DMP · Platforms · Real Time Bidding (RTB)

Five controversial predictions for programmatic advertising in 2016

 

robot-blog-flyer
This picture is as bad as my prediction that beacons would provide marketers with scaled, closed-loop attribution in 2016. Didn’t happen. Not even close.  

Programmatic advertising continued to creep into the generalist marketer’s consciousness in 2015.

 

If you’re interested, we recently wrote up a handy digest of some of 2015’s programmatic trends.

But enough of looking backwards, let’s look in the crystal ball and ask ‘What’s in store for 2016?’

Yet again, I’ve recruited two experts to help. Chris O’Hara, VP Strategic Accounts at Krux Digital (and Econsultancy’s programmatic guru – see his articles and research here), and James Bourner, Head of Display at Jellyfish.

Here’s what they had to say…

Real-world attribution may become… well, a reality

Chris O’Hara, VP Strategic Accounts at Krux Digital

One of the biggest gaps with digital media, especially programmatic, is attribution. We still seem to have the Wannamaker problem, where “50% of my marketing works, I just don’t know which 50%.”

Attitudinal “brand lift” studies and latent post-campaign sales attribution modeling have been the defacto for the last 15 years, but marketers are increasingly insisting on real “closed loop” proof. e.g. “Did my Facebook ad move any items off the shelf?”

We are living in a world where technology is starting to shed some light on actual in-store purchases, such that we are going to be able to get ecommerce-like attribution for Corn Flakes soon.

In one real world example, a CPG company has partnered with 7-11, and placed beacon technology in the store.

Consumers can receive a “get 20% off” offer on their mobile device, via notification, when the they approach the store; the beacon can verify whether or not they arrive at the relevant shelf or display and an integration with the point-of-sale (POS) system can tell (immediately) whether the purchase was made.

These marketing fantasies are becoming more real every day.

7/11

Cross-device targeting is important, but so is improving mobile inventory

James Bourner, Head of Display at Jellyfish

2016 will be the year of mobile: Just kidding!

Although on a more serious note, 2016 will be the year of more measured and more integrated mobile activity – we have only really just started to get to grips with cross-device targeting and tracking on a macro level.

While the big companies who are making a play for control of the ad tech industry will put a lot of emphasis on cross-device targeting and tracking in their battle plans, I think there will be a lot of improvements to the quality of inventory, especially in apps.

A lot of mobile supply is from developers, not traditional publishers, which has led to quality issues.

However, as we are now becoming very discerning in what we buy in mobile hopefully the developers will respond to the data and not be tempted to place banners quite so close to where accidental clicks may occur!

Google has been trying to prevent accidental clicks since 2012.

google combating accidental clicks

Frequency management will reduce waste and improve UX

Chris O’Hara, VP Strategic Accounts at Krux Digital

Before marketers could effectively map users to all of their various devices (cross-device identity management) and also match users across various execution platforms (hosting a “match table” that assures user #123 in my DMP is the same guy as user #456 in DataXu, as an example), they were helpless to control frequency to an individual.

Recent studies have revealed that, when marketers are only frequency capping at the individual level, they are serving as many as 100+ ads to individual users every month, and sometimes much, much more.

What if the user’s ideal point of effective frequency is only 10 impressions on a monthly basis? As you can see, there are tremendous opportunities to reduce waste and gain efficiency in communication.

This means big money for marketers, who can finally start to control their messaging – putting recovered dollars back into finding more reach, and starting to influence their bidding strategies to get users into their “sweet spot” of frequency, where conversions happen.

It’s bad news for publishers, who have benefitted from this “frequency blindness” inadvertently. Now, marketers understand when to shut off the spigot.

tap

Increased creativity will harness new forms of inventory

James Bourner, Head of Display at Jellyfish

From the buy side we will be looking forward to more video-on-demand inventory and new areas of supply opening up, especially for the UK market, which is hugely exciting.

Closely linked to this will be far more involvement from the creative guys.

There have been rumblings in the programmatic community for some time that we do not exploit creativity enough and we need to encourage our creative counterparts to the possibilities of programmatic, whether that be simply more permutations of ads to complement targeting or a more subtle but fundamental shift in some of the tools used to build creative.

Additionally, 2016 will see more large and impactful formats, skins and take-over placements served programmatically. This is obviously excellent for both media planners and buyers as well as the creative teams.

On the subject of placements there will be a proliferation of in-feed display (or native-type placements) becoming available programmatically. 2016 will also see more connected TV and digital radio exchanges being added into the programmatic supply line.

Programmatic out of home has been on the horizon for a while but I would predict connected TV will be the faster growing element of these.

An in-stream Guardian ad format. James expects more in-feed display to be available programmatically.

guardian ad unit

We should probably let the machines decide

Chris O’Hara, VP Strategic Accounts at Krux Digital

The adoption of advanced data technology is starting to change the way media is actually planned and bought. In the past, planners would use their online segmentation to make guesses about what online audience segments to target, and test-and-learn their way to gain more precision.

Marketers basically had to guess the data attributes that comprised the ideal converter. Soon, algorithms will start doing the heavy lifting.

What if, instead of guessing at the type of person who buys something, you could start with the exact composition of that buyer? Today’s machine learning algorithms are starting at the end point in order to give marketers a huge edge in execution.

In other words, now we can look at a small group of 1,000 people who have purchased something, and understand the commonalities or clusters of data attributes they all have in common.

Maybe all buyers of a certain car share 20 distinct data attributes. Marketers can have segments automatically generated from that data, and expand it from there.

This brand new approach to segmentation is a small harbinger of things to come, as algorithms start to take over the processes and assumptions of the past 15 years and truly transform marketing.

robot

Econsultancy runs Creatve Programmatic, a one day conference in London, as well as providing training on programmatic advertising.

DMP · Media Buying · Media Measurement · Media Planning

Trends in Programmatic Buying

 

thefuture
The digital marketing future we were promised years ago looks pretty lame in retrospect. This is an image of a trading desk supervisor at Razorfish, circa 2013.

2015 has been one of the most exciting years in digital driven marketing to date. Although publishers have been leading the way in terms of building their programmatic “stacks” to enable more efficient selling of digital media, marketers are now catching up. Wide adoption of data management platforms has given rise to a shift in buying behaviors, where data-driven tactics for achieving effectiveness and efficiency rule. Here’s a some interesting trends that have arisen.

 

Purchase-Based Targeting

Remember when finding the “household CEO” was as easy as picking a demographic target? Marketers are still using demographic targeting (Woman, aged 25-44) to some extent, but we have seen a them shift rapidly to behavioral and contextually based segments (“Active Moms”), and now to Purchase-Based Targeting (PBT). This trend has existed in categories like Automotive and Travel, but is now being seen in CPG. Today, marketers are using small segments of people who have actually purchased the product they are marketing (“Special K Moms”) and using lookalike modeling to drive scale and find more of them. These purchase-defined segments are a more precise starting point in digital segmentation—and can be augmented by behavioral and contextual data attributes to achieve scale. The big winners here are the folks who actually have the in-store purchase information, such as Oracle’s Datalogix, 84.51, Nielsen’s Catalina Solutions, INMAR, and News Corp’s News America Marketing.

Programmatic Direct

For years we have been talking about the disintermediation in the space between advertisers and publishers (essentially, the entire Lumascape map of technology vendors), and how we can find scalable, direct, connections between them. It doesn’t make sense that a marketer has to go through an agency, a trading desk, DSP an exchange, SSP, and other assorted technologies to get to space on a publisher website. Marketers have seen $10 CPMs turn into just $2 of working media. Early efforts with “private marketplaces” inside of exchanges created more automation, but ultimately kept much of the cost structure. A nascent, but quickly emerging, movement of “automated guaranteed” procurement is finally starting to take hold. Advertisers can create audiences inside their DMP and push them directly to a publisher’s ad server where they have user-matching. This is especially effective where marketers seek as “always on” insertion order with a favored, premium publisher. This trend will grow in line with marketers’ adoption of people-based data technology.

Global Frequency Management

The rise in DMPs has also led to another fast-growing trend: global frequency management. Before marketers could effectively map users to all of their various devices (cross-device identity management, or CDIM) and also match users across various execution platforms (hosting a “match table” that assures user #123 in my DMP is the same guy as user #456 in DataXu, as an example), they were helpless to control frequency to an individual. Recent studies have revealed that, when marketers are only frequency capping at the individual level, they are serving as many as 100+ ads to individual users every month, and sometimes much, much more. What is the user’s ideal point of effective frequency is only 10 impressions on a monthly basis? As you can see, there are tremendous opportunities to reduce waste and gain efficiency in communication. This means big money for marketers, who can finally start to control their messaging—putting recovered dollars back into finding more reach, and starting to influence their bidding strategies to get users into their “sweet spot” of frequency, where conversions happen. It’s bad news for publishers, who have benefitted from this “frequency blindness” inadvertently. Now, marketers understand when to shut off the spigot.

Taking it in-House

More and more, we are seeing big marketers decide to “take programmatic in house.” That means hiring former agency and vendor traders, licensing their own technologies, and (most importantly) owning their own data. This trend isn’t as explosive as one might think, based on the industry trades—but it is real and happening steadily. What brought along this shift in sentiment? Certainly concerns about transparency; there is still a great deal of inventory arbitrage going on with popular trading desks. Also, the notion of control. Marketers want and deserve more of a direct connection to one of their biggest marketing costs, and now the technology is readily available. Even the oldest school marketer can license their way into a technology stack any agency would be proud of. The only thing really holding back the trend is the difficulty in staffing such an effort. Programmatic experts are expensive, and that’s just the traders! When the inevitable call for data-science driven analytics comes in, things can really start to get pricey! But, this trend continues for the next several years nonetheless.

Closing the Loop with Data

One of the biggest gaps with digital media, especially programmatic, is attribution. We still seem to have the Wannamaker problem, where “50% of my marketing works, I just don’t know which 50%.” Attitudinal “brand lift” studies, and latent post-campaign sales attribution modeling has been the defacto for the last 15 years, but marketers are increasingly insisting on real “closed loop” proof. “Did my Facebook ad move any items off the shelf?” We are living in a world where technology is starting to shed some light on actual in-store purchases, such that we are going to able to get eCommerce-like attribution for corn flakes soon. In one real world example, a CPG company has partnered with 7-11, and placed beacon technology in the store. Consumers can receive a “get 20% off” offer on their mobile device, via notification, when the they approach the store; the beacon can verify whether or not they arrive at the relevant shelf or display; and an integration with the point-of-sale (POS) system can tell (immediately) whether the purchase was made. These marketing fantasies are becoming more real every day.

Letting the Machines Decide

What’s next? The adoption of advanced data technology is starting to change the way media is actually planned and bought. In the past, planners would use their online segmentation to make guesses about what online audience segments to target, an test-and-learn their way to gain more precision. Marketers basically had to guess the data attributes that comprised the ideal converter. Soon, algorithms will atart doing the heavy lifting. What if, instead of guessing at the type of person who buys something, you could start with the exact composition of that that buyer? Today’s machine learning algorithms are starting at the end point in order to give marketers a hige edge in execution. In other words, now we can look at a small group of 1000 people who have purchased something, and understand the commonalities or clusters of data attributes they all have in common. Maybe all buyers of a certain car share 20 distinct data attributes. Marketers can have segment automatically generated from that data, and expend it from there. This brand new approach to segmentation is a small harbinger of things to come, as algorithms start to take over the processes and assumptions of the past 15 years and truly transform marketing.

It’s a great time to be a data-driven marketer!

 

Programmatic Direct

Programmatic trends in 2015

2015-trends-blogBoy oh boy, 2015 was a big year for advertising debate.

To try and bring some closure to a year of fervid discussion on the Econsultancy blog, we asked two experts on performance marketing to give us their view on programmatic in 2015.

And if you want to learn more on this topic, book yourself a place at our Programmatic Training Course.

Is there a creativity vacuum?

David Carr, Strategy Director at DigitasLBi 

As we all raced to keep up with the exponential increases in options and terminology, maybe new realism began to creep in.

Had media left creative behind? Was programmatic only about cheaper buys and cheaper dynamic creative optimization with production efficiencies, real-time price updates and maybe a “personalized” colour-way and call to action based on someone’s browsing history?

When you asked around the industry for great creative examples the same ones would come back: Axe Brasil’s Romeo Reboot with its 100,000 dynamic videos, Diesel Decoded’s 400 bespoke copylines and the Amanda Foundation’s digital “Pawprint” work.

Yet programmatic is not just about direct response and CPAs. Programmatic is people. Programmatic allows creative to build a tailored story arch for the individual.

This makes brand ideas and human truths more important than ever to stimulate and organize the work making it consistent, relevant and distinct.

It means rethinking storytelling through a lens of data and technology to give personalization at scale and enable a brand relationship that learns – not just buying on a DSP.

Axe Brasil’s Romeo Reboot was an example of dynamic video.

romeo reboot

Brands seek transparency and control

David Carr, Strategy Director at DigitasLBi 

It is this technology lens that means new ways of organizing an agency are needed along with new client-agency relationships.

Creative, media and technology need to be re-integrating or at least work far more closely together. This way savings from spend can be used to create more effective work and technology can give greater transparency.

When at most 45 cents in the dollar reaches publishers and even an in-house or managed service on a shared platform leads to unknown ad-tech, DSP sell-side, reseller SSP and primary SSP fees plus data leakage to competitor algorithms, transparency is vital.

Taking a brand-first approach where clients control the bidding strategy AND the tech roadmap while not being lumbered with platform development and management might be a solution here?

Chris O’Hara, VP Strategic Accounts at Krux Digital (author of Econsultancy’s Programmatic Branding report)

More and more, we are seeing big marketers decide to “take programmatic in house.”

That means hiring former agency and vendor traders, licensing their own technologies, and (most importantly) owning their own data.

This trend isn’t as explosive as one might think, based on the industry trades – but it is real and happening steadily.

What brought along this shift in sentiment? Certainly concerns about transparency; there is still a great deal of inventory arbitrage going on with popular trading desks.

Also, the notion of control. Marketers want and deserve more of a direct connection to one of their biggest marketing costs, and now the technology is readily available.

Even old school marketers can license their way into a technology stack any agency would be proud of.

The only thing really holding back this trend is the difficulty in staffing such an effort. Programmatic experts are expensive, and that’s just the traders!

When the inevitable call for data-science driven analytics comes in, things can really start to get pricey! But, this trend continues for the next several years nonetheless.

Only app development is outsourced more than display advertising (source:Organisational Structures and Resourcing Best Practice Guide)

outsourced disciplines

Users suffer (especially on mobile) without union of creative, data and tech

David Carr, Strategy Director at DigitasLBi 

As all media continued to go mobile in 2015 the underbelly of programmatic was exposed. Hundreds of competing cookies on a page with javascript that bloated page weights above 1mb – if they even allowed the page to render at all – became a too common occurrence.

In this context programmatic became not just the future of ad buying but perhaps the best advert for Adblockers you could have.

Creative, data and technology consolidation for a mobile world is one potential solution but ultimately the only way that programmatic can live up to its promise is for all three to work together.

That way we can get back to people. Where do they go, what are they interested in, how do they respond to content and messages and how do we offer them something useful, usable and delightful?

Targeting with purchase data improves segmentation

Chris O’Hara, VP Strategic Accounts at Krux Digital

Remember when finding the “household CEO” was as easy as picking a demographic target?

Marketers are still using demographic targeting (Woman, aged 25-44) to some extent, but we have seen them shift rapidly to behavioral and contextually based segments (“Active Moms”), and now to Purchase-Based Targeting (PBT).

This trend has existed in categories like automotive and travel, but is now being seen in consumer packaged goods.

Today, marketers are using small segments of people who have actually purchased the product they are marketing (“Special K Moms”) and using lookalike modeling to drive scale and find more of them.

These purchase-defined segments are a more precise starting point in digital segmentation – and can be augmented by behavioral and contextual data attributes to achieve scale.

The big winners here are the folks who actually have the in-store purchase information (such as Oracle’s Datalogix, 84.51, Nielsen’s Catalina Solutions, INMAR, and News Corp’s News America Marketing).

archery targets

Programmatic direct as a route through complexity

Chris O’Hara, VP Strategic Accounts at Krux Digital

For years we have been talking about the disintermediation in the space between advertisers and publishers (essentially, the entire Lumascape map of technology vendors), and how we can find scalable, direct, connections between them.

It doesn’t make sense that a marketer has to go through an agency, a trading desk, DSP, an exchange, SSP, and other assorted technologies to get to space on a publisher website.

Marketers have seen $10 CPMs turn into just $2 of working media.

Early efforts with “private marketplaces” inside of exchanges created more automation, but ultimately kept much of the cost structure.

A nascent, but quickly emerging, movement of “automated guaranteed” procurement is finally starting to take hold. Advertisers can create audiences inside their DMP and push them directly to a publisher’s ad server where they have user-matching.

This is especially effective where marketers seek an “always on” insertion order with a favored, premium publisher. This trend will grow in line with marketers’ adoption of people-based data technology.

For more on programmatic in 2015, see other blog posts and research by Chris O’Hara.

Ben Davis

Published 7 December, 2015 by Ben Davis @ Econsultancy

Ben Davis is a senior writer at Econsultancy. He lives in Manchester. You can contact him at ben.davis@econsultancy.com, follow at @herrhuld or connect via LinkedIn.

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DMP

Match Game 2015

 

Match game
Ask me what my match rates are. I have no clue, and neither do you.

 

If you work in digital marketing for a brand or an agency, and you are in the market for a data management platform, you have probably asked a vendor about match rates. But, unless you are really ahead of the curve, there is a good chance you don’t really understand what you are asking for. This is nothing to be ashamed of – some of the smartest folks in the industry struggle here. With a few exceptions, like this recent post, there is simply not a lot of plainspoken dialogue in the market about the topic.

Match rates are a key factor in deciding how well your vendor can provide cross-device identity mapping in a world where your consumer has many, many devices. Marketers are starting to request “match rate” numbers as a method of validation and comparison among ad tech platforms in the same way they wanted “click-through rates” from ad networks a few years ago. Why?

As a consumer, I probably carry about twelve different user IDs: A few Chrome cookies, a few Mozilla cookies, several IDFAs for my Apple phone and tablets, a Roku ID, an Experian ID, and also a few hashed e-mail IDs. Marketers looking to achieve true 1:1 marketing have to reconcile all of those child identities to a single universal consumer ID (UID) to make sure I am the “one” they want to market to. It seems pretty obvious when you think about it, but the first problem to solve before any “matching” tales place whatsoever is a vendor’s ability to match people to the devices and browser attached to them. That’s the first, most important match!

So, let’s move on and pretend the vendor nailed the cross-device problem—a fairly tricky proposition for even the most scaled platforms that aren’t Facebook and Google. They now have to match that UID against the places where the consumer can be found. The ability to do that is generally understood as a vendor’s “match rate.”

So, what’s the number? Herein lies the problem. Match rates are really, really hard to determine, and they change all the time. Plus, lots of vendors find it easier to say, “Our match rate with TubeMogul is 92%” and just leave it at that—even though it’s highly unlikely to be the truth. So, how do you separate the real story from the hype and discover what a vendor’s real ability to match user identity is? Here are two great questions you should ask:

What am I matching?

This is the first and most obvious question: Just what are you asking a vendor to match? There are actually two types of matches to consider: A vendor’s ability to match a bunch of offline data to cookies (called “onboarding”), and a vendor’s ability to match a set of cookie IDs to another set of cookie IDs.

First, let’s talk about the former. In onboarding—or matching offline personally identifiable information (PII) identities such as an e-mail with a cookie—it’s pretty widely accepted that you’ll manage to find about 40% of those users in the online space. That seems pretty low, but cookies are a highly volatile form of identity, prone to frequent deletion, and dependent upon a broad network of third parties to fire “match pixels” on behalf of the onboarder to constantly identify users. Over time, a strong correlation between the consumer’s offline ID and their website visitation habits—plus rigor around the collection and normalization of identity data—can yield much higher offline-to-online match results, but it takes effort. Beware the vendor who claims they can match more than 40% of your e-mails to an active cookie ID from the get-go. Matching your users is a process, and nobody has the magic solution.

As far as cookie-to-cookie user mapping, the ability to match users across platforms has more to do with how frequently the your vendors fire match pixels. This happens when one platform (a DMP) calls the other platform (the DSP) and asks, “Hey, dude, do you know this user?” That action is a one-way match. It’s even better when the latter platform fires a match pixel back—“Yes, dude, but do you know this guy?”—creating a two-way identity match. Large data platforms will ask their partners to fire multiple match pixels to make sure they are keeping up with all of the IDs in their ecosystem. As an example, this would consist of a DMP with a big publisher client who sees most of the US population firing a match pixel for a bunch of DSPs like DataXu, TubeMogul, and the Trade Desk at the same time. Therefore, every user visiting a big publisher site would get that publisher’s DMP master ID matched with the three separate DSP IDs. That’s the way it works.

Given the scenario I just described, and even accounting for a high degree of frequency over time, match rates in the high 70 percentile are still considered excellent. So consider all of the work that needs to go into matching before you simply buy a vendor’s claim to have “90%” match rates in the cookie space. Again, this type of matching is also a process—and one involving many parties and counterparties—and not just something that happens overnight by flipping a switch, so beware of the “no problem” vendor answers.

What number are you asking to match?

Let’s say you are a marketer and you’ve gathered a mess of cookie IDs through your first-party web visitors. Now, you want to match those cookies against a bunch of cookie IDs in a popular DSP. Most vendors will come right out and tell you that they have a 90%+ match rate in such situations. That may be a huge sign of danger. Let’s think about the reality of the situation. First of all, many of those online IDs are not cookies at all, but Safari IDs that cannot be matched. So eliminate a good 20% of matches right off the bat. Next, we have to assume that a bunch of those cookies are expired, and no longer matchable, which adds another 20% to the equation. I could go on and on but, as you can see, I’ve just made a pretty realistic case for eliminating about 40% of possible matches right off the bat. That means a 60% match rate is pretty damn good.

Lots of vendors are actually talking about their matchable population of users, or the cookies you give them that they can actually map to their users. In the case of a DMP that is firing match pixels all day long, several times a day with a favored DSP, the match rate at any one time with that vendor may indeed be 90-100%–but only of the matchable population. So always ask what the numerator and denominator represent in a match question.

You might ask whether or not this means the popular DMP/DSP ”combo” platforms come with higher match rates, or so-called “lossless integration” since both the DMP and DSP carry an single architecture an, therefore, a unified identity. The answer is, yes, but that offers little differentiation when two separate DMP/DSP platforms are closely synched and user matching.

In conclusion

Marketers are obsessing over match rates right now, and they should be. There is an awful lot of “FUD” (fear, uncertainty, and doubt) being thrown around by vendors around match rates—and also a lot of BS being tossed around in terms of numbers. The best advice when doing an evaluation?

  • Ask what kind of cross-device graph your vendor supports. Without the fundamental ability to match people to devices, the “match rate” number you get is largely irrelevant.
  • Ask what numbers your vendor is matching. Are we talking about onboarding (matching offline IDs to cookies) or are we talking about cookie matching (mapping different cookie IDs in a match table)?
  • Ask how they are matching (what is the numerator and what is the denominator?)
  • Never trust a number without an explanation. If your vendor tells you “94.5%” be paranoid!
  • And, ask for a match test. The proof is on the pudding!
DMP

Programmatic has a personal side

incite“It’s not a technology revolution, it’s a mind-set revolution,” said Jeremy Hlavacek, VP for Programmatic at the Weather Channel. It’s about building data around customers to target relevant ads: the right message in the right place at the right time. It’s called programmatic, and there’s more to it than you might think.

What is it?

It was one of the key buzzwords of 2014, and everyone involved in selling and buying ad inventory seems set to be talking about “programmatic”–and figuring out what it really means–for years to come. Still young, and increasingly disruptive, it’s both a set of technologies and a mind-set, and it could change marketing and advertising in ways hardly yet foreseen.

Perhaps the simplest way to think of it is by analogy with the modern stock market. Where traders once walked the floor (yes, some still do), shaking hands on deals, most stock transactions these days take placeat lightning speed on automated markets. The programmatic market for digital ad inventory is similar, leveraging software to purchase inventory in a way which also automates pricing–and it’s extending its reach to traditional (TV and billboard) ads too. Essentially, it’s about machines buying ads, thus setting the market price, with humans removed from the process as much as possible.

But is that all it is? Just a way of doing what ad tech already does, but ever faster, and on an ever larger scale? From what I heard at the Incite Programmatic Summit in New York this week, it has the potential to be much more than that.

Putting it Together

The Incite Summit audience might not have been huge, but the concentration of major brands, as speakers or audience members, was impressive: Jaguar, Stolichnaya, ESPN, Sega, Fox News, Wells Fargo. Speaking with attendees between sessions or over lunch, I was surprised to hear no skepticism about programmatic at all. People I met were either completely new to programmatic, or had been using it in some form or other for no more than a couple of years–but everyone thought the potential for business transformation was huge.

Fertile ground for disruption.

Early days, then. As Chris O’Hara of Krux said, the programmatic market is so crowded–there are so many possible choices of vendor or approach–that it’s “fertile ground for disruption; for someone to just come in and change the model.” In other words, we may not even be looking at the true shape of programmatic yet.

Krux is one of the major players in the data management platform segment, sitting between publishers, agencies and brands to optimize the value of inventories and marketing budgets. O’Hara’s breakdown of the data universe helps show both the potential of programmatic, and the challenges facing it, when it comes to delivering personalized messages at blinding speed. There are three kinds of data:

  • First party data: a brand’s own data about their customers based on purchase behavior and other touch-points. Easy to access, in some cases (financial services, for example), very rich indeed, but not usually very extensive.
  • Second party data: the data readers choose to give to publishers and social media platforms. Also very rich, and large in scale, but–like Facebook data–generally in walled gardens, and can be expensive.
  • Third party data: available from data vendors in huge quantities, but the third party providers have incentives to sell as much of it as they can. and it’s regarded as highly unreliable.

If collecting good data is the first challenge, the second lies in identifying customers, especially across multiple channels and devices. As Hlavacek pointed out, with imperfect data sets one can’t expect perfect customer identification. But probabilistic identification can be enough. Even bad data is better than no data, and results which are only ten percent accurate can be very valuable.

If that’s what you want to do, you don’t need all of this.

Programmatic can be used, of course, just to firehose customers with content, but Hlavacek would say, “If that’s what you want to do, you don’t need all of this.” For those customers accurately identified, algorithms can be leveraged to dynamically model the messages they should be receiving. Isn’t that what marketers have always done, with or without the algorithms?  Yes, but programmatic means automating the process on a large scale, at very high speed, and integrating it directly with the purchase of ad inventory, and across multiple channels.

Speakers admitted there’s a still a big gap between the concept of personalized programmatic, and what the creative side–accustomed to developing one compelling message for a large market–is geared up to provide.

Even relevant messaging can be intrusive, of course. Jim Caruso, VP of product strategy at Varick Media Management, a programmatic vendor, had it about right: “Customers are everywhere, but don’t want to be reached everywhere.” But if customer identities can be established and centralized, automated frequency management should be able to cap repeat messaging just at the sweet-spot of providing enough reinforcement without becoming an annoyance.

A Programmatic Future

If you want to take a deeper dive into programmatic, you could do much worse than check outProgrammaticAdvertising.Org. It’s sponsored by the B2B digital marketing company Multiview, but far from being a market-place for the sponsor, it carries wide-ranging and clear-eyed commentary on all things programmatic, from analytics to standards. I spoke with publisher Nicholas Henderson about where programmatic is now–and where it’s going.

“Right now it’s all very high-level and jargony,” he said. “For stakeholders that’s fine, but it can tend towards increasing confusion for marketers.” Henderson emphasizes the human size of programmatic. That’s almost counter-intuitive, given its proffer of large scale automation, but Henderson insists that people aren’t buying mechanication and algorithms, but human creative thinking.

Imagine how it would revolutionize a consumer’s experience.

“There’s a lot of buzz around dynamic creative,” he told me. “Imagine how it would revolutionize a consumer’s experience.” Mobile has all but made the website cookie extinct, but collecting contextual and behavioral data via logins or unique device IDs should make it even more possible to tailor unique and relevant experiences. “Done properly it can be very subtle.” Right now, the real-time analytics involved probably need to be outsourced to something like a data management platform vendor, but there are so many in the space that the skill-sets seem ripe for purchase and integration by brands or large agencies.

The bottom line? Caruso summed it up: “Programmatic is not about pricing and buying ads. It’s about building data around customers to target relevant ads.” We may not be seeing quite the right business model yet, or clean enough data–and creative may not yet realize what’s possible–but once those pieces fall into place, hold tight for a programmatic future.

DMP

DMP 4-5-6

NEXTLEVELAs I’ve previously discussed, there are several basic use cases of the modern data management platform (DMP) for marketers. They include getting “people data” from addressable devices into a single system, controlling how it’s matched with different execution platforms and managing the frequency of messaging across devices.

In a world of ultra-fragmented device identity and multiple addressable media channels, you should be able to tie them together and make sure consumers get the optimal amount of messages. Big marketers use these tactics to save tons of money by chopping off the “long tail” of impressions, such when marketers deliver more than 30 impressions per user each month, and reinvesting to find more deduplicated reach.

There is so much more to the successful application of a DMP, though. The most cutting-edge marketers are taking DMPs to the next level, after investing the time in building consumer identity graphs and getting their match rates with execution platforms as high as possible.

There are several plays you can run when you start to dig in and put the data to work. 

Supercharge The Bidding Strategy

After identifying the long tail of impression frequency and diverting that investment into reach, where users are served up to three impressions per month, the key is driving users down into the sweet spot of frequency. This is where users are more likely to download more coupons, for example, or complete more video views.

If that sweet spot is between four and 20 impressions, marketers can adjust their strategy in biddable environments to ensure they are willing to pay more to “win” users who have only been exposed to three impressions so far. DMPs can match users with fidelity and deliver in near real time these types of targeting sets to multiple execution platforms, including those for display, video and search.

Optimize Partner Investment Through Reach Analysis  

It’s a great start to manage addressable media delivery on a global basis, but what happens after you have identified all of those wasted impressions?

Naturally, the money marketers are spending reaching consumers for the 100th time can be better spent looking for net new consumers. But how do you get them?

For a diaper manufacturer that wants to reach the estimated 6 million new mothers in market every year, it’s critically important to get to 100% reach against that audience. Many marketers start with a single, broad reach partner, such as Yahoo, and see how close they can get to total reach.

It’s fantastic to leverage big spending power to drive down prices and get massive customer service attention to spread a message to as many unique users as possible. But no single partner can get a marketer to 100%. That’s where the DMP comes in.

It’s not just about filling in the missing 25% of an audience that matters; the diaper manufacturer wants to hit those incremental moms across quality, well-lit sites. Determining where you can get a few more million deduplicated moms is the first step. The key is to then decide where to find them more effectively from an investment standpoint, which requires an overlap analysis.

Enhance Partner Selection Through Overlap Analysis 

Say our diapers manufacturer found 4 million new moms on Yahoo at a reasonable CPM. The DMP can then look across all addressable media investments and run a “Where are my people?” type of analysis.

Maybe this advertiser has another 20 partners on the plan after getting the bulk of unique reach from a single partner. How many more unique moms were found on Meredith? Moreover, how about finding moms on classic news and entertainment sites, such as NBC or Turner properties, or even non-endemic sites? Maybe there is an incremental 500,000 first-party “diaper moms” on a particular site, but now the advertiser can decide, based on performance KPIs and price, how valuable those particular moms are.

If those moms on a popular news site can be had for $5 CPM, maybe they are a more valuable reach vehicle than those found on the obvious “Moms.com” site. Without the DMP, they’ll never know.

Plus, marketers are also starting to optimize the way they procure such audiences, by leapfrogging over the existing ad tech ecosystem and doing audience-based programmatic direct buying using their new DMP pipes.

Understand KPIs Drivers Through Journey Building

Marketers that have deduplicated their audience and built an effective reach strategy can now go to the next level and start finding how those diaper moms moved from their first touch point in the customer journey to an actual action, such as downloading a retail coupon or requesting a sample package. When an audience is unified through a DMP, it’s possible to see the channels through which people move across their “customer journey” from awareness to action.

As an example, more large CPG companies are putting more investment into online video and, in fact, one of the world’s largest marketers has embraced a “ban the banner” approach and values engagement more than any other KPI – a metric more easily understood with video. With that in mind, a journey analysis can show marketers if seeing a few search impressions helped drive more completed views on (expensive) video and drive more brand engagement.

Did consumers download more coupons after viewing two equity (branding) impressions or before seeing the “buy now” (direct-response) message? The ability to understand how messages work together sequentially is the ultimate guide to being able to inform media investment strategy.

These are just a few of the next-level media use cases that can be accomplished once DMP fundamentals are put in place. DMPs are starting to shine a light on the “people data” that will drive the next decade of smart media investment. I think we will look back on the last 15 years of addressable marketing and wonder how we ever made such decisions without a clear view of audience first.

DMPs are starting to shine a light on the effectiveness of marketing, and giving marketers lots of new knobs and levers to pull.

It’s a great time to be a data-driven marketer.

Follow Chris O’Hara (@chrisohara) and AdExchanger (@adexchanger) on Twitter

Big Data · Data Management Platform

Leapfrogging the Lumascape

 

Leapfrogging-baby-elephants-at-chester-zoo-by-Mike-Shaw
Trying to make money with programmatic advertising is as easy as….

 

Marketers have always craved access to quality audience at scale. That was once as easy as scheduling buys on the top three broadcast networks and buying full-page ads in national newspapers. Today, the world is more complicated, with attention shifting into a splintered digital universe of thousands of channels across multiple media types.

Ad tech companies have tried to corral a massively expanding world of inventory in ad exchanges, along with the means to bid inside them. This “programmatic” world of inventory procurement is deeply flawed, yet still the best thing we have at the moment.

It’s flawed because it mostly offers access to commoditized “display” ad units of dubious value and struggles to deliver real audiences, rather than robots. But it’s also good because we have taken the first steps past a ridiculous paradigm of buying media through relationships and fax machines, while starting to bring an analytical discipline to media investment that is based on measurement.

So, as we sled the downward slope of the programmatic buying Hype Cycle, we are starting to see some new trends in inventory procurement – namely, a strategy that involves replacing some or all of the licensed programmatic architecture, as well a growing reliance on one’s own data.

But first, before we get into the nuts and bolts of how that works, some history:

The Monster We Created

After convincing ourselves of the lack of scalability in the direct model, where we would call an ad rep, we have set up a lot of distance between a marketer and their desired audience.

chrisoharachart1-921

Imagine I am a cereal manufacturer and have discovered through media mix modeling that digital moms on Meredith sites drive a lot of offline purchases. They are the “household CEOs” that drive grocery store purchasing, try new things and are influential among their peer group, in terms of recommending new products. In today’s new media procurement paradigm, there are many “friends” standing between my target and me:

  • Media agency: This is a must-have, unless marketers want to add another 100 people to their headcount with an expertise in media, but this adds 5% to 10% in costs to media buys.
  • Trading desk: Although many marketers are starting to take this functionality in-house, whether you trade internally or leverage an agency trading desk, you can expect 10% to 15% of media costs to go to the personnel needed to run this type of operation.
  • Demand-side platform (DSP): Don’t forget about the technology. A 15% bid reduction fee is usually required to leverage the smart tools necessary to find your inventory at scale across exchanges.
  • Private marketplace: But wait! We use private marketplaces to make exclusive deals among a small pool of preferred vendors. Yes, but they operate inside DSPs and carry transactional fees that can add between 5% and 10% extra.
  • Third-party data: You can’t target effectively without adding a nice layer of audience data on your buy, but expect to pay at least $1 CPM for the most basic demographic targeting – a significant percentage of cost even on premium buys.
  • Exchanges: Maybe you pay for this via your DSP, but someone is paying for a seat on an ad exchange and that cost is passed through a provider, which can add another several percentage points.
  • Supply-side platform (SSP): It’s not just the demand side that needs to leverage expensive technology to navigate the new world of digital media. Publishers pay up to 15% in fees to deploy SSPs, a smart inventory management technology to help them manage their “daisy chain” of networks and channel sales providers to get the best yield. This is baked into the media cost and passed along to the advertiser.
  • Ad server: Finally, the publisher pays a fee to get the ad delivered to the site. It is a somewhat small price, but one that is passed along to the advertiser, usually baked in to the media cost.

This is essentially the middle of a crowded LUMAscape, a bunch of different disintermediating technologies that stand between an advertiser and the publisher. Marketers pay for everything I just described. They may not license the publisher’s SSP for them, but they are subsidizing it. After running this gauntlet, marketers with $10 to spend on “cereal moms” end up with much less than half in media value – the amount the publisher ends up with after the disintermediation takes place. This can be anywhere from 10% to 40% of the working media spend.

That’s probably the biggest problem in ad tech right now.

We’ve essentially created a layer of technology so gigantic in between marketers and audiences, that 60% to 70% of media investment dollars land up in venture-funded technology companies’ hands, rather than the media owner creating the perceived value. How do we change that paradigm?

chrisoharachart2-921

Leapfrogging the Middleware

Data management technology is increasingly replacing some of the middleware in this procurement equation, effectively writing the third chapter in the saga we know as programmatic direct.

Here is a bit of background.

What I call “Programmatic Direct 1.0” was the short-lived period in which companies leveraging the DoubleClick for Publishers (DFP) ad-serving API built static marketplaces of premium inventory.

For example, a premium publisher like Forbes might decide to place a chunk of 500,000 home page impressions in a marketplace at a $15 CPM. Buyers could go into an interface, transact directly with the publisher and secure the inventory. The problem that inventory owners had a hard time valuing their future inventory and buyers weren’t keen to log into yet another platform to buy media. This phase effectively ended with the Rubicon Project buying several leaders in the space, ShinyAds and iSocket, and AdSlot taking over workflow automation software provider Facilitate Media. Suddenly, “programmatic direct” platforms started to live inside systems where media planners actually bought things.

Programmatic direct’s second act (2.0) is prevalent today. Companies use deal IDs or build PMPs within real-time systems and exchanges to have more control over procurement than what is available in an auction environment. Sellers can set prices and buyers can secure rights to inventory at a set, transparent cost. This works pretty well, but comes with the same gigantic stack of providers as before and includes additional transaction fees. This is akin to making a deal to buy a house directly from the owner, but agreeing to pay the real estate broker fee anyway. The thing about programmatic direct transactions is that they are fundamentally different than RTB because they don’t have to take place in “real time,” nor do they involve bidding. A brand-new set of pipes is required.

“Programmatic direct 3.0” – or whatever we decide to call it – looks a bit different. Let’s say the big cereal company uses a data-management platform (DMP) to collect its first-party data and creates segments of users from both offline user attributes and page-level attributes from site visitation behavior. The marketers have created a universal ID (UID) for every user. Let’s imagine they discovered 200,000 were females, 24 to 40 years old, living in two-child households with income greater than $150,000 and interested in health and fitness. Great.

Now imagine that a huge women’s interest site deployed its own first-party DMP and collected similar attributes about their users, who were assigned UIDs. If the marketer and publisher have the same enterprise data architecture, they could match their users, make a deal and discover that there’s an overlap of 125,000 of users on the site. Maybe the marketer agrees to spend $7 CPM to target those users, along with users who are statistically similar, every time they are seen on the site for November.

The DMP can push that segment directly into the publisher’s DFP. No trading desk fees, DSP fees, third-party data costs or SSPs involved. The same is true for a variety of companies that have built header bidding solutions, although they see less data than first-party DMPs.

With this 3.0 approach, most of the marketer’s $7 is spent on media, rather than a basket of technologies, and the publisher gets to keep quite a bit of that revenue.

Sounds like a good deal.

Follow Chris O’Hara (@chrisohara) and AdExchanger (@adexchanger) on Twitter. 

DMP

DMP 1-2-3

Blank Whiteboard IsolatedAlmost every marketer is starting to lean into data management technology. Whether they are trying to build an in-house programmatic practice, use data for site personalization, or trying to obtain the fabled “360 degree user view,” the goal is to get a handle on their data and weaponize it to beat their competition.

In the right hands, a data management platform (DMP) can do some truly wonderful things. With so many use cases, different ways to leverage data technology, and fast moving buzzwords, it’s easy for early conversations to get way too “deep in the weeds” and devolve into discussions of “match rates” and how cross-device identity management works. The truth is that data management technology can be much simpler than you think.

At its most basic level, DMP comes down to “data in” and “data out.” While there are many nuances around the collection, normalization, and activation of the data itself, let’s look at the “data in” story, the “data out” story, and an example of how those two things come together to create an amazing use case for marketers.

 

DataIn_DataOut
The DMP Tie Fighter: The left wing shows data coming into the DMP, and rhe right wing shows the data actvated on various channels. 

 

 

The “Data In” Story

To most marketers, the voodoo that happens inside the machine isn’t the interesting part of the DMP, but it’s really where the action happens. Understanding the “truth” of user identity (who are all these anonymous people I see on my site and apps?) is what makes the DMP useful in the first place, making one-to-one marketing and understanding customer journeys something that goes beyond AdExchanger article concepts, and starts to really make a difference!

  • Not Just Cookies: Early DMPs focused on mapping cookie IDs to a defined taxonomy and matching those cookies with execution platforms. Most DMPs—from lightweight “media DMPs” inside of DSPs to full-blown “first-party” platforms—handle this type of data collection with ease. Most first-generation DMPs were architected as cookie collection and distribution platforms, meant to associate a cookie with an audience segment, and pass it along to a DSP for targeting. The problem is that people are spending more time in cookie-less environments, and more time on mobile (and other devices). That means today’s DMPs have to have the ability to do more than organize cookies, but also be able to capture a large variety of disparate identity data, which can also include hashed CRM data, data from a point-of-sale (POS) system, and maybe even data from a beacon signal.
  • Ability to Capture Device Data: To a marketer, I look like eight different “Chris OHara’s:” three Apple IDFAs, several Safari unique browser signatures, a Roku device ID, and a hashed e-mail identity or two. These “child identities” must be reconciled to a “Universal ID” that is persistent and collects attributes over time. Most DMPs were architected to store and manage cookies for display advertising, not cross-device applications, so platforms’ ability to ingest highly differentiated structured and unstructured data are all over the map. Yet, with more and more time dedicated to devices instead of desktop, cookies only cover 40% of today’s pie.
  • Embedded Device Graph: Cross-device identification is notoriously difficult, requiring both the ability to identify people through deterministic data (users authenticate across mobile and desktop devices), or the skill to apply smart algorithms across massive datasets to make probabilistic guesses that match users and their devices. Over the next several years, the word “device graph” will figure prominently in our industry, as more companies try and innovate a path to cross-device user identity—without data from “walled garden” platforms like Google and Facebook. Since most algorithms operate in the same manner, look for scale of data; the bigger the user set, the more “truth” the algorithms can identify and model to make accurate guesses of user identity.

The “data in” story is the fundamental part of DMP—without being able to ingest all kinds of identifiers and understand the truth of user identity, one-to-one marketing, sequential messaging, and true attribution is impossible

Data Out

While the “data in” story gets pretty technical, the “data out” story starts to really resonate with marketers because it ties three key aspects of data-driven marketing together. Here’s what a DMP should be able to do:

  • Reconcile Platform Identity: Just like I look like eight different “Chris O’Haras” based on my device, I also look like 8 different people across media channels. I am a cookie in DataXu, another cookie in Google’s DoubleClick, and yet another cookie on a site like the New York Times. The role of the DMP is to user match with all of these platforms, so that the DMP’s universal identifier (UID) maps to lots of different platform IDs (child identities). That means the DMP must have the ability to connect directly with each platform (a server-to-server integration being preferable), and also the chops to trade data quickly, and frequently.
  • Unify the Data Across Channels: To a marketer, every click, open, like, tweet, download, and view is another speck of gold to mine from a river of data. When aggregated at scale, these data turn into highly valuable nuggets of information we call “insights.” The problem for most marketers that operate across channels (display, video, mobile, site-direct, social, and search, just to name a few) is that the fantastic data points they receive all live separately. You can log into a DSP and get plenty of campaign information, but how do you relate a click in a DSP with a video view, an e-mail “open,” or someone who has watched a YouTube on an owned and operated channel? The answer is that even the most talented Excel jockey running twelve macros can’t aggregate enough ad reports to get decent insights. You need a “people layer” of data that spans across channels. To a certain extent, who cares what channel performed best, unless you can reconcile the data at the segment level? Maybe Minivan Moms convert at a higher percentage after seeing multiple video ads, but Suburban Dads are more easily converted on display? Without unifying the data across all addressable channels, you are shooting in the dark.
  • Global Delivery Management: The other thing that becomes possible when you tie both cross-device user identity and channel IDs together with a central platform is the ability to manage delivery globally. More on this below!

gdmGlobal Delivery Management

If I am a different user on each channel—and each channel’s platform or site enables me to provide a frequency cap—it is likely that I am being over-served ads. If I run ads in five channels and frequency cap each one at 10 impressions a month per user, I am virtually guaranteed to receive 50 impressions over the course of a month—and probably more depending on my device graph. But what if the ideal frequency to drive conversion is only 10 impressions? I just spent 5 times too much to make an impact. Controlling frequency at the global level means being able to allocate ineffective long-tail impressions to the sweet spot of frequency where users are most likely to convert, and plug that money back into the short tail, where marketers get deduplicated reach.

In the above example, 40% of a marketer’s budget was being spent delivering between 1-3 impressions per user every month. Another 20% was spent delivering between 4-7 impressions, which conversion data revealed to be where the majority of conversions were occurring. The rest of the budget (40%) was spent on impressions with little to very little conversion impact.

In this scenario, there are two basic plays to run: Firstly, the marketer wants to completely eliminate the long tail of impressions and reinvest it into more reach. Secondly, the marketer wants to push more people from the short tail down into the “sweet spot” where conversions happen. Cutting off long tail impressions is relatively easy, through sending suppression sets of users to execution platforms.

“Sweet spot targeting” involves understanding when a user has seen her third impression, and knowing the 4th, 5th, and 6th impressions have a higher likelihood of producing an action. That means sending signals to biddable platforms (search and display) to bid higher to win a potentially more valuable user.

It’s Rocket Science, But It’s Not

If you really want to get deep, the nuts and bolts of data management are very complicated, involving real big data science and velocity at Internet speed. That said, applying DMP science to the common problems within addressable marketing is not only accessible—it’s making DMPs the must-have technology for the next ten years, and global delivery management is only one use case out of many. Marketers are starting to understand the importance of capturing the right data (data in), and applying it to addressable channels (data out), and using the insights they collect to optimize their approach to people (not devices).

It’s a great time to be a data-driven marketer!

Digital Media Ecosystem · DMP

A Brief History of Banner Time

mighty-jointIt’s been a long time since publishers have truly been in control of their inventory, but new trends in procurement methodologies and technology are steadily giving premium publishers the upper hand.

The story of display inventory procurement started with the Publisher Direct Era, when publishers were firmly in control of their banners, and kept them safely hidden behind sales forces and rate cards. Then the Network Era crept in, and smart companies like Tacoda took all the unwanted banners and categorized them. Advertisers liked to buy based on behavior, and publishers liked the extra check at the end of the month for hard-to-sell inventory.

That was no fun for the demand side though. They started the Programmatic Era, building trading desks, and leveraging DSPs to make sure they were the ones scraping a few percentage points from media deals. Why let networks have all of the arbitrage fun? The poor publisher was left to try and fight back with SSPs and more technology to battle the technology that was disintermediating them, kind of like a robot fight on the Science Channel.

But all of the sudden, publishers realized how silly it was to let someone else determine the value of their inventory, and launched the DMP Era. They ingested first-party data from their registration and page activity and created real “auto intenders” and “cereal moms” and wonderful segments that they could use to effectively sell to marketers. Now, every smart publisher knows more about their inventory than 3rd parties, and they can also find their readers across the wider Web through exchanges. A win-win!

Then all of the marketers in the world started reading AdExchanger, and saw the publisher example, and thought, “Wow, good call!” They started to truly understand how much money Programmatic companies were taking out of the investment they earmarked for media (silly marketers, Y U no read Kawaja’s first IAB deck?), and decided to use their own technology and data to power audience targeting. If it were a baseball game, this DMP Era for Marketers would be in the first or second inning, but the pitcher is throwing at a fast pace.

The next thing that happened was the Programmatic Direct Era, which lasted about ten minutes and effectively jumped the shark when Rubicon bought two of the more prominent companies involved (ShinyAds and iSocket). Programmatic Direct marketplaces promised a flip of the yield curve for publishers to expose the “fat middle” of undervalued impressions. They attempted this by placing blocks of inventory in a marketplace, and enabled the publisher to set rates, impression levels, and provide API access directly into their ad server. Alas, a tweak to Google’s API did not an industry make. Marketers loved the idea, but since they use audience as the primary mechanism to value inventory, PD marketplaces failed as stand-alone entities and were gobbled up. Under the steady hand of RTB-based technologies, they slowly evolve based on buy-side methodologies. Again, the demand side foils a perfectly reasonable, publisher-derived procurement scheme!

So, what’s next?

The Programmatic Direct Era still lives, albeit within private marketplaces (PMPs) and Direct Deal functionality. The IAB’s Open Direct protocol remains stuck at 1.0, but there is hope—and this time it’s a change that is positive for both marketers and publishers. The latest Era in inventory procurement is what I call Total Automation. Let me explain.

Say a big auto manufacturer has a DMP and has identified, via purchase information, the exact profile of everyone who buys their minivan. Call then “Van Moms.” Then suppose the publisher, who licenses an instance of the same DMP, is a women-friendly publication chock full of those Van Moms—and women who just happen to look like Van Moms. It’s pretty easy to pipe those Moms from the marketer right to the publisher. That process, which you might call Programmatic Direct 2.0, is interesting.

It requires no exchanges, no 3rd party data, no DSPs, no “private marketplace” no SSP, and potentially no agencies (spare the thought!). All it requires is some technology to map users and port them directly into an ad server.

What I just described is happening today, and moving quickly. Marketers are discovering that the change from demo-based buying to purchase-based buying through 1st party data is winning them more customers. Publishers are asking for—and commanding—high CPMs, and those CPMs are backing out for marketers. Thanks to all the crap in open exchanges, paying more for quality premium, “well lit” inventory actually works better than slogging through exchanges trying to find the audience needle in a haystack full of robots and “natural born clickers.”

The new Era of Total Automation will start putting publishers back on the map—but not all of them. The big distinction between the winners and losers will not only be the quality of their audience but, more importantly, the first-party data used to derive that audience. Not long ago, it was easy to apply a layer of 3rd party data and call someone an “auto intender” if they brushed past an article on the latest BMW. But compare that to the quality of an “auto intender” on a car site that has looked at 5 sedans over the last 2 weeks, and also used a loan calculator. There’s no comparison. The latter “intender,” collected from page- and user-level attributes directly by the publisher is 10 times more valuable (or $30 CPM rather than $3, if you like). The reason? That user volunteered real, deterministic information about herself that the publisher can validate. I am willing to bet that an auto manufacturer would pay a high CPM for access to an identified basket of those intenders on an ongoing, “always on” basis.

This is fantastic news for publishers that have great, quality inventory and have implemented a first-party data strategy. It’s even better news for the marketers that have embraced data management, and can extract and find their perfect audience on those sites. The Era of Total Automation will be over when every single marketer has a DMP. At that time, we will discover that there is no longer a glut of display inventory—all of the quality “Van Moms” and “Business Travelers” and the like will be completely spoken for. What will be left is a large pile of unreliable, long tail inventory available for the brave DR marketer and his DSP.

I think both marketers and publishers should welcome this new Era of data-driven one-to-one marketing. The crazy thing is that, once we get it right, it looks just like an anonymized version of direct mail—perhaps the oldest, greatest, most effective and measurable marketing tactic ever invented!

[This post originally appeared in AdExchanger on 7/2/15]

Digital Media Ecosystem

An Ad Tech Temperature Check

 

HotInHerre
Ad Technology: It’s hot in herre.

 

Clayton Christensen, the father of “disruptive innovation,” would love the ad technology industry.

With more than 2,500 Lumascape companies across various verticals chasing an exit, venture funding drying up for companies that haven’t made an aggressive SAAS revenue case and the rapid convergence of marketing and ad technology, the next few years will see some dramatic shifts.

The coming tsunami of powerful megatrends is driving ad technology relentlessly forward at a time when data is king and the companies that best package and integrate it into multichannel inventory procurement will be the rulers.

In a world where scale matters most, the big are getter bigger and smaller players are getting forced out, which is not necessarily good for innovation.

Data: Powering The Next Decade Of Ad Tech

Data, especially as it relates to “people data,” is and will be the dominant theme for ad technology going forward.

Monolithic companies with access to a people-based identity graph are leaning in heavily to identity management, trying to own the phone book of the connected device era. Facebook’s connection to Atlas leverages powerful and deeply personal deterministic data, continually volunteered on a daily basis by its users, to drive targeting. Google is attaching its massive PII data set garnered through Gmail, search and other platforms to its execution platforms with its new DMP, DoubleClick Audience Manager.

Both platforms prefer to keep information on audience reach safely within their domains, leaving marketers wondering how smart it really to tie the keys of user identity in a “walled garden” with media execution.

Will large marketers embrace these platforms for their consumer identity management needs, or will they continue to leverage them for media and keep their data eggs in another basket?

While some run into the arms of powerful cloud solutions that combine data management with media execution, many are choosing to take a “church and state” approach to data and media, keeping them separate. Marketers have to decide whether the risk of tying first-party data together with someone’s media business is worth having an all-in-one approach.

Agencies Must Adapt Or Die As Consultancies Edge Into Programmatic

Media agencies have also been challenged to provide more transparency around the way they procure inventory, the various incentive schemes they have with publishers and their overall methodology for finding audiences. With cross-device proliferation, agencies must be able to identify users to achieve one-to-one marketing programs, and they need novel ways to reach those users at scale.

That means a commitment to automation, albeit one that may come at the expense of revenue models derived through percentage of spend and arbitrage. Agencies will need new ways to add value in a world where demand-side players are finding closer connections to the supply side.

As media margins collapse, agencies need to act as data-driven marketing consultants to lift margins and stay relevant. They face increasing competition from large consultancies whose bread and butter has been technology integration. It’s a tough spot but opportunities abound for smart agencies that can differentiate themselves.

Zombie Companies Die Off But Edge-Case Innovation Continues

We’ve been talking about “zombie ad tech” for years now, but we are finally starting to see the end of the road for many point solution companies that have yet to be integrated into larger mar tech “stacks.”

Data-management platforms with native tag-management capabilities are displacing standalone tag-management companies. Retargeting is a tactic, not a standalone business, which is now a status quo part of many execution platforms. Fraud detection systems are slowly being dragged into existing platforms as add-on functionality. Individual data providers are being sucked into distribution platforms and data exchanges that offer customer exposure at scale. The list goes on and on.

This is an incredibly positive thing for marketers and publishers, but it is also a challenge. Cutting-edge technologies that give a competitive advantage are rarely so advantageous after they’ve moved into a larger “cloud.” Smart tech buyers must strike a balance between finding the next shiny objects that confer differentiating value, while building a stable “stack” that can scale as they grow.

That said, the big marketing technology “clouds” offered by Adobe, Oracle and Salesforce continue to grow, as they gobble up interesting pieces of the digital marketing “stack.”

Will marketers go all-in on someone’s cloud, build their own “cloud” or leverage services offerings that bring a unified capability together through outsourcing?

Right now, the jury is out, mostly because licensing your own cloud takes more than just money, but also the right personnel and company resources to make it work. Yet, marketers are starting to understand that the capability to build automated efficiency is no longer just a function of marketing, but a way to leverage people data to drive value across the entire company.

Today’s media targeting will quickly give way to tomorrow’s data-driven enterprise strategy. It’s happening now, and quickly

New Procurement Models Explode Exchanges, Drive Direct Deals

I think the most exciting things happening in ad technology are happening in inventory procurement.

Programmatic direct technologies are evolving, adding real audience enablement. Version 1.0 of programmatic direct was the ability to access a futures marketplace of premium blocks of inventory. Most buyers, used to transacting on audience, not inventory, rejected the idea.

Version 2.0 brings an audience layer to premium, well-lit inventory, while changing the procurement methodology. I think most private marketplaces within ad exchanges are placeholders for a while, as big marketers and publishers start connecting real people data pipes together and start to buy directly. It’s happening now – quickly.

I also can see really innovative companies leaning into creating a whole new API-driven way of media planning and buying across channels that makes sense. In the near future, the future-driven approaches of companies like MassExchange will bring to cross-channel inventory procurement a methodology that is more regulated, transparent and reminiscent of financial markets. It’s a fun space to watch.

Who will begin adding algorithmic, data-science driven automation and proficiency to the planning process, not just execution and optimization in the programmatic space?

Many of those in the ad technology and media game are here for the challenge, the rapid pace of innovation and the opportunity to change the status quo. We are all getting way more than we imagined lately, in a fun, exciting and fast-moving environment that punishes failure harshly, but rewards true market innovation. Stay safe out there.

[This post was originally published in AdExchanger on 6.16.15]