Speaker 1: Lord, Welcome to another episode of The Odd Laws Podcast.
Speaker 1: I'm Joe Wisenhall and I'm Tracy Alloway. Tracy, do you
Speaker 1: know what powers the internet? Um? Hamsters on wheels? No, Um, electricity.
Speaker 1: I don't know. Data. Is this like a metaphor? I
Speaker 1: guess you could probably say. I have a lot of
Speaker 1: different answers. Electricity is probably one. Someone could say data.
Speaker 1: I was gonna say online advertising. Ah, now this is interesting. Um.
Speaker 1: Do you mean in the sense that online advertising pays
Speaker 1: for websites to actually run? Yeah. Basically, it's like if
Speaker 1: you think about virtually anything we do on the internet,
Speaker 1: if you think about at least some aspect of our
Speaker 1: careers having written on the internet, if you think of
Speaker 1: all the social networking, literally everything in some way, it
Speaker 1: was probably paid for the online advertising. Yeah, I guess
Speaker 1: you're right. I mean, none of these things are really
Speaker 1: provided for for free. And I guess the cliche is
Speaker 1: always if you're not paying for the service, Uh, then
Speaker 1: you know the product they're selling is is probably you
Speaker 1: and your personal data, and they're selling that to advertisers,
Speaker 1: right exactly. And of course, as we know, a lot
Speaker 1: of the practices and online advertising these days are pretty controversial,
Speaker 1: and people are concerned about privacy and as you said,
Speaker 1: your personal data and the reader or the user being
Speaker 1: the product. But have you ever wondered, really how they
Speaker 1: figure out what ad to serve you? In the split
Speaker 1: second you go to a website and there's already an
Speaker 1: AD that's perfectly tailored to your interest. Have you ever
Speaker 1: really explored how that happens? I'm not lying when I
Speaker 1: say I have actually wondered this specifically, I've wondered why
Speaker 1: the pair of shoes that I looked at for like
Speaker 1: five seconds two weeks ago follow me around the internet
Speaker 1: from months later begging to be bought. That really annoys me.
Speaker 1: So if this is what we're going to talk about,
Speaker 1: then I'm on board, Joe. But what does it have
Speaker 1: to do with markets exactly? Well, it's uh, first of all,
Speaker 1: this is exactly what we're gonna be talking about, So
Speaker 1: you're in luck. And of course it's a market thing
Speaker 1: because someone has to buy and sell that ad inventory
Speaker 1: and essentially you go to a website or go to
Speaker 1: Facebook or whatever, and there has to be some sort
Speaker 1: of process for allocating that advertising space and how much
Speaker 1: are people going to pay for it? And how much
Speaker 1: is that worth and how much are uh they is
Speaker 1: the website going to sell it for? And that is
Speaker 1: a marketplace. So the Internet is fundamentally run by market structure,
Speaker 1: is what you're telling me. I love this episode already exactly. Well,
Speaker 1: I'm very excited about it. So without further ado, I
Speaker 1: want to bring in our guest. His name is Option Bdelli.
Speaker 1: He is a systems engineer for online ad platforms, and
Speaker 1: we are going to talk about market structure as it
Speaker 1: pertained to the world of online advertising. Thank you very
Speaker 1: much for coming on. Thank you, and hi Tracy Hija.
Speaker 1: So what happens when I go to a website? Wow,
Speaker 1: let's just let's just jump right in. Great, so today
Speaker 1: on average for the average website, let's go through what happens.
Speaker 1: So you load up your favorite web page, whatever it is,
Speaker 1: let's say bloom Bloomberg dot com of course, and chances
Speaker 1: are you visited Bloomberg dot com before. This is not
Speaker 1: the first time you visited this site, so that's important.
Speaker 1: We'll get back to that in a bit. What happens
Speaker 1: the moment you load the page there is a race.
Speaker 1: People want to find out who you are and whether
Speaker 1: it's worth their time to show you an AD, And
Speaker 1: the hope is if they show you an AD, maybe
Speaker 1: you will in some way engage or convert or respond
Speaker 1: to that ad in different ways, and there are different
Speaker 1: methods for measuring exactly how you convert. So the whole
Speaker 1: game at this point is to show you an AD
Speaker 1: as quickly as possible, that is, as revenue impacting as
Speaker 1: possible for both the person purchasing the ad and the
Speaker 1: purchase the person selling you the ad, the publisher and
Speaker 1: the advertiser. So I have a bunch of questions already
Speaker 1: about how the buyers and the sellers kind of come together.
Speaker 1: But I guess before we get to that, can you
Speaker 1: describe what kind of personal data is available if Joe
Speaker 1: or I, you know, actually click on a website, be
Speaker 1: at the Bloomberg website or something else. Sure, so we
Speaker 1: can divide this generally into three buckets of data. Let's
Speaker 1: say there's the data that we know about the user
Speaker 1: as as soon as they visit the page, the data
Speaker 1: that is live that is part of the request. Then
Speaker 1: there's the data the Bloomberg in this example has from
Speaker 1: your past visits. And then there's the data that the
Speaker 1: advertiser has purchased from a third party that has been
Speaker 1: collected from you over the course of months and years,
Speaker 1: from all sorts of different sources. The party that is
Speaker 1: in charge of providing this last piet of data, which
Speaker 1: I'm guessing is the most interesting to you, is called
Speaker 1: a d MP, a data management platform. It's the job
Speaker 1: of these DMPs to find out as much data as
Speaker 1: they can possibly get about you, and aggregate from all
Speaker 1: sorts of different databases and data sources, and correlate it
Speaker 1: to the information that is returned to them by the publisher,
Speaker 1: in this case, Bloomberg. So I have a bunch of
Speaker 1: potentially different interests. I'm interested in financial market, I'm interested
Speaker 1: in boxing, I'm interested in barbecuing. So in theory, there's
Speaker 1: a lot of different types of advertisers, very different from
Speaker 1: each other, that would might want to compete for my attention,
Speaker 1: that might want to put up some product that they're
Speaker 1: selling in front of me. So how what is the
Speaker 1: process by which that collection of data that the d
Speaker 1: MP has on me turns into someone putting an atom
Speaker 1: from ah? So that is the job of the DSP,
Speaker 1: the demand side platform. Are you enjoying the outfits? I
Speaker 1: I am already I have a feel like there's more
Speaker 1: to come to there are Okay, So the demand side
Speaker 1: platform partners with several d MPs, and it's their job
Speaker 1: to look at your incoming data, your page visit data
Speaker 1: that happens when you visit bloomber dot com in this
Speaker 1: one moment and correlated to all the different data it
Speaker 1: gets from the d MPs. And how they go about
Speaker 1: that is sort of proprietary. It's the special sauce. But
Speaker 1: imagine they have just about any imaginal data stores that
Speaker 1: they can possibly call on you based on your email
Speaker 1: address and how who you've given to you in the past,
Speaker 1: cookies that you've often websites in the past, even location
Speaker 1: begins from from brick and mortar real life stories that
Speaker 1: you visit in the past, and they're correlating it all
Speaker 1: and that single moment while you're viewing the page in
Speaker 1: order to figure out which ad is best to serve
Speaker 1: to you. It's weird to think there's like a behind
Speaker 1: the scenes battle happening every time you open up a
Speaker 1: new web page. I'm wondering how how did this structure
Speaker 1: actually come into place? Like who made the decision that
Speaker 1: this was going to be the way um that the
Speaker 1: Internet essentially works and that advertising is sold online. So
Speaker 1: let's go through a brief history of how the market
Speaker 1: micro structure of online advertising evolved. Those are words I
Speaker 1: never thought I'd be saying before, but here we are.
Speaker 1: So in the beginning that this is why they welcome
Speaker 1: to the outlaws. So in the beginning there were advertisers
Speaker 1: and publishers. So in this case, there's let's say Bloomberg
Speaker 1: and someone who wants to advertise on Bloomberg, and around
Speaker 1: let's say twenty years ago, was fairly easy for these
Speaker 1: two parties to find each other and match up. There
Speaker 1: were not that many people who were advertising, and they're
Speaker 1: not many people publishing websites. But more and more advertisers
Speaker 1: and more and more publishers makes this market microstructure really inefficient.
Speaker 1: Imagine the equipment of an open outcry pit, but only
Speaker 1: about five people in each pit, scattered all around the world.
Speaker 1: How to buyers and sellers match up with each other
Speaker 1: after they've reached a certain volume. The solution to that
Speaker 1: first became advertising networks. This would be an aggregation of
Speaker 1: advertisers under one umbrella being presented to publishers. That solves
Speaker 1: one problem, how do you get more supply to satisfy demand,
Speaker 1: but it exposes another problem. To give an example where
Speaker 1: that problem is, let's put some color on what ad
Speaker 1: we're serving on Bloomberg dot com. Let's say we're serving
Speaker 1: an ad for men's shoes. So if I present that
Speaker 1: add to Joe when he's being a boomer dot com,
Speaker 1: there's probably a higher percentage of a chance that he's
Speaker 1: going to convert actually engage with that ad. Then if
Speaker 1: I were to show those same men shoes to Tracy,
Speaker 1: who I'm guessing is not in the market for Adidas
Speaker 1: right now, so we need to wait now. So we
Speaker 1: need a way to solve that problem. We need a
Speaker 1: way to actually show Joe the ads that he cares
Speaker 1: about and Tracy the ads that she cares about. So
Speaker 1: that led to add exchanges, and that in turn became
Speaker 1: demand side platforms. The evolution how we went from ad
Speaker 1: networks to add exchanges to demand side platforms basically came
Speaker 1: from the recognition of this problem that although we have
Speaker 1: the ability to match up advertising publishers, buyers, and sellers,
Speaker 1: neither of them really know how to match up the
Speaker 1: best inventory with each other. They know how to give
Speaker 1: each other, you know, a certain lot size, a certain
Speaker 1: amount of certain quantity in terms of number of ads
Speaker 1: are in terms of revenue spent, but they don't really
Speaker 1: know how to connect to Joe to give him the
Speaker 1: best add that he wants. Tracy, I'm listening to off
Speaker 1: and describe this. I've been thinking back to some of
Speaker 1: our early episodes with Chris White talking about bond market
Speaker 1: structure and just the sheer multitude of potential players in
Speaker 1: the game and varieties of bonds that people could be selling,
Speaker 1: different coupons, different times, different structures, and the sort of
Speaker 1: complicated challenges that that poses in terms of bringing everyone together. Yeah. Absolutely,
Speaker 1: I wonder in that case again, how they're able to
Speaker 1: do it so quickly that we don't even notice that
Speaker 1: it's actually happening in the background. So Offiction, what's the
Speaker 1: technology that's actually allowing people to do this? So the
Speaker 1: technology is referred to as you ready, Joe, another acronym
Speaker 1: RTB Real time didn't and it's meant to happen as
Speaker 1: quickly as possible to connect advertisers and publishers through supplies
Speaker 1: lie platforms SSPs and demands like platforms DSPs. Why it's
Speaker 1: so fast. Well, there's an interesting wrinkle or two there.
Speaker 1: First off, the publisher always has the option of showing
Speaker 1: you a house AD if they don't get a proper
Speaker 1: real time bid that they can match open in time.
Speaker 1: The reason you might want to show a house ad
Speaker 1: versus a targeted AD is because you never want to
Speaker 1: leave a user waiting for an ad load. I'm sure
Speaker 1: you've all had the experience of visiting a page and
Speaker 1: waiting for to load, and waiting for to load, and
Speaker 1: when it does, you get a pop up or something
Speaker 1: more innoxious. Generally you want to avoid that. What URTV
Speaker 1: allows publishers to do is present the best possible targeted AD.
Speaker 1: But if they can't do that in time, they will
Speaker 1: just be happy to show you any ad. Maybe they
Speaker 1: have their own personal brands they'd like to show off
Speaker 1: to you. Maybe they have a preferred private partner who's
Speaker 1: as they have an inventory that they would front and
Speaker 1: run in front of their RTV ads. It really depends
Speaker 1: on the publisher. So explain to us the auction process
Speaker 1: real time bidding. It's essentially describing an auction, So to
Speaker 1: walk us through what is that micro second or whatever
Speaker 1: that or that add is delivered. How the bidding works? Sure? Okay,
Speaker 1: so let's walk through our example again of Joe visiting
Speaker 1: Bloomberg dot Com. Joe visits Bloomberg, Bloomberg sends Joe's data
Speaker 1: to their SSP, their supply side platform. The SSP in
Speaker 1: turn transforms this data into a bid and passes it
Speaker 1: off to the ad exchanges. The ad exchanges in turn
Speaker 1: pass this off to their d MPs. Are you following
Speaker 1: the microxtra platforms? Sorry? I say d MPs and a
Speaker 1: d MP DSPs Even I get confused. The d MPs
Speaker 1: are the data management platforms. They're in charge of gain
Speaker 1: the data. The DSPs are the demand side platforms, and
Speaker 1: the demand side platforms in turn look for the best
Speaker 1: inventory the bids that match up with the eyeballs that
Speaker 1: they're getting from Bloomberg and try to make the best match.
Speaker 1: It's the DSPs side too, or a job to service
Speaker 1: sort of the match engine here. So this all goes
Speaker 1: through three counterparties and end if you're counting. So I'm
Speaker 1: curious what pricing actually looks like under this system, because
Speaker 1: if I think back to old style advertising, um, and
Speaker 1: you know, please let me engage in some media naval
Speaker 1: gazing here. But if you were selling newspapers, um, for instance,
Speaker 1: you would know roughly what the audience for your newspaper
Speaker 1: circulation was, and you would have a decent idea of
Speaker 1: you know their income levels and where they live, and
Speaker 1: what they like to spend money on and things like that.
Speaker 1: But now these online ads are so targeted, does that
Speaker 1: drive down the pricing because you're not You're not really
Speaker 1: targeting like a big swathe of the population that you're
Speaker 1: hoping might buy your product. You're actually targeting a specific person.
Speaker 1: It actually seems to drive up the pricing because you
Speaker 1: don't really want to flood the market with ad volume
Speaker 1: that most people won't engage, and you want to hit
Speaker 1: the lowest number of people because remember there's a ton
Speaker 1: of technical effort going behind all this. There are servers
Speaker 1: that need to run and engineers that need to be paid,
Speaker 1: and all that. You want to put the least amount
Speaker 1: of effort into pushing your ads to the most the
Speaker 1: people who are most likely to engage with them. You
Speaker 1: never want to have any extra ad target hit that
Speaker 1: hits someone who's unlikely to click it. Ideally, not think
Speaker 1: about it like this. Let's say you have a thousand
Speaker 1: ads that you want to push to people, you have
Speaker 1: the choice. And this is how it used to work
Speaker 1: about ten or fifteen years ago, of just agreeing to
Speaker 1: a rate upfront with a publisher and saying these thousand
Speaker 1: ads will sell for ten dollars and leaving the publisher
Speaker 1: in charge of running those ads as fast as they
Speaker 1: can sell. And some people will be engaging with those
Speaker 1: ads Monday around two pm, some it's Saturday four am
Speaker 1: in the morning. Some from the United States, some from Indonesia.
Speaker 1: They'll all get the same rate. That's really not efficient
Speaker 1: from an advertiser's point of view. They're really not getting
Speaker 1: the most bang for their buck. So what they would
Speaker 1: like to do instead is push to people who they
Speaker 1: know as much information about and for whatever reason, they've
Speaker 1: decided have the highest confidence and their ability to convert
Speaker 1: with the add Now I'm curious about sort of you know,
Speaker 1: if we really dive into the sort of financial market
Speaker 1: aspect of this, are there players in the market that
Speaker 1: attempt to for better lack of better warriors engage in
Speaker 1: arbitrage buy up cheap, buy up inventory that they think
Speaker 1: they could get on the cheap, and then sort of
Speaker 1: repackage it in some way and resell up more expensive
Speaker 1: like essentially, you know, essentially become traders. Absolutely, And there
Speaker 1: are several different arbitraged strategies, just as you'll find in
Speaker 1: any other market microstructure. There are plenty of companies who
Speaker 1: are in the business of taking in raw ad supply
Speaker 1: and sort of sharry picking what they think is the
Speaker 1: best possible converting ad. And on the other side, there
Speaker 1: are plenty of people who in the business of aggregating
Speaker 1: eyeballs and only presenting the best possible eyeballs to vertisers. Fortunately,
Speaker 1: I'm not sure I can give any of their names
Speaker 1: on air. How profitable is that business? Then, like can
Speaker 1: you make a ton of money out of it? Given
Speaker 1: that the pricing is quite low, it is extraordinarily profitable
Speaker 1: in the tens of billions of dollars a year. So
Speaker 1: from time to time you mentioned secret sauce, I think
Speaker 1: at one point and you talked about this, and you
Speaker 1: hear about this world of companies called ad tech, and
Speaker 1: I never am totally clear what they do. And a
Speaker 1: lot of them seem to rise really fast and fall
Speaker 1: really fast. So what are some of the competing strategies?
Speaker 1: Some company comes along and says we have something new.
Speaker 1: We have a new way that you can reach reach
Speaker 1: your clients more officially, or we're going to use AI
Speaker 1: or machine learning or big data or whatever. Like. What
Speaker 1: are these ad tech companies? Where are they in the process,
Speaker 1: and how do they attempt to make things more efficient?
Speaker 1: So there are really two basic strategies for these at
Speaker 1: tech companies. One is to make the process faster to
Speaker 1: present adds faster to eyeballs that are willing to view
Speaker 1: them and gain a sort of latency arbitrage edge. The
Speaker 1: second strategy is to gather as much information as possible
Speaker 1: from all these different accurate sources and as quickly as
Speaker 1: possible match it up to the visitor that's trying to
Speaker 1: view the ad. Most add to companies focus on the
Speaker 1: second strategy. There in the business of gathering tremendous amounts
Speaker 1: of data, correling it all very very quickly, and presenting
Speaker 1: it to you quicker than the page will load. I'm
Speaker 1: curious about the data gathering aspect. So let's say you
Speaker 1: and I or the three of us here, we're like, okay,
Speaker 1: we want to start a new ad tech company, and
Speaker 1: where are we how do we start gathering that data,
Speaker 1: like where are there wholesale brokers of it? Or that
Speaker 1: can we collected ourselves? Like what's the process there? So
Speaker 1: first we were probably partnered with several publishers like Bloomberg
Speaker 1: for example, to go back to the beginning, we would
Speaker 1: also partner with third party data providers. We would want
Speaker 1: a combination of raw fresh data coming in from new
Speaker 1: visits so we can build our own database, and we
Speaker 1: want to have the ability to correlate it with as
Speaker 1: many pre existing data sources. You probably don't want to
Speaker 1: reinvent the wheel plenty. There are plenty of public data
Speaker 1: basines and four paid databases out there where you can
Speaker 1: find out, for example, from your email address or from
Speaker 1: your IP address, what country you're in, what city you're in,
Speaker 1: what sites you've signed up for the past, what your
Speaker 1: LS you visited in the past, what time zone you're in,
Speaker 1: what language you speak, all that sort of stuff. So
Speaker 1: what's the most lucrative area when it comes to this
Speaker 1: ecosystem of online ad selling is that the data collection?
Speaker 1: Is it? You know, the underlying technology of the bidding system?
Speaker 1: Is it if you're you know, arbitraging big blocks of
Speaker 1: potential eyeballs to sell to people. What makes the most money. So,
Speaker 1: judging from the market cap of the at two companies
Speaker 1: out there today, I'm going to guess it's a combination
Speaker 1: of selling the ad and also taking a bit of
Speaker 1: the cut of every ad that's sold. So good example
Speaker 1: here might be Google, which most publishers use as a
Speaker 1: demand side partner. So Google gets their money two ways.
Speaker 1: If you're using their product DFP double clip for publishers,
Speaker 1: if you're connecting to Google through DFP, they get a
Speaker 1: cut of every ad that is served through DFP, but
Speaker 1: also they have the option of serving ads from their
Speaker 1: own inventory through their exchange addicts. So for an analogy,
Speaker 1: think if, for example, on the COMEX, you both ran
Speaker 1: the exchange and also ran a gold mining company and
Speaker 1: offered a gold etf on the exchange, you not only
Speaker 1: get a cut of every trade, but you're sourcing inventory
Speaker 1: that seems to be the most profitable. So Google not
Speaker 1: only runs the exchange, they also are participated on the
Speaker 1: exchange and are selling a raw commodity on that same exchange,
Speaker 1: and so they make money multip both ways from that
Speaker 1: trade from that environment exactly. In fact, they even it
Speaker 1: sounds like Google's pretty good business, yeah, which I thought of?
Speaker 1: That is Facebook similar in that respect, they have a
Speaker 1: similar business. It's slightly different for social media, but similar.
Speaker 1: So I'm curious about the matching of the right add
Speaker 1: to my interest because maybe I'm interested in buying a
Speaker 1: yacht or a new car, some very lucrative thing, or
Speaker 1: if I clicked on the ad someone could make a
Speaker 1: ton of money. But I might also be interested in
Speaker 1: buying a book from Amazon that you know, it's probably
Speaker 1: the margins are small. So how does it balance those
Speaker 1: disparate potential outcomes to figure out which one is the
Speaker 1: best to serve? Well, the best way to describe it
Speaker 1: would be the free market. The DSP is connected to
Speaker 1: several advertisers. Each one of them is willing to bid
Speaker 1: on your impression. So it really comes down to is
Speaker 1: the yacht seller advertiser in the bookseller advertiser? How much
Speaker 1: do they both value your eyeballs? Maybe the yacht seller
Speaker 1: thinks you're not really convert on an ad because he's
Speaker 1: looked up your date and says, oh, well, you know
Speaker 1: you live here in this time. I've never bought a
Speaker 1: yacht before. Yeah, so it's say that you probably would
Speaker 1: in the future, but you've probably bought plenty of books,
Speaker 1: so maybe the yacht seller is not going to fight.
Speaker 1: It's harder for you using the price mechanism than the
Speaker 1: bookseller would. So if in one case, let's say the
Speaker 1: yacht seller bids fifty cents for your eyeballs and the
Speaker 1: bookseller bids a dollar the books, that would win and
Speaker 1: they'll end up paying fifty one cents, just enough to
Speaker 1: beat the yacht sellers bid. Got it, So the bookseller
Speaker 1: might end up bidding fifty one cents and on a
Speaker 1: transaction that you know, maybe we'll make them fifty five
Speaker 1: cents or a dollar or something like that. Right, And
Speaker 1: the idea is you will probably lose money on most
Speaker 1: of these in aggregate, but the winners are real winners.
Speaker 1: And the odds seller occasionally is going to get that
Speaker 1: ad and it's probably going to serve thousands and thousands
Speaker 1: of ads that don't turn to anything, maybe millions, but
Speaker 1: then when they do, those are huge jackpots. Yes, and
Speaker 1: I'll add one more wrinkle. We're talking about ads that
Speaker 1: ads come in many different forms. This isn't just a
Speaker 1: race for eyeballs, could be raising for video ad views.
Speaker 1: Or application installs. Even so I have a sort of
Speaker 1: related question, um sort of maybe even a philosophical question.
Speaker 1: But so much of the online advertising world seems to
Speaker 1: be about targeted advertising. So does anyone just you know,
Speaker 1: throw stuff out there nowadays just on the off chance
Speaker 1: that someone might see a good product and want to
Speaker 1: buy it to Joe's y odd example, you know what
Speaker 1: if someone has a really really nice yacht and they
Speaker 1: think people will buy it if only they were aware
Speaker 1: of it. Sure that does happen. It's also pretty rare
Speaker 1: because in order to actually get a high enough conversion
Speaker 1: ratio for this to be worth the advertiser's time, you
Speaker 1: really have to buy a tremendous amount of volume if
Speaker 1: you're not doing any targeting on who your customer is,
Speaker 1: and now she might be putting up a billboard versus
Speaker 1: sending a direct mail flyer. If you don't know much
Speaker 1: about the person who's engaging on the other side of
Speaker 1: the bye, who knows if they're going to bite? Is there? Though?
Speaker 1: Like sometimes you see like a huge site takeover, like
Speaker 1: on the New York Times or something like that. You'll
Speaker 1: see some massive they'll buy every ad on the website
Speaker 1: or on the front page or something like that. It's
Speaker 1: clearly not particularly targeted. There is still some sort of
Speaker 1: like brand advertising on the internet. Right, Yes, those are
Speaker 1: private auctions, and those are great, but they're really only
Speaker 1: great for publishers who can demand that sort of leverage.
Speaker 1: For bloomber dot com in this example, that would be
Speaker 1: a great example of the site that would have enough
Speaker 1: cashier to pull a private auction. I can imagine plenty
Speaker 1: of brands would want to have exclusive access to the
Speaker 1: eyeballs at the basing dot com. My personal blog probably
Speaker 1: not so much. I'm probably not gonna be able to
Speaker 1: land that big deal with Pepsi or Coca Cola to
Speaker 1: have them take over my site for the next thirty days.
Speaker 1: And that's something that would be negotiated among humans, and
Speaker 1: there would be a deal maybe like there was just
Speaker 1: the big ad conference and canned or people like partied
Speaker 1: on yachts and stuff like that, and those are the
Speaker 1: kinds of deals that get negotiated there rather than through
Speaker 1: some sort of like algorithmic matching engine. Right, and think
Speaker 1: about it if you will. There's the difference between buying
Speaker 1: at the market price on an exchange versus setting up
Speaker 1: an OTC Trade. We like to talk about changes to
Speaker 1: market structure quite a lot on the Pots podcast. Are
Speaker 1: there any big changes or big pressures or disruptions on
Speaker 1: the horizon for the current structure of the way these
Speaker 1: online ads are sold. I'm glad you asked. Let's talk
Speaker 1: about header bidding. Header bidding. Header bidding is what seems
Speaker 1: to be what's going to come next after real time bidding,
Speaker 1: and let's explain what it is and what the motivations
Speaker 1: are behind it. I mentioned Google's DFP double cliff for
Speaker 1: publishers before as an interesting feature. First off, most people
Speaker 1: most of the time use Google for advertising, just in
Speaker 1: the same way that most people most of the time
Speaker 1: use Google for search, and most publishers are content to
Speaker 1: just trust Google to sort of drive the process and
Speaker 1: they don't really dig too much into it because Google
Speaker 1: wouldn't always serve an ad that seems to satisfy both
Speaker 1: the advertisers and the publishers. The reason, in part that
Speaker 1: Google is able to do this, besides the fact that
Speaker 1: they're so great at aggregating data, is what's known as
Speaker 1: the waterfall. Here's how it works. You call out to Google,
Speaker 1: you asked them if they can give an ad to
Speaker 1: you so you can serve it to your visitors. Google
Speaker 1: goes through their inventory to see who the best ad
Speaker 1: is and where it's coming from. They have the option
Speaker 1: of serving it from another exchange or their own exchange addicts,
Speaker 1: so they have the ability to front run basically any
Speaker 1: other advertising ex change if you allow them to. And
Speaker 1: because Google is where all of the publishers show up to,
Speaker 1: all the other exchanges sort of put up with it
Speaker 1: or forced you. Header bidding is meant to change that.
Speaker 1: Header bidding is different from real time bidding, and that
Speaker 1: instead of where in real time bidding, where a publisher
Speaker 1: calls out to a DSP and waits for a response,
Speaker 1: header bidding allows the publisher to call out to multiple
Speaker 1: DSPs all at the same time and select the best
Speaker 1: bid incoming themselves. So you lose the convenience of just
Speaker 1: letting Google drive, but you gain the price competition between
Speaker 1: all of these different bidders. It sounds obviously better for
Speaker 1: the publisher. So why is that not already the norm? Well,
Speaker 1: because Google doesn't like it that way. Most people most
Speaker 1: of the time are actually not as enamored with his
Speaker 1: market mixed structure as you and I are a publisher
Speaker 1: is probably the business of publishing content and doing whatever
Speaker 1: their core businesses. Actually managing and driving this process yourself
Speaker 1: is really tricky. In order to for example, for head
Speaker 1: to be to be really profitable for you, you'd have
Speaker 1: to actually, you know, care about those bids and put
Speaker 1: some thought into whether you might for one partner over another.
Speaker 1: Would you need to build your own technology to determine
Speaker 1: If you're getting multiple bids from multiple exchanges, then it's
Speaker 1: I guess it's on you to actually determine which one
Speaker 1: is the best. Whereas if you're just on one exchange,
Speaker 1: then that determines what's best. And so I imagine to
Speaker 1: take some more supply side infrastructure to build that up. Exactly.
Speaker 1: It's the difference between you know, setting up effectually your
Speaker 1: own prop shop for ads versus just trusting your broker
Speaker 1: to disup why you with what you're asking for. Well,
Speaker 1: that has been absolutely fascinating discussion, and I just love
Speaker 1: the fact that the kind of market structure that topics
Speaker 1: that Tracy and I talked about frequently on this show
Speaker 1: are actually at the root of how the entire Internet works.
Speaker 1: And I didn't even realize that so I'll shooting Bedelli.
Speaker 1: Thank you very much for joining us. Thank you for
Speaker 1: having me, Joe Yea. So, Tracy, you said that you've
Speaker 1: been thinking about online ads and you've been wondering about
Speaker 1: how they get served up to you. Do you feel
Speaker 1: that your questions have been answered? I mean, I definitely
Speaker 1: have a better sense of it now, but I will
Speaker 1: never look at a web page loading the same ever again.
Speaker 1: You know, every time I go someplace, I'm going to
Speaker 1: be thinking about the intense auction process that's currently happening
Speaker 1: in the background with a bunch of people trying to
Speaker 1: bid on my specific profile. It is pretty amazing. I mean,
Speaker 1: sometimes websites or absolute load a little bit slower than
Speaker 1: you would like, but even with that, it is pretty amazing.
Speaker 1: How much has to happen in that short period of time.
Speaker 1: You don't even notice it, but all this information goes out,
Speaker 1: it's analyzed, it's put into some profile. There's a bitting habit,
Speaker 1: a bitty war happens in an ad is served to
Speaker 1: basically in an instant. I don't know if it's exactly
Speaker 1: a miracle, because it's kind of creepy. It's it's kind
Speaker 1: of creepy, right, but it is kind of amazing, isn't it. Yeah,
Speaker 1: I guess it gets to a point that we've talked
Speaker 1: about before on the show, which is also about inequality
Speaker 1: and the ability of algorithms to sort of reinforce a
Speaker 1: certain position that a person is already in. So, for instance,
Speaker 1: you know, if when you're twenty four years old, you
Speaker 1: need to pay day loan to survive until your next
Speaker 1: month's paycheck comes in, and you search online for payday loans,
Speaker 1: those ads might follow you around for years and years
Speaker 1: to come. Um, when other people who have never needed
Speaker 1: to pay day loan might see, you know, advertisements for
Speaker 1: four one case or things like that. I've never really
Speaker 1: thought of it like that before, but I've been thinking.
Speaker 1: We had a whole episode on this, Joe. No, no, no, absolutely,
Speaker 1: I never thought about that before with regards to online
Speaker 1: ads specifically, But I have been thinking about how like
Speaker 1: I went through a phase where I needed to buy
Speaker 1: some clothes. I need to buy some shoes, and then
Speaker 1: I got served tons of ads for weeks and months
Speaker 1: on and on similar stuff. And then I started worrying that, wait,
Speaker 1: am I buying more than I need to now on
Speaker 1: these things? Because I went through this period where I
Speaker 1: made these purchases, and so thinking about the sort of
Speaker 1: lasting impacts of a certain behavior on the type of
Speaker 1: ads were inclined to see for a long time is
Speaker 1: really fascinating and absolutely does speak to that discussion we
Speaker 1: had earlier about the influence of algorithms on our lives. Yeah,
Speaker 1: you cannot escape your Internet history, no matter how hard
Speaker 1: you try. All right, Uh, well, this has been another
Speaker 1: episode of the Odd Thoughts podcast. I'm Tracy Alloway. You
Speaker 1: can follow me on Twitter at Tracy Alloway, and I'm
Speaker 1: Joe Wise though you could allow me on Twitter at
Speaker 1: the Stalwart, and you should follow our producer tofur Foreheads
Speaker 1: on Twitter at Foreheast, as well as the Bloomberg head
Speaker 1: of podcasts, Francesca Levie at Francesca Today. Thanks for listening.