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!
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]
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]