Creating the Fabled 360 View of the Consumer

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]

 

Death of the Digital Media Agency (Redux)

Last year, I wrote that the digital agency was dead. I was mostly talking about how platform technology was going to knock a lot of digital media agencies out of business. In a world where over five trillion banner impressions are available every month, I argued, it was simply too much for humans to navigate through the choices and wring branding effect and performance out of campaigns. Well, digital media agencies are still around—but they continue to lose share to platforms as the amount of programmatically bought media increases. With RTB-based spending estimated to rise at an annualized rate of nearly 60% a year, according to market intelligence firm IDC, we could see as much as $14 billion in spending by 2016, or 27% of total display spending. Looks like the machines are slowly taking over.

Fairfax Cone, the founder of Foote, Cone, and Belding once famously remarked that the problem with the agency business was that “the inventory goes down the elevator at night.” That’s a big problem for an industry that relies on 23 year-old media planners to work long hours grinding on Excel spreadsheets and managing vendors to produce fairly mediocre media plans. Cone was talking about IP—what, exactly is the digital agency’s core intellectual property when the majority of the work seems to be hard labor? Digital creative agencies have no such worry. In this world of ubiquity, where everyone has access to wonderful SaaS-model technology that enables real-time bidding and access to trillions of exchange-based advertising impressions, the one place an agency can make an impact is on the creative side. Agencies that can create the miracle of getting more than 1 in 10,000 people to click on an ad, or watch a :30 pre roll video to completion are considered geniuses. But, what about the media shops? Can they really buy more efficiently than machines? More importantly, can they leverage the right machines to once again own the middle position between the advertiser and his prospect?

As I write in my recent report, looking at the history of display advertising, the future doesn’t favor the agency. In the beginning, agencies’ favored relationships with publishers made them a great way to buy media. Publishers aligned their content with the audiences that advertisers wanted (ESPN for sports enthusiasts), and largely controlled their inventory and audience data. Soon enough, the Network Era told hold, and smart companies like Tacoda started segmenting audiences based on context and behavior. By using technology to understand audiences better than the publishers themselves, they put yet another layer of IP between agencies and audiences. Then the DSP Era started, which further decoupled audiences from media. Agencies scrambled to create new vendor relationships with the MediaMaths of the world—but grew nervous that they would be disintermediated, and formed their own trading desks. This era is now evolving in the DMP Era.

After all of promises of easy audience targeting and automation, advertisers are looking at the same disturbingly low click-through rates, near impossibility of true attribution measurement, and spending waste—and determining that their own data is more valuable than most data that they can buy. Their desire to activate their “first party” data has given rise to the “DMP era.” Andy Monfried, who has brought his company Lotame through this transition, sees it this way, “Agencies are attempting to become technology providers for their clients, and from our perception, clients are hesitant to adopt. The larger agency holding companies have made an attempt at understanding first-party data but have come to be just a solution for clients to leverage third-party data. This is due to the lack of agency technology and lack of trust that clients put in agencies accessing their first-party data in a raw state.”

So, what happens now? Are advertisers simply going to license DMP technology, and build small practice groups for audience segmentation, targeting, and analytics? Or, are agencies going to adopt and learn how to become the centralization point for evaluating and helping clients implement new advertising technology? Media Kitchen digital head Darren Herman thinks the way through the trees is through education: “We are super bullish about teaching our strategists to learn the skills of data scientists. While the average media strategists will probably not have the skills of a robust data scientist with a PhD, from Stanford, an entire organization that learns to embrace data and make it useful will be more powerful than a few data scientists sprinkled [through] many. Knowledge of how to action data must both come from the top down and bottom up and be embraced by all. Building a culture that does this is hard as many people resist, but retooling and finding people who want this type of career is what we’re doing.”

Is your agency ready to hire a data scientist? Looks like the days of agencies hiring armies of English majors is over, and the next MIT recruiting session you see may have a few agency folks in attendance. Are digital media agencies dead? The data says not yet.

This post originally appeared on the EConsultancy blog on 12/13/12.

Can you Buy “Brand?”

Understanding Social Affinity Data

Marketers are increasingly turning to social platform data to understand their customers, and tapping into their social graphs to reach more of them. Facebook “likes” and Twitter “follows” are religiously captured and analyzed, and audience models are created—all in the service of trying to scale the most powerful type of marketing of all: Word-of-mouth.  With CRM players (like Salesforce, who recently acquired Buddy Media and Radian6) jumping into the game, digitally-derived social data is now an established part of traditional marketing.

But, are marketers actually finding real signals amid the noise of social data? In other words, if I “like” Lady Gaga, and you “like” Lady Gaga, and my ten year old daughter also “likes” Lady Gaga, then what is the value of knowing that? If I want to leverage social data to enrich my audience profiles, and try and get the fabled “360 degree” view of my customer, “likes” and “follows” may contribute more noise than insight. I recently sat down with Colligent’s Sree Nagarajan to discuss how brand marketers can go beyond the like, and get more value out of the sea of social data.

Colligent (“collectively intelligent,” if you like) goes beyond “likes” and actually measures what people do on social sites. In other words, if you merely “like” Lady Gaga, you are not measured, but if you post a Lady Gaga music video, you are. By scraping several hundred million Facebook profiles, and accessing the Twitter firehose of data, Nagarajan’s company looks at what people are socially passionate about—and matches it against other interests. For example, the data may reveal that 5% of Ford’s socially active fanbase is also wild about NASCAR. That’s great to know. The twist is that Colligent focuses on the folks who are nuts about NASCAR—and like Ford back. That’s called mutual engagement and, arguably, a more powerful signal.

Nagarajan’s focus on this type of data has many questioning the inherent value of targeting based on social media membership. “In any social network’s lifecycle, likes (or ‘follows’ or friends) start out as genuine signals of brand affinity. However as more and more like the page their audience gets increasingly diluted, making likes less of an indicator of brand’s true audience. True engagement as measured by comments, photo posts, re-tweets, hashes, etc. became much better indicators of brand affinity and engagement.”

Colligent data recently convinced Pepsi to choose Nicki Minaj as their spokesperson, since the data revealed a strong correlation between socially activated Pepsi and Minaj fans. Think about that for a second. For years, major brands have used softer, panel-based data (think “Q Score”) to decide what celebrities are most recognizable, and capture the right brand attributes. Now, getting hard metrics around the type of people who adore your brand are just a query away.  Digital marketers have been talking about “engagement” for years, and have developed a lexicon around measurement including “time spent” and “bounce rate.” Social affinity data goes deeper, measuring true engagement. For Nagarajan, “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 Pepsi fans to engage with Nicki Minaj; enough Nicki fans have to engage with Pepsi (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 the noise in social networks to surface meaningful relationships.”

So, what else can you learn from social affinity data? With so many actively engaged fans and followers, throwing off petabytes of daily data, these networks offer a virtual looking glass for measuring real world affinities. If you think about the typical Facebook profile, you can see that many of the page memberships are driven by factors that exist outside the social network itself. That makes the data applicable beyond digital:

• Television: Media planners can buy the shows, networks, and radio stations that a brand’s fans are highly engaged with.
• Public Relations: Flacks can direct coverage towards  the media outlets a brand’s fans are engaged with.
• Sponsorships: Marketers can leverage affinity data to determine which celebrity should be a brand’s spokesperson.
• Search: SEM directors can expand keyword lists for Google and Facebook buys using social affinity-suggested keywords.
• Display: Discover what sites Ford’s socially activated consumers like, and buy those sites at the domain level to get performance lift on premium guaranteed inventory buys.

Are we entering into a world in which marketers are going to use this type of data to fundamentally change the way they approach media buying?  What does it mean to “buy brand?” Sree Nagarajan sees this type of data potentially transforming the way offline and online media planners begin their process. “Much of the audience selection options available in the market today are media based. Nielsen defines TV audience, Arbitron radio, ComScore digital sites, MRI magazines, etc. Brand marketers are forced to define their audiences in the way media measures audience: by demographics (e.g., 18-49 male),” remarks Sree.  “Now, for the first time, social data allows marketers to define audiences based on their own brand and category terms. Now, they can say ‘I want to buy TV shows watched by Pepsi and more generally, Carbonated Soft Drinks audience.’ This will truly make marketing brand-centric instead of media-centric. Imagine a world where brand and category GRPs can be purchased across media, rather than GRPs in a specific media.”

Look for this trend to continue, especially as company’s become more aggressive aligning their CRM databases with social data.

[This article originally appeared in ClickZ on 12/11/12]

Discover more on this topic and others by downloading my new whitepaper, Best Practices in Data Management

Agencies: Working Hard or Hardly Working?

A recent meeting with a large agency’s digital planning team left me wondering who is doing the real work these days: agencies or ad networks? I was there to talk about our solution for making sense of an increasingly crowded and complicated digital space. Today’s media planners and buyers have to be able to navigate through a 300,000 channel world for their clients — and be able to take advantage of dozens of new creative executions, placements, and targeting capabilities. Their clients trust them to find a receptive audience wherever they are on the web — and deliver enough scale and performance to make it effective and affordable.

One of the planners in the room was responsible for a seven-figure pharmaceutical budget. When I asked him how he was evaluating new traffic sources, he said, “I buy on two networks. They find me headache suffers and my client is satisfied, why would I want to risk it by moving money around?”

“I buy on two networks.” Surely he couldn’t be serious.

After I left the meeting, I continued to be astonished by the reply. Sure, buying on those networks was easy (and probably pretty effective) but what was the agency bringing to the table? Why wouldn’t the client simply place those two network buys themselves, and gain an extra 10% in performance by eliminating the agency’s fee?

Furthermore, what if the client’s CMO asked that planner where his ads were running? He couldn’t tell him with any certitude. It seemed to me like a pretty expensive and risky marketing strategy.

The agency is passing along their job along to a network, who is keeping all the data from the campaign. Even if the company sold a ton of migraine pill prescriptions, they still don’t know how they were successful—and who responded to their ads. Even worse, that network can now go and pitch all of the client’s competitors, who now stand to gain for the investment they made building an audience.

If I were the client, I would be justified in firing this agency.

The successful agency not only continually works to discover new pockets of high-performing traffic for their clients but they actively manage the campaign, and share performance results with them. If I want to reach migraine sufferers, the easiest thing in the world is to call WebMD and sponsor their migraine section; I am guaranteed a contextually-relevant placement in a high quality setting. Easy.

Same thing as buying a car. If I want a really reliable German automobile that seats 5 adults, with leather seats, all-wheel drive, and impeccable handling, I just go the Mercedes dealer and pick up a new S-Class.

The problem starts to arise when I get my monthly bill. Is $1,200 a month too much to pay when I can get to work in the same relative comfort in a $600 a month Audi, or a $350 a month Volkswagen?

Maybe, as a media planner, I can find five health sites that target migraine sufferers and string together the same audience for a lot less money. In addition, maybe there are premium opportunities I can get on smaller, more vertically focused sites that the leading site cannot or will not offer me?

Don’t get me wrong, WebMD is a great place to advertise. But that’s something even my mother knows. Do you really need to pay 15% to an agency for them to recommend that strategy?

So, how hard is your agency working for you, anyway? Every advertiser who uses the services of a media agency for their media planning and buying should ask themselves and their agency this question every single day. If they did, I think they would unfortunately find in many cases, the answer to be: not very hard.

How can an agency then justify the fees that they are collecting? They can do it by continually looking for better performing traffic. The only way to do that is to spread dollars around, find pockets of traffic either through other networks, or direct-to-publisher sites. They can do it by deploying smaller per-publisher budgets, while benefiting from smaller incremental risk.

Sure, it will take more work, but that’s what the client is paying for.

[This originally appeared in Adotas on 3/9/2010]

The New, New Agency Model

Is Your Digital Agency Configured for Success?

With all the new technology and access to data, you would think running a digital agency in 2011 would be tempting. After speaking with a few hundred digital agency principals over the last several years, I think I would rather work at a car wash. At least you are outdoors doing low-value repetitive tasks. Let me explain.

I think most digital agencies were started by really smart people who saw the opportunity to provide their clients with the “magic” of media. Interactive ads, true measurement, real user engagement, ROI, and cross-platform messaging that reached consumers where brands wanted to be found. That is still true. The early ones nurtured their accounts from direct mail to e-mail, and then from broadcast into the web, a little budget at a time. When digital media truly arrived, digital agencies were at the vanguard of a new era: technology-driven creative shops and data-driven media agencies that crammed brand messages into the 728×90 mini billboards we love to hate, but occasionally produced some real internet marketing magic.

After a while, the magic was gone.

Digital campaigns have a tendency to suck every penny of margin out of an agency. The client wants to serve rich media, but doesn’t want to pay for it. They have $50,000 to spend but they want 10 A-tier sites on a plan, all of which have a $25,000 minimum. They want to run 5 creatives per placement, and switch them every two days, based on performance. They need their ads pixeled, and hooked up to their Google Analytics platform, which reports traffic numbers that never match up with their ad server. Then they want to know why. Most importantly, they want to be billed correctly, and that means making demand-side and publisher-side ad servers talk together, and agree on impression amounts (which, from my experience over the last 15 years, has never happened once). That’s an awful lot of work.

That’s why (as the 4As reports), digital margins can be 10 times lower than the margins on traditional media campaigns. That’s called mucho trabajo, poco dinero. Since digital agencies don’t seem to be going away anytime soon, they are going to have to figure out how to make more margins from their business, rather than leverage the traditional agency model of overworking extremely young employees until they burn out (essentially, the mucho trabajo, poco dinero cheap labor model). Here are a few things the modern digital shop must embrace for long term success:

1)      Use a Platform (or two): I paraphrase from David Kenny’s remarkable keynote address at the OMMA 2010, “if you are still using people to do work that servers can do, you are already irrelevant.” What value is there in providing ad operations for your clients (none, they just want their ads to work properly). How about reconciling and billing against different delivery numbers? Again, how does that provide value for your client? Those are low-value tasks that must be accomplished, but things that don’t make you a better agency for your clients. There are many systems out there that can centralize these low-value tasks (ad trafficking, billing reconciliation, reporting, etc) so your agency can focus on your clients.

2)      Hire some Nerds: I’m talking about math nerds. Media used to be about finding audience based on panel-based surveys. Now, media is about finding audience by using data, and then using performance and audience measurement data to perfect that audience—and using quantitative analytics to bid on that audience and optimize your results. Since media seems to be about understanding and leveraging data, you are going to need a few people who speak the language. They aren’t the same old English majors from liberal arts colleges in the northeast, either. And the good ones are expensive.

3)      Be Strategic: This sounds obvious, but sometimes the definition of “strategic” gets lost in the weeds when it comes to digital. Sometimes, an agency feels it is being “strategic” when they partner with enough technology companies that offer their clients a variety of digital tactics (social, video, mobile). But having those partnerships and capabilities is far different than using them smartly, in a way that gives your shop the edge over your competition (they have access to all the same technology as you do). True strategy involves finding what works for your clients and creating repeatable processes that lead to long term success. When your clients say they “want to do social” are you smart enough to determine whether they simply need access the Facebook API—or are they looking to find their customers through conversational density around their products, such as Buzz Logic offers? How you offer “social” to your clients should come with its own, unique strategic model.

4) Partner: Agencies are really just an extension of their clients, and they should operate that way. Now, we are seeing agencies building their own technology to leverage media buying power (and even earning commissions from inventory sources), and acting a lot like technology and media companies. I’m not sure (at least for media agencies) this is sustainable. Building great creative that drives forward brands (whether through sales, or just audience exposure) is key—and finding new audience to interact with those brands in the new multiple screen world is where the core competency of today’s digital agency should be. Let the technologists build the technology. They are happy to let you use it, and willing to partner (with both their technology and people) to share success.

Looking around at all the different technology available to digital agencies these days, we aren’t far away from when starting an effective campaign, building amazing creative, deploying it to the exact audience you need, measuring it, optimizing it, and billing it will be as simple as….well, doing it on Facebook. That means that, once you and every other agency begin to avail yourself of that technology, you better be left with something unique to sell your clients.

[This article appeared in ClickZ on 2/18/11]

Choosing between Performance and Branding in Digital Display?

Depending on how you are measuring success, maybe you don’t have to.

The New Data Ecosystem

According to Blue Kai, I am a tech savvy, social-media using bookworm in the New York DMA, currently in the market for “entertainment.” At least that’s what my cookie says about me. Simply by going to the Blue Kai data exchange’s registry page, you can find out what data companies and resellers know about you, and your online behavior and intent.

In this brave new world of data-supported audience buying, every individual with an addressable electronic device has been stripped down to an anonymous cookie, and is for sale. My cookie, when bounced off various data providers, also reveals that I am male (Axciom), have a competitive income (IXI), 3 children in my family (V12), a propensity for buying online (TARGUSinfo), and am in senior management of a small business (Bizo). I am also in-market for a car (Exelate), and considered to be a “Country Squire,” according to Nieslen’s PRIZM, which is essentially a boring white guy from the suburbs who “enjoys country sports like golf and tennis.” Well, I am horrible at tennis, but everything else seems to be accurate.

As a marketer, you now have an interesting choice. Instead of finding “Country Squires” or “Suburban Pioneers” on content-specific sites they are known to occupy (golfdigest.com, perhaps), now I can simply buy several million of these people, and find them wherever they may be lurking on the interconnected web. This explains why you suddenly see ads for Volkswagens above your Hotmail messages right after you looked at that nice Passat wagon on the VW website. Today’s real-time marketing ecosystem works fast, and works smart. But, what are the advantages of buying users versus the place where they are found?

Putting aside the somewhat “spooky” aspect of web targeting (such as using insurance claim data to target web visitors based on their medical conditions), I think every marketer agrees that these capabilities are where online media is going, and they present a powerful opportunity to both find and measure the audiences we buy. But, how do you decide whether to buy the cookie, or the site?

A Different Way to Measure Performance

Most marketers will insist that audience buying is meant for performance campaigns. This is largely a pricing consideration. Obviously, if I want to sell sneakers to young men that are well down the purchase funnel, it makes sense to buy data, and find 18-35 year old males who are “sneaker intenders” based on their online behavior and profile, and reach them at scale across the ad exchanges. Combined data and media will likely be under $4CPM, and probably less since both the data and media can be bid upon in real time. For most campaigns with a CPA south of $20, you need to buy “cheap and deep” to optimize into that type of performance.  It sounds pretty good on paper. There are a few problems with this, however:

What are they doing when you find them? Okay, so you found one of your carefully selected audience members, and you know he’s been shopping for shoes. Maybe you even retargeted him after he abandoned his shopping cart at footlocker.com, and dynamically presented him with an ad featuring the very sneakers he wanted to buy, and you did it all for a fraction of a cent.   The problem is that you reached him on Hotmail, and he’s engaged in composing an e-mail. What are the chances that he is going to break task, and get back into the mindset of purchasing a pair of sneakers? Also, what kind of e-mail is he composing? A work-related missive? A consolation note to a friend who has lost a loved one? Obviously, you don’t know.  Maybe you reached that user on a less than savory site, or perhaps on a social media site, where he is engaged in a live chat session with a friend. In any case, you have targeted that user perfectly…and at just the wrong time. This type of “interruption” marketing is exactly what digital advertising purports not to be. Perhaps a better conversion rate can be found on ESPN.com, or a content page about basketball, where that user is engaged in content more appropriate to your brand.

How do you know where the conversion came from? Depending on your level of sophistication and your digital analytics toolset, you may not be in the best position to understand exactly where your online sales are coming from. If you are depending on click-based metrics, that is even more true. As Comscore’s recent article points out, the click is somewhat of misleading metric. There are a lot of data that contribute to that notion but, put simply, clicks on display ads don’t take branding or other web behavior into account when measuring success. Personally, I haven’t clicked on a display ad in years, but seeing them still drives me to act. Comparing offline sales sales life over a four week period, Comscore reports that pure display advertising provides average lift of 16%, pure SEM provides lift of 82%–but search and display combined provide sales lift of 119%. That means you simply can’t look at display alone when judging performance—and you really have to question whether you are seeing  performance lift because you are targeting—or whether you are achieving it because your buyer has been exposed to a display ad multiple times. If it is the latter, you may be inclined to save the cost of data and go even more “cheap and deep” to get reach and frequency.

How do you value an impression? Obviously, the metric we all use is cost-per-thousand (CPM), but sometimes the $30 CPM impression on ESPN.com is less expensive than the $2 RTB impression from AdX. Naturally, your analytics tools will tell you which ad and publisher produced the most conversions. Additionally, deep conversion path analysis can also tell you that “last impression” conversion made at Hotmail, might have started on ESPN.com, so you know where to assign value. But, in the absence of meaningful data, how do we really know how effective our campaign has been? I really believe that display creates performance by driving brand value higher, and some good ways to measure that can now be found using rich media. When consumers engage within a creative unit, or spend time watching video content about your brand, they are making a personal choice to spend time with your message. There is nothing more powerful than that, and that activity not only drives sales, but helps create lifetime customers.

For today’s digital marketer, great campaigns happen when you understand your customer, find them both across the web and on the sites for which they have an affinity—and find them when they are engaged in content that is complimentary to your brand message. Hmmm…that kind of sounds like what we used to do with print advertising, and direct mail. And maybe it really is that simple after all.

[This article appeared 1/12/11 in AdWeek]

PLATFORM WARS #2: The Future of Display

The Future of Display Advertising will depend on Content, Data, Integration, and Control

It’s funny, but if you are around the display advertising business long enough—whether on the agency, publisher, or technology side—you tend to forget that the acronyms “DSP” and “RTB” didn’t even exist until recently. Now, we take for granted that we live in this “digital ecosystem,” surrounded by technology and data everywhere we look. But, what does the future of digital display look like?

** * Content: It is the content, stupid. Always has been and always will be. It’s why WebMD, WSJ, and TripAdvisor get $30 CPMs and everyone else gets $2. You want to buy audience? Why not buy it from the sites that have the right content to attract it? And, guess what? Those are the same consumers who have the “purchase intent” and you don’t need a million data-injected cookies to tell you that. The future of display advertising is bright for publishers that produce the kind of content that warrants high CPMs, and insist on valuing their content. I think that much of that content will inevitably be stored behind pay walls, creating two distinct Internets: the free, ad-supported one; and the paid one.

***  Data: The world is changing, and the data marketplace we know isn’t going to be very long-lived. Even if you believe (as I do) that cookies are fairly harmless and somewhat convenient (I would personally rather see relevant ads than not), you know the current situation must change. The Wall Street Journal’s recent “Data: What They Know” series simply stirred an already simmering pot a half-turn. The future is going to involve a great deal more transparency, and the ability for consumers to opt in and out of a cookie pool easily.

***  Integration: Tomorrow’s winners will also have to embrace open technology. Everybody knows the symbiotic relationship that display and search share. Why, then, is it so difficult to mate data from the two disciplines in a meaningful way for the average advertiser? Why is it so difficult to manage audience buying and guaranteed buying with the same tools? The future in display will offer advertisers the ability to easily discover, buy, and manage display buys—powered by insights that go beyond stale panel-based analytics. Imagine being able to model, in advance, how a display buy will perform alongside a complimentary search campaign, and then optimize both with the same tool? We are very close. Display is not going to be about display anymore.

***  Control: The future is a world where the publishers and advertisers wrest control back from the technology players. Why are agencies building their own DSPs? Because they are being disintermediated by technology players who know how to get the advertising performance that they don’t. Hell, if finding a bunch of quants and coders is what it takes to stay in the game, it’s only money, right? Holding companies have never been afraid to invest their clients’ money on the latest and greatest technologies and trends over the years. Why are publishers building their own platforms (i.e., Glam)? Because they getting $1 CPMs for their content, and exchanges are selling it for $8. All of that is going to end—badly. Over the next 2 years, the winning platforms will be those that offer both sides of the market transparency and control over buying and selling media.

So, all of this speculation is certainly very exciting. Then again, it’s the year 2010 and most agencies are still buying digital media by using fax machines and collating spreadsheets. What is very clear is that the current display advertising ecosystem is unsustainable. The wide array of technology players layered between advertiser and publisher is already shrinking, as companies consolidate or are absorbed, and the winners and losers are chosen. The conversation has been dominated by data lately—and that’s where it should be. Most of the display advertising out there is the kind of commoditized inventory that is worth only 75 cents, and data can play an important role in making even the worst inventory find a relevant audience. However, one of the reasons that companies like AdVerify are gaining so much steam, is the fact that an abundance of low-quality goods inevitably leads to a gray market.

The future of display will be one in which brand advertisers use technology tools to mix audience buying and guaranteed buying—informed by search (and other) data—in the same platform. Buying campaigns from reputable publishers will be painless and easy, and marketers will be able to make optimization decisions based on real data—both historical and forward-looking. Brand advertisers will buy premium audience segments through opted-in cookie pools from top-quality sites, and pay commensurate CPMs. Performance buyers will still be able to buy audience from networks and exchanges, but may settle for lower quality audience segments (cookie pools from publisher networks with lower quality content).

I am looking forward to the future.

[Published 10/6/10 in iMediaConnection]