The New Premium: How Programmatic Changes The Way Advertisers Value Inventory

By Eric Picard (Originally Published on AdExchanger.com Thursday, February 5th, 2015)

Five years ago, if I told anyone in our industry that I wanted to buy or sell “premium” inventory, we’d all picture the same thing: inventory that was bought or sold directly between a media buyer and publisher’s salesperson. Maybe it would be home page inventory or a section front, a page takeover or rich unit. Or perhaps it would just involve a specific publisher that we agreed equated to “premium.”

New programmatic technologies are radically changing how we think of inventory overall, especially the term “premium.” Inventory is no longer one- or two-dimensional – the definition has become much more complex. It is a multidimensionally defined set of attributes that includes traditionally “publisher-controlled” inputs, such as page location, dimensions of the creative, category and content adjacencies. But today there are additional overlaid attributes that flesh out the definition.

Advertisers can bring their own data to the dance, which we’ll hesitantly call “first party,” and overlay additional data sources, which we’ll hesitantly call “third party.” And beneath the surface level attributes are underlying components that can be much more dynamic. These components can help predict how effectively an impression can drive a campaign’s goals or outcomes.

Programmatic buying platforms historically were tied to open exchange inventory, but increasingly, they are used as primary buying platforms across open RTB, private marketplaces, direct publisher integrations and even to support direct buys. This more holistic approach ultimately leads to a “programmatic first” point of view, as the new inventory definitions being rightly demanded by advertisers become their starting point on media buys. While RTB “only” represents 20% to 40% of budgets today, it’s clear that the rapid growth of programmatic will drive these broader inventory definitions across the buyer-seller boundary.

Achieving Symmetry

Publishers are embracing the newly empowered media buyers, allowing them to bring their own data for direct buys. They are also allowing buyers to connect directly to their ad servers for programmatically enabled direct buys and buy-side inventory decisioning in real time. For the past few years, the asymmetry of information in programmatic – publishers had no idea why advertisers bought their inventory on the exchange – has been a sore point.

Publishers point out that if buyers work with them, they can open paths to the inventory, inclusive of audiences, that buyers are looking for on the exchange. As we see more collaboration between buyers and sellers on these points, pockets of highly valuable inventory that were lying dormant inside the publisher’s ad server (dare we say “premium”) will suddenly open up.

To use a mining analogy, publishers previously sold unrefined chunks of ore to media buyers, who found a variety of metals inside, but only some of it was valuable to them. So buyers started buying inventory through other marketplaces that allowed them to use their own tools and data to locate the chunks of ore that contained the metals they cared about. Now publishers are saying, “If you’re willing to pay us what you think that metal is worth, we can find more of it than you’re getting on those secondary marketplaces. But you have to work with us to get access to it.”

This new approach is both exciting and refreshing. The industry is getting over old suspicions and reluctance to share information. The asymmetry is becoming more symmetrical, and everyone involved gets more value. Days are still early, and only the most advanced players are figuring out how to make this work, but it won’t be long before this new way of defining “premium” is the standard.

Evolving Definition

How do we define “premium” in this new programmatically enabled world? Premium inventory matches the advertiser’s holistic goals, inclusive of where the ad will run – publisher, category, page location or format – and the multidimensional profiles of anonymous users behind the impressions, including first- and third-party audience data definitions, as well as geographic, demographic and other data elements provided by publishers and other parties. The advertiser believes the premium inventory will help fulfill their goals and drive outcomes that they desire.

That’s a mouthful, eh? How about this: Premium inventory matches the goals of the advertiser well enough that they’re willing to pay a premium for access.

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How private marketplaces actually work

By Eric Picard (Originally Published on iMedia – December 13, 2014)

Recently Ricardo Bilton wrote an article for Digiday about the difficulties that publishers have had embracing private marketplaces (PMPs). The validity of his article is arguable, and he called out a few of the buy-side platforms as causing some of the difficulty — despite the massive and growing volumes those platforms are actually driving in the PMP world. So instead of rebutting his article, let me define how these things work, and what the scope and difficulties are in making use of private marketplaces, but also what benefits can come from them.

Before we get into it, let’s talk about complex vs. complicated. They actually mean different things. Complex implies that the difficulty of embracing something is unavoidable — some things are just complex, have lots of moving parts and lots of opportunities to implement. Complicated implies that the difficulty is avoidable, and could be designed around. Private marketplaces today are both complex and complicated. We need to remove the complications.

History

Back in the dark ages of the programmatic world, let’s say 2008, publishers were wary of the newly emerging programmatic landscape. In order to convince them to put their inventory into the proto-exchanges that existed, the concept of a private exchange or private marketplace evolved. Keep in mind that up to that point, mostly the inventory that flowed on the exchanges came from ad networks daisy-chaining their inventory together. But as publishers began participating, and SSPs entered the scene, these private marketplace mechanisms were rolled out to support publisher concerns about yield optimization — and especially cherry picking and cream skimming — buying strategies that were major concerns for publishers in those early days.

As a result, the first private marketplaces were fairly simple to understand, and were nice ways for publishers to get their feet (or at least their toes) wet in the programmatic space. The basic concept was simple: Publishers could expose some or their entire inventory to an exchange or SSP. They could hand-pick which advertisers were invited to come into the private marketplace. Only those invited to have access could bid on the inventory.

The problem with this early approach was that it missed out on some very important fundamentals of exchange-based buying and selling. One important fundamental is bid density: For every impression that is exposed to an auction, you need as many bidders (buyers) as possible competing for that inventory in order to have the price reach a reasonable amount — especially in a second price auction.

What is a second price auction?

It’s a pretty simple idea, really — if three people participate in a second price auction for an Apple, all three people put in the highest price they’re willing to pay for that apple. Person A bids $1.00. Person B bids $1.50. Person C bids $0.50. Person B would win the auction, but only pay $1.01 for the Apple. The reason for a second price auction rather than a first price auction (in the example above for a first price auction, person B would still win, but would pay $1.50) is to encourage the bidder to put their true price into the auction. Second price auctions are generally understood to have less “gaming” of the auction — since the high bidder is protected from overpaying.

But in a world where only one or two advertisers are bidding on the same impression, there’s often no second price to use. So private marketplaces by nature are problematic when it comes to bid density — and many early private marketplaces ultimately failed to succeed. There are mechanisms that can be tried — for instance using a first price auction for private marketplaces — but of course this can lead to rampant gaming of the auction — and rarely will a buyer put the actual price they’re willing to pay into a first price auction. Another mechanism is price floors –which protect the publisher from having the impression fall on the floor for close to nothing — but often a PMP price floor in those days became a price, rather than a floor due to the lack of bid density.

As our industry evolved away from the original exchanges and toward real-time bidding, a whole host of new complex issues were uncovered — but also amazing new capabilities. One of the key things that this drove in the PMP world were new innovations like dynamic floor pricing — where the SSP or even the publisher ad server was able to analyze demand across the ad server, the SSP, the PMP, and the open exchange and set the floor on a per-impression basis.

As publishers got over their initial fear of programmatic selling, they began to put their inventory into the open exchange and blend the private and open bids into the same auction. Publishers quickly realized that they needed to give the buyers that had private marketplace access a set of preferences so that they would continue in the PMP rather than bounce out to the exchange. This led to all kinds of mechanisms — across various systems that have brought us to our modern programmatic landscape for private marketplaces.

Private marketplaces today

Private marketplaces today are very confusing. They’re both complex and complicated. There’s no clear and simple definition of a PMP that means exactly the same thing to everyone because there are so many ways to implement one. And depending on your ad server, your choice of SSP and/or exchange, and the buyer’s DSP, it’s fairly impossible to know in advance how the PMP will instantiate itself. Literally if you took five impressions of a PMP and reviewed them, each could be delivered completely differently from the others.

For publishers looking to start using private marketplaces today — without any legacy configurations or expectations, there are some benefits of having waited. Today PMPs are really about giving the publisher control over the way their preferred customers get treated by the auction. As everyone knows, when you have a big customer, who spends a large amount with you annually, you probably want to give them some discounts and benefits for working with you. Private marketplaces today are evolving into sets of controls for protecting the relationship with the buyer, and often for giving them either a discount, or giving them better access and control over the inventory they want to buy. It is the latter scenario — giving the buyer control — that makes some publishers very nervous, but is the real benefit of the PMP in today’s market.

In this scenario, the publisher lets their big spending customers get some additional control over defining the audience and the inventory that they have access to. Sellers frequently will bundle this additional control with a larger overall buy, or with a high minimum CPM, or with a high minimum overall budget. And publishers are finding that this approach makes everyone on all sides of the deal much happier. Everyone wins, as long as the complexity required to pull this off is embraced.

One of the biggest innovations in the programmatic world, and one that causes a lot of the complexity behind the issues this space has been saddled with, is the Deal ID. Deal ID was supposed to solve many problems in the programmatic space, but they have added another layer of complexity. The trick is to embrace the complexity without structuring things in such a way that they become unnecessarily complicated.

What is deal ID?

It became clear that while RTB was a vastly superior way to buy and sell ads than anything else we’d seen as an industry — there were touch-points between the old systems and the new systems that were confusing. Nowhere was this confusion worse than when a buyer wanted to execute a guaranteed deal over the RTB infrastructure.

But that Deal ID mechanism has now been used in much more flexible ways than its original driving intent. Think of a Deal ID as a way to prioritize a buy against supply. And the features for how you prioritize the bid vary by ad server, by exchange, by DSP, and by SSP. Sometimes the combination of each of those things leads to a different set of capabilities.

If you’re feeling confused, you’re getting the picture. This isn’t simple stuff. But that’s okay, because with complexity comes opportunity. Here’s a complex, but powerful scenario that Deal ID opens up:

All of these bids are Deal ID bids — prices are CPM:

  • Advertiser A sets up a dynamic bid that lands at $5 for the impression. Publisher floor prices this advertiser at $7.
  • Advertiser B sets a dynamic bid at $6 for the impression. Publisher floor prices this advertiser at $4.
  • Advertiser C sets up a dynamic bid for $17 for the impression. Publisher floor prices this advertiser at $20.
  • Advertiser D sets up a dynamic bid for $1 for the impression. Publisher floor prices this advertiser at $3.

In the above scenario, Advertiser B would win the auction, and pay $6 CPMs for that impression. Since one of the Deal ID bids won the auction, the impression never makes it to the open exchange. If for some reason the publisher had set the floor price for advertiser B at $7, then this auction would have flowed through to open exchange, and then advertiser C would have likely won the auction (assuming nobody in the open exchange bid higher than $17). Advertiser C would end up paying whatever the next highest bidder was willing to pay, plus $0.01.

How was that for complex? Want it to be more complex?

Some ad platforms can support a Deal ID with a dynamic bid, or a fixed price with a priority cascade. So while price mattered a lot in the example above, if one of those bids (even the low bid) was a fixed price, it would have won the auction at the fixed price. That’s how you give your preferred advertisers ways to find their preferred audience while giving them a fixed price. The trick is to negotiate well on the price on both sides so everyone gets what they want.

So while Deal ID is just one mechanism that may or may-not be part of a private marketplace, the two concepts are becoming somewhat inextricably linked together. What is a private marketplace today? It’s a complex set of interacting tools, systems, mechanisms, and approaches that can be used to give the publisher control over the prioritization of their supply against the demand represented over the exchange. Easy to understand? No. Easy to configure? No. Easy to execute against? Not yet. But worth using? Absolutely!

This complexity means power, but the complexity leads to confusion and complications. So when we have people who aren’t practitioners writing articles about very complex systems and how they are used, and then going to sources for quotes about adoption of these complicated scenarios, the answer is going to either be vague (not quotable) or clear (not accurate.) And these clearer quotes, which aren’t really very accurate in many cases, paint a picture of the space that looks like it isn’t working.

Private marketplaces give publishers control over the prioritization of buys coming from the programmatic channel. As an industry, we’re still figuring private marketplaces out — but vast and growing dollars are being spent over them in the meantime, and those buyers and sellers willing to take the time and effort to understand the complexity are winning. Yes, we need to make the execution of private marketplaces less complicated. It would be nice if we could also make them less complex, but only if we don’t lose the power that comes with the complexity. And in the meantime, the channel is growing and productive.

Don’t Believe The Lies About Digital Media

By Eric Picard (Originally published on AdExchanger Monday, December 1st, 2014)

For years, there has been a series of bad memes spreading throughout our industry. Some of the big ones have caused a huge amount of misunderstanding in our space.

Here’s my favorite: “There is infinite supply of ad inventory. With this overabundance of supply, the cost of inventory will be driven to zero.”

This way of thinking caused the publisher side of the industry to fear and block adoption of programmatic buying and selling until the last year or two, when it was proved to be false.

A simple truth: All ad impressions (in a nonfraudulent world) are created by a person seeing an ad. I estimate that there are 5 trillion monthly digital ad impressions in North America. If we divide this against roughly 300 million active Internet users in North America, assuming they spend on average five hours a day consuming content on the Internet, that breaks down to approximately 100 ads per hour.

So we as an industry have 100 chances an hour, or about 500 chances per day, to reach each person in North America with a digital ad. Of the 300 million people we can reach, advertisers only care about a sliver of the total audience.

Once you break down the audience to the desired number of people to reach, with the relevant targeting, the question becomes: Of the 500 daily opportunities, how many times do you want to reach that group of people? It comes down to several factors: what mechanisms you, as a buyer, can use to identify them and deliver an ad to them, the format of the ad and how effective you believe your opportunity to reach them will be.

So, no – the number of impressions is not infinite. And if we believe that some percentage of the ads in that 5 trillion monthly statistic are fake, meaning fraudulent or simply not viewable, then the number of chances to reach consumers could be much smaller, from 100 to as low as 50 ad opportunities per hour.

Suddenly the lie is turned on its head and it becomes more about maximizing the opportunity with your target audience. And for those opportunities, the cost is definitely not heading toward zero.

Not True: Ad Inventory Can Be Defined By The Publisher And Divided Into Pools Of Undifferentiated Impressions

Ad inventory is made up of a group of individual, unique ad impressions. Every impression has hundreds of points of data surrounding it. The problem with this belief is that it assumes limitations that don’t exist. Publishers define inventory in broad, relatively undifferentiated buckets, which are the lowest common denominator from a complex media plan sent with a fairly detailed RFP by the buyer.

For instance, buying a million impressions of “soccer moms” from a publisher creates a very limited view of that inventory. The range of income, interests, product ownership or geography is broad for the individuals behind those impressions. And some “soccer moms” may be worth more than others depending on an advertiser’s campaign goals.

In a world where inventory is publisher-defined, this lowest common denominator approach was the only way to operate a scale media business. But that’s no longer the case. Buy-side decisioning allows the buyer to define the inventory – and that inventory definition by nature can be more complex than ever.

So what is an impression? Ultimately an impression is a human being engaging in a monetizable experience via a computer or digital device, in a certain modality. By modality, we mean that they’re either passively consuming content (such as watching a video), actively consuming content (reading an article or email), actively participating in an interruptible interactive experience (playing a game with breaks between levels) or actively participating in a non-interruptible interactive experience (writing an email or engaged in a video chat).

All the data about the person behind the impression is captured in first-party (buy side and sell side) and third-party data platforms. And all the data about the content being consumed, and the user’s modality, belongs to the publisher. It is by matching both types of data that we can truly unlock the value of inventory. The more open and transparent we make things, the more value we unlock. By giving buyers access to the unfettered truth of inventory and the ability to peruse and pay for their desired inventory, price ultimately tends to go up, not down. The old world of limited, siloed and blocked data is responsible for this lie.

As we’ve opened up inventory sources and unlocked access to audience and modality data, the market responded by equalizing prices. As a result, publishers can make just as much money from programmatic channels as direct sales. Publishers that are allowing demand from programmatic sources to compete directly with guaranteed inventory are becoming pleasantly surprised with the results. Publishers that started early on this path are gaining some significant advantages that could be sustainable over the long haul.

Not True: Publishers Don’t Let Buy-Side Systems Access Inventory Because Of Potential Data Leakage

This is an old misconception. Back around 2005, some publishers invested in technology to enable creation of “publisher first-party” audience targeting data. They tracked individual audience members’ activity on the publishers’ sites and put these content consumption behaviors into behavioral targeting segments. They could then sell these segments as inventory definitions, rather than just selling locations.

This was very useful when publishers had small pools of valuable inventory that would sell out, such as auto-related inventory that would sell out months in advance. So publishers tracked users who read articles in the “auto content” bucket and created a segment called “auto intenders” so they could sell ads targeted to those users when they were browsing other pages of the publisher’s content. If they charged $15 CPMs for auto content ads, they would sell behaviorally targeted “auto intenders” for $10. They’d deliver those ads on pages that were probably selling for $5 CPMs, more than doubling the yield on those impressions.

The problem is that only the largest publishers with big user bases that consumed lots of their content could assemble enough valuable behavioral data. The small window into a person’s web-surfing behavior that any one publisher had access to was not enough to really create sustainable value. However, that data was created, sold and valued by the market, although it was less valuable to buyers because it originated from user activity on just one publisher, rather than data pooled across publishers.

Right around that time, the first behavioral ad networks, typified by Blue Lithium, figured out that they could supercharge their behavioral targeting segmentation by buying guaranteed targeted buys from publishers and stealing segmentation data from the publishers. They maximized reach by keeping the frequency cap as low as the publisher would allow, then dropped their own cookies on those users and added the publisher’s targeting definition to their own.

For instance, say a buy of “auto intenders” from Yahoo had a frequency cap that was set to one. If the ad network bought 1 million impressions for a $10 CPM, they would add 1 million unique users to their own cookie pool of “auto intenders” for just $10,000. They could then find those same users on cheaper sites, and eventually buy the inventory over ad exchanges for less than $1 but sell it for $8, allowing them to arbitrage the market. Since these networks could turn small expensive direct buys into feeders of their behavioral targeting pools, and then extend those buys to cheap inventory sources, publishers obviously became very concerned about this “data leakage.”

Most publishers, other than the very largest, stopped investing in their own first-party behavioral data technologies, leading to the creation of the lie that publishers are deathly afraid of data leakage. But what people missed is that most publishers simply gave up fighting this battle. Instead they partnered with third-party data providers that paid publishers for the rights to collect behavioral data.

They would then push the behavioral segments back into the publisher’s ad server so they could sell the data as part of their direct buys. The data leakage problem led to the creation of the third-party data marketplace and the tracking of users across publishers, which marketers find more valuable.

The real value that publishers can provide is not in turning the behavior of their audiences into targeting data. Instead publishers can give buy-side decisioning systems access to data about the content being consumed (category-level data) and what users are doing on those pages (modality data). They can also enable the buyer to bring their own first-party data, which is far more valuable for buyers than anything the publisher could assemble.

Everybody wins when publishers open up competition between decisions made by a demand-side or buy-side ad platform and direct buys booked through their sales force. When given the opportunity, buyers are willing to pay similar or higher rates for access to this inventory, compared to what they’d pay for publisher-packaged inventory that only offers publisher-based ad decisions. The two methods competing with one another increases the value of the inventory and maximizes the yield for each impression. It’s a “win-win” or a non-zero-sum game – a good thing for everyone.

How To Use RTB For Targeted Reach Instead Of Retargeting

By Eric Picard (Originally published on AdExchanger)

I was recently told by an executive in a position to know that 70 to 80% of revenue in the RTB space comes from retargeting. I found that stunning because it basically tells us that the RTB space is incredibly immature. If the vast majority of revenue in the space is retargeting, then nearly all the spending comes from ecommerce companies.

That means we have huge upside in this space because ecommerce companies certainly don’t make up anything near the majority of advertising spending.

Nearly 90% of advertising spend “all-up” is done on a targeted reach basis. In other words, the advertiser has come up with an ideal marketing persona (or series of marketing personas – many brands have five to 10 defined marketing personas) and their media plan is designed to reach people matching that persona. Using old-school methods, such as Nielsen or comScore, they find publishers with audiences matching their marketing personas, and that’s where they’ll buy impressions.

The problem is that this is extremely inaccurate, and wastes budget by spreading it across the whole audience that visits this publisher. On one hand, it’s wasteful because it pushes the message on audiences that don’t match campaign goals. On the other hand, it’s OK if there’s some “waste” in media spending because there’s value in getting the message in front of slight target mismatches.

Case in point: I don’t have cable at home. We watch Hulu, Amazon Prime and Netflix when we consume TV content. But recently, while traveling, I saw a few hours of TV in my hotel each night. I was shocked by the vast number of pharmaceutical ads on broadcast television – especially on the news (which I hardly watch anymore).

Most ads related to conditions I’m not facing today – so in a sense those ads were wasted. But should I ever contract one of those conditions, I’ll likely remember those products exist. Or should one of my close friends or loved ones get stricken with those conditions, I’ll recall that a medication exists and engage in conversation with them.

So yes – this broadcast brand strategy certainly does have some value. As I’ve said before: There’s value in the fact that I know Dodge Ram owners are “RAM Tough.”

On the other hand, we can be much more precise now than in the past — if you can find the data. And if you believe in the methodology that created the data, there are ways to more precisely reach your target personas and target audiences of all flavors.

Find The Right Tools

Using demand-side platforms and social media marketing tools, including the self-service tools within Facebook, it’s now possible to find your target audience in a variety of ways. You can be very narrow or very broad. You can control exactly which sites on which you’ll reach that audience, or you can simply specify on which sites you don’t want to reach your audience.

For brands that are very particular about running ads only on approved content, there is the white list – a specific list of domains matching against publishers that you specifically approve to run ads on. This does limit scale, but there’s no limit on the size of the white list you can create.  And there are vendors like Trust Metrics that you can use to build a custom white list for you, which both hones the targeting to sites that match your brand safety metrics and massively reduces fraud.

Or if you want, you can use private marketplaces to execute buys only on the sites you specifically negotiate with for access to their audiences over RTB. This has a lot of value for pharma and marketers that are extremely sensitive to running ads on sites that match their brand values.

If you want to specify a tightly targeted user base, one that is so targeted that it limits the audience size to only a few thousand users, you can do that using tools like Facebook’s advertising that lets you specify many different elements and tells you how limited the size of your audience is.

Or there are tools like Optim.al, which hones the audience and offers ways to expand or contract it. Or tools that let you find audiences similar to your targeting with less targeting but greater impression volume. (Disclosure: My company Rare Crowds does this.) Or you could use MediaMath’s built-in features to automatically find the right audience that performs best for your campaigns.

Nearly every company playing in the RTB space has functionality designed to meet the needs of advertisers that want to reach specific audiences, not just retarget people who visited your website or who are existing customers. There is the potential to reach people you haven’t reached before, find new customers and prospect for them.

The biggest growth sector for RTB this year is clearly going to be brand advertisers and those that use RTB for targeted reach — just like 90% of all media spending.

Programmatic buying: The FAQ every marketer needs

By Eric Picard (Originally Published in iMedia – November 15, 2014)

I was at the ad:tech conference in New York last week, and in one of the sessions, three different people asked about programmatic. They didn’t ask any nuanced questions. They effectively asked, “What is programmatic?” They were embarrassed that they didn’t know, but after the first person spoke up, others in the room were emboldened.

For someone so steeped in the programmatic space, this took me by surprise. Certainly, I thought, no one in our industry doesn’t know what programmatic is. Adding to my consternation was that this specific panel was focused on SEO — and I figured that anyone working in search must be in the know on what was happening in programmatic. So I walked around and asked people for the rest of the conference what they knew about programmatic, just so I could see how out of touch I was from the mainstream. While most people were relatively up to date, I was surprised by the lack of general knowledge and the amount of misinformation there was out there.

So I figured it was time to step back and go over the very basics in this classic frequently asked questions (FAQ) format.

What does the term “programmatic” mean?
The term “programmatic,” which I’ve been told I coined back in 2009, really just is the umbrella term for automated buying and selling of media. While this is how I use the term, and what the market generally tries to use it to mean, many people use it to refer just to one part of the “programmatic ecosystem” — real-time bidding (RTB).

What are ad exchanges?
Much like in the finance world where stocks, commodities, and derivatives are sold over “exchanges,” we now have mechanisms to sell advertising over exchanges. Think of this as an auction-based mechanism to sell ads. Most exchanges are second-price auctions, meaning that whoever bids the highest for an ad wins the ad impression but pays the price (sometimes plus one penny) that the second-highest bidder was willing to pay. And nearly all of these exchanges have moved to RTB. Ad exchanges typically perform the function of providing liquidity to the marketplace, letting supply and demand match fluidly. Ad exchanges are not typically where the dollars accumulate; they’re a relatively inexpensive conduit through which demand and supply flow.

What is real-time bidding?
RTB is an auction-based mechanism for media buyers to bid on advertising at the impression level, as the ad impression takes place. When the ad impression takes place, a call is made to the exchange, which submits the impression to all bidders (participants with seats on the exchange). Those bidders have a very short time — usually less than 100 milliseconds — to respond to the auction with their bids. Unlike in the world of paid search, where all the demand for ads sit within the ad system of the search engine, ad exchanges federate out the auction, meaning that each bidder contains its own demand and only submits what it chooses to the exchange. This makes the exchange more of a clearing mechanism, rather than the revenue-generating mechanism that the paid search auction is.

What value does an advertiser or media buyer get by using RTB?
RTB enables a media buyer to specify exactly what their goals or outcomes are and look only for ad inventory that matches against those goals. Sometimes those goals are performance based; sometimes they are audience based. In other words, buyers can specify what audiences they want to reach and buy only those ad impressions that match. This is very different from the experience of buying from publishers directly, where the publisher specifies the inventory definition. Over RTB, the buyers specify the inventory definition and only buy what they want.

Are exchanges only available for banner ads?
RTB and programmatic exchanges are not in any way limited to one inventory type. Pretty much any available media inventory (ironically except for paid search) is available this way. Display, mobile, video, social, and even some traditional media such as television, radio, and print are either already available over exchanges or will be soon.

How do I buy ads on an exchange?
Buying mechanisms for ad exchanges are typically referred to as demand-side platforms, or DSPs. Some ad networks also enable exchange buying but in some cases are not transparent about this (i.e., they might be buying ads on the exchanges and reselling them to their customers). DSPs are available from companies like MediaMath, Turn, DataXu, The Trade Desk, AppNexus, and others.

How do publishers sell ads over exchanges?
Publishers that are quite large can sometimes offer their inventory directly over an ad exchange. Some even have their own. But most publishers use an aggregator of one kind or another — either an ad network or a specialty platform called a supply-side platform (SSP). SSPs are kind of the inverse of a DSP and have specialized software for managing supply on the publisher’s behalf. Some exchanges are incorporating the functionality of SSPs directly such that publishers don’t need a separate vendor to support this need. And some SSPs are beginning to behave as exchanges on their own.

Can I buy directly from publishers programmatically?
Yes, many publishers make their inventory available over the exchange, and most DSPs can specify publishers they wish to include in a buy. Many publishers also have rolled out “private marketplaces” using either ad exchanges or supply-side platforms. These private marketplaces are kind of like private ad exchanges where the publisher makes its inventory available only to specific buyers. These have all the benefits of RTB to the buyer but give the publishers more control over floor prices they want to set — or even fixed rate deals they want to support with specific buyers or advertisers.

Can I execute direct buys, or guaranteed buys, programmatically?
Yes, there’s a whole subset or category of the programmatic ecosystem that is appropriately called programmatic direct. Solutions in this space are less well defined, as it is newer. But the general goal is to provide more automation to the buying and selling of media. These buys can happen over display, mobile, video, social, and even television, radio, and print. The ecosystem has vendors supporting the needs of buyers and sellers independently — and a few that are hybrid solutions. Companies in this space include Bionic Ads, Shiny Ads, Yieldex, iSocket, BuySellAds, and others. Many DSPs are now plugging into the programmatic direct inventory sources as well, allowing one-stop-shop buying of both RTB and direct inventory.

Is programmatic replacing more traditional ways of buying and selling media?
Yes. Interpublic Group, one of the biggest agency holding companies, has stated that it wants to move 50 percent of its media buying to programmatic methodologies by 2015, and ultimately do that across all media types. In public and private conversations across the industry with executives at both marketing and media agencies, the zeitgeist is definitely moving in this direction. Publishers were the holdup until the last few years, when they started to see the benefits of programmatic selling on their own. Many publishers are finding that programmatic selling provides higher yield, either because their cost of sales are lower or because the inventory is being used more efficiently.

MediaMath Acquires Rare Crowds And Its Founder, Eric Picard

By Zach Rodgers (Originally published on AdExchanger, November 10th, 2014)

MediaMath has snapped up Rare Crowds, a small, 2-year-old startup founded by ad tech trailblazer Eric Picard, AdExchanger has learned.

Under the all-stock transaction, Picard will join MediaMath as VP of strategic partnerships as the media-buying platform builds out products around private marketplaces and “automated guaranteed” inventory (i.e., direct site buys).

The deal has the markings of an acqui-hire. Picard, whose title at MediaMath will be VP of strategic partnerships, is the only exec from Rare Crowds’ small team going over to MediaMath. Co-founder and CTO Scott Tomlin will consult with MediaMath through the transfer of Rare Crowds’ technology.

A well-known figure in the ad tech space, Picard founded Bluestreak, an early ad server and rich media platform. Later he was an architect of Microsoft’s ad platform strategy, and he also held a senior product role at TRAFFIQ before that company exited ad tech and repositioned as an agency.

His responsibilities at MediaMath will include oversight of the company’s relationship with Akamai, from which it acquired Advertising Decision Sciences – along with its pixel-free ad targeting technology – in January 2013.

“MediaMath is making an investment in moving beyond real-time bidding, and taking RTB technologies really far forward into other parts of the ecosystem,” Picard told AdExchanger. “The company is investing in the rest of the media plan that is not currently accessible.”

Rare Crowds was initially focused on helping publishers better package their inventory in a programmatic selling environment, but in the last year pivoted to the buy side.

Here’s how Picard described the Rare Crowds value proposition in an AdExchanger interview two years ago:

“The whole industry has been very focused on prediction. We have to predict how much inventory we are going to have so we can sell it in advance, when you are talking about premium inventory. In RTB, all of the systems that have been developed really allow you to target much better and do not have to worry about prediction.

We’re finding this hypertargeted inventory that has more than four attributes, that’s what we define as a ‘rare crowd,’ all the way out to 12 or 15 or 20 attributes. If it exists, we’ll find it.”

Rare Crowds was backed by angel investors including Hulu’s SVP for ad sales, Peter Naylor, Interactive Advertising Bureau founder Rich LeFurgy and Mediasmith CEO Dave Smith.

The Digital Advertising Industry Needs An Open Ecosystem

By Eric Picard (Originally published on AdExchanger Tuesday, November 4th, 2014)

Thanks to amazing new offerings from Facebook, Google, Amazon and others on deeply connected identity and tracking solutions, we are seeing two major developments. For the first time, connected identities across entire populations are available for targeting, tracking, reporting and analytics. But these identity pools exist within walled gardens, siloed to just one provider.

From a tactical and strategic point of view, I completely understand why companies create these walled-garden identity solutions. And to some extent, they will open their walls – metaphorically allowing outside vendors and partners to enter through checkpoints, accompanied by security and wearing clearly labeled badges. Nobody can fault a company like Facebook or Google for being careful about allowing entrée to their walled gardens. The potential for a PR backlash is significant, and that could cause the overall value of their offering to decline. So yes – it’s good to be cautious.

But it does create a significant issue for every publisher outside the top five or so because their first-party data pool is limited to the activity on their own site or apps. They don’t get access to cross-site activity, nor do they have a way to compete with the efforts of the biggest players on their own. It will be hard for publishers – even the large ones – to resist the momentum that will build to plug into these walled gardens, forcing publishers to effectively commoditize themselves in exchange for access to identity, targeting and analytics data.

I’ve long been a proponent of open approaches in the ad-tech space, including open source, open architecture or open APIs. I also am a big fan of well-considered and coordinated industry or consortium efforts. I believe that efforts like OpenRTB, which is pushing for an open API standard for real-time bidding, will be key to helping the industry grow.

Open efforts like this help ensure that the biggest players don’t create huge competitive moats like we saw with paid search, where Google AdWords’ creative, functionality and APIs became the effective industry standard. As a result, any time Google makes any change, all other paid search players must immediately copy Google because of its massive dominance in this area.

Even the biggest players should support these open initiatives because regardless of any disproportionate boost one or two players may get, we’re in a massive growth phase and an open approach has proven a better way to expand industries and sectors. Building significant traction is easier with scale – and by pooling scale, the whole space has the opportunity to accelerate growth.

That said, it’s highly unlikely that Google and Facebook will take a completely open approach on their key initiatives. For one, they have enough scale to catalyze efforts and markets on their own. But more importantly, it’s not in their self-interest to be open. Remaining closed gives them opportunity to maintain control and position in the market while marginalizing smaller players in the ecosystem.

I predict that we will see more industry consortiums created around areas like identity, directly in response to the very large walled gardens that are being built now. It’s really the only way that everyone else in the industry can protect against commodification and ensure a level playing field.

Programmatic’s place at the top of the marketing funnel

By Eric Picard (Originally Published in iMedia – October 11, 2014)

For decades, modern marketers have developed significant marketing plans with detailed analysis of target audiences. Often before products are designed, significant amounts of market research have been developed and applied against the product or service development process.

When a brand decides to spend millions of dollars to create a product or service, it typically then spends tens to hundreds of thousands of dollars on market research and product planning to get ready to launch it.  And then hundreds of thousands to millions of dollars to market the product.

Most of that market research and product strategy folds over into the marketing plan. And as part of that process, typically very detailed marketing personas are created — sometimes a handful, sometimes more than a dozen. These marketing personas are decomposed into the marketing plan and drive many of the media mix decisions that are used to divvy up budget among channels. And often these do get distributed to the media agency as part of the marketing plan’s translation into media planning and strategy.

But in my experience, it is fairly common that by the time the media buyer gets the media plan from the planners, the marketing personas have been stripped off. And this is even more true when we bring programmatic media into view. As an example, consider a conversation I had this past year with a media buyer at a major trading desk.

This trading desk handles the media buying for a major home improvement retailer. And when I talked with the trading desk buyer about how the company approaches this customer’s media buys over its DSP partner, the buyer looked a little puzzled. To that person, it was about only two things:

  • Buying the “home improvement” segment
  • Setting the rest of the budget to optimize spend against CPA on its web pages and letting the DSP figure the rest out

The problem with this approach is that it’s extremely one dimensional — and loses much of the value that exists within the systems used. It’s like using an F-16 to commute to work. Or an aircraft carrier to run to the store.

I haven’t seen the marketing plan for the client, but I can imagine (having seen a lot of them over the years) that the retailer has several different ones. I’ll make up a few that probably exist in part, and explain how I’d have approached the campaign using a DSP.

Persona 1: Reggie is a 28-year-old single male who lives in a major metropolitan area in a condo that he owns. He makes more than $50,000 a year and mostly shops at the client’s stores to buy décor items, fans, DIY project materials, and probably will buy things like air conditioners, painting supplies, hand tools, etc.

Personas 2 and 3: Sophie is a 35-year-old stay-at-home mother who lives in the suburbs of a major metropolitan area and is married to Tim, a 35-year-old executive who works in the city and commutes. Together they own a house that is more than 4,000 square feet and has at least half an acre of land. Tim is a weekend DIY warrior, who takes on various home improvement projects. He’s likely to take on light construction projects, buying building materials, painting materials, plumbing and electrical, and lots of landscaping tools such as riding mower, blowers, etc. Sophie is an avid gardener who buys numerous plants and gardening materials, and takes frequent courses on design and gardening at the store.

Persona 4: Arthur is 65 years old. He is retired, lives in a modest home in the suburbs, which he owns outright. He is in the process of getting ready to sell the house as he and his wife are looking to move to a smaller place or a retirement community. But he has three adult children who own homes nearby, and he frequently putters and does projects around their houses. He’s likely to buy building and painting materials.

Although I just made up these personas, they’re fairly typical of the kinds of personas I have seen over my career — if anything, they’re a bit light. Additional information that would typically accompany a persona includes the numbers of each of these personas that exist in each DMA in the U.S., perhaps even broken down by ZIP code within each DMA. And then marketing teams typically will use whatever tools are at their disposal to begin matching against mechanisms like PRISM clusters and do some media mix modeling about how to reach these audiences.

At the handoff to media agency partners for digital media, the planners at that point begin using various tools to determine what sites have traffic that matches their target audiences, and an overall media plan and strategy is devised.

Once the plan is handed off to media buyers and their trading desk partners, the thinking is usually quite distilled. Buyers going directly to publishers will send over an RFP that simplifies the media plan (they may also send the media plan) for sending to publishers. They then wait to hear back regarding what inventory is available. The trading desk partners typically decide what audience attributes align against available data segments for their goals.

Now let’s go back to the example I used above about the trading desk with a major client in the home improvement retail space. Given its customer personas, I’d have recommended a few other ways to engage and find audiences.

Perhaps it could target users who own homes of a certain size or homeowners who have been in their home for a certain number of years. It could target each of these segments by age and geography. It could differentiate both creative and offer by each of these. It could vary what products to highlight in its advertising based on some of the criteria, such as age, gender, and other elements. It could target households with children differently than households with adult children not living in the home. It could even target based on the age of children, assuming parents of college age students might be moving kids into apartments or dorms at the end of summer or fall. Or it could target urban apartment dwellers with fans in the summer and suburban homeowners with leaf blowers in the early fall, snowblowers in the late fall, and lawnmowers in the early spring.

In programmatic, we far too often fall into the trap of only feeding the portion of the purchase funnel that is focused only on CPA at low costs of media plus data. As a market, we need to expand how we see programmatic media and really try to dig into the market for data and the use of sophisticated DSP platforms.

The 7 types of targeting you need to know

By Eric Picard (Originally Published in iMedia – May 10, 2014)

For as long as people have been buying ads, they have been targeting their desired audiences. The science behind this obviously has changed over the years. In the beginning — say, back in ancient Greece — it was as simple as putting the name of your pottery shop on a few of your clay pots. This evolved to more location-based models over the millennia, of course, and today we can geo-target your mobile device. End of story? Not quite.

As we think about the evolution of targeted advertising over the past 50 years, there are panel-based “currency” data providers such as Nielsen, Arbitron, and others. These services allow buyers to place ads on specific published content across numerous media, with an understanding of the overall audience breakdown that views this content. Buyers can place their ads on content where their desired audience makes up some percentage of the audience that consumes that content. By doing this across a certain number of publications or shows, they can be relatively confident that they are reaching a certain number of members of their target audience.

This is easy when you’re selling a product or service that has a very broad audience — say, toothpaste. But when you have a very targeted customer you’re trying to reach, it can be much more difficult. Other than niche publications clearly aligned with your target customer — say, knitting magazines or websites — it has been hard to find enough touchpoints to reach prospective customers easily.

That has changed significantly over the last few years. Let’s focus on digital media for our purposes. The core types of targeting available today include the following.

Panel-based data

Panel-based data is the most broadly used today, from providers such as Nielsen, comScore, and others. These panels are used as described above — to understand the overall audiences that consume content provided by a publisher. This “whole milk” approach works well for brand advertisers that have large audiences that are easy to find.

Geography

This category includes geo-targeting and geo-derived information such as Nielsen PRIZM clusters that merge information about households in specific geographies. This is much more important today than in the past, given that mobile devices offer information about where audiences are at the moment of the ad delivery, thereby taking location-based advertising to new heights. In mobile devices, this matters a lot, as some of the mechanisms available on the web are either not available on mobile, or much less available due to technical limitations related to cookies.

First-party audience data

First-party audience data is available from either the advertisers directly (data they have about their existing customers) or from publishers directly (data they have about their individual audience members). First-party data is derived either from explicitly provided information or from observed behavior.

On the advertiser side, this is typically CRM data; generally these are either customers or prospects with whom the advertiser has had direct contact. Perhaps the person in question has purchased from the advertiser before, or perhaps that person has signed up for a newsletter. In the case of e-commerce, perhaps the user has visited the site but hasn’t purchased, in which case a click-path analysis might derive some information about the person’s interests.

In the case of publishers, this information can be captured through registration (which actually tends to be much more accurate than professionals believe; as it turns out, many people don’t put in fake information) or from observed behavior (users who read financial news get put into a finance bucket to be targeted when consuming other kinds of content).

Third-party audience data

Third-party audience data is available from numerous providers. Typically these data points are derived from observing the behavior (anonymously) of the end users as they’re moving across numerous websites. Sometimes this data is derived from other sources, such as credit card activity matched anonymously to users via cookie matching.

Third-party retargeting data

Third-party retargeting data is available from numerous providers. These companies will typically place targeting tags on both the advertiser and publisher websites and then link those together in order to execute media buys. Because the provider needs to have matched cookies on both the advertiser and publisher websites, typically these services run as ad networks, since they need to close the loop directly. But there are providers that allow advertisers to create their own retargeting cookie pools and reach their customers and prospects over ad exchanges and through their own direct publisher relationships. This is frequently being referred to as second-party targeting.

Look-alike targeting

Look-alike targeting is available from numerous providers as well, which enables the buyer to provide the look-alike vendor or network with a pool of cookies or data definitions. The providers will then find matching audiences who “look like” the users you’ve provided to them. This allows the buyer to get value similar to retargeting campaigns, but for much larger audiences.

Custom micro-segmentation

Custom micro-segmentation is available from a few providers. This enables the buyer to specify extremely targeted audiences that are orders of magnitude more targeted than what is available over the open market and that match their ad campaign goals exactly or much more closely. This type of targeting can be used for brand campaigns or for performance.

The types of targeting above are broad bucket definitions, and there are now literally hundreds of thousands, if not millions, of available targeting segments on the market. Vendors should be more than happy to educate buyers (and sellers) on the opportunity and methodologies behind the data segmentation. I highly recommend that one or more buyers within every buying group become an expert in the types of available segmentation and the data models involved.

The buyer’s role in shaping programmatic’s future

By Eric Picard (Originally Published on iMedia – April 12, 2014)

Media buying has been moving to more and more automated mechanisms over the last few years. When I talk to buyers about why this is the trend, they nearly always say something like, “Publishers package inventory that they want to sell, but I want control. I want access to inventory I want to buy.”

In 2006, I wrote an article called “Content Distribution: The Final Media Revolution.” The point I was making is one that would be hard for people in 2014 to ignore — that consumers are in control of their media consumption habits and that media companies should embrace this rather than battle it. Heck, back in 2003 I wrote another article on the same exact topic called “Control, the Killer App,” which was more focused on advertising conceptual design.

Now let’s talk about the concept of control from the perspective of the buyer. Today the vast majority of media dollars are spent on direct buys where the buyer has sent the seller an RFP and a media plan and asked the seller to put together a proposal. This process has developed over the years in digital as a way for buyers to push the grunt work (and frankly, sometimes the creative work) of media planning and buying off to the seller.

This evolved because, in digital media, the buyer has no idea what’s available to buy. In television, the buyers know in advance what shows are on television and how many ad slots are available in which pods. When they execute a buy, they’re just seeing if the slots they want have been sold yet. Magazines are a bit more complex, but buyers still have an immense amount of knowledge about what’s available. In digital media, the world is very opaque. Buyers don’t know what the seller has to offer, let alone what’s “left” to purchase. This has put publishers in a position to craft packages of inventory that they push to the buyer.

Some media directors see it as their jobs to take the packages offered by publishers and break them up. The problem is that buyers tell the publisher what they want, and publishers bundle together the desirable inventory with undesirable inventory that they force upon the buyer. This effectively would be like going to the grocery store for bananas and being told that in order to buy bananas, you also had to take some plantains and avocados. You can imagine that as the buyer you would tell the seller to jump in a lake — at which point the seller would say, “Well, maybe I can throw the avocados and plantains in for cheap.” After negotiating for a few minutes, the seller effectively lowers the price on the junk you don’t want to the point that it’s almost free — so you simply take it. This is how the sellers move the inventory they can’t sell; they bundle it together and effectively lower the price of all the inventory in the bundle until the buyer is willing to accept it.

This has “worked’ for the last 15 to 20 years mostly because buyers didn’t have much choice in the matter. There was too much work on the buying side to bother trying to wrest control back from the seller. And sellers were happy to pick up the slack; it gave them great opportunities to package inventory and increase sell-through.

But things have changed, and all media buying is heading down the path toward programmatic mechanisms. Today programmatic comes effectively in two flavors: RTB and direct. They’re supported by two separate software stacks and reflect the two different ways to buy media:

RTB: RTB is buyer-centric and enables buyers to take full control over what they’re getting. The buyers define the inventory they want to buy, and then the tools procure that inventory over the advertising exchanges. Companies playing in this space include AppNexus, MediaMath, Turn, DataXu, [x+1], Rubicon, PubMatic, and many others.

Direct: Direct is seller-centric and enables publishers to package inventory and expose it to buyers in programmatic means — but keep the publishers in control of defining the inventory and bundling it in ways that meet their sell-through goals. Companies playing in this space include Yieldex, iSocket, Bionic Ads, Adslot, Shiny Ads, and many others.

The problem with the programmatic direct stack and methodologies is that buyers want to be in control. The rapid (and massive) growth of the RTB stack has been driven as much by the control that buyers have gotten over their media buying as anything else. Buyers want to be in control.

Of course, publishers want to be in control too — which is why they’re adopting the programmatic direct technologies at a rapid pace. And the RTB buying tools vendors are lining up to plug into the APIs provided by the direct vendors.

At the end of the day, programmatic media buying and selling is the future. But I’m convinced that ultimately the buyer will demand control. And publishers simply don’t have the means to refuse this demand. We’ll see lots of mechanisms designed to plug the buyers into the sellers’ systems over the next few years, with significant effort placed on giving the buyer more insight and control over what they’re buying.