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How arbitrage works in advertising today

How arbitrage works in advertising today Eric Picard

The idea that ad networks, trading desks, and various technical "traders" in the ad ecosystem are "arbitraging" their customers is fairly well understood. The idea that an intermediary of some kind sells ad inventory to a media buyer, but then buys it somewhere else for a lower price is a pretty basic reality in our industry. But what most of us don't understand is how it gets done and especially how technically advanced firms are doing it.


So let's talk about this today -- how arbitrage is enacted by various constituents, and I'd love to hear some reactions in the comments about how marketers and media buyers feel about it, especially if they weren't aware of how it was done. Note: There are many ways to do this; I'm just going to give you some examples.


Old school networks


Back in the day, ad networks would go to large publishers and negotiate low price remnant buys (wholesale buys) where they'd buy raw impressions for pennies on the dollar, with the rule being that the network could only resell those impressions without identifying the publisher (blind inventory resale).


The networks that have done this well traditionally apply some targeting capabilities to sell based on context/content and also audience attributes. But even this is all very old school. The more advanced networks even back in the old days employed a variety of yield optimization technologies and techniques on top of targeting to ensure that they took as little risk on inventory as possible.


RTB procurement


Many networks now use the exchanges as their primary "procurement" mechanism for inventory. In this world there's very little risk for networks, since they can set each individual campaign up in advance to procure inventory at lower prices than they've been sold. There is a bit of risk that they won't be able to procure inventory -- i.e. there isn't enough to cover what they've pre-sold. But the risk of being left holding a large amount of inventory that went unsold is much lower and saves money.


Once you mitigate that primary risk and then add in the ability to ensure margin by setting margin limits, which any DSP can do "off the shelf," the risk in managing an ad network is so low that almost anyone can do it -- as long as you don't care about maximizing your margins. That's where a whole new class of arbitrage has entered the market.


Technical arbitrage


There are many different ways that companies are innovating around arbitrage, but I'll give you the baseline summarization so you can understand why many of the networks (or networks that advertise as if they're some kind of "services based DSP") are able to be successful today.


Imagine a network that has built an internal ad platform that enables the following:



  • Build a giant (anonymous) cookie pool of all users on the internet.

  • Develop a statistical model for each user that monitors what sites the network has seen them on historically on a daily/day-of-week basis.

  • Develop a historical model showing how much inventory on each site tends to cost in order to win the inventory on the exchange, perhaps even each individual user.

  • When you run a campaign trying to reach a specific type of user, the system will match against each user, then in the milliseconds before the bid needs to be returned, the network's systems will determine how likely they are to see this user that day -- and if they will find them on sites where historically they've been able to buy inventory for less money than the one they're on at the moment.

  • If the algorithm thinks it can find that user for less money, it will either bid low or it will ignore the bid opportunity until it sees that user later in the day -- when it probably can win the bid.

This kind of technology is now running on a good number of networks, with many "variations" on this theme -- some networks are using data related to time of day to make optimization decisions. One network told me that it finds that users are likely to click and convert first thing in the morning (before they start their busy day), in mid-morning surfing (after they've gotten some work done), after lunch (when they're presumably trying to avoid nodding off), and in the late afternoon before going home for the day. They optimize their bidding strategy around these scenarios either by time of day or (in more sophisticated models) depending on the specific user's historical behavior. 


You shouldn't begrudge the networks based too much on this "technical arbitrage," since all that technology requires a significant upfront investment. They're still giving you access to the same user pool -- but one question that nags at me is that they may be giving you that user on sites that are not great.


It also begs the question that if these very technical networks are buying their inventory on a per-impression basis, all the stories about fraud get me a little worried. A truly sophisticated algorithm that matches a unique ID should be able to see that those IDs are getting too many impressions to be human. But I haven't done any analysis on this -- it's just a latent concern I have.


Eric Picard is the CEO and founder of Rare Crowds.


On Twitter? Follow Picard at @ericpicard. Follow iMedia Connection at @iMediaTweet.

Eric Picard is Vice President, Strategic Partnerships, at MediaMath. He was previously the Founder and CEO of MediaMath-acquired Rare Crowds, an open source ad technology company that provides a completely open advertising technology stack for...

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Comments

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Commenter: Jessie Mamey

2013, August 01

The evolution of display buying is pretty astounding and makes a ton of sense. I'll admit that it took me a while to understand what was really going on behind the scenes and I'd say that most partners in my plans are utilize RTB inventory to find efficiency for my advertisers. I will choose to work with a media partner (or perhaps we should call them something else) based on the whether they (1) have unique data to fuel the targeting of the buy, (2) have access to unique inventory - i.e. not just the major exchanges or (3) have an optimization technology that outperforms other partners or our own campaign management team's performance.

I'm all for paying that technology cost markup, as it seems clear to me that the development of those algorithms are incredibly complex. It's an area I'm very interested in learning more about. If a "network" can develop that technology and meet or exceed performance goals, I'm on board.

Questions about algorithms:
An interesting idea popped into my mind during a conference last week where a panel was discussing algorithms. The comment was specifically focused on how algorithms are designed to take into account an insane amount of attributes - from page content, user cookie information, browser access point, potentially other 3rd party feeds (i.e. weather) -"Big Data". Companies are compensating and evolving by investing in the tech folks, but also the data scientists. Algorithms are being developed by these data scientists and are fueled by baselines and campaign performance against those standards.

I'm curious to know how these algorithms are taking into account the type of product being advertised - does it matter? Should the same algorithm designed to optimize against CPA look for the same performance thresholds for time of day performance when assessing a QSR/B2C advertiser vs. a Tech/B2B advertiser. Is anyone creating these types of category customized algorithms or is that something that is being done without being publicized?