CDIM is Table Stakes in the Data Management Wars

IdentityCrisisA 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]

Best Practices in Digital Display Media (Interview)

Digital display is remarkably complex. Standard campaigns can involve multiple vendors of different technologies and types of media.

Today, eConsultancy launches Best Practices in Digital Display Advertising, a comprehensive look at how to efficiently manage online advertising. We asked the author, Chris O’Hara, about the report and work that went into it.

Why did you write Best Practices in Digital Display Media?

In my last job, a good part of my assignment was traveling around the country visiting with about 500 regional advertising agencies and marketers, large and small, over three years. I was selling ad technology. Most advertisers seemed extremely engaged and interested to find out about new tools and technology that could help them bring efficiency to their business and, more importantly, results to their clients. The problem was that they didn’t have time to evaluate the 250+ vendors in the space, and certainly didn’t have the resources (financial or time) to really evaluate their options and get a sense of what’s working and what isn’t.

First and foremost, I wanted the report to be a good, comprehensive primer to what’s out there for digital marketers including digital ad agencies. That way, someone looking at engaging with data vendors, say, could get an idea of whether they needed one big relationship (with an aggregator), no data relationships, or needed very specific deals with key data providers. The guide can help set the basis for those evaluations. Marketers have been basically forced to license their own “technology stack” to be proficient at buying banner ads. I hope the Guide will be a map through that process.

What was the methodology you used to put it together?

I essentially looked at the digital display ecosystem through the lens of a marketer trying to take a campaign from initial concept through to billing, and making sure I covered the keys parts of the workflow chain. What technologies do you employ to find the right media, to buy it, and ultimately to measure it? Are all of these technologies leading to the promised land of efficiency and performance? Will they eventually? I used those questions as the basis of my approach, and leveraged the many vendor relationships and available data to try and answer some of those questions.

What’s the biggest thing to take away from the report?

I think the one thing that really runs through the entire report is the importance of data. I think the World Economic Forum originally said the “data is the new oil” [actually, the earliest citation we can find is from Michael Palmer in 2006, quoting Clive Humby] and many others have since parroted that sentiment. If you think about the 250-odd technology companies that populate the “ecosystem,” most are part of the trend towards audience buying, which is another way of saying “data-driven marketing.” Data runs through everything the digital marketer does, from research through to performance reporting and attribution. In a sense, the Guide is about the various technologies and methodologies for getting a grip on marketing data—and leveraging it to maximum effect.

There’s an explosion of three letter acronyms these days (DSP, DMP, SSP, AMP, etc) that marketers are still trying to sort out. Do we need all of them? Is there another one around the corner?

I am not really sure what the next big acronym will be, but you can be certain there will be several more categories to come, as technology changes (along with many updates to Guides such as these). That being said, I think the meta-trends you will see involve a certain “compression” by both ends of the spectrum, where the demand side and supply side players look to build more of their own data-driven capabilities. Publishers obviously want to use more of their own data to layer targeting on top of site traffic and get incremental CPM lift on every marketable impression. By the same token, advertisers are finding the costs of storing data remarkably cheap, and want to leverage that data for targeting, so they are building their own capabilities to do that. That means the whole space thrives on disintermediation. Whereas before, the tech companies were able to eat away at the margins, you will see the real players in the space build, license, or buy technology that puts them back in the driver’s seat. TheBest Practices in Digital Display Advertising Guide is kind of the “program” for this interesting game.

To learn more about the Best Practices in Digital Display Advertising Guidedownload the report here.

The Five Things to Expect in a DMP

Getting back control over their inventory is giving publishers a lot to think about.

“We want to make sure that we’re controlling what happens with data . . . we want to make sure we control pricing. Control’s a very important message. We don’t want there to be a cottage industry built on our backs” – Nick Johnson, SVP, NBC Universal

What do publishers really want? It’s simple, really: Power and control. In order to survive the ad technology era, publishers need the power to monetize their audiences without relying on third parties, and complete control over how they sell their inventory. In this era of “Big Data,” there is a fire hose stream of tremendously valuable information for publishers to take advantage of, such as keyword-based search data, attitudinal survey data, customer declared data from forms, page-level semantic data, and all the 3rd party audience data you can shake a stick at.

All of this data (cheap to produce, and ever-cheaper to store) has given rise to companies who can help publishers bring that data together, make sense of it, and use it to their advantage. Currently, ad technology companies have been using the era of data to their advantage, utilizing it to create vertical ad networks, ad exchanges, data exchanges, DSPs, and a variety of other smart-sounding acronyms that ultimately purport to help publishers monetize their audiences, but end up monetizing themselves.

Rather than power the ad tech ecosystem, what if data could actually help publishers take back their audiences? If “data is the new gold” as the pundits are saying, then smart publishers should mine it to increase margins, and take control of their audiences back from networks and exchanges. Here are the five things a good data management platform should enable them to do:

  • Unlock the Value of 1st Party Data: Publishers collect a ton of great data, but a lot of them (and a LOT of big publishers) don’t leverage it like they should. Consider this recent stat: according to a recent MediaPost article, news sites only use in-site audience targeting on 47% of their impressions, as opposed to almost 70% for Yahoo News.  By leveraging site-side behavioral data, combined with CRM data and other sources, it is possible to layer targeting on almost every impression a publisher has. Why serve a “blind” run-of-site (ROS) ad, when you can charge a premium CPM for audience-targeted inventory?
  • Decrease Reliance on 3rd Parties: The real reason to leverage a DMP is to get your organization off the 3rd party crack pipe. Yes, the networks and SSPs are a great “plug and play” solution (and can help monetize some “undiscoverable” impressions), but why are publishers selling raw inventory at $0.35 and letting the people with the data resell those impressions for $3.50? It’s time to turn away those monthly checks, and start writing some to data management companies that can help you layer your own data on top of your impressions, and charge (and keep) the $3.50 yourself. Today’s solutions don’t have to rely on pre-packaged 3rd party segments to work, either, meaning you can really reduce your data costs. With the right data infrastructure, and today’s smart algorithm-derived models, a small amount of seed data can be utilized to create discrete, marketable audience segments that publishers can own, rather than license.
  • Generate Unique Audience Insights: Every publisher reports on clicks and impressions, but what advertisers are hungry for (especially brand advertisers) are audience details. What segments are most likely to engage with certain ad content? Which segments convert after seeing the least amount of impressions? More importantly, how do people feel about an ad campaign, and who are they exactly? Data management technology is able to meld audience and campaign performance data to provide unique insights in near real-time, without having to write complicated database queries and wait long times for results. Additionally, with the cost of storing data getting lower all the time, “lookback windows” are increasing, enabling publishers to give credit for conversion path activity going back several months. Before publishers embraced data management, all the insights were in the hands of the agency, who leveraged the data to their own advantage. Now, publishers can start to leverage truly powerful data points to create differentiated insights for clients directly, and provide consultative services with them, or offer them as a value-added benefit.
  • Create New Sales Channels: Before publisher-side data management, when a publisher ran out of the Travel section impressions, he had to turn away the next airline or hotel advertiser, or offer them cheap ROS inventory. Now, data management technology can enable sales and ops personnel to mine their audience in real time and find “travel intenders” across their property—and extend that type of audience through lookalike modeling, ensuring additional audience reach. By enabling publishers to build custom audience segments for marketers on the fly, a DMP solution ensures that no RFP will go unanswered, and ROS inventory gets monetized at premium prices. 
  • Create Efficiency: How many account managers does it take to generate your weekly ad activity reports? How much highly paid account management time are publishers burning by manually putting together performance reports? Why not provide an application that advertisers can log into, set report parameters, and export reports into a friendly format? Or, better yet, a system that pre-populates frequent reports into a user interface, and pushes them out to clients via an e-mail link? You would think this technology was ubiquitous today, but you would be wrong. Ninety-nine percent of publishers still do this the hard (expensive) way, and they don’t have to anymore.

It’s time for publishers to dig into their data, and start mining it like the valuable commodity it is. Data used to be the handcuffs which kept publishers chained to the ad technology ecosystem, where they grew and hosted a cottage industry of ad tech remoras. The future that is being written now is one of publishers’ leveraging ad technologies to take back control, so they can understand and manage their own data and have the freedom to sell their inventory for what it is truly worth.

That’s a future worth fighting for.

[This post originally appeared in ClickZ on 2/29/12]

Same Turkey, New Knife

The way the ad tech world looked pre-DSP...and pre-DMP

Technology may still capture the most advertising value, but what if publishers own it?

A few years ago, ad technology banker Terence Kawaja gave a groundbreaking IAB presentation entitled, “Parsing the Mayhem: Developments in the Advertising Technology Landscape.” Ever since then, his famed logo vomit slide featuring (then) 290 different tech companies has been passed around more than a Derek Jeter rookie card.

While the eye chart continues to change, the really important slide in that deck essentially remains the same. The “Carving up the stack” slide (see above), which depicts how little revenue publishers see at the end of the ad technology chain, has changed little since May 2010. In fact you could argue that it has gotten worse. The original slide described the path of an advertiser’s $5 as it made it’s way past the agency, through ad networks and exchanges, and finally into the publisher’s pocket.

The agency took about $0.50 (10%), the ad networks grabbed the biggest portion at $2.00 (40%), the data provider took two bits (5%), the ad exchange sucked out $0.35 (7%), and the ad server grabbed a small sliver worth $0.10 (2%), for a grand total of 64%. The publisher was left with a measly $1.80. The story hasn’t changed, and neither have the players, but the amounts have altered slightly.

While Kawaja correctly argued that DSPs provided some value back to both advertisers and publishers through efficiency, let’s look ahead through the lens of the original slide. Here’s what has happened to the players over the last 2 years:

  • Advertiser: The advertiser continues to be in the cat bird seat, enjoying the fact that more and more technology is coming to his aid to make buying directly a fact of life. Yes, the agency is still a necessary (and welcomed) evil, but with Facebook, Google, Pandora, and all of the big publishers willing to provide robust customer service for the biggest spenders, he’s not giving up much. Plus, agency margins continue to shrink, meaning more of their $5.00 ends up as display, video, and rich media units on popular sites.
  • Agency: It’s been a tough ride for agencies lately. Let’s face it: more and more spending is going to social networks, and you don’t need to pay 10%-15% to find audiences with Facebook. You simply plug in audience attributes and buy. With average CPMs in the $0.50 range (as opposed to $2.50 for the Web as a whole), advertisers have more and more reason to find targeted reach by themselves, or with Facebook’s help. Google nascent search-keyword powered display network isn’t exactly helping matters. Agencies are trying to adapt and become technology enablers, but that’s a long putt for an industry that has long depended on underpaying 22 year olds to manage multi-million dollar ad budgets, rather than overpaying 22 year old engineers to build products.
  • Networks: Everyone’s talking about the demise of the ad network, but they really haven’t disappeared. Yesterday’s ad networks (Turn, Lotame) are today’s “data management platforms.” Instead of packaging the inventory, they are letting publishers do it themselves. This is the right instinct, but legacy networks may well be overestimating the extent to which the bulk of publishers are willing (and able) to do this work. Networks (and especially vertical networks) thrived because they were convenient—and they worked. Horizontal networks are dying, and the money is simply leaking into the data-powered exchange space…
  • Data Providers: There’s data, and then there’s data. With ubiquitous access to Experian, IXI, and other popular data types through 3rd party providers, the value of 3rd party segments has declined dramatically. Great exchanges like eXelate give marketers a one-stop shop for almost every off-the-shelf segment worth purchasing, so you don’t need to strike 20 different license deals. Yet, data is still the lifeblood of the ecosystem. Unfortunately for pure-play segment providers, the real value is in helping advertisers unlock the value of their first party data. The value of 3rd party data will continue to decline, especially as more and more marketers use less of it to create “seeds” from which lookalike models are created.
  • Exchanges: Exchanges have been the biggest beneficiary of the move away from ad networks. Data + Exchange = Ad Network. Now that there are so many plug and play technologies giving advertisers access to the world of exchanges, the money had flowed away from the networks and into the pockets of Google AdX, Microsoft, Rubicon. PubMatic, and RMX.
  • Ad Serving: Ad serving will always be a tax on digital advertising but, as providers in the video and rich media space provide more value, their chunk of the advertiser pie has increased. Yes, serving is a $0.03 commodity, but there is still money to be made in dynamic allocation technology, reporting, and tag management. As an industry, we like to solve the problems we create, and make our solutions expensive. As the technology moves away from standardized display, new “ad enablement” technologies will add value, and be able to capture more share.
  • Publisher: Agencies, networks, and technologists have bamboozled big publishers for years, but now smart publishers are starting to strike back. With smart data management, they are now able to realize the value of their own audiences—without the networks and exchanges getting the lion’s share of the budget. This has everything to do with leveraging today’s new data management technology to unlock the value of first party data—and more quickly aggregate all available data types to do rapid audience discovery and segmentation.

 The slide we are going to be seeing in 2012, 2013 and beyond will show publishers with a much larger share, as they take control of their own data. Data management technology is not just the sole province of the “Big Five” publishers anymore. Now, even mid-sized publishers can leverage data management technology to discover their audiences, segment them, and create reach extension through lookalike modeling. Instead of going to a network and getting $0.65 for “in-market auto intenders” they are creating their own—and getting $15.00.

Now, that’s a much bigger slice of the advertising pie.

[This post originally appeared in ClickZ on 2/1/12]

Signal to Noise

What Data Should Inform Media Investment Decisions?

The other day, I was updating my Spotify app on my Android device. When it finally loaded, I was asked to log in again. I immediately loaded up a new playlist that I had been building—a real deep dive into the 1980s hardcore music I loved back in my early youth. I’m not sure if you are familiar with the type of music that was happening on New York City’s lower east side between 1977 and 1986, but it was some pretty raw stuff…bands like the Beastie Boys (before they went rap), False Prophets, the Dead Boys, Minor Threat, the Bad Brains, etc. They had some very aggressive songs, with the lyrics and titles to match.

Well, I put my headphones in, and started walking from my office on 6th Avenue and 36th street across to Penn Station to catch the 6:30 train home to Long Island…all the while broadcasting every single song I was listening to on Facebook. Among the least offensive tunes that showed up within my Facebook stream was a Dead Kennedys song with the F-word featured prominently in the song title.  A classic, to be sure, but probably not something all of my wife’s friends wanted to know about.

As you can imagine, my wife (online at the time), was frantically e-mailing me, trying to tell me to stop the offensive social media madness that was seemingly putting a lie to my carefully cultivated, clean, preppy, suburban image.

So why, as a digital marketer, would you care about my Spotify Facebook horror story?

Because my listening habits (and everything else you and I do online, for that matter) are considered invaluable social data “signals” that you are mining to discover my demographic profile, buying habits, shoe size, and (ultimately) what banner ad to serve me in real time. The only problem is that, although I love hardcore music, it doesn’t really define who I am, what I buy, or anything else about me. It is just a sliver of time, captured digitally, sitting alongside billions of pieces of atomic level data, captured somewhere in a massive columnar database.

Here are some other examples of data that are commonly available to marketers, and why they may not offer the insights we think they might:

— Zip Code: Generally, zip codes are considered a decent proxy for income, especially in areas like Alpine, New Jersey, which is small and exclusive. But how about Huntington, Long Island, with an average home value of $516,000? That zip code contains the village of Lloyd Harbor (average home value of $1,300,000) and waterside areas in Huntington Bay like Wincoma, where people with lots of disposable income live).

— Income: In the same vein, income is certainly important and there are a variety of reliable sources that can get close to a consumer’s income profile, but isn’t disposable income a better metric? If you earn $250,000 per year, and your expenses are $260,000, then you are not exactly Nordstrom’s choicest customer. In fact, you are what we call “broke.” Maybe that was okay back in the good old days of government-style deficit spending but, these days, luxury marketers need a sharper scalpel to separate the truly wealthy from the paper tigers.

— Self-Declared Data: We all like to put a lot of emphasis on the answers real consumers give us on surveys, but who hasn’t told a little fib from time to time? If I am “considering a new car” is my price range “$19,000 – $25,500” or “35,000 – $50,000?” This type of social desirability bias is so common that reaearchers have sought other ways of inferring income and purchase behavior. When people lie about themselves, to themselves (in private, no less)  you must take a good deal of self-declared data with a hearty grain of salt.

— Automobile Ownership: Want to know how much dough a person has? Don’t bother looking at his home or zip code. Look at his car. A person who has $1,800 a month to burn on a Land Rover is probably the same person liable to blow $120 on mail order steaks, or book that Easter condo at Steamboat. Auto ownership, among other things, is a great proxy for disposable income.

It would be overly didactic to rehearse all of the possible iterations of false data signals that are being used by marketers right now to make real-time bidding decisions in digital media. There are literally thousands—and social “listening” is starting to make traditional segmentation errors look tame. Take a recent Wall Street Journal article that reported that the three most widely socially-touted television shows fared worse than those than shows which received far less social media attention.

Sorry, but maybe that hot social “meme” you are trying to connect with just isn’t that valuable as a “signal.” We all hear the fire truck going by on 7th Avenue. The problem is that the only people who turn to look at it are the tourists. So what is the savvy marketer to do?

Remember that all data signals are just that: Signals. Small pieces of a very complicated data puzzle that you must weave together to create a profile. Unless you are leveraging reliable first-party data, second-party data, and third party data, and stitching that data together, you cannot get a true view of the consumer.

In my next column, we’ll look at how stitching together disparate data sources can reveal new, more reliable, “signals” of consumer interest and intent.

[This article was originally published in ClickZ on 12/2/2011]