Watch to explore the possibilities of using mobile location and beacon data to better segment, target, and analyze consumers. Featuring FreckleIOT, Ticketmaster/Live Nation, and PlaceIQ.
The term “real time” is bandied about in the ad technology space almost as heavily as the word “programmatic.”
Years later, the meaning of programmatic is finally starting to be realized, but we are still a few years away from delivering truly real-time experiences. Let me explain.
The real-time delivery of targeted ads basically comes down to user matching. Here is a common use case: A consumer visits an auto site, browses a particular type of minivan, leaves the site and automatically sees an ad on the very next site he or she visits. That’s about as “real-time” as it gets.
How did that happen? The site updated the user segment to include “minivan intender,” processed the segment immediately and sent that data into a demand-side platform (DSP) where the marketer’s ID was matched with the DSP’s ID and delivered with instructions to bid on that user. That is a dramatic oversimplification of the process but clearly many things must happen very quickly – within milliseconds – and perfectly for this scenario to occur.
Rocket Fuel, Turn and other big combo platforms have an advantage here because they don’t need to match users across an integrated data-management platform (DMP) and DSP. As long as marketers put their tags on their pages and stay within the confines of a single execution system, this type of retargeting gets close to real time.
However, as soon as the marketer wants to target that user through another DSP or in another channel, user matching comes back into play. That means pushing the “minivan intender” ID into a separate system, but the “real-time” nature of marketing starts to break down. That’s a big problem because today’s users move quickly between channels and devices and are not constrained by the desktop-dominated world of 10 years ago.
User matching has its own set of challenges, from a marketer’s ability to match users across their devices to how platforms like DMPs match their unique IDs to those of execution platforms like DSPs. Assuming the marketer has mapped the user to all of his or her device IDs, which is a daunting challenge, the marketer’s DMP has to match that user as quickly as possible to the execution platform where the ads are going to be targeted and run.
Let’s think about how that works for a second. Let’s say the marketer has DMP architecture in the header of the website, which enables a mom to be placed in the “minivan” segment as soon as the page loads. After processing the segment, it must be immediately sent to the DSP. Now the DSP has to add that user (or bunch of users) to their “minivan moms” segment. If you picture the internet ID space as a big spreadsheet, what is happening is that all the new minivan moms are added to the DSP’s big existing table of minivan moms so they are part of the new targeting list.
Some DSPs, such as The Trade Desk, TubeMogul and Google’s DBM, do this within hours or minutes. Others manage this updating process nightly by opening up a “window” where they accept new data and process it in “batches.” Doesn’t sound very “real-time” at all, does it?
While many DMPs can push segments in real time, the practical issue remains the ability of all the addressable channels a marketer wants to target to “catch” that data and make it available. The good news is that the speed at which execution channels are starting to process data is increasing every day as older ad stacks are re-engineered with real-time back-end infrastructure. The bad news is that until that happens, things like global delivery management and message sequencing across channels will remain overly dependent upon how marketers choose to provision their “stacks.”
The Future Is Dynamic
Despite the challenges in the real-life execution of real-time marketing, there are things happening that will put the simple notion of retargeting to shame. Everything we just discussed depends on a user being part of a segment. I probably exist as a “suburban middle-aged male sports lover with three kids” in a variety of different systems. Sometimes I’m an auto intender and sometimes I’m a unicorn lover, depending on who is using the family desktop, but my identity largely remains static. I’m going to be middle aged for a long time, and I’m always going to be a dad.
But marketers care about a lot more than that. The beer company wants to understand why sometimes I buy an ice-cold case of light beer (I’m about to watch a football game, and I might drink three or four of them with friends) and when I buy a six-pack of their craft-style ale (I’m going to have one or two at the family dinner table).
The soda company is competing for my “share of thirst” with everything from coffee to the water fountain. They want to know what my entry points are for a particular brand they sell. Is it their sports drink because I’m heading to the basketball court on a hot day, or is it a diet cola because I’m at the baseball game? The coffee chain wants to know whether I want a large hot coffee (before work) or an iced latte macchiato (my afternoon break).
This brings up the idea of dynamic segmentation: Although I am always part of a static segment, the world changes around me in real time. The weather changes, my location changes, the time changes and the people around me change constantly. What if all of that dynamic data could be constantly processed in the background and appended to static segments at the moment of truth?
In a perfect world, where the machines all talked to each other in real time and spoke the same language, this might be called real-time dynamic segmentation.
This is the future of “programmatic,” whatever that means.
[This originally appeared in AdExchanger on 8/31/2016]
It’s a hoary old chestnut, but “understanding the customer journey” in a world of fragmented consumer attention and multiple devices is not just an AdExchanger meme. Attribution is a big problem, and one that marketers pay dearly for. Getting away from last touch models is hard to begin with. Add in the fact that many of the largest marketers have no actual relationship with the customer (such as CPG, where the customer is actually a wholesaler or retailer), and its gets even harder. Big companies are selling big money solutions to marketers for multi-touch attribution (MTA) and media-mix modeling (MMM), but some marketers feel light years away from a true understanding of what actually moves the sales needle.
As marketers are taking more direct ownership of their own customer relationships via data management platforms, “consumer data platforms” and the like, they are starting to obtain the missing pieces of the measurement puzzle: highly granular, user-level data. Now marketers are starting to pull in more than just media exposure data, but also offline data such as beacon pings, point-of-sale data (where they can get it), modeled purchase data from vendors like Datalogix and IRI, weather data and more to build a true picture. When that data can be associated with a person through a cross-device graph, it’s like going from a blunt 8-pack of Crayolas to a full set of Faber Castells.
Piercing the Retail Veil
Think about the company that makes single-serve coffee machines. Some make their money on the coffee they sell, rather than the machine—but they have absolutely no idea what their consumers like to drink. Again, they sell coffee but don’t really have a complete picture of who buys it or why. Same problem for the beer or soda company, where the sale (and customer data relationship) resides with the retailer. The default is to go to panel-based solutions that sample a tiny percentage of consumers for insights, or waiting for complicated and expensive media mix models to reveal what drove sales lift. But what if a company could partner with a retailer and a beacon company to understand how in-store visitation and even things like an offline visit to a store shelf compared with online media exposure? The marketer could use geofencing to understand where else consumers shopped, offer a mobile coupon so the user could authenticate upon redemption, get access to POS data from the retailer to confirm purchase and understand basket contents—and ultimately tie that data back to media exposure. That sounds a lot like closed-loop attribution to me.
Overcoming Walled Gardens
Why do specialty health sites charge so much for media? Like any other walled garden, they are taking advantage of a unique set of data—and their own data science capabilities—to better understand user intent. (There’s nothing wrong with that, by the way). If I’m a maker of allergy medicine, the most common trigger for purchase is probably the onset of an allergy attack, but how am I supposed to know when someone is about to sneeze? It’s an incredibly tough problem, but one that the large health site can solve, largely thanks to people who have searched for “hay fever” online. Combine that with a 7-day weather forecast, pollen indices, and past search intent behavior, and you have a pretty good model for finding allergy sufferers. However, almost all of that data—plus past purchase data—can be ingested and modeled inside a marketer DMP, enabling the allergy medicine manufacturer to segment those users in a similar way—and then use an overlap analysis to find them on sites with $5 CPMs, rather than $20. That’s the power of user modeling. Why don’t site like Facebook give marketers user-level media exposure data? The question answers itself.
Understanding the Full Journey
Why is “data science the new measurement?” Because, when a marketer has all of that data at their fingertips, something close to true attribution becomes possible. Now that marketers have the right tools to draw with, the winners are going to be the ones with the most artists (data scientists).
It’s a really interesting space to watch. More and more data is becoming available to marketers, who are increasingly owning the data and technology to manage it, and the models are growing more powerful and accurate with every byte of data that enters their systems.
It’s a great time to be a data-driven marketer!
[This post originally appeared in AdExchanger on 8/12/16]
As a longtime digital practitioner, I sometimes feel ashamed that I haven’t clicked on many banner ads in the last 10 years or so. It’s not that I don’t like banner ads. I recognize that advertising is the thing that supports all of the great content I read. I don’t even mind lots of ads in my paid, expensive, print and online versions of The Wall Street Journal – I sometimes even read them.
But standard banners rarely get any consideration or clicks from me, unless they are incredibly relevant. Standard banner ads aren’t particularly engaging – and the marketers buying them are getting frustrated with an ecosystem rife with fraud, technology taxes and nonhuman traffic.
Some of the world’s top marketers are actively working on “ban-the-banner” initiatives, driven by the theory that nothing but engagement matters – a KPI more easily correlated to watching an entire video than the much-maligned click. They believe great brands should tell great stories, so it seems obvious that the scant real estate and functionality offered by banner slots makes creating consumer engagement difficult, if not impossible.
At the intersection of an amazing technology-driven programmatic buying landscape and the increasingly creative social-led atmosphere of the new web is online video. It kind of snuck up on us, steadily creeping into our social feeds, blogs and favorite website destinations. That’s a very good thing: The reason linear television continues to command the lion’s share of media dollars is because people like to be entertained, and watching something is so much easier than reading something. Watching is a passive experience, but an emotional one.
Video is a place where brands can tell amazing stories, make a great pitch and drive consumer engagement. After years of perfecting 15-second, 30-second and one-minute spots, media agencies are eager to leverage their linear creative in new formats to reach audiences that seem to be abandoning traditional television in droves. This, coupled with a few other factors, is causing advertisers to rapidly move away from animated banners to video.
Millennials Don’t Like Television
Perhaps the most pressing dynamic forcing more video adoption among marketers is thatmillennials – who comprise an estimated 80 million-plus US consumers and will spend $200 billion next year – don’t really watch television anymore.
This is both from a delivery and physical dynamic; they do not watch video on television sets as much as they consume it on tablets, phones and other devices, and they also prefer on-demand viewing to scheduled programming. It makes sense. Even a 13” laptop with a retina display beats a 70” HDTV when held several inches in front of one’s face.
And the rise of streaming services has matured, giving consumers a legitimate option to “unplug” from traditional cable services and consume all the content they want on demand. Marketers must adapt to a reality that makes mobile the top priority for younger consumers, and adjust to the fact that many of the places millennials consume their content are relatively ad-free zones – at least in terms of traditional advertising units. It just so happens that video advertising fits into this new world nicely.
Time-Spent: The New Currency
If video ad delivery is going to be the mainstream unit, then it also follows that things like impressions and clicks are becoming irrelevant quickly. For video, the coin of the realm is time spent, and it is actually a pretty strong sign of engagement and valuable proxy for brand sentiment.
While it may be true that we are forced to consume some pre-roll before engaging with organic content, the best part of online video is that consumers waiting for content on their iPhones are much less likely to take a trip to the kitchen for a snack, as they do when standard commercials come on the tube. Instead of a solid three-minute block of commercials, they only have to engage with a single ad. Also, that ad can be tailored to individual preferences. That means even more engagement, less ad abandonment and a lot of measurability.
Data management platforms are helping marketers segment audiences that are prone to engage through an entire length of video and understand the types of content that produce longer viewing times and true engagement – and modeling those audiences to find lookalikes.
Linear And Online Video Must Connect For True Attribution
Probably the greatest thing about online video is the hope of leveraging data to connect online audiences with linear ones, and getting a better sense of media mix modeling and multitouch attribution.
Comcast certainly gets it. Connecting set-top box data with online ad serving means being able to touch a consumer with video across multiple screens – and bring real measurement of audiences that are increasingly device agnostic. Large telecoms, such as Verizon, are acquiring companies that provide the “last mile” of value from their broadband pipes, and that mile is as much about online video ad delivery as it is about website content.
The battle to do this correctly will be won at the “people level,” which is why we are seeing such a pitched battle of cross-device graphs; unless marketers can connect people with all of their devices, true attribution is simply impossible, rather than just being hard.
It’s an interesting time for modern marketers and publishers as they try and grow out of what we will see as the very early days of addressable advertising, and into a world dominated by on-demand content across a multitude of screens. The common denominator is video advertising, and I’m going long on the companies in the ecosystem that are going to power this new reality.
[This article originally appeared in AdExchanger on 6.30.16]
The report looks at the challenges and opportunities for agencies that want to become trusted stewards of their clients’ data.
I sat down with the author, Chris O’Hara, to find out more.
Q. It seems like the industry press is continually heralding the decline of media agencies, but they seem to be very much alive. What’s your take on the current landscape?
For a very long time, agencies have been dependent upon using low-cost labor for media planning and other low-value operational tasks.
While there are many highly-skilled digital media practitioners – strategists and the like – agencies still work against “cost-plus” models that don’t necessarily map to the new realities in omnichannel marketing.
Over the last several years as marketers have come to license technology – data management platforms (DMP) in particular – agencies have lost some ground to the managed services arms of ad tech companies, systems integrators, and management consultancies.
Q. How do agencies compete?
Agencies aren’t giving up the fight to win more technical and strategic work.
Over the last several years, we have seen many smaller, data-led agencies pop up to support challenging work – and we have also seen holding companies up-level staff and build practice groups to accommodate marketers that are licensing DMP technology and starting to take programmatic buying “in-house.”
It’s a trend that is only accelerating as more and more marketer clients are hiring Chief Data Officers and fusing the media, analytics, and IT departments into “centers of excellence” and the like.
Not only are agencies starting to build consultative practices, but it looks like traditional consultancies are starting to build out agency-like services as well.
Not long ago you wouldn’t think of names like Accenture, McKinsey, Infinitive, and Boston Consulting Group when you think of digital media, but they are working closely with a lot of Fortune 500 marketers to do things like DMP and DSP (demand-side platform) evaluations, programmatic strategy, and even creative work.
We are also seeing CRM-type agencies like Merkle and Epsilon acquire technologies and partner with big cloud companies as they start to work with more of a marketer’s first-party data.
As services businesses, they would love to take share away from traditional agencies.
Q. Who is winning?
I think it’s early days in the battle for supremacy in data-driven marketing, but I think agencies that are nimble and willing to take some risk upfront are well positioned to be successful.
They are the closest to the media budgets of marketers, and those with transparent business models are really strongly trusted partners when it comes to bringing new products to market.
Also, as creative starts to touch data more, this gives them a huge advantage.
You can be as efficient as possible in terms of reaching audiences through technology, but at the end of the day, creative is what drives brand building and ultimately sales.
Q. Why should agencies embrace DMPs? What is in it for them? It seems like yet another platform to operate, and agencies are already managing DSPs, search, direct buys, and things like creative optimization platforms.
Ultimately, agencies must align with the marketer’s strategy, and DMPs are starting to become the single source of “people data” that touch all sorts of execution channels, from email to social.
That being said, DMP implementations can be really tough if an agency isn’t scoped (or paid) to do the additional work that the DMP requires.
Think about it: A marketer licenses a DMP and plops a pretty complicated piece of software on an agency team’s desk and says, “get started!”
That can be a recipe for disaster. Agencies need to be involved in scoping the personnel and work they will be required to do to support new technologies, and marketers are better off involving agencies early on in the process.
Q. So, what do agencies do with DMP technology? How can they succeed?
As you’ll read in the new guide, there are a variety of amazing use cases that come out of the box that agencies can use to immediately make an impact.
Because the DMP can control for the delivery of messages against specific people across all channels, a really low-hanging fruit is frequency management.
Doing it well can eliminate anywhere from, 10-40% of wasteful spending on media that reaches consumers too many times.
Doing analytics around customer journeys is another use case – and one that attribution companies get paid handsomely for.
With this newly discovered data at their fingertips, agencies can start proving value quickly, and build entire practice groups around media efficiency, analytics, data science – even leverage DMP tech to build specialized trading desks. There’s a lot to take advantage of.
Q. You interviewed a lot of senior people in the agency and marketer space. Are they optimistic about the future?
Definitely. It’s sort of a biased sample, since I interviewed a lot of practitioners that do data management on a daily basis.
But I think ultimately everyone sees the need to get a lot better at digital marketing and views technology as the way out of what I consider to be the early and dark ages of addressable marketing.
The pace of change is very rapid, and I think we are seeing that people who really lean into the big problems of the moment like cross-device identity, location-based attribution, and advanced analytics are future-proofing themselves.
Although it’s starting to become a well-worn aphorism, “data is the new oil” resonates more than ever. Like oil, data is an abundant resource, but it doesn’t become useful until it is refined for use and turned into fuel.
Without the proper refinement, big data may be worthless. The stock of big data unicorn Palantir, for example, sunk on news that it lost key client relationships due to a lack of perceived value. The company collected abundant data from CPG companies but was unable to apply it to practical use cases, according to a recent article.
Marketers are starting to turn away from using abundant, yet commoditized, third-party data sources in exchanges and move toward creating peer-to-peer data relationships and leveraging second-party data for targeting. This speaks to the refinement of targeting data: Better quality in the raw materials always yields more potent fuel for performance. Not all data is the same, and not every technology platform can spin data straw into gold.
Marketers have been using available data for addressable marketing for years, but now are starting to mine their own data and get value from the information they collect from registrations, mobile applications, media performance and site visitation. Data management platforms (DMPs) are helping them collect, refine, normalize and associate their disparate first-party data with actual people for targeting.
This is a beautiful thing. Technology is enabling marketers to mine their own data and own it. Yet many marketers are still just scraping the surface of what they can do, and using data primarily for the targeting of addressable media.
Some, however, are starting to deliver customer experiences that go beyond targeting display advertising by using data to shape the way consumers interact with their brands beyond media.
The case for personalization – customer experience management, or CX – is palpable. When the Watermark Group studied [PDF] the cumulative stock performance of Forrester Research-rated “leaders” or “laggards” in customer experience, the results were staggering. During a period in which the S&P 500 grew by 72%, those focused on personalized experiences outperformed the market by 35%, and the laggards underperformed by 45% on average. That’s a delta of nearly 80% in stock price performance between the winners and losers.
Moreover, 89% of customers who have a, unsatisfactory experience will leave a brand, according to a recent study; the cost of reacquiring a churned customer can run up to seven times the amount it took to win a new customer.
The stakes could not be higher for marketers and publishers looking to drive bottom-line performance. For many companies, whether they are marketing print or online subscriptions, promoting their content or selling products off the shelf, it’s hard to justify the heavy costs associated with licensing platforms to gather the right data and use that data to drive relevant customer experiences to their CFOs. Yet, when looking at big company priorities on multiple surveys, the desire to “create more relevant customer experiences” is right up there with “earn more revenue” and “increase profits.” Why?
The simple answer is that customer experience has an enormous impact on both revenue and profitability. Giving new customers the right experience provides a higher probability of winning them, and giving existing customers relevant experiences reduces churn – and creates opportunities to sell them more products, more often. When both top-line revenue and profitability can be driven through a single initiative, most CFOs start to invest and will continue to invest as results confirm the initial thesis.
Take the “heavy user” of a quick-service restaurant who dines several times a week and consistently transacts an over-average per-visit receipt. QSRs understand the impact these valued customers have on the bottom line. These users provide a strong baseline of predicable revenue, are usually the first to try new product offerings and respond to market-facing initiatives, such as discounting and couponing, which can strategically increase short-term receipts. Smart marketers should not be content to sit back and let this valuable segment remain stagnant or find new offerings with a competitive restaurant. They must show these users that they are valued, ensure they retain or increase store visits and keep them away from the hamburger next door.
That can be as simple as offering a coupon for a regular’s favorite order. Or it can be as complex as developing a mobile application that enables the customer to order his food in advance and pick it as soon as it’s ready.
Since the restaurant collects point-of-sale data and has authenticated user registration data from the mobile app, it can now personalize the customer’s order screen with his most popular orders to shorten the mobile ordering experience. Perhaps the app can offer special discounts to frequent diners for trying – and rating – new menu items. When on the road, the app can recommend other locations and direct him right to the drive-in window through popular map APIs. The possibilities are endless when you start to imagine how data can drive your next customer interaction.
Marketers and publishers are quickly embracing their first-party data and aligning it with powerful applications that drive customer experience, increase profits, reduce customer churn and boost lifetime value.
It’s a great time to be a data-driven marketer.
[This post originally appeared in AdExchanger on 5/23/16]
Any AdExchanger reader probably knows more about data management technology than the average Joe, but many probably associate data management platforms (DMPs) with creating audience segments for programmatic media.
While segmentation, audience analytics, lookalike modeling and attribution are currently the primary use cases for DMP tech, there is so much more that can be done with access to all that user data in one place. These platforms sitting at the center of a marketer’s operational stack can make an impact far beyond paid media.
As data platforms mature, both publishers and marketers are starting to think beyond devices and browsers, and putting people in the center of what they do. Increasingly, this means focusing on giving the people what they want. In some cases that means no ads at all, while in others it’s the option to value certain audiences over others and serve them an ad first or deliver the right content – not just ads – based on their preferences.
Beyond personalization, there are DMP plays to be made in the areas of ad blocking and header bidding.
DMPs see a lot of browsers and devices on a monthly basis and strive to aggregate those disparate identities into a single user or universal consumer ID. They are also intimately involved in the serving of ads by either ingesting ad logs, deploying pixels or having a server-to-server connection with popular ad servers. This is great for influencing the serving of online ads across channels, but maybe it can help with one of the web’s most perplexing problems: the nonserving of ads.
With reports of consumers using applications to block as many as 10% of ads, wouldn’t it be great to know exactly who is blocking those ads? For publishers, that might mean identifying those users and suppressing them from targeting lists so they can help marketers get a better understanding of how much reach they have in certain audience segments. Once the “blockers” are segmented, publishers can get a fine-grained understanding of their composition, giving them insights about what audiences are more receptive to having ad-free or paid content experiences.
A lot of these issues are being solved today with specialized scripts that either aren’t very well coded, leading to page latency, or are scripted in-house, adding to complexity. Scripts trigger the typical “see ads or pay” notifications, which publishers have seen become more effective over time. The DMP, already installed and residing in the header across the enterprise, can provision this small feature alongside the larger application.
Speaking of DMP architecture being in the header, I often wonder why publishers who have a DMP installed insist on deploying a different header-bidding solution to manage direct deals. Data management tech essentially operates by placing a control tag within the header of a publisher website, enabling a framework that gives direct and primary access to users entering the page. Through an integration with the ad server, the DMP can easily and quickly decide whether or not to deliver high-value “first looks” at inventory.
Today, the typical large publisher has a number of supply-side platforms (SSPs) set up to handle yield management, along with possibly several pieces of infrastructure to manage that critical programmatic direct sale. Publishers can reduce complexity and latency by simply using the pipes that have already been deployed for the very reason header bidding exists: understanding and managing the serving of premium ads to the right audiences.
Maybe publishers should be thinking about header bidding in a new way. Header-bidding tags are just another tag on the page. Those with tag management-enabled DMPs could have their existing architecture handle that – a salient point made recently by STAQ’s James Curran.
Curran also noted that the DMP has access, through ad log ingestion, to how much dough publishers get from every drop in the waterfall, including from private marketplace, programmatic direct header and the open exchanges. Many global publishers are looking at the DMP inside their stack as a hub that can see the pricing landscape at an audience level and power ad servers and SSPs with the type of intelligent decisioning that supercharges yield management.
In ad technology, we talk a lot about the various partners enabling “paid, owned and earned” exposures to consumers, but we usually think of DMPs as essential only for the paid part.
But the composition of a web page, for example, is filled with dozens of little boxes, each capable of serving a display ad, video ad, social widget or content. Just as the DMP can influence the serving of ads into those little boxes, it can also influence the type of content that appears to each user. The big automaker might want to show a muscle car to that NASCAR Dad when he hits the page or a shiny new SUV to the Suburban Mom who shuttles the kids around all day.
Or, a marketer with a lot of its own content (“brands are publishers,” right?) may want to recommend its own articles or videos based on the browsing behavior of an anonymous user. The big global publisher may want to show a subscriber of one magazine a series of interesting articles from its other publications, possibly outperforming the CPA deals it has with third parties for subscription marketing.
This one-to-one personalization is possible because DMPs can capture not only the obvious cookie data but also the other 60% of user interactions and data, including mobile apps, mobile web, beacon data and even modeled propensity data from a marketer or publisher’s data warehouse.
Wouldn’t it be cool to serve an ad for a red car when the user has a statistically significant overlap with 10,000 others who have purchased red cars in the past year? That’s how to apply data science to drive real content personalization, rather than typical retargeting.
These are just some of the possibilities available when you start to think as the DMP as not just a central part of the ad technology “stack” but the brains behind everything that can be done with audiences. This critical infrastructure is where audience data gets ingested in real time, deployed to the right channels at speed and turned into insights about people. In a short period of time, the term “DMP” will likely be shorthand for the simple audience targeting use case inside of the data-driven marketing hub.
It’s a great time to be a data-driven marketer.
We’ve been hearing about big data driving marketing for a long time, and to be honest, most is purely aspirational.
Using third-party data to target an ad in real time does deploy some back-end big-data architecture for sure. But the real promise of data-driven marketing has always been that computers, which can crunch more data than people and do it in real time, could find the golden needle of insight in the proverbial haystack of information.
This long-heralded capability is finally moving beyond the early adopters and starting to “cross the chasm” into early majority use among major global marketers and publishers.
Leveraging Machine Learning For Segmentation
Now that huge global marketers are embracing data management technology, they are finally able to start activating their carefully built offline audience personas in today’s multichannel world.
Big marketers were always good at segmentation. All kinds of consumer-facing companies already segment their customers along behavioral and psychographic dimensions. Big Beer Company knows how different a loyal, light-beer-drinking “fun lover” is from a trendsetting “craft lover” who likes new music and tries new foods frequently. The difference is that now they can find those people online, across all of their devices.
The magic of data management, however, is not just onboarding offline identities to the addressable media space. Think about how those segments were created. Basically, an army of consultants and marketers took loads of panel-based market data and gut instincts and divided their audience into a few dozen broad segments.
There’s nothing wrong with that. Marketers were working with the most, and best, data available. Those concepts around segmentation were taken to market, where loads of media dollars were applied to find those audiences. Performance data was collected and segments refined over time, based on the results.
In the linear world, those segments are applied to demographics, where loose approximations are made based on television and radio audiences. It’s crude, but the awesome reach power of broadcast media and friendly CPMs somewhat obviate the need for precision.
In digital, those segments find closer approximation with third-party data, similar to Nielsen Prizm segments and the like. These approximations are sharper, but in the online world, precision means more data expense and less reach, so the habit has been to translate offline segments into broader demographic and buckets, such as “men who like sports.”
What if, instead of guessing which online attributes approximated the ideal audience and creating segments from a little bit of data and lot of gut instinct, marketers could look at all of the data at once to see what the important attributes were?
No human being can take the entirety of a website’s audience, which probably shares more than 100,000 granular data attributes, and decide what really matters. Does gender matter for the “Mom site?”Obviously. Having kids? Certainly. Those attributes are evident, and they’re probably shared widely across a great portion of the audience of Popular Mom Site.
But what really defines the special “momness” of the site that only an algorithm can see? Maybe there are key clusters of attributes among the most loyal readers that are the things really driving the engagement. Until you deploy a machine to analyze the entirety of the data and find out which specific attributes cluster together, you really can’t claim a full understanding of your audience.
It’s all about correlations. Of course, it’s pretty easy to find a correlation between only two distinct attributes, such as age and income. But think about having to do a multivariable correlation on hundreds of different attributes. Humans can’t do it. It takes a machine-learning algorithm to parse the data and find the unique clusters that form among a huge audience.
Welcome to machine-discovered segmentation.
Machines can quickly look across the entirety of a specific audience and figure out how many people share the same attributes. Any time folks cluster together around more than five or six specific data attributes, you arguably have struck gold.
Say I’m a carmaker that learned that some of my sedan buyers were men who love NASCAR. But I also discovered that those NASCAR dads loved fitness and gaming, and I found a cluster of single guys who just graduated college and work in finance. Now, instead of guessing who is buying my car, I can let an algorithm create segments from the top 20 clusters, and I can start finding people predisposed to buy right away.
This trend is just starting to happen in both publishing and marketing, and it has been made available thanks to the wider adoption of real big-data technologies, such as Hadoop, Map Reduce and Spark.
This also opens up a larger conversation about data. If I can look at all of my data for segmentation, is there really anything off the table?
Using New Kinds Of Data To Drive Addressable Marketing
That’s an interesting question. Take the company that’s manufacturing coffee machines for home use. Its loyal customer base buys a machine every five years or so and brews many pods every day.
The problem is that the manufacturer has no clue what the consumer is doing with the machine unless that machine is data-enabled. If a small chip enabled it to connect to the Internet and share data about what was brewed and when, the manufacturer would know everything their customers do with the machine.
Would it be helpful to know that a customer drank Folgers in the morning, Starbucks in the afternoon and Twinings Tea at night? I might want to send the family that brews 200 pods of coffee every month a brand-new machine after a few years for free and offer the lighter-category customers a discount on a new machine.
Moreover, now I can tell Folgers exactly who is brewing their coffee, who drinks how much and how often. I’m no longer blind to customers who buy pods at the supermarket – I actually have hugely valuable insights to share with manufacturers whose products create an ecosystem around my company. That’s possible with real big-data technology that collects and stores highly granular device data.
Marketers are embracing big-data technology, both for segmentation and to go beyond the cookie by using real-world data from the Internet of Things to build audiences.
It’s creating somewhat of a “cluster” for companies that are stuck in 2015.
We have all heard about the Democratic Party’s skill with data, and there is no doubt the Obama campaign’s masterful use of first-party registration data to drive online engagement, raise funds and influence political newbies helped put him over the line.
Four years later, the dynamics are mostly similar, but we have moved into a world where mobile is dominant, more young new voters are highly engaged and the standard segmentation – at least on the Republican side – might as well be thrown out the window.
In other words, everyone is getting influenced on their mobile phone, especially through news and social channels. There are a ton more mobile-first, new voters out there, and nobody is really sure which voters make up this weird new Trump segment.
To get a handle on this, political advertisers need to properly onboard and analyze their data to identify who they should target, where they live and what they like.
Understand Voter Identity
In politics, a strong “ground game” is key. That means real, old-school retail politics, such as knocking on doors and getting voters in specific precincts out on Election Day. All campaigns have the voter rolls and can do their fill of direct mail, robocalls and door knocking.
But how to influence voters well before Election Day who are tethered to their devices all day and night? It requires a digital strategy that can reach voters across the addressable channels they are on, including display, video, mobile and email. This strategy should leverage an identity graph to ensure the right messaging is hitting the same voter – at the right cadence.
Maybe “Joe the Firefighter,” a disaffected moderate Democrat who has had it with the Clintons, visited the Donald’s website and is ready to “Make America great again.” Before cross-device capabilities were strong, you could only retarget Joe the next time you saw his cookie online.
Today, Joe can get an equity message reinforced on display (“Make America great again!”), a mobile “nudge” to take action when we see Joe on his tablet at night (“Donate now!”) and follow up with an email a few days before the big rally (“Come see the Donald at the Civic Center!”).
Beyond this capability is the incredibly important task of laddering up individual identity into householding, so we can understand the composition of Joe’s family, since households often vote together and contain more than one registered voter.
Nail Geographic Targeting by County and District
Since “all politics is local,” it follows that all digital advertising should be locally targeted. This is table stakes for digital providers that work with campaigns, and targeting down to the ZIP+4 level has brought a level of precision to district-level outreach that approaches direct mail.
But direct mail (household targeting) is the crown jewel and digital is still trying to cross that divide, but is held back by a fragmented ecosystem of identity and, more importantly, privacy considerations.
This has always been a key consideration, given the fact that a small percentage of key districts can flip the presidency to one party or another.
Affiliation Modeling Through Behavior
Sometimes getting an understanding of someone’s party affiliation is super obvious, such as “liking” a specific candidate on social media. But, sometimes, a user’s affinity has to be derived through attributes derived through his or her behavior and the context of content consumed over time.
Data management platforms are bringing more precision to this type of modeling. Functionality, such as algorithmic segmentation, is helping digital analysts go beyond the basics. It’s fairly easy to correlate two or three attributes, such as income and gender, to estimate party affiliation. In this cycle, for example, we have seen a strong bias toward Trump from lower-income males with less than a college degree.
However, it’s hard for humans to correlate eight or more distinct attributes. Maybe those lower-education, low-income, rural males who love NASCAR actually lean toward Bernie Sanders in certain districts. Letting the machines crunch the numbers can give digital campaign managers an unseen advantage, and that capability has just now become available at scale.
“In 2016, relying on TV advertising to sway voters is no longer a solid campaign tactic,” JC Medici, Rocket Fuel’s national director of politics and advocacy, told me via email. “To secure the White House in November, candidates must now add a strong digital media strategy by utilizing best-in-class AI, correlated with strong voter and propensity data assets to ensure they are delivering ads to the right voter, on the right screen, at the right time.”
One of the hot new areas for political campaign targeting is social affinity, the idea that there is a mutual affinity that can be measured between interests.
Yes, when someone “likes” Hillary, you have an obvious target. But, how about those folks who haven’t stated an obvious choice? Maybe 80% of Hillary fans also liked cat shelters, yellow dresses and Chris Rock.
When strong correlations between deterministic social behavior are shown, it becomes fairly easy to leverage that data for targeting – and make informed choices regarding media. People who liked Hillary also like certain TV shows, actors, causes and websites. Campaign managers can leverage data from Affinity Answers, Affinio and other companies to understand these relationships and exploit them to build support for candidates, while leveraging the ability to geotarget at very granular levels on Facebook.
The Free State Project, an organization committed to getting 20,000 “liberty-loving” people to move to New Hampshire and work toward limited government, just reached its goal – talk about a tough conversion. President Carla Gericke credits the use of data-driven targeting on Facebook for the achievement.
Speaking of social, it is also highly important to get the context right.
“Programmatic has introduced two new challenges: bots (who don’t vote) and brand safety,” Trust Metrics CRO Marc Goldberg told me. “In the age of immediate and shocking news, it has become more important that a political ad does not end up next to porn, hate or issues that are contradictory to the politician’s beliefs. One screen shot and bam, you are on Twitter.”
Onboarding And Offboarding
Perhaps the most critical functionality for digital political campaigns continues to be the ability to “onboard” offline data, such as phone numbers, email addresses and party affiliation, and match it to an online ID for targeting purposes. This is essentially table stakes, considering the years of political investment in collecting offline records for phone banks and direct mail campaigns.
Previously, the onboarding of such data was limited to associating it with an active cookie for retargeting use. But with the emergence of real cross-channel device graphs, this data can now be tied to a universal consumer ID that is persistent and collects attributes over time.
Simply put, that onboarded email – now a UID – can be mapped to a number of identities, including Apple and Android mobile identifiers, third-party IDs from Experian and the like and device IDs from Roku and other OTT devices. In other words, the device graph enables that email to be associated with the voter’s omnichannel footprint, giving campaigns the ability to sequentially target messages, map creative to execution channels and truly understand attribution.
What’s even more exciting is the idea of offboarding some digital data back into the CRM. How valuable would it be to know that a potential voter watched an entire YouTube video on a candidate after being reached by the phone bank? Certain types of behavioral data, depending on compliance with privacy policies, can be brought back into the CRM to impact the effectiveness of offline voter outreach.
It is fair to say that 2016 is the most exciting campaign season we’ve had in a generation – and it’s only the primary season. As data-driven marketers, we will see campaigns push the limit in applying big marketing dollars to digital channels, trying to unlock new, mobile-first millennial voters, while persuading independents through more addressable advertising then ever.
It’s a great time to be a data-driven marketer.
If you think about the companies with perhaps least amount of consumer data, you would automatically think about consumer packaged goods (CPG) manufacturers. Hardly anybody registers for their website or joins their loyalty clubs; moms don’t flock to their branded diaper sites; and they are at arms-length from any valuable transaction data (store sales) until well after the fact. So, with little registration, website, or offline sales data, why are so many large CPG firms licensing an expensive first-party data management platform?
While CPG companies will never have the vast amounts of point-of-sale, loyalty-card, app, and website data that a big box retailer might have, they do spend a ton of dough on media. And, as we all know, with large media expenditures come tons of waste. Combine this with the increasingly large investment and influence that activist investors and private equity companies have in CPG, and you can see where this leads. PE companies have installed zero-based budgeting that forces CPG concerns to rationalize every penny of the marketing budget—which, until lately, has been subject to the Wannamaker Rule (“I know half of my budget is working, but not which half”). Enter the DMP for measurement and global frequency control, cutting off and reallocating potentially millions of dollars in “long tail” spending. Now, the data that the CPG marketer actually has in abundance (media exposure data), can be leveraged to the hilt.
This first and most obvious CPG use case has been discussed extensively in past articles. But there is much more to data management for CPG companies. Here are just a few tactics big consumer marketers have written into their data-driven playbooks:
The Move to Purchase-Based Targeting (PBT)
Marketers have come a long way from demographic targeting. Yes, gender, age and income are all reliable proxies for finding those “household CEOs,” but we live in complicated times and “woman, aged 25-54, with 2 children in household” is still a fairly broad way to target media in 2016. Today, men are increasingly as likely to go grocery shopping on a Thursday night. Marketers saw this and shifted more budget to behavioral, psychographic, and contextual targeting—but finding cereal buyers using proxies such as site visitation sharpened the targeting arrow only slightly more than demography.
Packaged goods marketers have long understood the value of past purchases (loyalty cards and coupons), but until the emergence of data management technologies, have struggled to activate audiences based on such data. Now, big marketers can look at online coupon redemption or build special store purchase segments (Datalogix, Nieslen Catalina, News America Marketing) and create high value purchase-based segments. The problem? Such seed segments are small, and must be modeled to achieve scale. Also, by the time the store sales data comes in, it’s often far too late to optimize a media plan. That said, CPG marketers are finding that product purchasers share key data attributes that reveal much about their household composition, behavior, and—most interestingly—affinity for a company’s other products. It may not seem obvious that a shopping basket contains diapers and beer—until you understand that Mom sent Dad out to the store to pick up some Huggies, and he took the opportunity to grab a cold six-pack of Bud Light. These insights are shaping modern digital audience segmentation strategy, and those tactics are becoming more and more automated through the use of algorithmic modeling and machine-learning. CPG has seen the future, and it is using PBT to increase relevant reach.
Optimizing Category Reach
CPG marketers are constantly thinking about how to grow the amount of product they sell, and those thoughts typically vary between focusing on folks who are immensely loyal (“heavy” category buyers) versus those who infrequently purchase (“light” or “medium” category buyers). Who to target? It’s an interesting question, and one answered more decisively with purchase-based sales data.
Take the large global soda company as an example. Their average amount of colas their customer consumes is 15 a year, but that is an immensely deceptive number. The truth is that the company has a good amount of “power users” who drink 900 colas a year (two and a half per day), and a lot of people who may only drink 2-3 colas during the entire year. Using the age-old “80/20 Rule” as a guideline, you would perhaps be inclined to focus most of the marketing budget on the 20% of users who supposedly make up 80% of sales volume. However, closer examination reveals that heavy category buyers may only be driving as little as 50% of total purchase volume. So, the marketer’s quandary is, “Do I try and sell the heavy buyer his 901st cola, or do I try and get the light buyer to double his purchase from two to four colas a year?”
Leveraging data helps CPG companies not have to decide. Increasingly, companies are adopting frequency approaches that identify the right amount of messaging to nurture the heavy users (maybe 2-3 messages per user, per month) and bring light buyers to higher levels of purchase consideration (up to 20 messages per month). Moreover, by using DMP technology to segment these buyers based on their category membership, creatives can be adjusted based on the audience. Heavy buyers get messages that reinforce why the love the brand (“share the love”), and light buyers can receive more convincing messages (“tastes better”).
Increasing Lift through Cross-Channel Messaging
CPG marketers have some highly evolved models that show just how much lift a working media dollar has on sales, and they use this guide to decision on media investment by both channel and partner. With the power of DMPs for cross-channel measurement, CPG companies are finally able to apply even small insights they can to tweak sales lift.
What if the data reveal that a 50% mixture of equity and direct response ad creatives lifts coupon downloads by 200%? In other words, instead of just showing “Corn Flakes are Yummy” ads, you mixed in a few “Buy Flakes now at Kroger and save!” creatives afterwards, and you saw a huge impact on your display performance? Sadly, this simple insight was not available before data management platforms corralled cross-channel spending and associated it with an individual, but now these small insights are adding up to appreciable sales lift.
In another example, a large CPG company sees massive lift in in-store coupon redemptions by running branded display ads on desktop all throughout the week—but giving a “mobile nudge” on the smartphone on Friday night when it’s time to fill the pantry. This cross-channel call-to-action has seen real results, and only involves grabbing a brand-favorable consumer’s attention on another device to create a big impact. Again, a simple tactic—but also impossible without the power of a DMP.
CPG marketers have been able to achieve a ton of progress by working with relatively sparse amounts of data. What can you do with yours?
Marketers are getting frustrated with spending up to 60% of their working media dollars to fund intermediaries between themselves and their publishing partners. By the time a marketer pays his agency, trading desk, exchange, third-party data provider, and subsidizes the publisher’s ad serving stack, dollars turn into dimes. Marketers want less fraud, more people, less ad tech, and to put more media dollars to work to drive performance. Quality publishers, who for so long sacrificed control for access to an always-on stream of programmatic cash, are now seeing balance return, as shady sources of inventory leave the ecosystem and start to create scarcity for premium supply.
Publishers with desired audiences are starting to leverage hacks like “header bidding” and private marketplaces to get more control and capture more revenue from transactions. But they are also starting to look at data-only transactions among trusted demand-side partners. Now that marketers are catching up with DMP technology, securely sharing audiences becomes possible, opening up a new era where “second party” data is poised to reign supreme. Before we talk about how that happens, let’s first define some data terms:
First-party data is proprietary data that marketers and publishers have collected – with permission, of course – and, therefore, own. It can be cookies collected from a site visit, offline data onboarded into addressable IDs and even data from marketing campaigns. Second-party data is simply someone else’s first-party data. Second-party data gets created any time two companies strike up a deal for data that is not publicly available. The most common use case is that of a marketer – say a big airline –getting access to data for a publisher’s frequent travelers. Big Airline might say to Huge News Site with business travelers, “Let’s user match, so every time I see one of my frequent flyers on your site, I can serve him an ad.” Huge News Site may decide to allow Big Airline to target its users wherever they are found (a “bring your own data” deal) or make such a deal incumbent upon buying media. Either way, Big Airline now has tons of really valuable Huge News Site reader data available in its data-management platform (DMP) for modeling, analysis and targeting.
Despite the much heralded death or merely diminution of third-party data, it is still a staple of addressable media buying. This is data that is syndicated and made available for anyone to buy. This data could describe user behavior (Polk “auto intenders” of various stripes) or bucket people into interesting addressable segments based on their life circumstances (Nieslen “Suburban Strivers”), describe a user’s income level (Acxiom or Experian) or tell you where a user likes to go via location data (PlaceIQ or Foursquare). Most demand-side platforms (DSPs) make a wide variety of this data available within their platforms for targeting, and DMPs enable users to leverage third-party data for segment creation – usually allowing free usage for analytics and modeling purposes and getting paid upon successful activation. Data Quality And Scale So, which kind of data is the best? When asked that question by a marketer, the right question is inevitably, “all of it.” But, since that’s an annoying answer, let’s talk about the relative scale and value of each type of data. It’s easily visualized by this wonderfully over-simplified triangle:
First-party data is the most limited in scope, yet the most powerful. For marketers –especially big CPG marketers who don’t get a lot of site traffic – first-party data is incredibly sparse but is still the absolute most valuable signal to use for modeling. Marketers can analyze first-party data attributes to understand what traits and behaviors consumers have in common and expand their reach using second- or third-party data. Retail and ecommerce players are more fortunate. A Big Box Store has first-party data out the wazoo: loyalty card data, point-of-sale system data, app data, website registration data, site visit data and maybe even credit card data if it owns and operates a finance arm. It can leverage a DMP to understand how media exposure drove a store visit, where customers were in the store (beacons!), what was purchased, how many coupons were remitted and whether or not they researched their purchase on the site. Talk about getting “closed loop” sales attribution. The power of first-party data is truly amazing.
The biggest problem with third-party data is that all of my competitors have it. In programmatic marketing, that means both Ford and Chevy are likely bidding on the same “auto-intender” and driving prices up. The other problem is that I don’t know how the data was created. What attributes went into deciding whether or not this “auto intender” is truly in-market for a car? There are no real rules about this stuff. A guy who read the word “car” in an article might be an “auto-intender” just as someone who looked a four-door sedans three times in the last 30 days on reputable auto sites. Quality varies. That being said, there is huge value in having third-party data at your disposal. Ginormous Music App, for example, has built a service that is essentially a DMP for music; it knows how to break down every song, assign very granular attributes to it and delivers highly customized listening experiences for free and paid users. Those users are highly engaged, have demonstrated a willingness to buy premium services and are, by virtue of their mobile device, easily found at precise geolocations. Yet, for all of that, the value to a marketer of a Maroon Five segment is rather small. Everyone likes Maroon Five, from grandmothers to tweens to Dads. A Maroon Five segment provides little value to an advertiser. Yet, if Ginormous Music App could push its app-based user data (IDFAs) into the cookie space and find a user match, it could effectively use third-party data to understand the income, behavior and general profile of many Maroon Five fans. And that’s what their advertisers like to buy. That’s pretty damn valuable.
So, how about “second-party” data? These are the “frequent business travelers” on Huge News Site and the “Mitsubishi intenders” on Large Auto Site. These are real users, with true demonstrated intent and behavior that has been validated on real properties. One of the most valuable things about audiences built on second-party data is that there is usually transparency regarding how those users found their way into a segment.
The ironic and kind of beautiful thing about the emergence of second-party data is that it is most often merely a connection to a premium publisher’s users. However, it can be uncoupled from a publisher’s media sales practice. Marketers, increasingly sick of all the fraud and junk in the programmatic ecosystem are turning toward second-party data to access the same audiences they bought heavily in print 30 years ago. This time, however, they are starting to get both the quality – and the quantitative results – they were looking for. On the flip side, quality publishers are starting to understand that, when offered in a strict, policy-controlled environment that protects their largest asset – audience data – they can make way more money with data deals than media deals.
Put simply, second-party data is heralding a return to the good old days when big marketers depended on relationships with big publishers as the stewards of audiences, and they created deep, direct relationships to ensure an ongoing value exchange. Today, that exchange increasingly happens through web-based software rather than martini lunches.
[This article originally appeared in AdExchanger on 1/25/16]
2015 was a fantastic year for many data-driven marketers, with data management platforms (DMPs), consultancies and marketers getting something nice under their trees.
Unfortunately, 2015 also saw legacy networks, supply-side platforms (SSPs) and some less nimble agencies receive coal in their respective stockings for failing to keep up with the rapidly changing paradigm as marketing and ad technology merge.
In the great tradition of end-of-year prediction articles, here’s my take on the year’s biggest developments and what we’ll see in 2016, including a rapid technology adoption from big marketers, a continuing evolution of the agency model and an outright revolution in how media is procured.
I thought 2015 was supposed to herald the “death of the digital agency model.” As agencies struggled to define their value proposition to big marketers that were increasingly bringing “programmatic in house,” agencies were reputed to be on the ropes. Massive accounts with billions of dollars in marketing spend were reviewed, while agencies churned through cash pitching to win new business – or at least trying keep old business.
The result? Agencies swapped a ton of money, but were abandoned by no serious marketers. Agencies got a lot smarter, and starting spinning new digital strategies and DMP practices to combat the likes of system integrators and traditional consultancies. And the band played on.
In 2016, we will continue to see agencies strengthen their digital strategy bench, start moving “marketing automation” practices into the DMP world and offer integration services to help marketers build bespoke “stack” solutions. Trading desks will continue to aggressively pursue unique relationships with big publishers and start to embrace new media procurement methodologies that emphasize their skillset, rather than the bidded approach in open exchanges (more on that below).
Marketers Hug Big Data
Marketers started to “cross the chasm” in 2015 and more widely embrace DMPs. It’s no longer just “early adopters” such as Kellogg’s that are making the market. Massive top-100 firms have fully embraced DMP tech and are starting to treat online data as fuel for growth.
Private equity and activist investors continue to put the squeeze on CPG companies, which have turned to their own first-party data to find media efficiency as they try to control the one line item in the P&L usually immune to risk management: marketing spend.
Media and entertainment companies are wrangling their consumer data to fuel over-the-top initiatives, which put a true first-party relationship with their viewers front and center. Travel companies are starting to marry their invaluable CRM data to the anonymous online world to put “butts in seats” and “heads in beds.”
If 2015 saw 15% of the Fortune 500 engage with DMPs, 2016 is when the early majority will surge and start to make the embrace of DMP tech commonplace. The land grab for 24-month SAAS contracts is on.
It used to be a that a senior-level digital guy would get sick of his job and leave it (or his job would leave him), leading to a happy consultant walking around advising three or four clients on programmatic strategy. In 2015, that still exists but we’ve seen a rise in scale to meet the needs of a rapidly changing digital landscape.
Marketers and publishers are hiring boutique consultancies left and right to get on track (see this excellent, if not comprehensive, list). Also, big boys, including Accenture, Boston Consulting Group and McKinsey, are in the game, as are large, media-centric firms, such as MediaLink.
These shops are advising on data strategy, programmatic media, organizational change management and privacy. They are helping evaluate expensive SAAS technology, including DMPs and yield management solutions, and also doing large systems integrations required to marry traditional databases with DMPs.
Match Rates (Ugh)
Perhaps unpublicized, with the exception of a few nerdy industry pieces, we saw in 2015 a huge focus on “match rates,” or the ability for marketers to find matches for their first-party data in other execution systems.
Marketers want to activate their entire CRM databases in the dot-com space, but are finding only 40% to 50% of cookies that map to their valuable lists. When they try to map those cookies to a DSP, more disappointment ensues. As discussed in an earlier article, match rates are hard to get right, and require a relentless focus on user matching, great “onboarding services,” strong server-to-server connections between DMPs and DSPs (and other platforms) and a high frequency of user matching.
This was the year that marketers got disappointed in match rates and started to force the industry to find better solutions. Next year, huge marketers will take bold steps to actually share data and create an available identity map of consumers. I think we will see the first real data consortium emerge for the purposes of creating an open identity graph. That’s my big prediction – and hope – for 2016.
Head For The Headers
2015 was the year of “header bidding,” the catch-all phrase for a software appliance that gives publishers the chance to offer favored demand-side partners a “first look” at valuable inventory. I am not sure if “header bidding” will ultimately become the de facto standard for “workflow automation,” but we seem to be relentlessly marching back to a world in which publishers and marketers take control of inventory procurement and get away from the gamesmanship inherent in exchange-based buying.
Big SSPs and networks that have layered bidding tech onto open exchanges are struggling. Demand-side platforms are scrambling to add all sorts of bells and whistles to their “private marketplaces,” but the industry evolves.
Next year, we will see the pace of innovation increase, and we have already seen big trade desks make deals with DMPs to access premium publisher inventory. It’s nice to see premium publisher inventory increase in value – and I believe it will only continue to do so.
2016 will be the year of “second-party data” and the winners will be the ones with the technology installed to easily transact on it.
2015 was a great year for data-driven marketing, and 2016 will be even more fun. Stay safe out there.
This post originally appeared in AdExchanger on 12/17/2015
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.
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.
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!
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.
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!
As 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.
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.
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?
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.
Almost 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.
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
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!
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!
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]
With companies like Kraft and Kellogg’s starting to leverage the programmatic pipes for equity advertising, we are starting to hear a lot of buzz about the potential for “programmatic branding,” or leveraging ad tech pipes to drive upper-funnel consumer engagement. It makes sense. Combine 20 years in online infrastructure investment with rapidly shifting consumer attention from linear to digital channels, and you have the perfect environment to test whether or not digital advertising can create “awareness” and “interest,” the first two pieces of the age old “AIDA” funnel.
The answer, put simply, is yes.
Online reach is considerably less expensive than linear reach, and we are starting to have the ability to reliably measure how that brand engagement is generated. Marketers want an “always-on” stream of equity advertising that comes with measurement—but they also need it. With attention rapidly shifting from traditional channels, investments in linear television are starting to return fewer sales. But most marketers are just starting to gain the digital competency to make programmatic branding a reality.
That competency is called data management—the ability to segment, activate, and analyze consumer audiences in a reliable way at scale. Why is that so?
The most fundamental problem with digital branding is that it is truly a one-to-one marketing exercise. If we dream of the “right message, right person, right time,” then matching a user with her devices is table stakes for programmatic branding. How do I know that Sally Smith on desktop is the same as Sally Smith on tablet? Cross-device identity management (CDIM or, alternatively, CDUI) is the key. Device IDs must be mapped to cookies, other mobile identifiers, and Safari browser signals in order to get a sense of who’s who. Once you unlock user identity, many amazing things become possible.
Global Frequency Capping
One of the reasons programmatic branding has yet to gain serious ground with marketers is because of waste. This is both real (lots of wasted impressions due to invisible ads or robotic traffic) and perceived (how many impressions are ineffective due to frequency issues). The former problem is getting solved by smart technology, and already somewhat mitigated by market pricing. But the latter problem is solvable with data management. Assuming the marketer understands the ideal effective frequency of impressions per channel, or on a global basis, a DMP can manage how many impressions an individual sees by controlling segment membership in various platforms. Let’s say the ideal frequency for cereal advertising aimed at Moms is 30 per day across channels. The advertiser knows less than 30 impressions lessens effectiveness—and over 30 impressions has negligible impact. Advertisers using multiple channels (direct-to-publisher, plus a mobile, video, and display DSPs) are likely over serving impressions in each channel, and maybe underserving in key channels like video. Connecting user identity helps control global frequency, and can save literally millions of dollars, while optimizing the effectiveness of cross-channel advertising.
If “right person” technology is enabled as above, then it makes sense to try and get to “right place and right time.” Data management can enable this Holy Grail of branding, helping marketers create relevance for consumers as they embark on the customer journey. What brand marketers have dreamed of is now possible, and starting to happen. Dad, in the auto-intender bucket, gets exposed to a 15 second pre-roll ad before logging into his newspaper subscription on his tablet in the morning; gets the message reinforced by more equity display ads in the afternoon at work; and, while checking messages on his mobile phone on the way home, gets an offer for $500 off with a qualified test drive. After he hits the dealership and checks in via the CRM system, he receives an e-mail thanking him for his visit and reminding him of the $500 coupon he earned. These tactics are not possible without tying both user identity and systems together. Doing so not only enables sequential messaging, but also the ability to test and measure different approaches (A/B testing).
Cross Channel Attribution
How about attribution? It’s impossible to perform cross-channel attribution without knowing who saw what ad. At the end of the day, it’s really about the insights. Proctor and Gamble is famous for spending millions of dollars every year to understand the “moment of truth,” or why people choose Tide over Surf detergent. Although they know consumer segmentation and behavior better than anyone, even the biggest brand marketers struggle to gain quality insights from digital channels. Data management is starting to make a more reliable view possible. Brand advertising is just another form of investment. Money is the input, and the output is sales and—as important—data on what drove those sales. In the past, brand marketers were reliant upon panel-based measurement to judge campaign effectiveness. Now, data management helps brands understand which channels drove results—and how each contributed. It is early days for truly reliable cross-channel attribution modeling, but we are finally starting to see the death of the “last click” model. Smart marketers are using data to author their own flexible attribution models, making sure all channels involved receive variable credit for driving the final action. In the near future, machine learning will help drive dynamic models, which flex over time as new signals are acquired. We will then start to see just how effective (or not) tactics like standard display advertising are for driving upper funnel engagement.
So, is 2015 the year for programmatic branding? For a select group of marketers that are leveraging data management to enable the best practices outlines above, yes. The more accurately marketers can map online user identity and understand results, the more investment will flow from linear to addressable channels.
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]
I-COM Global Summit: Panel Discussion on Leveraging Big Data to take Programmatic to the Next Level – Chris O’Hara, Krux Digital, Eric Picard, Mediamath & Tom Simpson, MediaQuark
Chris O’Hara, VP Strategic Accounts, Krux Digital, USA, Eric Picard, VP Strategic Partnerships, Mediamath, UK and Tom Simpson, CEO, MediaQuark, Singapore were speakers and David Smith, CEO & Founder, Mediasmith, USA was moderator in Leveraging Big Data to take Programmatic to the Next Level. This discussion had no presentation.
11 May 2015 · By Western iMedia
The varied facets of programmatic advertising have become more mainstream in the media industry. Those heavily involved at their media companies see a promising future for this once niche revenue opportunity.
“Efficiency is the name of the game,” said Matt Prohaska, CEO and principal of Prohaska Consulting, speaking about programmatic buying at the World Congress in New York City on Monday. Prohaska turned the discussion over to a panel of experts that explored the future of programmatic.
There are two major aspects of programmatic advertising, said Jeremy Crandall, senior vice president of operations and client services at Adroit Digital. Automated programmatic, which includes real-time bidding (RTB), is the side heard more often.
“To me, programmatic is like a BLT sandwich,” she said. “RTB is the bacon.”
The other aspect is data-driven programmatic, in which buyers leverage data to make informed decisions about which ad impressions to buy.
Crandall explained the different modes of programmatic buying and selling. Open RTB leverages ads not sold by a direct salesperson. These ads are are remnant impressions and the transactions take less than 100 milliseconds.
“It really is a many-to-many marketplace,” she said.
Crandall described private marketplace (PMP) transactions as a walled garden, in which select buyers are invited to participate and usually involves price floors. PMP is not as impersonal as RTB, Crandall said. Sharing data makes a significant difference in efficacy of the buy.
“It is really those relationships that still matter a lot,” she said.
Chris O’Hara, vice president of strategic accounts at Krux Digital, gave insight from the data management platform (DMP) side of the programmatic equation.
O’Hara outlined the evolution of publisher ad sales as moving from publisher direct, to ad network 1.0, to the introduction of the DSP era. We are currently in the DMP area, one of “programmatic direct,” O’Hara said.
We are moving toward an era of total automation across channels. Efficient automation, where publishers retain more revenue and advertisers get increased reach for budget, is part of that future.
“This is the wave of the future; it’s happening and it’s super exciting,” O’Hara said.
Jon Usry, director of digital platforms at Dallas Morning News Media, shared his company’s strategic approach to the current and future states of programmatic.
When programmatic first entered the market, the reaction from publishers was one of fear, as buyers were perceived to have the advantage and the quality of these ads seemed low.
This is not the case now, Ursy said. Programmatic is seen as an opportunity rather than a threat.
“Certainly, a lot of things have changed significantly,” he said.
Dallas Morning News made the strategic decision to build or purchase digital marketing solutions as programmatic grew in influence.
The current state of programmatic is a level playing field, Ursy said. The company hold regular meetings about how to leverage programmatic. They explore all options, he said: “There might be certain situations where we want programmatic to be competing with direct sells.”
Developing a strong plan for programmatic is important to the company. Programmatic media spend is set to double in the U.S. alone, Ursy said.
Usry shared where The Dallas Morning News is focusing as they develop their future programmatic strategy:
As data continues to grow and marketers get better tools, Usry says programmatic has a positive future: “For the future of programmatic, I think it looks promising for all parties.”
Read more: http://www.inma.org/blogs/world-congress/post.cfm/programmatic-advertising-once-a-threat-is-now-considered-an-opportunity#ixzz3bGw6O7yI
Read more: http://www.inma.org/blogs/world-congress/post.cfm/programmatic-advertising-once-a-threat-is-now-considered-an-opportunity#ixzz3bGvnIBgK
In early 2012, when data management technology was somewhat nascent, I wrote about “the five things to expect from a DMP.” They were: To unlock the power of one’s first party data; decrease reliance upon third party data; generate unique audience insights; use data to audience power new channels; and create efficiency. A little over three years later, those things still continue to drive interest in DMP technology—and great value for both publishers and marketers.
The “table-stakes” functionality of DMPs—segmentation, lookalike modeling, targeting, and analytics—continue to resonate. Even the least advanced DMPs have those abilities, and this is what people who buy DMP software should expect from any system. Unfortunately, there are now dozens of “platforms” that claim DMP technology. Some are legitimate players, born from the ground up to be “first-party” DMPs. Some have been created as “lightweight” DMPs to collect and distribute cookies for display advertising. And still others are legacy tag management or network platforms that have bolted on DMP functionality as they work towards a fuller “stack” solution that marketers say they want.
Writing this article again, three years later, I would still encourage software buyers to evaluate their DMP choice on the ability of their partner to meet the above-listed criteria. But, there has been so much nuance and development over the last several years. Therefore, additional selection criteria present themselves if one is expected to make a reasonably informed choice in DMP selection going forward.
Here’s what the modern DMP consumer should be looking out for:
- Lookback: Three years ago I talked about “lookback windows” in the context of giving publishers the ability to attribute future conversion events to ads shown previously on their site. That is still a compelling publisher user case. What “lookback windows” really refer to is whether or not your DMP can capture 100% of the raw, log-level user event data—and store it. This necessitates an open taxonomy (because “you don’t know what you don’t know,”) and also the ability to store tons of data and make it accessible quickly. This is considered to be complete data architecture. Many DMPs operate with a rigid, defined taxonomy and only collect segment IDs—not the underlying data. That’s a problem for businesses that need to move fast and activate new segments opportunistically. Ask how—and for how long—your DMP stores data.
- Onboarding: Lots of DMPs claim to have the ability to easily ingest CRM and other offline data and match it to cookies, but the truth is everyone depends on a limited set of “onboarding” vendors to provide the matches. That’s fine, but there are some nuances and subtleties involved in the process by which offline data enters the online identity space (hashing). DMPs should enable seamless connection to all three major onboarding providers, the ability to select the methodology by which offline identity is matched to online, and also be able to automatically choose which onboarding partner is right for each identity. Ask how each DMP you evaluate works with each vendor, what kind of match rates you can expect, and how each stores persistent user identity to insure better matches over time.
- Measurement: Let’s face it, the ability to tweak programmatic audience delivery to online video viewability numbers up a few percentage points is great, but nothing moves the needle like linear television. Marketers spend a ton of money there, and will continue to do so for the foreseeable future—all the while moving incremental percentages of their budget into the digital channels where folks are spending an increasing amount of time. But, they are never really going to go full throttle with digital until they can reconcile reach and frequency across channels—and those channels must include linear! Your DMP should be able to handle overlap reporting, light attribution, and cross-channel media performance—but it should also start making some highly informed guesses about how linear audiences map to digital ones, in order to enable true attribution and media mix models. Ask how your DMP is positioned to tie the linear and digital strings together from a measurement perspective.
- CDIM: Three years ago, we were still waiting for the “year of mobile” to occur, so “cross device identity management” was still largely pre-funded slideware on some entrepreneur’s computer. Jump to today, and “CDIM” and “CDUI” are at the tip of every ad tech tongue! As more and more people move from device to device—almost none of which support the traditional cookie as an identifier—marketers and publishers desperately need to map devices to people. It’s the only way to deliver the fabled “360 degree view” of the user. Ask your DMP vendor how they are prepared to deliver deterministic matches and, more importantly, how they reconcile identity without seeing a user logging in across devices. Doing great probabilistic matching necessitates not only strong algorithms but, more importantly, scale of users which breeds precision models. What is the size of their “truth set” of user data with which to probabilistically determine user identity? The quality and scale of that data will determine your choice.
- Data Governance: I think the biggest question to ask a potential DMP vendor is their philosophy on data ownership. For both marketers and publishers, audience data is likely one of their top three assets. Trusting such data to a technology vendor is not something to be considered lightly. How is that data stored? What are the policy controls available to help you share that data with trusted partners? What about privacy and governance? How can my platform help me activate data in different places, where different rules about PII and data collection and storage apply? Knowing the answers to these before you buy can save lots of heartache (and legal fees) later. More importantly, how independent is your data? Is your partner also in the business of selling media or data? That can create some conflicts of interest—especially if your data might be valuable to a competitor. Finally, what if you want your data back? You have the right to get it out quickly, and in a useable format.
The bad news is that choosing a DMP isn’t any easier than it was three years ago. It’s a lot more complex, and you really need to dig in deeply to understand the very small nuances between platforms that appear, on the surface, to be very much the same. The good news is that there is a great deal of selection available, and some very high quality vendors to choose from. Take your time, put your vendors through a very rigorous process that includes asking the questions outlined above, and choose wisely!
[This post originally appeared in the EConsultancy blog on 5.11.15]
If you read AdExchanger regularly, you might think that nearly every global marketer has a programmatic trading strategy. They also seem to be leveraging data management technology to get the fabled “360-degree view” of their customers, to whom they are delivering concise one-to-one omnichannel experiences.
The reality is that most marketers are just starting to figure this out. Their experience ranges from asking, “What’s a DMP?” to “Tell me your current thinking on machine-derived segmentation.”
A small, but significant, number of major global marketers are aggressively leaning into data-driven omnichannel marketing, pioneering a trend that is not going anywhere anytime soon. Over the next five years, nearly every global marketer will have a data-management platform (DMP), programmatic strategy and “chief marketing technologist,” a hybrid chief marketing officer/chief information officer that marries marketing and technology. These are exciting times for people in data-driven marketing.
So, what are marketers looking for from technology today? Although these conversations ultimately become technical in nature, you soon discover that marketers want some pretty basic, “table stakes” type of stuff.
Better Segmentation Through First-Party Data
Marketers spend a lot of time building customer personas. Once a customer is in their customer relationship management (CRM) database and generates some sales data, it’s pretty easy to understand who they are, what they like to buy and where they generally can be found. From a programmatic perspective, these are the equivalent of a car dealer’s “auto intenders,” neatly packaged up by ad networks and data providers to be targeted in exchanges.
That’s still available today, but the amazing amount of robotic traffic, click fraud and media arbitrage has made marketers realize just how loose some segment definitions may be. Data companies have a great deal of incentive to create and sell lots of auto intenders, so marketers are starting to look deeper at how such segments are actually created. It turns out that some auto intenders are people who brushed past a car picture on the web, which lumped them into a $12 cost per mille (CPM) audience segment.
Those days seem to be coming to an abrupt close as marketers increasingly use their own data to curate such segments and premium publishers, which do have auto intenders among their readerships, use data-management tools to make highly granular segments available directly to the demand side. Marketers are now willing to pay premium prices for premium audiences in a dynamic being driven by more transparency into how audiences are created in the first place. Audiences comprised of first- and second-party data will win every time in a transparent ecosystem.
Less Waste, More Efficiency
Part and parcel of better audience segmentation is less waste and more media efficiency. The old saw, “I know half of my marketing works, I just don’t know which half,” goes away with good data and better attribution.
As an industry, we promised to eliminate waste 20 years ago. The banner ad was supposed to usher in a brave new world of media accountability, but we ended up creating a hell of a mess. Luckily, venture money backed “solutions” to the problems of click fraud, faulty measurement and endless complexity in digital marketing workflow.
Marketers don’t want to buy more technology problems they need to fix. And they don’t want to spend money chasing the same people around the web. They want to limit how much they spend trying to achieve reach. Data-management technology is starting to rein in wasteful spending, via tactics including global frequency management, more precise segmentation, overlap analysis and search suppression.
Marketers want to use data to be more precise. They are starting to leverage systems that help them understand viewability and get a better sense of attribution by moving away from stale last-click models. The days are numbered for marketers with black-box technology that creates a layer between their segmentation strategies and how performance is achieved against it.
One-To-One Communication Via Cross-Device Identity
Maybe the biggest trend and aspiration among marketers is the ability to truly achieve one-to-one marketing. A few years ago, that meant email, telemarketing and direct mail. Today, if you want to have a one-to-one customer relationship, you must be able to associate the “one” person with as many as five or six connected devices.
That is extremely difficult, mostly because we have been highly dependent on the browser-based “cookie” to determine identity. Cookie-based technologies evolved to ensure different cookies match up in different systems, but it’s a new world today.
Really understanding user identity means being able to reconcile different device signals with a universal ID. That means lots of cookies from different browsers, Safari’s unique browser signature, IDFAs, Android device IDs and even signals from devices like Roku, not to mention reliably “onboarding” anonymized offline data, such as CRM records.
Without device mapping, an individual looks like seven different devices to a marketer, making it impossible to deliver the “right message, right place, right time.” Frequency management is tougher, attribution models start to break and sequential messaging is hard to do. Marketers want a reliable way to reconcile user identity across devices so they can adapt their messages to your situation.
Marketers inject tons of dollars into the advertising ecosystem and expect detailed performance reports. Each dollar spent is an investment. Some dollars create sales results, but all dollars spent in addressable channels create some kind of data.
Surprisingly, that data is still mostly siloed, with social data signals not connected to display results. Much of it is delivered in the form of weekly spreadsheets put together by an agency account manager. It seems crazy that marketers can’t fully take advantage of all the data produced by their digital marketing, but that is still very much the reality of 2015.
Thankfully, that dynamic is changing quickly. Data technology is rapidly offering a “people layer” of intelligence across all channels. Data coming into a central system can look at campaign performance across many dimensions, but the key is aggregating that data at the people level. How did a segment of “shopping cart abandoners” perform on display vs. video?
Marketers now operate under the new but valid assumption that they will be able to track performance in this way. They are starting to understand that every addressable media investment can create more than just sales – it can produce data that helps them get smarter about their media investments going forward.
It’s a great time to be a data-driven marketer.
[This post originally appeared in AdExchanger on 4.6.15]
A recent analyst report made an astute observation that all marketers should consider: It’s not about “digital marketing” anymore – it’s about marketing in a digital world. The nuance there is subtle, but the underlying truth is huge. The world has changed for marketers, and it’s more complicated than ever.
Most consumers spend more time on web-connected devices than television, creating a fragmented media landscape where attention is divided by multiple devices and thousands of addressable media outlets. For marketers, the old “AIDA” (attention, interest, desire and action) funnel persists, but fails in the face of the connected consumer.
When television, print and radio dominated, moving a consumer from product awareness to purchase had a fairly straightforward playbook. Today’s always-on, connected consumer is on a “customer journey,” interacting with a social media, review sites, pricing guides, blogs and chatting with friends to decide everything from small supermarket purchases to big investments like a new house or car.
Marketers want to be in the stream of the connected consumer and at key touch points on the customer journey. But, in order to understand the journey and be part of it, they must be able to map people across their devices. This is starting to be known as cross-device identity management (CDIM), and it is at the core of data-driven marketing.
In short, identity lies at the heart of successful people data activation.
Until very recently, managing online identity was largely about matching a customer’s online cookie with other cookies and CRM data, in order to ensure the desktop computer user was aligned with her digital footprint. Today, the identity landscape is highly varied, necessitating matching ID signals from several different browsers, device IDs from mobile phones and tablets, IDs from streaming devices and video game consoles and mobile app SDKs.
Matching a single user across their various connected devices is a challenge. Matching millions of users across multiple millions of devices is both a big data and data science challenge.
Real one-to-one marketing is only possible when the second party – the customer – is properly identified. This can be done using deterministic data, or information people volunteer about themselves, in a probabilistic manner, where the marketer guesses who the person is based on certain behavioral patterns and signals. Most digital marketing companies that offer identity management solutions take what data they have and use a proprietary algorithm to try and map device signals to users.
The effectiveness of device identity algorithms depends on two factors: the quality of the underlying deterministic data – the “truth set” – and its scale.
Data Quality Matters
There is data, and then there is data. The old software axiom of “garbage in, garbage out” certainly applies to cross-device user identity. Truly valuable deterministic data include things like age, gender and income data. In order to get such data, web publishers must offer their visitors a great deal of value and be trusted to hold such information securely. Therefore, large, trusted publishers – often with subscription paywalls – are able to collect highly valuable first-party user data.
Part of the quality equation also relates to the data’s ability to unlock cross-device signals. Does the site have users that are logged in across desktop, mobile phone and tablet? If so, those signals can be aggregated to determine that Sally Smith is the same person using several different devices. Publishers like The Wall Street Journal and The New York Times meet these criteria.
Scale Is Critical
In order to drive the best probabilistic user matches, algorithms need huge sets of data to learn from. In large data sets, even small statistical variances can yield surprising insights when tested repeatedly. The larger the set of deterministic data –the “truth” of identity – the better the machine is able to establish probability. A platform seeing several million unique users and their behavioral and technographic signatures may find similarities, but seeing billions of users will yield the minuscule differences that unlock the identity puzzle. Scale breeds precision, and precision counts when it comes to user identity.
As digital lives evolve beyond a few devices into more connected “things,” having a connected view of an individual is a top priority for marketers that want to enable the one-to-one relationship with consumers. Reliably mapping identity across devices opens up several possibilities.
Global Frequency Management: Marketers that leverage multiple execution platforms, including search, email, display, video and mobile, have the ability to limit frequency in each platform. That same user, however, looks like five different people without centralized identity management.
Many marketers don’t understand what ideal message frequency looks like at the start of a campaign, and most are serving ads far above the optimal effective frequency, resulting in large scale waste. Data management platforms can control segment membership across many different execution platforms and effectively cap user views at a “global” level, ensuring the user isn’t over-served in one channel and underserved in another.
Sequential Messaging: Another benefit of cross-device identity is that a user can be targeted with different ads based on where they are in the consumer journey. Knowing where a consumer is in an established conversion path or funnel is a critical part of creative decisioning. Optimizing the delivery of cross-channel messages at scale is what separates tactical digital marketers and enterprise-class digital companies that put people data at the heart of everything they do.
Customer Journey Modeling: Without connecting user identity in a centralized platform, understanding how disparate channels drive purchase intent is impossible. Today’s models bear the legacy of desktop performance metrics, such as last click, or have been engineered to favor display tactics, including first view. The true view of performance must involve all addressable channels, and even consider linear media investment that lacks deterministic data. This is challenging but all but impossible without cross-device identity management in place.
The ubiquity of personal technology has transformed today’s consumers into “digital natives” who seamlessly switch between devices, controlling the way they transmit and receive information. Marketers and publishers alike must adapt to a new reality that puts them in control of how editorial and advertising content is accessed. Delivering the right consumer experience is the new battleground for CMOs. Unlocking identity is the first step in winning the war.
[This post originally appeared in AdExchanger on 3.16.15]
Twenty years after the first banner ad, the programmatic media era has firmly taken hold. The Holy Grail for marketers is a map to the “consumer journey,” a circuitous route filled with multiple addressable customer touchpoints. With consumers spending more of their time on mobile devices – and interacting with brands like never before through social channels, review sites, pricing comparison sites and apps – how can marketers influence customers everywhere they encounter a brand?
It’s a tough nut to crack, but starting to become an achievable reality to companies dedicated to collecting, understanding and activating their data. Marketers are starting to turn towards data management platforms (DMP), which help them connect people with their various devices, develop granular audience segments, gain valuable insights and integrate with various platforms where they can activate that data. In addition to technology, marketers also have to configure their entire enterprises to align with the new data-driven realities on the ground.
The question is: Where do marketers turn for help with this challenging, enterprise-level transition?
Many argue that agencies cannot support the type of deep domain expertise needed for the complicated integrations, data science and modeling that has become an everyday issue in modern marketing. But should data management software selection and integration be the sole province of the Accentures and IBMs of the world, or is there room for agencies to play?
For lots of software companies, having an agency in between an advertiser and their marketing platform sounds like a problem to overcome, rather than a solution. Many ad tech sellers out there have lamented the process of the dreaded agency “lunch and learn” to develop a software capability “point of view” for a big client.
Yet, there are highly compelling ways agencies add value to the software selection process. The best agencies insert themselves into the data conversation and use their media and creative expertise to influence what DMPs marketers choose, as well as their role within the managed stack.
From Digital To Enterprise
It makes perfect sense that agencies are involved with data management. The first intersection of data and media added the “targeting” column to the digital RFP. Agencies have started to evolve beyond the Excel-based media planning process to start their plans with an audience persona that is developed in conjunction with their clients. Today, plans begin with audience data applied to as many channels as are reachable. Audience data has moved beyond digital to become universal.
Agencies have also been at the tip of the spear, both from an audience research standpoint (understanding where the most relevant audiences can be found across channels) and an activation standpoint (applying huge media budgets to supply partners). Since they are on the front lines of where media dollars are expressed, they often get the first practical look at where data impacts consumer engagement. During and after campaigns conclude, the agency also owns the analytics piece. How did this channel, partner and creative perform? Why?
Having formerly limited agencies to doing campaign development and execution, marketers are now turning to the collected expertise of their agency media and analytics teams and asking them to embed the culture of audience data into their larger organization. When it’s time to select the DMP—the internal machine that will drive the people-based marketing enterprise—the agency is naturally called upon.
Data Management Is About Ownership
Although a small portion of innovative marketers have begun leveraging DMP technology and taken media execution “in-house,” the vast majority stills relies on agencies and ad tech platform partners to operate their stacks through a managed services approach. Whether a marketer should own the capability to manage its own ad technology stack is a matter of choice, but data ownership shouldn’t be. Brands may not want to own the process of applying audience data to cross-channel media, but they absolutely must own their data.
Where Agencies Play in Data Management
The Initial Approach: Most agencies have experience leveraging marketers’ first-party data through retargeting on display advertising. In an initial DMP engagement, marketers will rely on their agencies to build effective audience personas, map those to available attributes that exist within the marketer’s taxonomy and apply the segments to existing addressable channels. Marketers can and should rely on past campaign insights, attribution reports and other data insights from their agencies when test-driving DMPs.
Connect the Dots: For most marketers, agencies have been the de-facto connector of their diverse systems. Media teams operate display, video and mobile DSPs, ad serving platforms, and attribution tools. Helping a marketer and their DMP partner tie these execution platforms together, understand audience data, and the performance data generated from campaigns is a critical part of a successful DMP implementation.
Operator: Last, but not least, is the agency as operator of the DMP. Marketers want their data safely protected in their own DMP, with strong governance rules around how first-party data is shared. They also need a hub for utilizing third-party data and integrating it with various execution and analytics platforms. Marketers may not want to operate the DMP themselves, though. Agencies can win by helping marketers wring the most value from their platforms.
Marketers have strong expertise in their products, markets and customer base – and should focus on their core strengths to grow. Agencies are great at finding audiences, building compelling creative and applying marketing investment dollars across channels, but are not necessarily the right stewards of others’ data.
Future success for agencies will come from helping marketers implement their data management strategy, align their data with their existing technology stack and return insights that drive ongoing results.
[This post originally appeared in AdExchanger on 2.2.15]
With companies like Kraft and Kellogg’s starting to leverage the programmatic pipes for equity advertising, we are starting to hear a lot of buzz about the potential for “programmatic branding,” or the use of ad tech pipes to drive upper-funnel consumer engagement.
It makes sense. Combine 20 years in online infrastructure investment with rapidly shifting consumer attention from linear to digital channels, and you have the perfect environment to test whether or not digital advertising can create “awareness” and “interest,” the first two pieces of the age old “AIDA” funnel.
The answer, put simply, is yes.
Online reach is considerably less expensive than linear reach, and we are starting to have the ability to reliably measure how that brand engagement is generated. Marketers don’t just want an “always-on” stream of brand advertising that comes with measurement – they also need it. With attention rapidly shifting from traditional channels, investments in linear television are starting to return fewer sales.
But most marketers are just starting to gain the digital competency to make programmatic branding a reality. That competency is called data management – the ability to segment, activate and analyze consumer audiences in a reliable way at scale.
The most fundamental problem with digital branding is that it is truly a one-to-one marketing exercise. If we dream of the “right message, right person, right time,” then matching a user with her devices is table stakes for programmatic branding. How do I know that Sally Smith on desktop is the same as Sally Smith on tablet?
Cross-device identity management is the key. Device IDs must be mapped to cookies, other mobile identifiers and Safari browser signals to get a sense of who’s who. Once you unlock user identity, many amazing things become possible.
Global Frequency Capping
One of the reasons programmatic branding has yet to gain serious ground with marketers is because of waste. This is both real, including all those wasted impressions due to invisible ads or robotic traffic, and perceived, such as impressions that are ineffective due to frequency issues.
Smart technology and market pricing solves the first problem, while data management solves the second. Assuming the marketer understands the ideal effective frequency of impressions per channel, or on a global basis, a DMP can manage how many impressions an individual sees by controlling segment membership in various platforms. Let’s say, for example, the ideal frequency for cereal advertising aimed at moms is 30 per day across channels. The advertiser knows showing fewer than 30 impressions reduces effectiveness, while more than 30 impressions has a negligible impact. Advertisers using multiple channels, such as direct-to-publisher, plus mobile, video and display DSPs, are likely overserving impressions in each channel and possibly underserving in key channels like video. Connecting user identity helps control global frequency and can save literally millions of dollars, while optimizing the effectiveness of cross-channel advertising.
If “right person” technology is enabled as above, the next logical step is to try and get to “right place and right time.” Data management can enable this holy grail of branding, helping marketers create relevance for consumers as they embark on the customer journey. What brand marketers have dreamed of is now possible and starting to happen.
Dad, in the auto-intender bucket, is exposed to a 15-second pre-roll ad before logging into his newspaper subscription on his tablet in the morning. The message is reinforced by more equity display ads he sees in the afternoon at work. And while checking messages on his mobile phone on the way home, he receives an offer for $500 off with a qualified test drive. After Dad hits the dealership and checks in through the CRM system, he receives an email thanking him for his visit and reminding him of the $500 coupon he earned.
These tactics are not possible without tying user identity and systems together. Doing so not only enables sequential messaging, but also the ability to test and measure different approaches through A/B testing.
How about attribution? It’s impossible to perform cross-channel attribution without knowing who saw what ad. At the end of the day, it’s really about the insights.
Procter & Gamble is famous for spending millions of dollars every year to understand the “moment of truth,” or why people choose Tide over another detergent. Although they know consumer segmentation and behavior better than anyone, even the biggest brand marketers struggle to gain quality insights from digital channels.
Data management is starting to make a more reliable view possible. Brand advertising is just another form of investment. Money is the input. The output is sales and, just as important, the data on what drove those sales. In the past, brand marketers relied on panel-based measurement to judge campaign effectiveness. Now, data management helps brands understand which channels drove results and how each contributed.
It is early days for truly reliable cross-channel attribution modeling, but we are finally starting to see the death of the “last-click” model. Smart marketers use data to author their own flexible attribution models, making sure all channels involved receive variable credit for driving the final action. In the near future, machine learning will help drive dynamic models, which flex over time as new signals are acquired. We will then start to see just how effective – or not – tactics like standard display advertising are for driving upper-funnel engagement.
Is 2015 the year for programmatic branding? For marketers that are leveraging data management to enable the best practices outlined above, the answer is yes. The more accurately marketers can map online user identity and understand results, the more investment will flow from linear to addressable channels.
[This post originally appeared on 1.4.2015 in AdExchanger]
In this increasingly cross-device world, marketers have been steadily losing the ability to connect with consumers in meaningful ways. Being a marketer has gone from three-martini lunches where you commit to a year’s worth of advertising in November to a constant hunt for new and existing customers along a multifaceted “customer journey” where the message is no longer controlled.
Consumers’ attention migrates from device to device, where they spread their limited attention among multiple applications. It’s become a technology game to try and track them down, and starting to become a big data game to serve them the “right message, at the right place, at the right time.”
Modern ad tech is supposed to be the marketer’s savior, helping him sort out how to migrate budgets from traditional media, such as TV, radio and print, to the addressable channels where people now spend all of their time. Marketers and their agencies need a technology “stack,” but they end up with a hot mess of different solutions, including various DSPs for multiple channels, content marketing software and ad servers.
Operating and managing all of them is possible, but laborious and difficult to do right. Worse still, these systems are nearly impossible to connect. Am I targeting the same consumer over and over through various channels? How to manage messaging, frequency and sequencing of ads?
Since all of these systems purport to connect marketers to customers on the audience level, the coin of the realm is data. It’s not just “audience data” but actual data on the individuals the marketer wants to target.
Marketing is now a people game.
Yet, in the cross-channel, evolving world of addressable media, connecting people to their various devices is difficult. You need to see a lot of user data, and you have to not only collect web-based event data, but also mobile data where cookies don’t exist. Deterministic data, such as a website’s registration data, can lay the foundation for identity. When blended with probabilistic data and modeled from user behavior and other signals, it becomes possible to find an individual.
Right now, the overlords of the people marketing game are platforms like Google, where people are happy to stay logged in to their email application on desktop, mobile and tablet, or Facebook, which knows everything because we are nice enough to tell them. Regular publishers may be lucky enough to have subscription users that log in to desktop and mobile devices, but most publishers don’t collect such data. Their ability to deliver true one-to-one marketing to their advertisers is limited to their ability to identify users.
This dynamic rapidly makes the big “walled gardens” of the Internet the only place big marketers can go to unlock the customer journey. That might work for Google and Facebook shareholders and employees, but it’s not good for anyone else. In our increasingly data-dependent world, not all marketers are comfortable borrowing the keys to user identity from platforms that sell their customers advertising. Soon, everyone will have to either pay a stiff toll to access such user data, or come up with innovation that enables a different way to unlock people-centric marketing.
What is needed is an independent “truth set” that advertisers can leverage to match their anonymous traffic with rich customer profiles, so they can actually start to unlock the coveted “360-degree view of the user.” Not only does a large truth set of users create better match rates with first-party data to improve targeting, but it also holds the key to making things like lookalike modeling and algorithmic optimization work. Put simply, the more data the machine has to work with, the more patterns it finds and the better it learns. In the case of user identity, the probabilistic models most DMPs deploy today are very similar. Their individual effectiveness depends on the underlying data they can leverage to do their jobs.
In the new cross-device reality: If you can’t leverage a huge data set to target users, it’s time to take your toys and go home. Little Johnny doesn’t use his desktop anymore.
Think about the three principle assets most companies have: their brand, their intellectual property and products and their customer data. Why should a company make a third of their internal value dependent upon a third party, whether or not they pledge “no evil?” Those that offer a “triple play” of mobile, cable television and phone services are also part of the few companies that can match a user across various devices. The problem? They all sell, or facilitate the sale of, lots of advertising. Marketers are not sure they want to depend on them for unlocking the puzzle of user identity.
Some of the greatest providers of audience data are independent publishers who, banded together, can create great scale and assemble a truth set as great as Facebook and Google. Maybe it’s time to create a data alliance that breaks the existing paradigm. The “give to get” proposition would be simple: Publishers contribute anonymized audience identity data to a central platform and get access to identity services as a participant. This syndicate could enable the deployment of a universal ID that helps marketers match consumers to their devices and create an alternative to the large walled gardens.
The real truth is that, without banding together, even great premium publishers will have a hard time unlocking the enigma of cross-device identity for marketers. Why not build a garden with your neighbors, rather than play in somebody else’s?
[This post was originally published in AdExchanger on 12.11.14]
Back in 2007, a company called TRAFFIQ started one of the first programmatic futures exchanges. The idea was simple: publishers committed blocks of premium inventory into the exchange at a stated price (say, a block of 500,000 homepage rectangle units in November at $8 CPM), and advertisers could construct packages of premium inventory at discounted prices by making future commitments. Basically, a better, faster way to buy digital guaranteed.
The idea never really took off. Publishers didn’t really understand how to value their inventory in the future, real-time enablement was just starting to take off, and advertisers and their agencies were deeply stuck in manual inventory procurement run by spreadsheets and fax machines. (TRAFFIQ went on to build some highly innovative workflow automation software, and is now a successful technology- enabled digital agency).
Almost eight years later, we are living in a fully programmatic world—but many of the benefits of programmatic futures have yet to come true. Today’s “programmatic” is still very focused on RTB, inventory pools are still murky, and technology’s ability to value publisher inventory still has a long way to go. What’s missing?
The problem with today’s programmatic RTB environment is that the “exchanges” aren’t really true exchanges like we have in the financial markets. Although you can liken online inventory to stocks, the comparison is tough to justify. Lacking agreed measurement, value continues to be in the eye of the beholder. More importantly, the procurement process is still driven more by the buyers than the sellers. Private exchanges are starting to make inroads in terms of creating valid counterparty transactions, but the RTB pipes have not been engineered to handle the key aspects of transactional workflow.
The biggest, fundamental problem with RTB is that it values inventory in a singular way. In the open market, a 30-year old male car intender costs the same whether you find him on Cars.com or Hotmail. Although tweaks in RTB with private transactions can enable premium inventory procurement, it’s not scalable. The right exchange should be able to value audience separately from everything else.
Another issue is the problem of valuing inventory over time. As a publisher with 30 days to go in my quarter, my homepage inventory may be worth $10CPM. But, the day before the quarter closes, that same inventory may be worth only $1CPM if I haven’t sold it yet. Today’s networks and exchanges enable publishers to set a solid floor price, but have trouble managing value dynamically. That’s because future publisher pricing is not being matched with visible demand. Ironically, the real-time nature of today’s exchanges actually limits a publisher’s ability to manage yield, because every impression is always chasing an immediate bid. A real futures exchange would enable a publisher to value inventory dynamically, so it matches the value set not by bids—but by buy orders in the system (real, stated demand for future inventory).
Although the demand side has it pretty good right now, a true programmatic futures exchange could be truly game changing. Yes, today’s exchanges are serious arbitrage machines. Because the buyer has access to the entire market, they have more information available to them to manage their investment. The problem is, in programmatic RTB, they are stuck with a two-tiered system: secure “premium” inventory through private exchanges and/or DealID functionality for branding and demand creation, and drive lower-funnel activity through performance-driven bidded buying in the wilds of the exchange. Ask any trading desk manager—it’s still really hard to get exactly what you want without going to guaranteed buys, cross-channel buying still requires multiple systems, and communicating the value of the “media investments” you are making to clients is near impossible, because everything is bought and measured differently. So, going from “media buying” to true “media investment” necessitates a true programmatic future exchange, akin to NASDAQ or the NYSE.
In such an exchange, publishers would be able to value their inventory by utilizing a combination of their existing rate cards and product catalogs (for selling advance contracts), and data from buy orders in the market itself. Just like in the stock market, prices would fluctuate based on the spread between bid and ask pricing—and the contract date. Publishers could therefore execute any type of guaranteed buy in such a system (direct sales) as well as have the exchange handle direct deals (“programmatic direct” and “private market”). This is because such an exchange would manage matchmaking, not the execution piece. This is critical. Today, we are watching systems built from the ground up to deliver ads try and go in reverse to manage the process of buying them. As we have seen, the rise of “automated guaranteed” platforms suggests that RTB is not quite cut out for the job.
Why would the demand side want a true programmatic futures exchange? First of all, a true futures exchange treats media as a true commodity—and makes it tradable. The beauty of a commodities exchange is that, once you own a future contract for pork bellies, you can sell it. In digital media, once you have bought a bunch of 300x250s in Appnexus, you are stuck with them. Arbitrage is not the same as futures trading in a regulated market. A true programmatic futures exchange for media would actually enable well-heeled buyers to leverage their scale to consolidate positions in media, and resell them in bulk (or in chunks). Think about that. Imagine GroupM buying the entire Q4 consumer electronics inventory in February. What would that be worth to another agency representing Sony as the holidays approached?
The bottom line is that, despite the power of RTB pipes, we are a long way away from seeing the platforms where addressable media will be traded in the future. Eight years ago, my bet was on a programmatic futures exchange, and I am still long.
[This post originally appeared on 11/7/2014 in AdExchanger]
Not only are mobile devices nearing ubiquity – but research shows they’re owned by more than nine out of 10 earthlings – smartphones are nearing ubiquity in the developed world, too, with 56% penetration. People are on mobile all the time, and more than half of them use the mobile device as the primary way they access the Internet. At 1.8 hours a day, media consumption on mobile devices now surpasses both television (1.5 hours) and desktop (1.6 hours). If marketers would match their investment in mobile advertising, now at just 4% of media budgets, with the amount of time we spend there – 20% of our time – a lot of people would make a lot of money.
Not only is mobile the fastest growing, most exciting place to be in advertising right now, it’s where the hugest opportunities are. Did you know that 44% of Fortune 100 companies don’t have a mobile-optimized website? That is insane.
Mobile is now “first among equals” when it comes to marketing channels, and every advertiser should think that way when they start putting their plans together for 2015.
Everything Has Already Changed Forever
Proctor and Gamble loves to talk about “the moment of truth,” which is when a consumer stands in front of a store shelf and chooses between two products. Why did they buy Tide detergent instead of Surf? There are a lot of emotional connections between brands and people, whether you are buying soap or making a decision on your next high-ticket item, like a dishwasher. Although brands still need to make an emotional connection, there is an entirely new dynamic driving the many different “moments of truth” we have every day.
Today, we also have what Google calls the “zero moment of truth,” or the fact that every consumer with a smartphone can find out when they are standing in front of that shelf every good and bad thing ever written about a product. So, as a marketer, how do you handle that every one of your customers has the acquired knowledge of the universe in their hands at all times? They can get all the reviews, see all the coupons and deals, and ask their friends before making a decision. That’s going to keep us all busy for years to come in ad tech.
Stop Saying ‘Funnel’
Mobile killed the sales funnel. Somehow, over the last year or so, the AIDA funnel died a quiet death after 116 years. The idea of driving potential customers through a process of “attention, interest, desire and action” has been replaced with something we now call the “customer journey” – a circuitous route, where marketers must be in control, or quickly able to react to, all kinds of touchpoints.
If that sounds confusing, you are not alone. Most marketers struggle with the sheer data expertise needed to create and build sequential messages that follow a consumer from television to tablet to smartphone as they learn more about brands or products. In 2014, the customer journey is mostly handled through retargeting on as many devices as possible, but the lack of a universal ID makes telling a good story across screens pretty tough.
If you want to be able to do that as a marketer, or help marketers do that on your audience as a publisher, then it’s all about the data.
The Tom Cruise Thing
At every mobile conference, someone usually shows a slide with Tom Cruise from “Minority Report.” In the 2002 movie – released more than a decade ago! – we saw future Tom walking by interactive DOOH billboards for Lexus and the Gap, receiving all kinds of personalized offers after being retina scanned. Everything in that movie now exists, including facial identification, in-store beacons, real-time creative delivery, geolocation, RFID and personalization.
We are living in a “Minority Report” world, and sooner or later, we are going to figure out how to put all of the pieces together at scale. Was that a mobile ad that Tom Cruise saw, or will we be calling it something else? Does it matter?
This post appeared in AdExchanger on 9.18.14]
“Basically, we make technology that helps marketers buy different kinds of banner ads,” I told him.
“You mean the kind of annoying pop-up ads that everyone hates?” he asked.
His look of profound disappointment said it all. I explained that the kind of work we do wasn’t just about populating the Internet with the “Lose five pounds with one stupid trick” type of banner. But even though we are getting a lot right, my explanations eventually started sounding pretty weak.
I have been working in this business since 1995. Aside from doing some ad implementation testing, I have probably clicked on about a dozen banner ads in as many years. Today’s robust, real-time ad tech “stack” has been purpose-built to optimize the delivery of the kind of banner ads most people already hate: standardized IAB units, retargeted ads, auto-play video pre-roll units and even the dreaded pop-up and pop-under.
Publishers without robust direct sales options depend on networks and exchanges to monetize the endless streams of traffic they create, and they happily collect their $1.10 eCPM (cost per mille) payments. Advertisers looking for cheap reach and performance plumb the depths of such inventory to find the rare conversion, and hope they are getting what they pay for rather than a shady “last view” attributed banner.
Today, the highest and best use of the standardized banner has enabled marketers to leverage their first-party data to bombard site visitors with retargeted ads – an effective tactic, since they are essentially paying to accelerate a conversion that has a great chance of happening on its own.
As an industry, it seems pretty clear that we will look back on this era in digital ad technology and see how primitive it was. Have we built a trillion-dollar real-time ad serving machine for punch-the-monkey ads, or have we really innovated and created disruption?
RTB Is Dead, Long Live RTB
The recent acquisition of [X+1] by Rocket Fuel is a great sign for our industry. It basically validates the idea that, for programmatic RTB to be effective, real data science must inform targeting. [X+1] is one of the best at cross-channel targeting, and they have already started to figure out the cross-channel attribution puzzle. An everlasting always-on stream of RTB banners for branding and retargeting might prove to be a hugely important part of unlocking a broader multi-channel strategy – if the data can dictate it. If data management platform technology can be leveraged to truly optimize addressable marketing, then RTB will survive and thrive. With consumers always on the move, and every form of media starting to be addressable, real-time programmatic will be something marketers have permanently switched on, and we’ll see the true value of the pipes we have created.
How about inventory that is relatively standard, but a bit nicer than that found within the exchange environment? Transacting on this tier of inventory works quite nicely with all kinds of one-to-one connections within RTB, and buyers and sellers are quickly leveraging the pipes to make private marketplace deals.
If I am a quality financial publisher, why wouldn’t I sell within RTB for $8 CPMs, rather than pay a $200,000 salesperson to sell at $12 CPMs? The math just makes sense. Delivering higher tiers of inventory at scale to private buyers is a great use of RTB, but not a panacea for overall inefficiency in media procurement. But, we have seen those RTB pipes service entire new classes of inventory, and start to appeal to brand marketers.
The problem with getting really good inventory has always been the difficulty understanding rates and availability. That’s why the RFP exists today, and isn’t going away anytime soon. Publishers will always want full control over the really good stuff. Because they know their inventory better than any algorithm, there will always be a need for human control and creativity. Big, custom sponsorships and custom-curated native executions will only increase over time, as more television and print budgets shift into addressable digital. You just can’t automate those deals. Marketers and agencies will demand programmatic efficiency to compress an expensive, 42-step process for securing guaranteed inventory. This is one area that programmatic RTB has not been built to handle (these deals are neither “real time” nor “bidded”), but we are seeing real innovation from a number of companies trying to bring programmatic efficiency to guaranteed deals.
It’s hard to explain everything that we are getting right to a 14-year-old who spends more time on mobile apps than in an Internet browser. His assessment, in surfing the desktop Internet, is probably right – it looks like a lot of weight loss ads and sneaker retargeting. But, it’s still early days nearly 20 years after the first banner ad was served.
The New Mobile Display Ecosystem, an Econsultancy report published in association with OpenX, surveyed over 20 leaders in mobile marketing, publishing, and technology to find out the latest trends in mobile advertising, and what the future might hold. Chris O’Hara, the report’s author, answers some of our questions about the research.
So, is 2014 finally the “year of mobile?”
Well, this “year of mobile” has been coming for some time, but our survey panelists are starting to feel that mobile has finally emerged as a player on the overall advertising scene. There are still huge discrepancies between time spent on mobile devices (a lot) and ad spending in the sector (relatively small). According to some research, people spend more than 20% of their time on mobile devices, but ad spending is at 4%. That’s a multiple-billion dollar opportunity.
What is keeping mobile ad spend from growing?
Our research showed that a large issue for advertisers was mobile creative—specifically, the lack thereof. The units are mostly small and prone to “fat thumb” clicks in browsers, and most of the in-app ads were fairly plain “install ads.” Not great for brand building or telling a story. Also, it is still somewhat difficult to get to scale without a “mobile cookie,” or persistent ID. That’s changing now, but without having statistical identification available at scale across many systems, only the large players like Google and Apple can effectively identify users across devices. That’s a challenge.
Who is most impacted by the growth in smartphones in the ad ecosystem?
For me, the retailers and product folks have it the worst. Soon enough, smartphones will reach 50% penetration. That means every other person will have the combined knowledge of the entire world right at their fingertips. What that means for retailers is what Google is starting to call the “Zero Moment of Truth,” an adaptation of an old P&G saying. What it means is that, when a consumer is standing in front of a product with their smartphone, they can find out every single thing—good and bad—about a product that’s ever been written with the click of a button. And, of course, the right price to pay. That’s an incredible dynamic.
What’s the most shocking thing you learned while researching the report?
44% of Fortune 100 companies don’t have a mobile-enabled website. That’s pretty scary, considering the “Zero Moment” dynamic, but it’s also a huge opportunity.
You asked panelists what “Mobile First” really means. What did they say?
Everyone agreed that both marketers and publishers have to start with mobile, because that is where people are spending their time. You can’t ignore mobile, or just make an HTML5 site and call it a day. If you are building a new website, launching a new product, you must do that with a “mobile first” approach, and try to leverage the unique touchpoints the channel offers to consumers. That’s the obvious part. I was pretty surprised to see how passionate people were about the idea of “mobile first.” Many think mobile is the biggest single opportunity out there for business. Suffice it to say, it is ignored at your peril.
What about the creative problem? How are marketers taking advantage of the unique data and form factors at play in mobile?
Native is certainly a big focus. The IAB has identified about 6 different categories of native advertising, many of which apply to mobile devices. OpenX has recently launched a new mobile exchange for accessing native mobile units programmatically. Native units tend to leverage more of the mobile form factor, which is great. Marketers are still struggling to take advantage of all the great data that can be used (altitude, motion, facial recognition, biological data, activity, etc), but some really cool executions are starting to be deployed. We are essentially ready for our Tom Cruise “Minority Report” moment from 2002, with ads that can follow us around and talk to us personally based on our situation.
What are the biggest threats?
Although everyone I talked to loves their “triple play” deal, and Apple or Android phone, nobody wants telecoms or big technology companies to be the only ones with cross-device targeting capability. All thr panelists were interested in a more diverse ecosystem, more akin to display advertising, where the “cookie” (albeit controversial) has enabled real audience targeting at scale. Marketers need to tell a sequential story, as the consumer moves from device to device. That’s only possible when you can link users to all of their devices, and that’s hard to do now unless you are Verizon.
Any final thoughts?
I think video is the way we are going to see mobile eat into established marketing budgets. The ads play amazingly well on new larger-screen phones and HD tablets. There are great creatives already established (the 10, 55, and 30 second spot ad), and you can actually tell stories with video, which is what marketers want to do. Videos are also the ultimate “native” ad. Video is where the action is right now, but other native formats suited to mobile form factors will follow.
This originally appeared on the Econsultancy blog on 8/5/14]
Digital agencies used to get paid for unpacking an incredibly complicated digital landscape for marketers. Faced with all kinds of new marketing opportunities, advertisers turned to savvy digital agencies to figure out where to spend their money, and how much of it to dedicate to display, mobile and social channels.
The dingy little secret was that the agencies didn’t really plan much of anything. The way it worked was that agency planners would make an Excel template, create an RFP document, instruct the media owners to send back all kinds of creative ideas and fill out the media plan template. RFPs sent publisher teams spinning into action, churning out exciting-looking PowerPoints with screenshots and suggested spending levels.
Not much of this was scientific. Publishers often promised more inventory than could be delivered, knowing they would never get the full budget allocation. Agencies asked for various “budget levels,” knowing they would allocate only $50,000 per publisher – but asking to see $200,000 plans to get a better sense of where CPMs might be negotiated. At the end of the day, the agencies would pick the winners and losers, usually the five publishers on the last plan, plus a few “challengers” or new ideas to impress the client with “innovation.” Once the plan went live, publishers could count on a quick cancellation or massive change to the contracted plan. Nothing ever seemed written in stone once the first impression was served.
Sounds pretty lame, right? Sadly, a lot of media is still planned this way. But, thanks to all kinds of programmatic innovation, times are rapidly changing and digital agencies are going to have to find out how to change with it.
In the old paradigm, agencies largely provided value by dealing with the intricacies of negotiating with vendors, moving data from plans to ad servers and billing systems and keeping clients in the loop on how their digital media “investments” were performing. Optimization was largely defined as cancelling a bad deal and re-allocating budget into a better one.
Today’s ad technology has given marketers and their agencies a lot more knobs and buttons to push. We are rapidly seeing a shift away from manual, Excel-based processes to nimble, web-based planning technology, driven by centralized data.
There are no spreadsheets inside of MediaMath or AppNexus. Publishers don’t offer PowerPoints in iSocket or AdSlot. And agencies are pushing legacy media-buying systems like MediaOcean and Strata to adapt to a digital world without spreadsheets and fax machines. A host of new, web-based planning and buying systems (like Bionic!) are also starting to disrupt the status quo, as agencies try and reconcile the old ways of buying media with a world in which billions of ad impressions are available through interfaces and big clients like P&G say they are going to buy up to 70% of ads programmatically.
Recently, a big European group of publishers introduced an RFP to have their entire digital inventory catalogued and made available through “programmatic direct” technology. Publishers want to give advertisers the efficiency and access they crave but have complete control over pricing and availability. That’s where the world is heading.
So what happens to an agency whose sole digital expertise consists of sending out Excel templates for publishers to fill out with pricing and avails? Sounds like the value they have been providing – lots of manual horsepower to help with complicated workflow – is going to become completely irrelevant. You can buy all the social media you want through easy-to-use interfaces.
It’s easy to hire a few smart “traders” and give them access to a DSP and gain access to the universe of inventory available in programmatic RTB. And now it’s increasingly easy to negotiate premium inventory deals inside programmatic platforms and secure those guaranteed impressions. A number of big marketers have decided it’s so easy that they are starting to do it themselves by bringing digital marketing in-house.
Digital media agencies’ legacy business models are expiring faster than a Madison Avenue parking meter. What should innovative agencies be doing to change and continue to provide real value to their clients?
- Planning: “Planning” is not planning anymore. It’s investment management. Even though there are new ways to procure the media, your clients still need to know how it’s performing and moving the needle for their business. Figure out how to measure beyond clicks and common CPA metrics and try to get inside your clients’ real budget numbers. Are you gaining access to the client’s P&L and first-party data so you can help them measure by more important metrics, such as net new customers?
- Teaching: Just because desktop display and social ads are commoditized doesn’t mean clients don’t need to understand the latest ways to rise above the noise. Are you schooling your clients on nascent native mobile opportunities or the latest ways to leverage RTB video to enable branding at scale? These are ideas that come with the help of vendors and publishers, but agencies need to stop collating others’ ideas and start helping vendors translate their opportunities into the framework of the client’s business. That is where the right digital agency can provide value.
- Doing: The manual, spreadsheet-driven world of “22-year-old media planners” where labor, rather than strategy, was at a premium are over. But, in a programmatic world, execution – the “doing” – is more important than ever. Reallocating budgets to match performance cannot be totally algorithm-driven when spending is across multiple channels in systems that do not speak to each other. Agencies are perfectly positioned to be in the middle of dozens of systems, reconciling spending and performance against both long- and short-term client goals. That’s a job that can only be done by people.
The irony of today is that lot of systems are starting to make digital media planning less complicated from a transactional and workflow standpoint but the overall digital landscape is more complicated to navigate than ever. The digital media agencies that survive must change the way they plan, teach their clients and execute in order to survive and thrive.
[This post originally appeared in AdExchanger on 7.25.14]
Everything seems to be generating data nowadays: mobile devices, e-commerce transactions, Web browsing.
Savvy marketers use data mining, data visualization, text analytics, and forecasting to make more effective decisions and reach customers. But the savviest among them are innovating with fresh types of data—and attracting new business as a result.
Sensing Opportunity for an Upsell
“The data that devices collect are going to add all kinds of context to advertising,” says Chris O’Hara, cofounder of Bionic Advertising Systems, a digital advertising service. Marketers can know exactly where potential consumers are, the current time and temperature, and which of the consumer’s friends are nearby.
When might such factors come into play? O’Hara gives the example of sensors in grocery stores that can detect the items shoppers take off the shelves. That data, run through huge databases, enable marketers to instantly suggest—via tech such as smartphones or electronic shelf displays—other products for shoppers to add to their carts.
More and more, geography will help marketers zero in on demographics, says Kevin Lee, CEO of online advertising and marketing firm Didit. “Geotargeting is a great way to market not only at the hyper-local business level but also for national marketers looking to target specific demographic and psychographic groups,” he says.
Marketers have experimented for years with mobile geolocation-centered campaigns, primarily using couponing. However, since research shows that a whopping 72% of consumers say they’ll respond to sales calls-to-action within sight of the retailer, there are plenty more location-based opportunities that encourage customer loyalty, such as special gifts, alerts to flash sales and early access.
Cooking a Data Stew
With the evolution in data analytics, marketers can now mix different types of data to glean new insights. David L. Smith, CEO of media agency Mediasmith, sees this as the coming of age of the data management platform: tools that integrate data from several sources, including customer information, website data and digital advertising input. All of it serves to improve messaging.
“Messages that come from ad campaigns, direct mail and other communications to the consumer can be coordinated,” Smith says, “so that the consumer is always getting relevant information—not just standard communications.”
Collecting Data—While Respecting Customer Privacy and Security
All these data-driven trends can bring benefits to the consumer and improve marketing efficiency. But they also raise privacy and security issues—to which marketers are giving serious attention. “Privacy is going to remain a constant fear in the consumer’s heart,” says Michael Hardin, dean at the University of Alabama’s Culverhouse College of Commerce. “A lot of companies are going to be struggling mightily to deal with that.”
Smart marketers will learn how to walk this fine line and mine significant value from relatively little personal information, says O’Hara. One company strikes this balance with one of its products, an activity-tracker wristband: With just a little personal data input from its user, the wristband gives them athletic performance feedback.
These new technologies are changing the world of marketing—especially given the speed at which data are arriving, says Hardin. Shrewd marketers are contemplating how best to react in a way that benefits their companies.
[This post originally appeared on 6/16 in WSJ]
There has been a lot of talk about the pervasive amount of click fraud and bot traffic happening in digital. Marketers are reportedly spending anywhere from 30% to 70% of their digital budgets on fake impressions and clicks, and an entire cottage industry is cropping up to help marketers combat fraud and try and protect their digital marketing investments.
Some people claim that price of fraud is already built into the programmatic RTB ecosystem. Marketers are using programmatic RTB for direct marketing, and they are measuring sales using CPA metrics. If they are paying $100 per verified acquisition, should they care whether it takes 10 million or 20 million impressions to produce a conversion? Some say that they don’t, and take the view that they only pay for results, justified by their backend conversion metrics which take media cost into consideration.
I hope this is not the case. Ignoring fraud with these justifications is what ultimately may kill the digital advertising business before we ever get to scale.
Another big problem is faulty, fraud-like attribution. Let’s take the case of the big programmatic marketing platform that has been getting great conversions for their customers. Marketers look at the results of such platforms and think that the technology has managed to effectively separate the wheat from the chaff in popular ad exchanges and find the “sweet spot” of cookie targeting that converts. But, dig a little deeper and you notice that many of the conversions are happening on webmail subdomains (mail.yahoo.com). In other words, the platform is getting last-view attribution from successful e-mail marketing. This is a more subtle case of fraud…but really more of a tax on digital ignorance for marketers. But again, the marketer sees this channel producing results that align with his CPA goals. Did the conversions get attributed correctly? Maybe not, but those questions get overlooked when the blended CPA is on target.
Cookie bombing and other types of fraud are just as likely to limit digital advertising to performance budgets, and keep real growth at bay.
If we are being honest with ourselves, we must admit that there doesn’t seem to be a ton of desire to solve these inherent problems in programmatic RTB. There are too many people making too much money to want to fix it. And it’s going to destroy programmatic RTB as we know it. Who benefits from the current scenario?
- Publishers: Most publishers benefit greatly from the programmatic RTB revenue stream. Big publishers “fill” their long tail inventory with ads. Mid-sized publishers without large direct sales teams depend greatly on network and programmatic fill for their revenue. Long tail pubs are fully committed to their AdSense checks for survival. A lot of publishers’ Comscore numbers are a lot bigger than they should be, thanks to cheap inventory of unknown provenance.
- AdTech: Every vendor in programmatic RTB benefits from inventory flowing through their pipes. Most charge on a percentage-of-spend, which means they might sacrifice 50% of their revenue if they had to stop charging for fraudulent impressions. New fraud detection and measurement firms are also profiting (albeit in a virtuous way).
- Agencies: What would today’s big agencies do without the ability to leverage programmatic RTB to arbitrage inventory, or charge a premium for “unpacking the ad tech space” for their clients? The new programmatic landscape has been a boon to smart, nimble agencies that have built, bought, or leveraged ad technology to pivot their dying media businesses. How eager are agencies to expose the fundamental flaws within the programmatic RTB ecosystem?
The biggest loser in the entire room is the poor marketer, who ultimately pays the bills. But it’s easy to turn a blind eye, because the numbers look good. But how long will big marketers confuse true marketing success with today’s flawed digital attribution metrics? Marketers are starting to think about real measurement frameworks (net new customers), rather than CPA metrics. They are also keenly interested using brand messages to interact with their customers across screens. And they won’t be using CPA to measure brand growth.
So why do marketers continue to leverage programmatic RTB despite the inherent risk of fraud and current limitations for brand advertising? To paraphrase Clear Channel’s Bob Pittman at the recent IAB annual meeting, “Given a choice between quality and convenience, convenience always wins.”
The biggest question lately is whether or not we can make it as convenient (and cheap) to buy guaranteed media at scale. Seeing this opportunity, a lot of players in programmatic RTB are looking hard at the money being spent on guaranteed media (the “transactional RFP” channel), and trying to add new “programmatic direct” tools to their arsenals. RTB players know that brands are still uncomfortable executing brand campaigns in the wilds of the open exchange, and they know truly premium inventory won’t be available unless publishers have more granular control over pricing, availability, and partner selection. Put more simply, the lion’s share of digital money still gets transacted manually, with paper insertion orders, and successful automation means a big piece of the action.
Providing a layer of automation for direct deals helps with fraud (guaranteed deals, by their nature, offer inventory transparency), and adds the ability to scale within higher classes of inventory.
Marketers are actually looking forward to having their agencies leverage new technology to secure quality digital placements. Whether these innovations come from tweaking existing programmatic RTB technology (private exchanges) or from new, API-driven “programmatic direct” providers doesn’t matter to them. They need to execute cross-channel digital campaigns at scale, and those campaigns (if they are for brand purposes) cannot contain fraud.
Does this spell the end of programmatic RTB? Nope. I think there will always be exchanges and technologies that let direct marketers plumb the depths of the Web to drive online sales. Ten years ago, folks were writing about the death of shady affiliate, click, and CPA networks—but they are still around. But, will today’s programmatic RTB business have to fundamental transform to win brand dollars? Yes, and the path to success is what we have been calling programmatic direct. It will be interesting to see the various technology executions of programmatic direct, as they form the gateway for branding to flourish online.
I was recently at a Digital Marketing Association awards dinner where data legend Charles Stryker was being honored. After accepting his award, he told a famous story about data that every digital marketer should know.
A long time ago, the US Postal Service discovered they were paying a ton of money to deliver mail to deceased people. Charles was hired to help them get a handle on their records and create a sort of “Do Not Mail” list. Part of doing the work involved considerable A/B testing to ensure he was making the correct assumptions about the data. Direct response mailers were being sent to groups of dead people and similar groups of folks who were still alive. Something astonishing happened when the results came in.
The dead people responded at nearly twice the rate of the living.
Of course everyone at the dinner (about a hundred senior direct marketing executives) laughed uproariously. They have seen all kinds of unpredictable results with direct marketing. In today’s digital age, marketing is moving faster than ever. The velocity of data is increasing in orders of magnitude, and attribution is going to get even trickier.
What happened with the “dead” people was pretty interesting. It turns out that the successful mailers went to households where the husband of the family died, and the elderly spouses were taking great care to go through and read the mail of their deceased partners. The wives wanted to make sure there was nothing important in those letters—and probably were connecting with their husbands through that simple, daily task. Those widows made a great mailing list “select” since they actually opened and read the mail!
In today’s digital marketing, where we seem increasingly dependent on algorithms and attribution models for targeting and measurement, I wonder if we are too deep in the weeds. Are we forgetting the real, human element of marketing? Do we really understand how success and failure happen with our campaigns? At a recent iMedia agency conference, a lot of the talk was about trying not to forget that advertising is first and foremost about storytelling. Leading with emotion is so important. The marketer has to make an emotional connection with his audience and get them to care.
That struck me when watching a joint town hall workshop with Google and Kellogg’s about dynamic creative. Yes, changing the background color or “call to action” on a 300×250 ad in real time can bump the lift of a campaign incrementally—but are we tweaking a broken process?
Can we really tell great stories on standardized banner ads?
With the rapid rise of programmatic, a lot of platforms and data companies are fully committed to a standardized industry where scale is king. Display, video, and mobile—biddable and accessible at full scale—is the mandate. Kellogg’s wants inexpensive access to a large electronic palette where Frosted Flakes ads can be constantly tweaked to get optimal performance. Nothing wrong with that. American Express recently announced an “100%“programmatic” initiative for digital marketing. Why not? Both companies spend tons of money on TV, and optimizing the bottom of the funnel makes complete and utter sense.
But that’s all we are talking about: Optimizing the bottom of the funnel with standardized ads. Sorry, but we are not creating new customers with dynamic 300×250 ads that get a .05% click-through rate. If you are in this business, working for a venture backed startup or newly public adtech company whose value proposition is around driving audience targeting at scale, then you are not “creating stories” online.
As an industry, we need to create digital campaigns that get people to “open the mail.” This is incredibly hard to do with standard display banners, today’s woeful “native” executions, and interruptive social ads. Video has promise, but scale still eludes marketers, and low video completion rates erode available reach considerably.
So, how do you leverage programmatic technology to get great creative out at scale? The only real answer is to automate the workflow behind securing premium inventory and custom programs. That’s where the promise of programmatic direct comes in. Marketers want great ideas from publishers, access to the best inventory they have, and non-standardized units. They just don’t want to pay 10% of their media budgets for planners to cut and paste data into spreadsheets.
Innovation in the space is not just limited to programmatic direct companies with API connections into the publisher side (iSocket, ShinyAds, AdSlot) and workflow automation players (Centro, MediaOcean, and Bionic Advertising)—but also includes RTB players like Rubicon and MediaMath who are building new automation capabilities to augment their RTB stacks. In other words, it’s all about automating the deal right now.
Do they want access to evergreen programmatic campaigns that drive their most likely customers through the bottom of the sales funnel? Of course, and that’s a great job for programmatic RTB. But it does not, cannot, and will never replace the kind of media you can secure through a guaranteed transaction. Also, speaking of dead people responding better—that kind of sounds familiar. In programmatic RTB, some of the best click-through rates come from the dead—fake visitors created by robots.
That said, I think more and more digital advertising will go programmatic, and that programmatic RTB will command the lion’s share of performance budgets. But, when it comes to building brands, bringing automation to the process of securing quality inventory will win.
[This post originally appeared in AdExchanger on 5.13.14]
Despite years of online targeting, the idea of having a complete, holistic “360 degree view” of the consumer has been somewhat of a unicorn. Today’s new DMP landscape and cross-device identification technologies are starting to come close, but they are missing a key piece of the puzzle: the ability to incorporate key social affinities.
In the nearby chart, you can see that online consumers tell us all about themselves in a number of ways:
Viewing Affinities: Where they go online and what they like to look at provides strong signals of what they are interested in. Nielsen, comScore, Arbitron and others have great viewership/listenership data that is strong on demographics, so we can get a great sense of the type of folks a certain website or show attracts. This is great, but brands still struggle to align demographic qualities perfectly with brand engagement. 34 year old men should like ESPN, but they could easily love Cooking.com more.
Buying Affinities: What about a person’s buying habits? Kantar Retail, OwnerIQ, and Claritas data all tell us in great detail what people shop for and own—but they lack information on why people buy the stuff they do. What gets folks staring at a shelf to “The Moment of Truth” (in P&G parlance) when they decide to make a purchase? The buying data alone cannot tell us.
Conversational Affinity: What about what people talk about online? Radian6 (Salesforce), Crimson Hexagon, and others really dig into social conversations and can provide tons of data that brands can use to get a general sense of sentiment. But this data, alone, lacks the lens of behavior to give it actionable context.
Social Behavioral Affinity: Finally, what about the actions people take in social environments? What if we could measure not just what people “like” or “follow” online, but what they actually do (like post a video, tweet a hashtag, or engage with a fan page)? That data not only covers multiple facets of consumer affinity, but also gives a more holistic view of what the consumer is engaged with.
Adding social affinity data to the mix to understand a consumer can be a powerful way to understand how brands relate to the many things people spend their time with (celebrities, teams, books, websites, musicians, etc.). Aligning this data with viewing, buying, and conversational data gets you as close as possible to that holistic view.
Let’s take an example of actionable social affinity in play. Say Whole Foods is looking for a new celebrity to use in television and online video ads. Conventional practice would be to engage with a research firm who would employ the “Q Score” model to measure which celebrity had the most consumer appeal and recognition. This attitudinal data is derived from surveys, some with large enough sample sizes to offer validity, but it is still “soft data.”
Looking through the lens of social data, you might also measure forward affinity: how many social fans of Whole Foods expressed a Facebook “like” for Beyonce, or followed her account on Twitter? This measurement has some value, but fails at delivering relevance because of the scale effect. In other words, I like Beyonce, so does my wife, and so does my daughter . . . along with many millions of other fans—so many that it’s hard to differentiate them. The more popular something is, the broader appeal and less targetability that attribute has.
So, how do you make social affinity data relevant to get a broader, more holistic, understanding of the consumer?
Obviously, both Q Score and forward affinity can be highly valuable. But when mixing viewing, buying, and listening with real social affinity data, much more becomes possible. The real power of this data comes out when you measure two things against one another. Sree Nagarajan, CEO of Affinity Answers, explained this mutual affinity concept to me recently:
“In order for the engagement to be truly effective, it needs to be measured from both sides (mutual engagement). The parallel is a real-world relationship. It’s not enough for me to like you, but you have to like me for us to have a relationship. Mapped to the brand affinity world, it’s not enough for Whole Foods fans to engage with Beyonce; enough Beyonce fans have to engage with Whole Foods (more than the population average on both sides) to make this relationship truly meaningful and thus actionable. When true engagement is married with such mutual engagement, the result is intelligence that filters out the noise in social networks to surface meaningful relationships.”
As an example, this approach was recently employed by Pepsi to choose Nicki Minaj as their spokesperson over several other well-known celebrities.
What else can social affinity data do?
- Brands can use social affinity data to decide what content or sponsorships to produce for their users. Looking at their users’ mutual affinity between the brand and music, for example, might suggest which bands to sponsor and blog about.
- A publisher’s ad sales team can use such data to understand the mutual affinity between itself and different brands. A highly correlated affinity between activated social visitors to GourmetAds’ Facebook page and those who post on Capital One’s Facebook page may suggest a previously unknown sales opportunity. The publisher can now prove that his audience has a positive predisposition towards the brand, which can yield higher conversions in an acquisition campaign.
- What about media buying? Understanding the social affinity of fans for a television show can produce powerful actionable insights. As an example, understanding that fans of “Teen Wolf” spend more time on Twitter than Facebook will instruct the show’s marketing team to increase tweets—and post more questions that lead to increased retweets and replies. Conversely, an Adult Swim show may have more Facebook commenters, leading the marketer to amplify the effect of existing “likes” by purchasing sponsored posts.
- Keyword buying is also interesting. Probing the mutual affinities between brands and celebrities, shows, music acts, and more can yield long tail suggested keyword targets for Google, Bing/Yahoo, and Facebook that are less expensive and provide more reach than those that are automatically suggested. As an example, when “Beavis and Butthead” re-launched on MTV, Google suggested keywords for an SEM campaign such as “Mike Judge” (the show’s creator) and “animated show.” Social affinity data suggested that socially activated Beavis fans also loved “Breaking Bad.” Guess what? Nobody else was bidding on that keyword, and that meant more reach, relevance, and results.
I believe that understanding social affinity data is the missing piece of the “360 degree view” puzzle. Adding this powerful data to online viewing, buying, and social listening data can open up new ways to understand consumer behavior. Ultimately, this type of data can be used to generate results (and measure them) in online branding campaigns that have thus far been elusive.
Want a full view of the people who are predisposed to love your brand? Understand what you both mutually care about through social affinities—and measure it.
[This post originally appeared in AdExchanger on 4.14.14]
Even though programmatic RTB has seen the lion’s share of venture capital funding and an enormous amount of innovation, RTB buying only accounts for 20%-30% of all digital media dollars. The real money still flows through the direct buying process, with agencies spending up to 400 hours and $50,000 to create the typical campaign, and publishers burning through 1,600 hours a month and 18% of their revenue responding to RFPs. What a mess….and an opportunity.
Everybody’s battling for a slice of that direct sales pie, and the game is all about helping buyers and sellers automate the manual processes that drive almost 80% of transactional value.
The Holy Grail for both sides is a web based, connected platform that will enable planners and sellers to thrust aside Excel, and start to transact business in the cloud. Although a number of companies have tried and failed to deliver on the promise of workflow automation, the time seems ripe for true adoption, as agencies are being challenged by their clients to create the same programmatic efficiencies across all media channels that they have embraced with RTB. As we speak, winners and losers are being selected, so let’s look at the landscape.
When you look at all of the companies providing a slice of the end-to-end workflow just in digital media execution, it’s hard to imagine that there can be “one system to rule them all” or a true “OS” for digital media. Yet, the dream is just that: An end-to-end comprehensive “stack” that handles media from research through to billing, and eliminates the many manual tasks and man hours involved in connecting the dots. But what are the realities? Let’s saddle up this unicorn and take a ride:
The End of the End-to-End Stack?
The notion of a single end-to-end “stack” for the digital marketer is a tough vision to execute upon. Build a system that has every little feature that a huge agency needs and you have effectively built something no one else can use. The flip side is building something so standardized that individual organizations find little value in it. The “operating systems” of the future that will win should enable agencies and marketers to leverage a standard operating system, but customize it with their own pricing, performance, and vendor data. This enables the efficiency of standardization while enabling data to provide the “secret sauce” that media shops need to justify their fees. More importantly, the modern operating system for media must be extensible, to allow for a wide variety of point solutions to integrate seamlessly. The right system will certainly eliminate a few logins, but must not limit the numbers of tools that can be accessed through it. That concept necessitates a highly modern, scalable, API-driven, web-based platform. It will be interesting to see how today’s legacy systems (which are exactly the opposite of what I have described) adapt.
Hegemon Your Bets
Several years ago, I wrote that the merger between Mediabank and Donovan may actually be a good thing—provided it offered more choice, flexibility, and open standards. Looking some three years later, I am not sure agencies have any more of that today. Like any other near monopoly, Mediaocean has a disincentive to open up its ecosystem because it invites competition. So time will tell whether their nascent “Connect” effort will become a way for agencies to quickly consolidate their “stack” around a flexible operating system—or if it’s just an integration tax for vendors (a revenue strategy quickly becoming known as the “Lumascrape”). After an IPO, the company will face enormous quarterly pressure for growth. It will be hard to raise prices on already stretched agencies, so publishers will be in the crosshairs. I smell “marketplace” and some monetization strategies around “programmatic direct” enablement for guaranteed media. And what about open standards? Despite years of work by the IAB, the standards and protocols for creating electronic ordering and invoicing are still very much in flux.
Connecting the Dots
More than anything else, the most exciting thing happening in digital media is seeing real programmatic connections between buyers and sellers for guaranteed media. After so much innovation in programmatic RTB (hundreds of vendors, billions in venture capital), we now have some amazing pipes that impressions can flow through. Unfortunately, this has largely been limited to lower classes of inventory and focused almost exclusively on the DR space. Creating the same programmatic efficiencies for “premium” brand-safe inventory is now starting to happen. Whether it comes from new “programmatic direct” pure play technologies, or happens through the RTB pipes, it will not happen successfully without transparency. That means giving publishers control over their inventory, pricing, and what demand partners can access their marketplaces. Will these connections thrive? Not if vendors charge network-like fees, arbitrage media, or don’t provide transparency. Will the endemic fraud in programmatic RTB push more transactions outside the RTB pipes? I think so, and a lot of publishers (see Yahoo/AOL/Microsoft deal) are betting that there are better ways for buyers to access their inventory.
Time for Real Time
Look at all the RTB players who want a piece of the guaranteed action. Three of them (Rubicon, Appnexus, and Pubmatic) will IPO soon, and be under tremendous pressure to increase revenue, margins, and continue to innovate and find new markets. When international expansion stops providing double-digit growth increases, then it’s time to look toward new streams of demand generation—namely, the 80% of deals not currently flowing through their pipes. Those pipes have been engineered for real-time bidding, but guaranteed deals are neither real-time nor bidded. Can they innovate fast enough to provide real value between buyers and sellers? Can they apply years of innovation in DSP and SSP tech to the more prosaic problem of workflow automation? Probably, but there are still business model issues to work out. Most of these companies have put a stake in the ground for either publishers or marketers, and a transactional platform must be agnostic to sit in the middle. It will be interesting to see how new offerings are received in the marketplace.
As the Chinese curse says, “may you live in interesting times.” Indeed, the past several years of ad tech has been nothing but interesting, but the real action is just starting—and it’s taking place in what was the most uninteresting field of workflow automation.
[This post originally appeared in AdExchanger on 3.12.14]
I was recently talking to the Chief Digital Officer of a large agency that does a lot of digital media buying. He has been working closely with a number of software providers to standardize his operations on a media management system. Getting all his vendor information, order management, and billing information has been a huge undertaking. Apparently, half the battle at an agency is getting paid (getting paid in less than 120 days is the other half)!
We were talking about some of the upfront processes behind putting together a media plan, which were mostly manual: putting the actual plan together in Excel, trading e-mails back and forth with vendors in the RFP process, trafficking ad tags, collecting screenshots, etc. Wouldn’t it be valuable if computers could streamline much of that work, and connect buyers and sellers together more seamlessly?
He agreed that it would truly transform his business, but accepted much of that manual work as part of the cost of doing business (paid for, incidentally, by his clients). The real way to transform his business, he said, was to answer the following questions. If “programmatic direct” technologies simply nailed down these four things, the payoff would be enormous. I paraphrase his answers below:
How much should I buy? “I basically know that I am going to have AOL, Yahoo, Facebook, and GDN on almost every plan. For my more vertical clients, in auto for example, I also know 95% of the sites and networks I am going to be on. Sure, I use research tools to validate those recommendations to my clients, but media discovery is not a huge pain point. Where we struggle is answering the question of media investment allocation. Should I spend 30% of my budget with Facebook? 40%? I really don’t know, and often don’t have the right mix until the campaign is nearly over. It would be great to have some business intelligence built into a system that recommended my guaranteed media mix programmatically.”
What should I pay? “I also have a pretty good idea what things cost, thanks to the RFP process. When you RFP 40 publishers in a vertical, you find out pretty quickly what your best pricing for guaranteed media is, and you can leverage that information to insure you are giving your clients competitive rates. Unfortunately, it feels like we go through this exercise every time on every RFP. We have the historical pricing data, but it’s all over the place in spreadsheets—and often in the planner’s heads. It would be great if this information was in the same place, and if a system could make pricing recommendations up front in the process, which would also shorten the negotiation process with publishers.
Why am I recommending this? “The biggest thing we struggle with is justifying our media choices to our clients. When we present a recommendation, often we are asking our client to invest hundreds of thousands or even millions in an individual vendor. My deck has to have more in it than basic audience information. I have to talk about the media’s ability to perform and hit certain KPIs for the price. It would be really useful to have recommendations come with some metrics on how such placements performed historically, or even some data on how other, similar, investments moved the needle in the past. Right now, getting to that data is nearly impossible, and usual resides with your senior planner in the account. The other obvious problem with that is employee turnover. My best planners, along with everything they’ve learned over two or three years walk out the door along with my data and relationships. The right system should store all of that institutional knowledge.”
You need that when? “The other thing a system can help with is speed to market. Publishers hate it when we ask them for huge, innovative proposals—in 24 hours. The reason we do that is because our clients ask us for amazing and innovative media recommendations in 48 hours. The pressure to deliver plans is huge, and you can easily lose large chunks of business by reacting to such requests too slowly. What programmatic direct technology may be able to help with is giving planners access to tools that compress the pre-planning process down, and enable agencies to deliver thoughtful, data-backed recommendations out fast—and at scale.”
Especially for larger agencies, programmatic direct technology has to be more than just workflow efficiency tools and automating the insertion order. (Although that has to come first). The next generation of programmatic efficiency or guaranteed media has to include serious business intelligence tools that can solve the “how” while simultaneously answering “why.”
[This post orginally appeared in AdExchanger on 2.11.14]
Well, at least it’s not the “year of mobile” again. Or, maybe it is. After several days of media investment banking conferences (Gridley and JEGI), I can reliably report that 2014 will be “the year” of many uber-trends ,some of which will enrich the M&A bankers who have a focus on the increasingly frothy ad technology and marketing space. Here are five memes to consider:
If you were to believe every ad tech panelist, you might be inclined to throw your laptop in the East River. Apparently—despite desktop only slightly starting to lose overall time-spent share to mobile on a year-over-year basis—nobody is developing ad tech solutions for the desktop anymore. Everyone is “mobile first,” meaning that they are writing code for tablet and mobile phone browsers and apps before developing solutions for the poor laptop or desktop computer. Of course, mobile devices are showing explosive growth, and clearly where the majority of consumers’ time will be found (as evidenced by the 8 of 10 people at my conference table multitasking during the eMarketer presentation which laid out the mobile data). This might be only a slight exaggeration when it comes to mobile eCommerce, which is trending to dominate the vast majority of online sales transactions in just a short time. Also, in case you missed this, it is now passé to call “mobile” “mobile.” Companies are so hip to the growth in portable digital devices that they just talk about “reach” rather than distinguishing between “tablets” and “smartphones.” You know it’s really the “year of mobile” when it’s too lame to talk about.
“No Cookie, No Problem”
Guess what? Nobody is worried that cookies are going away. Again, if you spend all of your time in conference ballrooms listening to panelists, you naturally understand that cookies are a thing of the ancient past, rather than the data currency without which 80% of Lumascape companies could not credibly operate. In fact, if the cookie disappeared tomorrow, ad tech players would simply go with a “statistical ID” or another cool sounding identification technology that is being invented somewhere. I am really glad that no one is particularly worried but—hearing this meme several times over the past week—I would be interested in how many platforms and ad networks have developed and deployed data technologies that enable them to do audience targeting at scale without cookies. What I think the reality of the situation might be is that cookie technology is replaceable, but if legislation changes suddenly or Google Chrome decides to switch things up, there could be huge trouble in Luma land. So much value destruction in so short a period is just something not fun to talk about at M&A conferences.
The idea of the “technology stack” is not new for 2014, but what has changed is that tons of point solutions that were funded in 2008 are still unprofitable, their VCs are at the end of their fund lifespans, and it’s time to find an exit. That means someone unprofitable point solution can either become a part of another’s “stack” or everyone can take their toys and go home. The problem with everyone wanting you to have a “stack” is that they are expensive to build and also expensive to license via SaaS. Small players cannot afford a “stack” and the big players already have them. That dynamic is going to create a ton of M&A activity in 2014, as vulnerable point-solution providers, some with excellent technology, succumb to larger integrators. As repeatedly pointed out, the biggest players in the marketing space (IBM, Adobe, Salesforce, etc.) represent the vast majority of M&A dollar volume, all of which has gone towards augmenting “stacks”—and it doesn’t look like they are going to be done anytime soon. There are a lot of good engineers that aren’t going to exit big at their point solution company, and may be ready for a comparatively cushy work life in the bosom of corporate behemoths that offer unlimited Mountain Dew and Skittles in the company snack room. Look for lots more M&A, and much of it “aquihire” focused.
The Funnel is Dead…It’s Now the “Customer Journey”
Everyone now has to have an “omnichannel-capable programmatic offering.” That’s the one parked right next to my Unicorn. Not that the instinct is incorrect—the proliferation of screens means that marketers have to reach people along their “consumer journey.” It’s no longer a trip down the sales funnel, but a twisting landscape where the consumer pushes you information through various social interactions. The smart marketer has to be ready at the drop of a hat to deliver perfect, personalized messages into the consumer’s smartphone at the “moment of truth” before a purchase—and, at the very least, be prepared for various “Oreo” social media moments that can create “earned” media at scale. Sounds like marketers may actually start to miss the old “AIDA” funnel!
One of 2013’s memes was the notion the “Sutton Pivot,” or running where the display money is—namely, the 70% of digital dollars that get transacted through the RFP channel. That’s where we get to complain endlessly about funding the “23 year old media planner” with “sneaker parties.” David Moore remarked at the recent JEGI conference that “50% of the cost of a campaign” went into the complexity of planning and delivering it. That sounds like a lot, but might be only a slight exaggeration. Everyone wants everything more programmatically, but the problem is that publishers haven’t quite given up yet. They are still keeping the premium inventory to themselves and out of the exchanges. “Programmatic everywhere” may become a reality…in five or six years. But old habits (and buying methodologies) die hard. In the meantime, everyone with a “platform” is going to try and figure out how to automate the inefficient buying process and try and get some of that 70% flowing through a system that creates a nice “percentage of spend” platform fee. 2014 will see this trend accelerate.
Happy New Year!
Every one of these memes will produce a ton of innovation, lots of M&A, a good deal of mid- to senior- level hiring, and plenty of bankers fees, so don’t worry! 2014 looks like a great year for ad technology!
[Originally published in AdExchanger on 1.23.2014]
Direct mail is an amazing thing. It costs something like $750 CPM to put a glossy catalogue in the mail, but somehow direct marketers make those numbers work. Mailing lists are constantly optimized to make sure they hit the right houses, fresh lists acquired to create new demand, and non-performing lists ruthlessly culled if they don’t meet certain KPIs. Direct marketers actually can tell just how much money a mailing will produce in sales.
Contrast that with a banner campaign, in which “good” performance means a 0.05% click-through rate, 40% non-viewable inventory, and fairly dim transparency. Some of the greatest companies in the space, newly public and boasting hundreds of millions in run rates, are still challenged to justify spending to their marketing clients. Thankfully, last click attribution hasn’t gone anywhere. I recently overheard a marketer at a conference saying that 70% of clicks on her last campaign with a big, popular “platform” came from Yahoo Mail subdomains. It doesn’t take a genius to figure out that the marketer’s e-mail program was creating sales, but the fancy platform’s banners were making sure they were “last viewed” before the purchase.
So, how to get display advertising more like direct mail?
It must start with procurement. Marketers should be able to tell how much the media costs, who will view it, and who to buy it from. Unfortunately, unlike almost every other form of media on the planet, that doesn’t exist today for the digital marketer. Marketers can name their price in the programmatic RTB channel, but if they want access to directly sold inventory (making up as much of 70% of all digital media spend today), they need to purchase via the “transactional RFP” process.
I don’t know whose fault it was, but publishers didn’t help themselves when they decided to hide pricing information from agencies. With an endless supply of inventory (some 5 trillion impressions per month, according to Eric Picard), banner sales has always been a bit more art than science. Buy 1,000,000 homepage impressions at $20 CPM, and I’ll throw in 5,000,000 “ROS” impressions. Presto! You get a reasonable eCPM of just over three bucks. Everybody’s happy….except for publishers. In the long run, such practices devalue their inventory.
Media prices are still opaque in the transactional RFP channel, and agencies like it that way. In order to get basic pricing and availability information, they send out “requests for proposals,” which send publisher sales teams scrambling. According to recent research by Digiday and Adslot, publishers spend an average of 1,600 man hours a month on RFPs, and 18% of their revenue churning through RFPs that have an average “stick rate” of about 25% (campaigns that will deliver the contracted amount). Ouch! A lot can happen in 1,600 hours.
Peter Naylor, the IAB’s Publisher in Residence, speaking to publishers at a recent conference summed it up nicely when he said, “Agencies take the information they receive in RFP’s to get a view of the market.” In other words, agencies get access to all the pricing information, and publishers are left to wonder who they are competing with—and at what price.
Despite this, agencies would also like to see this procurement methodology perish also. They want to buy impressions at scale, control the price they pay, and be able to “out-clause” on demand. Programmatic RTB offers all of the above—but only on lower classes of inventory. New programmatic direct technologies seem to be the answer to the problem of transactional RFPs. Whether they leverage existing RTB pipes (private deals) or are API-driven solutions connected to the publisher’s ad server, more and more higher-class inventory is starting to find its way to programmatic channels. That’s a good thing. Sure, there will still be RFPs for sponsorships, but sooner or later, all commoditized banner inventory (including “mobile” and video) will likely be purchased programmatically.
The question for publishers is whether or not they are going to take a part in deciding what the next stage of digital media procurement looks like. Will it still be driven by the demand side, or can publishers have a bigger seat at the table, and help build the process by which they expose and sell their “premium” inventory?
The RFP is dying, and publishers may applaud the last breaths of an over complex and inefficient process. But they should be careful of what may take its place.
[Originally publisher in AdExchanger on 1.2.14]
Econsultancy: Why now? In other words, why has this “programmatic direct” trend been on the radar lately? What’s driving all of the conversation the space?
Chris O’Hara: It’s really something my boss Joe Pych calls the “Sutton Pivot,” inspired by the famous thief Willie Sutton who robbed banks “because that’s where the money was.” Over 70% of digital display dollars are transacted in a very manual way today. Despite all the LUMAscape hype over RTB, most of the digital money still gets transacted through the request-for-proposal (RFP) process. Everybody wants a piece of the action, hence the “Sutton pivot,” in which all the ad tech companies are running to try and provide automation technology for directly sold deals. It’s actually a good thing. Today’s process for buying guaranteed digital media can take over 40 steps and suck up over 10% of media budgets just in man hours.
Q: The concept of “programmatic direct” or “programmatic premium” is a relatively new phenomenon, but it’s really just about automating the buying process for digital media, right? What makes it different from the automation happening in real-time bidding? What’s the difference?
A: Real-time bidding, or what we are starting to call “programmatic RTB” has been a real boon to the industry. We now have a set of “pipes” which connect demand- and supply-side platforms that make the digital media procurement process hugely efficient. Today’s systems are modern, cloud-based, scalable, and super low latency. We are seeing the type of liquidity and deal flow that happens in systems like NYSE and NASDAQ. That said, 70% of buying that happens in digital is neither “real time” nor “bidded.” It’s just two organizations trying to make a deal. You need different technology to enable that kind of guaranteed transaction, and marketers are starting to wonder why they are paying so much in transactional costs to access higher classes of digital inventory. RTB proved that efficiency can happen in digital, and now marketers want faster and more efficient access to more than just remnant inventory.
Q: You say that agencies have a “perverse incentive” to embrace efficiency in buying. It would seem counter to everything that is happening in the programmatic space at the moment. How do demand side business models need to adapt for programmatic direct to become a reality?
A: Agencies make money when plans take 400 hours to create. Manually trafficking line items in an ad server, and cutting and pasting publisher insertion orders pays the bills for agencies who charge on a “cost-plus” basis. Digital media agencies have been operating that way for years: hire cheap, work the “23 year old media planner” hard, and earn a mark-up on their labor. Nothing wrong with work-for hire, but the RTB phenomenon—and marketers experience with easy-to-use programmatic platforms in search and social marketing—have changed the dynamic entirely. Agencies have to do more than heavy lifting now to survive. They need to hire fewer, smarter, people to leverage systems—and more great creative and analytical people to make sure they are driving digital messages that inspire—and meet KPIs. The days of getting paid to traffic ads in MediaVisor are over. That’s a big time cultural change for agencies. A lot of shops won’t survive the transition, and that’s a good thing.
Q: What are some of the things—beyond cultural change—that need to happen to create this new era of programmatic direct efficiency? What’s missing?
A: We tend to think of digital as this highly advanced form of marketing, but it’s really the most backwards. Direct mail costs something like $750 per thousand (CPM) to put a catalog in the mail—and marketers like LL Bean make that number work consistently. Digital struggles to make $10 CPA goals work on $5 products. That’s really lame. Part of the problem is the lack of basic information available to the marketer. If I want to buy a direct mail list, I can find out how many folks in the list live in San Francisco, and have purchased a product by credit card in the last month. I can find out how much it costs to by that list—and who sells it. Until recently digital media has had no such directory. Not only that, but the industry lacks even the most basic set of electronic ordering protocols, that can enable systems to understand each other in electronic transactions. The good news is that more work has occurred on this front in the last two weeks than has happened in the 5 years the IAB has been promoting “eBusiness” initiatives. Look for some significant announcements in this area soon.
Q: Who benefits most from adopting programmatic direct strategies? Publishers? Agencies? The marketers themselves? Are there winners and losers if this new tactic sees adoption at scale?
A: It’s easy to say that “everyone’s a winner” with programmatic direct adoption at scale, but that’s not entirely true. I think publishers are the big winners, because they are starting to take some control back over the procurement process from the demand side. I think longer tail sites that depend on RTB revenue streams will continue to be able to get access to demand at scale through RTB systems, and still get their AdSense money. But what really excites me is seeing high quality publishers that own high quality real estate on category specific properties finally get more control over pricing and partner selection. This will be even more critical as publishers expand their offerings cross-channel, into video. Publishers need a programmatic way to sell their higher classes of inventory, and not be so dependent on prevailing procurement methodologies which overvalues biddable, commoditized inventory. Agencies who value higher class inventory also win, of course.
Q: Right now, the conversation (and action) seems limited to display media. How does “programmatic direct” impact cross-channel buying?
A: Everything digital will be bought “programmatically” in 5 years. Some will be RTB display, and some will be display, native, and video inventory purchased through “programmatic direct” platforms. Addressable television, digital out-of-home (DOOH), and other channels will also factor in. Once we can get a true unique identifier that makes sense from a technology and privacy standpoint (big question, obviously), then marketers will really be living in programmatic heaven.
Q: You’ve been working in the “programmatic direct” space for a long time (staring at TRAFFIQ in 2008), and yet there seems to be fairly little adoption of the concept among agencies. Are you crazy? Why keep doing it? Will there be a big payoff in the end?
A: Change is really hard, especially when the pace of change is as rapid as in digital ad technology. When I was on the publisher sales side, there was always something that bothered me about getting a $200,000 insertion order for digital advertising through a fax machine. That stuff still happens today. Ultimately, I so believe that true process automation will happen in digital media, and that we can free people in the space to stop doing a lot of manual grunt work, and start being truly creative. I was watching a documentary the other night, and an engineer was talking about why he loved his job. He said he spent the last three years building a bridge that eliminated 10 minutes from the commute for some 20,000 people a day. “I saved people over 50,000 days of productivity last year,” the engineer explained, adding, “I wonder what those people are doing with all that extra time.”
There are a lot of young people who go into an agency thinking that they are going to help make the next kick-ass viral ad, but they end up working until 10 o’clock at night pasting line items into an ad server. I really think that, if we can change that, great things will happen.
[Originally published 12/5/2013 on the Econsultancy blog]
Lately, I have been working on a whitepaper about the “programmatic direct” phenomenon. Part of the research involved surveying a bunch of influential people in the space, and asking them where they thought this new buying methodology was in terms of adoption. Their answers kind of surprised me.
If “programmatic direct” was a baseball game, we are in the top of the second inning.
The game has basically just started, and a few balls have been put into play, but the action is just getting started—and the big sluggers have yet to step up to the plate. If you are a regular AdExchanger reader, you would be justified in thinking that programmatic direct was quickly gaining steam by progressive agencies and publishers. After all, there has been a good deal of hype surrounding the idea of enabling programmatic access to higher classes of inventory, and it seems like almost every ad technology player in the display space is getting into the game.
Sure, some real innovations are happening in programmatic RTB that are enabling private marketplace transactions. Initiation-only auctions and fixed rate deals inside of exchanges are only the tip of the iceberg, though. New web-based technology and advanced ad server APIs are starting to provide real process automation—the tools that will make it easier to buy and sell the 70% of inventory currently procured through the “transactional RFP” process.
However, there are a few major things that need to happen before “programmatic direct” can really take hold:
A Directory: It may sound strange, but one of the biggest failings of digital media has been the lack of a directory for buyers. In direct mail, you can look up how many people get the L.L. Bean mailing list, add all kinds of criteria (males of a certain age that have purchased with a credit card in the past three months), find out exactly what it costs, and who to buy it from. No such thing exists in digital media. Hence, the RFP process, where buyers have to go through hoops just to get a sense of pricing and availability. This simple act of discovery adds time and complexity to every transaction. Today’s programmatic direct systems are being built from the ground up—starting with good information, and also with dynamic pricing and availability information thanks to API connections to DFP and other publisher ad servers.
Standards for Electronic Ordering: Another obvious thing that needs to happen before real process automation can happen in digital is that a set of standards have to be agreed upon. The IAB has known this since 2008, but five years later the “eBusiness Task Force” (now called the “Digital Automation Task Force”) seems no closer to its original mandate. Its stated mission: Updating the XML schema and implementation testing for the electronic delivery of digital advertising business document.” Those documents include Requests for Proposals (RFPs), insertion orders (IOs), and invoices—documents that must be standardized in order for adoption of programmatic direct buying to occur at scale. However, there is urgency like never before to get such standards implemented, and a source close to the action says that “we will see more movement in the next nine months in standards and protocols than has happened in ten years.” Let’s hope so. The wide adoption of a common set of standards and protocols opens up the door to the electronic IO—the key to achieving scale in programmatic direct.
Culture Change: While a directory can be created and standards adopted with lots of hard work, those things are actually easier than the real key to programmatic direct adoption: culture change among agencies and publishers. Agencies must leverage technology to empower the “23 year old media planner” and give them a reason beyond sneaker parties to go to work. Technology will unleash their creativity and get them focused on solving real problems for clients. Likewise, publishers need to escape the “$200,000 a year salesman,” with his accompanying high T&E and schmoozy selling style. Publishers need data-driven sellers that understand how to drive programmatic adoption, and can sell based on the new “media investment” paradigm happening at agencies—understanding tactically how to spread digital dollars across a broad portfolio of channels. Agencies now they cannot remain stuck with the current cheap labor model. Publishers understand that they cannot keep their higher classes of inventory outside of programmatic channels. Change is hard, but it’s already here.
About a year ago, I said that 2013 would be the year of programmatic direct. It turns out that 2013 has been the year of programmatic direct hype, and a ton of valuable behind-the-scenes work on the technologies that will drive it in the future. But unlike the perennial “year of mobile” programmatic direct will become a reality quickly if some of the above building blocks come together.
[This post originally appeared in AdExchanger].
NextMark today announced the creation of Bionic Advertising Systems, a new division focused on delivering technology that streamlines digital advertising workflow for digital marketers, their advertising agencies, and publishers.
“The new Bionic brand represents our philosophy of delivering advertising technology that combines the strengths of humans and machines,” remarked Joe Pych, CEO of NextMark, and co-founder of Bionic. “Over the past few years, there’s been a battle of man versus machine in digital media. Neither side is winning. Instead of man or machine, the best ‘systems’ of the future will be a combination of both. The recent announcements by AOL,Yahoo!, and Microsoft around Programmatic Direct validate this belief and heralds a new age in digital advertising: the Bionic Age. As the name implies, our new Bionic unit is 100% dedicated to delivering solutions for this new era in digital advertising.”
Launched today, Bionic Advertising Systems will encompass NextMark’s solutions for digital advertising, including the latest Programmatic Direct technologies. Bionic’s software automates the mundane processes of digital media planning, buying, and ad operations. It frees media planners, buyers, and sellers to spend their time on higher-value tasks. It enables digital media planners to find advertising opportunities, gather information, create and send requests for proposals, negotiate with publishers, build media plans, execute orders, and implement their campaigns with the click of a button. With its modern API-driven architecture, it integrates with popular agency tools such as Doubleclick, MediaMind, and comScore. It’s currently integrating with leading sell-side Programmatic Direct technology providers Adslot, iSocket, and Yieldex. Bionic’s Digital Media Planner aims to tie together the many disparate systems used in digital advertising, giving them a single interface that simplifies the way they develop and deliver media plans.
“’Bionic’ is such a great concept for the digital media industry,” added Chris O’Hara, the business unit’s co-founder and Chief Revenue Officer. “A lot of companies in the space think that algorithms and robots are the answer. We know human creativity can be unleashed by automation, and that digital advertising works best when people are empowered by technology.”
Currently, more than forty advertising agencies are using the Bionic Digital Media Planner to create and execute their media plans. More than 900 publishers and networks are using the Bionic Digital Ad Sales System to promote more than 9,000 premium digital advertising programs—the largest directory of its kind, which also powers the IAB’s Digital Advertising Directory.
To learn more, visit the Bionic website: http://www.bionic-ads.com/