaker 4: I mean, the handful of CEOs who thought they were

830
00:48:04,440 --> 00:48:07,080
Speaker 4: just selling groceries, you know, need a firm to help

831
00:48:07,120 --> 00:48:09,400
Speaker 4: them realize they're actually supposed to be selling data, and

832
00:48:09,440 --> 00:48:12,560
Speaker 4: they need a firm to help them think through how

833
00:48:12,560 --> 00:48:15,520
Speaker 4: to maximize pricing in a world where cost cutting is

834
00:48:15,600 --> 00:48:19,400
Speaker 4: hit bone and shareholders expect more and more returns. Like,

835
00:48:19,440 --> 00:48:21,680
Speaker 4: there's got to be a revenue play too, right, And

836
00:48:22,120 --> 00:48:24,160
Speaker 4: a revenue play is going to be in part a

837
00:48:24,160 --> 00:48:26,600
Speaker 4: pricing play. So I think we're in a new world

838
00:48:26,640 --> 00:48:29,960
Speaker 4: here when it comes to pricing. And the Federal Reserve

839
00:48:30,040 --> 00:48:32,960
Speaker 4: is not known for being numble or fast moving or

840
00:48:33,000 --> 00:48:37,160
Speaker 4: particularly innovative when it comes to thinking about the economy.

841
00:48:37,200 --> 00:48:39,239
Speaker 4: They've been sort of running a same playbook for a

842
00:48:39,280 --> 00:48:42,040
Speaker 4: long time here, right, So I think only time will

843
00:48:42,040 --> 00:48:43,279
Speaker 4: tell whether or not they catch up.

844
00:48:43,760 --> 00:48:46,439
Speaker 2: By the way, I checked out the sample two day

845
00:48:46,440 --> 00:48:51,200
Speaker 2: agenda of the Idea Works Company and Cellary Revenue Masterclass,

846
00:48:51,200 --> 00:48:54,359
Speaker 2: and it really is a boot camp on charging more

847
00:48:54,800 --> 00:48:58,520
Speaker 2: ten fifteen Coffee Break ten thirty, Top ten things you

848
00:48:58,560 --> 00:49:01,720
Speaker 2: need to know about ancillary revenue in airlines eleven, Antilia

849
00:49:01,800 --> 00:49:04,280
Speaker 2: revenue boost the bottom line twelve Like it's really amazing.

850
00:49:04,600 --> 00:49:08,120
Speaker 2: David and lindsay, that was so fantastic. Really appreciate you

851
00:49:08,360 --> 00:49:10,759
Speaker 2: both coming on odd lots. Everyone should check out the

852
00:49:10,840 --> 00:49:15,120
Speaker 2: June third edition of The American Prospect. Really fascinating stuff

853
00:49:15,160 --> 00:49:17,200
Speaker 2: on a range of topics. Great chatting with both of you.

854
00:49:17,440 --> 00:49:29,279
Speaker 4: Thanks thanks for having us.

855
00:49:31,280 --> 00:49:33,239
Speaker 2: Tracy. I thought that was fantastic and there us a

856
00:49:33,239 --> 00:49:36,319
Speaker 2: lot there, actually, I thought Lindsay's point at the very

857
00:49:36,400 --> 00:49:39,200
Speaker 2: end I thought was a really great one, because obviously

858
00:49:39,640 --> 00:49:43,080
Speaker 2: people don't like higher prices, and the inflation data, probably

859
00:49:43,080 --> 00:49:46,080
Speaker 2: for better or worse, captures the general rise in prices

860
00:49:46,120 --> 00:49:48,920
Speaker 2: over the last several years and the disinflation over the

861
00:49:48,960 --> 00:49:51,680
Speaker 2: last couple of years. But then this idea that there's

862
00:49:51,719 --> 00:49:55,279
Speaker 2: something else out there that's really annoying. Maybe it is

863
00:49:55,440 --> 00:49:57,120
Speaker 2: a sort of polite way to put it, or like

864
00:49:57,200 --> 00:50:00,400
Speaker 2: aggravating about this economy and this sort of psychological and

865
00:50:00,440 --> 00:50:02,920
Speaker 2: feeling that to get the optimal price you have to

866
00:50:02,960 --> 00:50:05,200
Speaker 2: like download an app and all of this stuff that

867
00:50:05,239 --> 00:50:09,400
Speaker 2: I think sort of compound the aggravation of higher prices themselves.

868
00:50:09,480 --> 00:50:12,640
Speaker 3: No, absolutely, and also just the point about, well, the

869
00:50:12,719 --> 00:50:15,520
Speaker 3: genies kind of out of the bottle, and maybe we

870
00:50:15,640 --> 00:50:18,600
Speaker 3: are moving from an age in which it was all

871
00:50:18,640 --> 00:50:23,680
Speaker 3: about driving costs lower and building factories in places like

872
00:50:23,800 --> 00:50:27,200
Speaker 3: China or Vietnam or wherever in order to lower your

873
00:50:27,239 --> 00:50:30,080
Speaker 3: cost of production. But the thing that we saw from

874
00:50:30,120 --> 00:50:34,080
Speaker 3: the pandemic was that a you have supply chain issues

875
00:50:34,120 --> 00:50:37,719
Speaker 3: and so that production facility can close, and then b

876
00:50:38,320 --> 00:50:41,239
Speaker 3: you can also make money by raising your prices and

877
00:50:41,320 --> 00:50:43,560
Speaker 3: selling less of your stuff. And this has been an

878
00:50:43,560 --> 00:50:47,520
Speaker 3: ongoing theme on all blots, and we spoke with Samuel

879
00:50:47,600 --> 00:50:50,360
Speaker 3: Rans about this, of course, and you can see the

880
00:50:50,520 --> 00:50:54,040
Speaker 3: strategy going back to Lindsay's point at the beginning of

881
00:50:54,080 --> 00:50:57,160
Speaker 3: the conversation on the earnings calls. This is something that

882
00:50:57,280 --> 00:51:01,920
Speaker 3: CEOs very openly discuss and talk about totally.

883
00:51:02,040 --> 00:51:04,480
Speaker 2: By the way, our producer Kale came through the reference

884
00:51:04,560 --> 00:51:07,279
Speaker 2: the nineteen sixty seven book The Poor Pay More by

885
00:51:07,360 --> 00:51:11,120
Speaker 2: David Keplovitz looks really interesting and it hadn't really clicked

886
00:51:11,120 --> 00:51:13,640
Speaker 2: to me. David's point, which is that there is sort

887
00:51:13,680 --> 00:51:16,000
Speaker 2: of ability to pay. And yes, you know, the rich

888
00:51:16,160 --> 00:51:19,040
Speaker 2: in theory in practice have the ability to pay more,

889
00:51:19,480 --> 00:51:21,719
Speaker 2: but then the sort of willingness to pay about like, Okay,

890
00:51:21,760 --> 00:51:25,000
Speaker 2: you're in a desperate situation you need this, or as

891
00:51:25,040 --> 00:51:28,080
Speaker 2: Lindsay's point, like you may only have in your area

892
00:51:28,160 --> 00:51:30,840
Speaker 2: one competitor or wherever it is. And so the idea

893
00:51:30,880 --> 00:51:34,000
Speaker 2: that ability to pay is the only measure by which

894
00:51:34,080 --> 00:51:36,799
Speaker 2: a company would set a price is clearly wrong. For

895
00:51:36,880 --> 00:51:38,919
Speaker 2: some reasons that are obvious once you hear them.

896
00:51:39,200 --> 00:51:41,960
Speaker 3: I think that's such an important distinction. And then the

897
00:51:42,040 --> 00:51:43,799
Speaker 3: other thing I would just tack on to that is

898
00:51:44,040 --> 00:51:47,200
Speaker 3: going back to the advertising point. So you know, depending

899
00:51:47,280 --> 00:51:51,680
Speaker 3: on whatever proxy profile the algo is building about you,

900
00:51:52,239 --> 00:51:55,480
Speaker 3: all the prices that you're seeing, all the offerings might

901
00:51:55,520 --> 00:51:58,640
Speaker 3: be very different to someone who is better well off,

902
00:51:58,680 --> 00:52:01,120
Speaker 3: And so you never even maybe if you're living in

903
00:52:01,160 --> 00:52:04,880
Speaker 3: a certain zip code and you have certain demographics attached

904
00:52:04,880 --> 00:52:08,320
Speaker 3: to you, or certain buying patterns, or certain credit scores

905
00:52:08,400 --> 00:52:13,479
Speaker 3: or whatever, maybe you never even get ads for brokerage services, right,

906
00:52:13,560 --> 00:52:16,440
Speaker 3: And so the idea of building wealth through the stock

907
00:52:16,480 --> 00:52:18,840
Speaker 3: market is just something that you never encounter, and so

908
00:52:19,760 --> 00:52:22,759
Speaker 3: all of that inequality becomes sort of codified. God, I'm

909
00:52:22,800 --> 00:52:25,960
Speaker 3: depressing myself as I talk Joe. This is depressing. Wait

910
00:52:26,000 --> 00:52:28,920
Speaker 3: are you a little bit less relaxed about some of this?

911
00:52:29,080 --> 00:52:29,319
Speaker 1: Now?

912
00:52:29,440 --> 00:52:30,400
Speaker 3: Please tell me you are.

913
00:52:31,440 --> 00:52:34,680
Speaker 2: I still kind of think. I still think I would

914
00:52:34,680 --> 00:52:36,160
Speaker 2: be happy to pay more for an uber if my

915
00:52:36,160 --> 00:52:37,959
Speaker 2: phone were going to run out. But there are many

916
00:52:38,080 --> 00:52:42,399
Speaker 2: aspects of this that I find uncomfortable. Surge pricing does

917
00:52:42,440 --> 00:52:45,000
Speaker 2: not bother me the same way other things do. I

918
00:52:45,040 --> 00:52:48,720
Speaker 2: do want to attend an Ideal works Company ancillary revenue

919
00:52:48,760 --> 00:52:50,600
Speaker 2: master class. Maybe we could do that one day.

920
00:52:51,080 --> 00:52:53,960
Speaker 3: I do not let me just throw that out there, No,

921
00:52:54,120 --> 00:52:58,640
Speaker 3: I mean that list of junkxies that David was reading

922
00:52:58,719 --> 00:53:02,440
Speaker 3: where like a hair away from them basically charging for

923
00:53:02,520 --> 00:53:06,120
Speaker 3: oxygen in order to breathe. Right, Like, we're almost there.

924
00:53:06,640 --> 00:53:09,560
Speaker 2: That seems successive on the plane. It's like, does that

925
00:53:09,640 --> 00:53:10,480
Speaker 2: thing fall out?

926
00:53:10,719 --> 00:53:11,160
Speaker 5: Yeah?

927
00:53:11,239 --> 00:53:13,680
Speaker 2: Do you pay extra, pay extra to make sure that

928
00:53:14,280 --> 00:53:16,640
Speaker 2: the thing fall will fall out?

929
00:53:16,960 --> 00:53:17,240
Speaker 3: Yeah?

930
00:53:17,320 --> 00:53:17,520
Speaker 2: Well.

931
00:53:17,600 --> 00:53:19,200
Speaker 3: The one other thing I was going to throw in

932
00:53:19,360 --> 00:53:21,680
Speaker 3: is I know they talked about the Federal Reserve being

933
00:53:21,960 --> 00:53:25,440
Speaker 3: slow to approach this and to some extent, you know,

934
00:53:25,480 --> 00:53:27,960
Speaker 3: it's such a thorny issue. As soon as the word

935
00:53:28,040 --> 00:53:32,040
Speaker 3: greedflation comes up, people immediately start arguing about it. Maybe

936
00:53:32,040 --> 00:53:35,080
Speaker 3: you could couch it in different terms, you know, price,

937
00:53:35,160 --> 00:53:39,080
Speaker 3: pack architecture, more sophisticated pricing, personalized pricing, and all of that.

938
00:53:39,320 --> 00:53:42,439
Speaker 3: But I will say this is something that has come

939
00:53:42,520 --> 00:53:46,080
Speaker 3: up in our conversations with Richmond Fed President Tom Barkin,

940
00:53:46,200 --> 00:53:48,600
Speaker 3: where he talked I think he might have even used

941
00:53:48,600 --> 00:53:51,360
Speaker 3: the idea of genie out of the bottle, which is

942
00:53:51,400 --> 00:53:53,560
Speaker 3: one thing that companies have learned from the past couple

943
00:53:53,600 --> 00:53:57,200
Speaker 3: of years is that they can push price and experiment

944
00:53:57,520 --> 00:53:58,759
Speaker 3: with demand ealisticity.

945
00:53:59,160 --> 00:54:03,120
Speaker 2: That's true, I think to David's point, what it says

946
00:54:03,320 --> 00:54:05,640
Speaker 2: is that some of these things are like a whole

947
00:54:05,680 --> 00:54:08,280
Speaker 2: of government approach, and so the idea is like also,

948
00:54:08,320 --> 00:54:10,799
Speaker 2: it's like the FED does not have like tools to

949
00:54:10,800 --> 00:54:13,560
Speaker 2: go after like junk fees or whatever. But yeah, I

950
00:54:13,560 --> 00:54:14,480
Speaker 2: thought that was fascinating.

951
00:54:14,520 --> 00:54:15,239
Speaker 3: Shall we leave it there.

952
00:54:15,280 --> 00:54:16,319
Speaker 5: Let's leave it there, all right?

953
00:54:16,400 --> 00:54:19,000
Speaker 3: This has been another episode of the Odd Thoughts podcast.

954
00:54:19,080 --> 00:54:22,160
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.

955
00:54:22,360 --> 00:54:25,400
Speaker 2: And I'm Joe Wisenthal. You can follow me at The Stalwart.

956
00:54:25,719 --> 00:54:28,960
Speaker 2: Follow our guest David Dayan, He's at d Dayan. And

957
00:54:29,200 --> 00:54:33,200
Speaker 2: Lindsey Owens She's at Owens Lindsey One. And definitely check

958
00:54:33,239 --> 00:54:36,680
Speaker 2: out that new edition of the American Prospect magazine. Follow

959
00:54:36,719 --> 00:54:40,000
Speaker 2: our producers Carman Rodriguez at Carman armand dash Ol Bennett

960
00:54:40,000 --> 00:54:43,120
Speaker 2: at Dashbot and kel Brooks at Kelbrooks. Thank you to

961
00:54:43,160 --> 00:54:46,200
Speaker 2: our producer Moses Ondam. For more odd Lots content, go

962
00:54:46,200 --> 00:54:49,160
Speaker 2: to Bloomberg dot com slash odd Lots. We have transcripts

963
00:54:49,160 --> 00:54:51,680
Speaker 2: the blog and a newsletter and go on chat with

964
00:54:51,719 --> 00:54:54,840
Speaker 2: Hello listeners twenty four to seven, go to our discord Discord,

965
00:54:54,880 --> 00:54:56,400
Speaker 2: dot gg, slash od.

966
00:54:56,280 --> 00:54:58,880
Speaker 3: Lots and if you enjoy all thoughts. If you like

967
00:54:58,960 --> 00:55:01,680
Speaker 3: it when we dive into to price pack architecture, then

968
00:55:01,719 --> 00:55:05,120
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969
00:55:05,520 --> 00:55:08,359
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970
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971
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972
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973
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