I have been hearing a lot about data lakes lately. Progressive marketers and some large enterprise publishers have been breaking out of traditional data warehouses, mostly used to store structured data, and investing in infrastructure so they can store tons of their first-party data and query it for analytics purposes.
“A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed,” according to Amazon Web Services. “While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data.”A few years ago, data lakes were thought to be limited to Hadoop applications (object storage), but the term is now more broadly applied to an environment in which an enterprise can store both structured and unstructured data and have it organized for fast query processing. In the ad tech and mar tech world, this is almost universally about first-party data. For example, a big airline might want to store transactional data from ecommerce alongside beacon pings to understand how often online ticket buyers in its loyalty program use a certain airport lounge.
However, as we discussed earlier this year, there are many marketers with surprisingly sparse data, like the food marketer who does not get many website visitors or authenticated customers downloading coupons. Today, those marketers face a situation where they want to use data science to do user scoring and modeling but, because they only have enough of their own data to fill a shallow lake, they have trouble justifying the costs of scaling the approach in a way that moves the sales needle.
Figure 1: Marketers with sparse data often do not have enough raw data to create measureable outcomes in audience targeting through modeling. Source: Chris O’Hara.
In the example above, we can think of the marketer’s first-party data – media exposure data, email marketing data, website analytics data, etc. – being the water that fills a data lake. That data is pumped into a data management platform (pictured here as a hydroelectric dam), pumped like electricity through ad tech pipes (demand-side platforms, supply-side platforms and ad servers) and finally delivered to places where it is activated (in the town, where people live).As becomes apparent, this infrastructure can exist with even a tiny bit of water but, at the end of the cycle, not enough electricity will be generated to create decent outcomes and sustain a data-driven approach to marketing. This is a long way of saying that the data itself, both in quality and quantity, is needed in ever-larger amounts to create the potential for better targeting and analytics.
Most marketers today – even those with lots of data – find themselves overly reliant on third-party data to fill in these gaps. However, even if they have the rights to model it in their own environment, there are loads of restrictions on using it for targeting. It is also highly commoditized and can be of questionable provenance. (Is my Ferrari-browsing son really an “auto intender”?) While third-party data can be highly valuable, it would be akin to adding sediment to a data lake, creating murky visibility when trying to peer into the bottom for deep insights.So, how can marketers fill data lakes with large amounts of high-quality data that can be used for modeling? I am starting to see the emergence of peer-to-peer data-sharing agreements that help marketers fill their lakes, deepen their ability to leverage data science and add layers of artificial intelligence through machine learning to their stacks.
Figure 2: Second-party data is simply someone else’s first-party data. When relevant data is added to a data lake, the result is a more robust environment for deeper data-led insights
for both targeting and analytics. Source: Chris O’Hara.
In the above example (Figure 2), second-party data deepens the marketer’s data lake, powering the DMP with more rich data that can be used for modeling, activation and analytics. Imagine a huge beer company that was launching a country music promotion for its flagship brand. As a CPG company with relatively sparse amounts of first-party data, the traditional approach would be to seek out music fans of a certain location and demographic through third-party sources and apply those third-party segments to a programmatic campaign.But what if the beer manufacturer teamed up with a big online ticket seller and arranged a data subscription for “all viewers or buyers of a Garth Brooks ticket in the last 180 days”?
Those are exactly the people I would want to target, and they are unavailable anywhere in the third-party data ecosystem.The data is also of extremely high provenance, and I would also be able to use that data in my own environment, where I could model it against my first-party data, such as site visitors or mobile IDs I gathered when I sponsored free Wi-Fi at the last Country Music Awards. The ability to gather and license those specific data sets and use them for modeling in a data lake is going to create massive outcomes in my addressable campaigns and give me an edge I cannot get using traditional ad network approaches with third-party segments.Moreover, the flexibility around data capture enables marketers to use highly disparate data sets, combine and normalize them with metadata – and not have to worry about mapping them to a predefined schema. The associative work happens after the query takes place. That means I don’t need a predefined schema in place for that data to become valuable – a way of saying that the inherent observational bias in traditional approaches (“country music fans love mainstream beer, so I’d better capture that”) never hinders the ability to activate against unforeseen insights.Large, sophisticated marketers and publishers are just starting to get their lakes built and begin gathering the data assets to deepen them, so we will likely see a great many examples of this approach over the coming months.
We’ve been hearing about big data driving marketing for a long time, and to be honest, most is purely aspirational.
Using third-party data to target an ad in real time does deploy some back-end big-data architecture for sure. But the real promise of data-driven marketing has always been that computers, which can crunch more data than people and do it in real time, could find the golden needle of insight in the proverbial haystack of information.
This long-heralded capability is finally moving beyond the early adopters and starting to “cross the chasm” into early majority use among major global marketers and publishers.
Leveraging Machine Learning For Segmentation
Now that huge global marketers are embracing data management technology, they are finally able to start activating their carefully built offline audience personas in today’s multichannel world.
Big marketers were always good at segmentation. All kinds of consumer-facing companies already segment their customers along behavioral and psychographic dimensions. Big Beer Company knows how different a loyal, light-beer-drinking “fun lover” is from a trendsetting “craft lover” who likes new music and tries new foods frequently. The difference is that now they can find those people online, across all of their devices.
The magic of data management, however, is not just onboarding offline identities to the addressable media space. Think about how those segments were created. Basically, an army of consultants and marketers took loads of panel-based market data and gut instincts and divided their audience into a few dozen broad segments.
There’s nothing wrong with that. Marketers were working with the most, and best, data available. Those concepts around segmentation were taken to market, where loads of media dollars were applied to find those audiences. Performance data was collected and segments refined over time, based on the results.
In the linear world, those segments are applied to demographics, where loose approximations are made based on television and radio audiences. It’s crude, but the awesome reach power of broadcast media and friendly CPMs somewhat obviate the need for precision.
In digital, those segments find closer approximation with third-party data, similar to Nielsen Prizm segments and the like. These approximations are sharper, but in the online world, precision means more data expense and less reach, so the habit has been to translate offline segments into broader demographic and buckets, such as “men who like sports.”
What if, instead of guessing which online attributes approximated the ideal audience and creating segments from a little bit of data and lot of gut instinct, marketers could look at all of the data at once to see what the important attributes were?
No human being can take the entirety of a website’s audience, which probably shares more than 100,000 granular data attributes, and decide what really matters. Does gender matter for the “Mom site?”Obviously. Having kids? Certainly. Those attributes are evident, and they’re probably shared widely across a great portion of the audience of Popular Mom Site.
But what really defines the special “momness” of the site that only an algorithm can see? Maybe there are key clusters of attributes among the most loyal readers that are the things really driving the engagement. Until you deploy a machine to analyze the entirety of the data and find out which specific attributes cluster together, you really can’t claim a full understanding of your audience.
It’s all about correlations. Of course, it’s pretty easy to find a correlation between only two distinct attributes, such as age and income. But think about having to do a multivariable correlation on hundreds of different attributes. Humans can’t do it. It takes a machine-learning algorithm to parse the data and find the unique clusters that form among a huge audience.
Welcome to machine-discovered segmentation.
Machines can quickly look across the entirety of a specific audience and figure out how many people share the same attributes. Any time folks cluster together around more than five or six specific data attributes, you arguably have struck gold.
Say I’m a carmaker that learned that some of my sedan buyers were men who love NASCAR. But I also discovered that those NASCAR dads loved fitness and gaming, and I found a cluster of single guys who just graduated college and work in finance. Now, instead of guessing who is buying my car, I can let an algorithm create segments from the top 20 clusters, and I can start finding people predisposed to buy right away.
This trend is just starting to happen in both publishing and marketing, and it has been made available thanks to the wider adoption of real big-data technologies, such as Hadoop, Map Reduce and Spark.
This also opens up a larger conversation about data. If I can look at all of my data for segmentation, is there really anything off the table?
Using New Kinds Of Data To Drive Addressable Marketing
That’s an interesting question. Take the company that’s manufacturing coffee machines for home use. Its loyal customer base buys a machine every five years or so and brews many pods every day.
The problem is that the manufacturer has no clue what the consumer is doing with the machine unless that machine is data-enabled. If a small chip enabled it to connect to the Internet and share data about what was brewed and when, the manufacturer would know everything their customers do with the machine.
Would it be helpful to know that a customer drank Folgers in the morning, Starbucks in the afternoon and Twinings Tea at night? I might want to send the family that brews 200 pods of coffee every month a brand-new machine after a few years for free and offer the lighter-category customers a discount on a new machine.
Moreover, now I can tell Folgers exactly who is brewing their coffee, who drinks how much and how often. I’m no longer blind to customers who buy pods at the supermarket – I actually have hugely valuable insights to share with manufacturers whose products create an ecosystem around my company. That’s possible with real big-data technology that collects and stores highly granular device data.
Marketers are embracing big-data technology, both for segmentation and to go beyond the cookie by using real-world data from the Internet of Things to build audiences.
It’s creating somewhat of a “cluster” for companies that are stuck in 2015.
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]
Lately, I’ve been thinking a lot about the hourglass funnel. Most funnels stop at the thin bottom, when a customer “drops” out, having made the journey through awareness, interest, desire and action. After the “action,” or purchase, the customer gets put into a CRM to be included in more traditional marketing outreach efforts, such as calls, e-mails, and catalogue mailings. In the past, marketers often thought about how to turn customers into advocates, but couldn’t figure out how to do it at scale. Companies that were really good at multi-level marketing, like Amway, didn’t have easy-to-replicate business models.
Today, the situation has changed. Social-media platforms give marketers tools to engage customers in their CRMs and bring them back through the bottom of the funnel, turning them into brand advocates — and maybe even salespeople. This is why Salesforce has been snatching up social-media companies like Radian6 and Buddy Media, while Oracle bought Vitrue and Involver. These platforms can help get people talking about your brand– and, in turn, you get to listen to what they have to say. These platforms also can help you understand what it takes to get your customers to move from liking your page to actively sharing your content and to actually recommending your products and even selling them as an affiliate.
The ad-tech revolution of the last several years has supercharged our ability to drive people through this hourglass-shaped funnel. But instead of enabling this movement, we have instead – for the most part — focused on wringing efficiency out of reaching the customers we’re already very close to getting. For example, programmatic RTB makes it easy to bid on people in the “interest” layer, who behave like existing customers. Additionally, it’s a no-brainer to retarget those customers who have already expressed “desire” by visiting a product page or your website. And technology also makes it increasingly easy to invite customers already in your CRM to “like” your Instagram page, or to offer them incentives to “recommend” products through social sharing tools.
But what about the very top of the funnel (awareness) and the very bottom (advocacy)? Those are the two most critical parts of the marketing hourglass funnel, but the two least served by technology today. While we have tools to drive people through the marketing process more quickly or cheaply, technology doesn’t create brands or turn social-media fans into brand advocates.
However, the right strategy for both ends of this funnel can still boost awareness and advocacy by creating a branding vortex that is a virtuous circle. Let me explain:
You can’t start a customer down the sales funnel without making he or she aware of your product or service. Despite all of the programmatic promise in display, technology mainly emphasizes reaching our known audience most efficiently. It simply hasn’t yet proven that it can create new customers at scale. That’s why TV still gets the lion’s share of brand dollars. Cost-effective reach, pairedwith a brand-safe, viewable environment, is what TV supplies.
In my opinion, the digital answer for raising awareness is starting to look less and less like programmatic RTB and more like video and “native” formats, which are more engaging and contextually relevant. Also, new programmatic direct technologies are starting to make the process of buying guaranteed, premium inventory more measurable, efficient and scalable.
Programmatic RTB advocates will argue that you can build plenty of awareness across exchanges, but it’s hard to create emotion with three IAB standard units, and there still isn’t enough truly premium inventory available in exchanges today to generate a contextual halo for your ads. New “native” display opportunities, video and tablet advertising are where branding has the biggest impact. Adding those opportunities to social tools, such as Twitter and Instagram, would help you leverage your existing brand advocates and amplify your message.
Great digital branding at the “awareness” level of the funnel not only helps drive potential new customers deeper into the sales funnel, but also can help engage existing customers. This amplification effect is extremely powerful. Old-school marketers such as David Sarnoff understood that folks make buying decisions through their friends and neighbors. He also understood that, when you’re trying to sell the next big thing (like radio), you have to leverage existing media (print). Applied to digital marketing, this simply means leveraging awareness media — TV, video and “native” advertising — to stimulate word-of-mouth advertising, which is still the most powerful type. By using Facebook and other social sharing tools, the effect of any campaign can grow exponentially in a very short period of time. This virtuous circle of awareness media influencing brand advocates, who then create more awareness among their own social circles, is something that many marketers miss when they lead their campaigns with data rather than with emotion.
Everything In Between
I’m not saying that marketers can simply feed the top of the funnel with great branding and ignore the rest. That’s not true at all; the middle of the funnel is important too. I think it’s relatively easy, nowadays, to build a stack that also helps support all the hard work that brands are doing to create awareness. Most large marketers reinforce brand efforts with “always on” programmatic RTB that targets based on behavior, and all brands employ as much retargeting that they can buy. Once customers are in the CRM, it’s not hard to maintain a rewards/loyalty program, and messaging to an existing social fan base also is relatively simple.
But marketers are making a mistake if they think that this kind of programmatic marketing can replace great branding. With so many different things competing for customers’ attention, capturing it for more than a second is extremely difficult, and the challenge is only going to get harder.
The Datalogix Effect
So what does all this mean for for ad technology? The best way to think about this is to look at the Datalogix-Facebook partnership. Datalogix’s trove of customer offline purchase data essentially enables brands to measure whether or not all their social-ad spending resulted in more online sales. A few studies have pretty much proven that media selling soap suds on Facebook created more suds sales at the local Piggly Wiggly. In fact, ROI turns out to be easy to calculate, as well as positive.
This type of attribution seems simple, but I don’t think you can overstate its impact. It’s the way we finally move from clicks and views to profit-optimization metrics such as those offered by MakeBuzz. And this method of tying online activity with offline sales is already having a vast impact on the ecosystem. It shows, beyond doubt, that branding sells product.
Getting the attribution right, though, means that brands are going to have to care about creative and content more than ever. It means big wins for video, “native” ad approaches, and big tentpole marketing campaigns. If quality premium sites can be bought programmatically at scale, then it may also mean big wins for large, traditional publishers.
It also likely means that many retargeters, programmatic RTB technologies and exchanges could end up losing in the long run. Don’t get me wrong: These technologies are needed to drive the “always on” machine that powers the middle of the funnel. But just how many DSPs and exchanges does the industry need to manage its commoditized display channel?
As marketers realize that they are spending money to capture customers that were going to convert anyway, they’re likely to focus less on audience targeting and more on initiatives to create new customers — and turn existing customers into advocates.
[This post originally appeared in AdExchanger on 7/31/13]
A few years ago, I had coffee with Nick Langeveld, who left Nielsen to run business development for an interesting company called Affectiva. He was telling me how the company, an MIT labs spin-off, was going to make measurement in a new direction by measuring people’s facial expressions.
Now, marketers could see the exact moment when they captured surprise, delight, or revulsion in a consumer—and scale that effort to anyone with a webcam, who opted into their panel. This sounded great, but I wondered if and when large marketers would adopt such technology.
The question of adoption was answered early this year, when the company announced that both Unilever and Coca Cola would use the technology to measure all of their marketing efforts this year. In the consumer products wars, perhaps tweaking your video assets to get an extra smile or“wow moment” will give Coke what it needs to pick up market share from Pepsi. Even if that is not the case, the measurement will give their agency creative a very real—and real-time—indication of how impactful their content is.
I think this is a great sign.
The relentless automation of digital media seems to have taken a lot of emphasis off the creative. Now that digital marketers can buy audiences so precisely, delivering the right message doesn’t seem to matter as much. For agency people like R/GA’s Michael Lowenstern, who recently spoke at Digiday Agency Summit on this topic, the right equation is right message plus right creative equals more performance and lower costs. His recent Verizon campaign used hundreds of individual banner creatives, matched to audiences, and raised sales of FiOS 187%. That’s sales increase—not an increase in campaign performance.
When talking about higher funnel branding activities using rich media and video, creative matters even more. Building brands takes means connecting emotionally with consumers, so it would seem like technologies like Affectiva’s, which utilise “big data” approaches to drive branding, all the more relevant for this day and age. Research presented by The Intelligence Group’s Allison Arling-Giorgi at the recent 4As conference showed that Generation Y consumers find humor the most effective form of advertising, so Unilever leveraging technology to squeeze one more smile out of a 30-second spot starts to make a lot of sense.
In the case of Intel, adding a “webcam” to a set top box sounds really scary from a privacy perspective, but starts to make a lot of sense when you think about the migration of television ad dollars to display.
Theoretically, the shifting dollars reflect a desire on the advertisers’ part to achieve more granular targeting, and put the right ads in front of the right people at scale. The digital display channel offers lots of targeting, but the low-quality inventory, commoditized and bland creative units, and tremendous amount of fraud have kept a lot of TV money on the sidelines.
In 2009, Morgan Stanley analyst Mary Meeker estimated the gap between where people spend their time (increasingly online) and where advertisers spend their money (TV) represented a $50 billion global opportunity. While that has shrunk considerably, the gap is still measured in the tens of billions, despite the enormity of Google, and the embrace of ubiquitous social platforms such as Facebook and Twitter.
Connected set top boxes may be biggest online advertising disruptor
Despite being a mass reach vehicle today, plans like Intel’s threaten to disrupt online advertising in a far more fundamental way than digital has been impacting traditional ads. Connected set top boxes are connected to households—the composition of which are easy to access, both from a demographic and financial perspective. They also tend to deliver a much more engaging video experience for the brand advertiser than ignored “pre-roll” inside a tiny 300×250 pixel video player.
Now, add the element of being able to actually see who is on the couch watching—and tailor ad messaging to those viewers, based on their age, and contextual relevance of the content they are consuming. That’s powerful in a way that digital cannot ever become, despite the rise of “social TV” watchers who tweet during “appointment viewing” shows like The Walking Dead.
Delivering ads to TVs, depending on who is watching them? That’s something to really watch out for.
[This post originally appeared on the EConsultancy Blog on 4/8]
I was recently at a conference, and took a picture of a PowerPoint slide that I thought was pretty interesting. It showed the growth of tweets about television from Q2 2011 to last quarter. Basically, nobody was tweeting anything a few years ago, and then there were over 18 million unique people tweeting about TV in Q4 2012, representing a 182% year-over-year growth rate. If you are a modern marketer that spends money on television advertising, there are some implications in this data worth looking at.
Are you in the conversation?
Back in the 1980s, I would sometimes go to Times Square to see horror movies. The theatres were uniformly crumby, but the people were the best. Times Square movie theatres always featured an audience willing to give Jamie Curtis’ Halloween character plenty of advice in each scene. In fact, between the chatter and screaming, you could hardly hear the film. That was what passed for “social viewing” in the old days. Today, we are discovering that people still like to share viewing experiences together, and Twitter and other social tools lets you make every television show an Oscar party you can attend in your pajamas. Brand advertisers backing a particular show want the glow of good comedy or drama, and now extending that association may mean inserting yourself into the conversation via a Sponsored Tweet. What’s really interesting about that is your message can be received during the action, without interrupting.
Less TV, More Tweet
The rise of “Social TV” gives brand marketers yet another dimension to ponder as well. With a show’s active and engaged community just a Tweet away, how much media should you allocate to thirty second spots, and how much should go towards the social element? Moreover, social TV means that every consumer seeing your ad can get the chance to interact and talk back socially. We are seeing marketers hashtag their ads and drop into the social stream of conversation. Although this is still a form of “interruption marketing,” it’s the closest that brands have gotten to being a part of, rather than disturbing, the entertainment in a long time. These digital “native advertising” opportunities are proving effective, and starting to take market share away from commoditized 300×250 display advertising units.
Can your company dunk in the dark?
The latest test for marketers is The Oreo Challenge or, more simply put, do I have a social strategy for taking advantage of news and events? Although it seemed like a no-brainer during the Superbowl, “you can still dunk in the dark” was the result of a contemplated strategy. Oreo’s very responsive tweet is a phenomenon that digital marketers are still talking about—the kind of lightning on a bottle that produces tens of millions of dollars in “earned” media. But getting there requires your marketing team and agency to truly understand everything about the brand they are promoting. If your team can’t automatically speak in the brand’s “voice” and doesn’t truly understand the brand attributes and values, you can’t automatically respond to opportunity in the social space. Teams that live and breathe their brand—and, more importantly, their brand’s key constituency—must be trusted to speak socially…and sometimes loudly, if the occasion warrants it. Of course, there is a good chance your joke will go flat, but that’s okay when you are among your television “friends.”
[This post was originally published on 4/3/13 on The CMO Site]
Mark Zagorski, the CEO of data management platform eXelate has worked with dozens of big marketers to help them put all kinds of data to work, including their own.
“Right now most organizations are dealing with terabytes of data. Over a third [manage] more than 10 terabytes of data and one-fifth will manage half a petabyte of data within three years,” Zagorski tells me. “The key objective for marketers seeking to harness the power of big-data is to make it actionable.”
As a marketer, it is likely that you have access to a great deal of data, and maybe even the kind of big-data we’ve been hearing so much about. CRM data grows every day; point-of-sale data gets easier and less expensive to store; tag-collected data from websites and social sites expands daily; and there is a seemingly infinite amount of third-party data available for purchasing and mixing in with your own.
The modern CMO must find a way to value the data assets she has, learn to listen for the real signals among the noise, and find a way to put that data to use. Mostly, that means understanding customer attributes, what drives them to transact, and how much it costs to get them to do so more frequently.
For Darren Herman, in charge of digital media at forward-looking agency Media Kitchen, data is all about the way it can be leveraged for his clients. “We care less about big-data and more about actionable data. Our clients have tons of first-party data,” Herman tells me, adding that the real challenge is in “uniting the data between silos (usually within client organizations) and making them available and actionable for advertising and marketing decisions. Much of the time, the clients’ data is available through the IT organization, and it’s not quite understood how it will be used for marketing decisions.”
In many ways, data-driven CMOs face two challenges: Firstly, winning the internal battle with the CTO to get access to disparate data sources, and bringing them together in a way that creates the opportunity to glean global insights; and secondly, building the platform that enables them to normalize many discrete data types, query that data quickly, and “activate” that data to produce a sales outcome.
Think of a large, global consumer products organization. A company that sells soap suds around the world may have up to 20 regional operating companies, and as many as 200 separate datacenters throughout the organization. Within all of those data silos are digital stories of marketing success and failure. Imagine if you could duplicate the promotional dynamics that drove a 20 percent increase in Italian diaper sales across the entire global organization, or leverage the learnings that one operating company had when a key discounting scheme failed?
These types of insights can be obtained when the CMO asks the right questions, and when he has data management platforms behind him that can make it possible to get the answers. Being a data-driven marketer isn’t about how much data you can centralize in a single platform. The data may be big, but ultimately the data you store is only as valuable as your ability to extract insights from it — and act upon it.
[This post originally appeared in the CMO Site on 3/20/13]
Social media stands to help marketers better work the newly-emerged hourglass funnel.
Marketers have been using the AIDA model in one form or another since its invention in 1898. The path of “awareness, interest, desire, and action” has been relevant for more than 100 years, and even if individual marketing channels have their differences, the way people are brought through the purchase funnel has changed about as much as human nature over the same time period.
That is to say, very little.
Consumer behavior is the same, even if the tools of the trade are different. For example, Pinterest activity demonstrates “desire” in the lower part of the funnel just as much as clipping a coupon does. The fact that Pinterest activities are measurable (and infinitely more cost-effective and scalable) makes all the difference.
What has changed a good deal over the past several years is what happens when a consumer drops out of the bottom of the funnel. It used to be that a purchaser was put into a marketer’s CRM system, where he or she would start to receive new marketing messages via established channels like mail, telemarketing, and loyalty programs.
Of course, that is still happening, but now there is a whole new part of the funnel to work through. This new, inverted funnel explains, for instance, why Salesforce purchased Buddy Media and Radian6 — the marketing is just getting started after the consumer purchases.
Today’s CMO has to have a more developed strategy for what happens after the purchase than ever before. This new socially-enabled funnel means closely linking the traditional CRM to social platforms — not only for “listening” to what your customers are saying, but also to give them an opportunity to start selling on your behalf.
After purchase, you need to encourage your buyer to join your social sphere, and start extending the conversation. This means not only listening to sentiment, but also giving the consumer the incentives to get to the next phase in the post-conversion funnel: social activation.
Migrating customers from being passive “likers” and “followers” to socially-activated users with true brand affinity is difficult. How you communicate within platforms like Facebook and Twitter (both on an earned and paid basis) is critical, along with providing key incentives for such participation. Ultimately, the affinity group you curate can be turned into sellers, either real affiliate salespeople or, in a softer sense, “brand ambassadors” that go beyond social sharing to influence others to purchase.
Today’s successful CMOs have been seeing through the bottom of the funnel for a long time, and putting together the tools and support needed to migrate post-purchase marketing activity from CRM-driven tactics to social activation strategies.
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.
Much has been written about the notorious “logo vomit” map of famed internet banker Terence Kawaja. I reference his handy charts on my blog, and often his “Display LUMAscape” as a reference point for thinking about the digital display business, and what will happen to it. Many have tried to navigate through the various categories and dissect what may be “happening” in the space, which is a favorite pastime of company executives trying to raise money for many of the identified advertising technology outfits referenced within. Nobody ever really tries to explain the whole thing, though. It’s just too complicated, I guess. Allow me to try:
“A few years ago, people started to figure out that you could use technology to target advertising to people on the Web. Ever since then, 250 companies have placed themselves in the middle of the transaction between the advertiser and the inventory, confusing everyone. Now, most of them are running out of money and will sell cheap, get acquired, or go out of business.”
Perhaps that oversimplifies things slightly, but the reality is that there are many companies in the space that are primed for one of those three scenarios. Unfortunately, most of them will sell for less than their investment, or go out of business. Here are the three big reasons we have gotten here:
It was a Bad Idea
The whole point of most of the companies on the Kawaja map is to help advertisers use data to find exactly the right audience at the right time, serve them the right ad, and maybe find something out about them that helps drive branding or sales. In the past, most advertisers used to do that contextually (putting ads for shoes in Vogue, for example) and it seemed to work pretty well. When that Internet thing came along, publishers could get something nearing their print CPMs for “site sponsorships” and premium banner advertising alongside good content. Sooner or later, however, publishers decided to put banners ads on all of their pages, creating the advertising largest inventory glut known to man. That created a big problem.
All of that banner space needed to be monetized somehow, and publishers were quickly discovering that it was hard to make money on the trillions of monthly advertising impressions they had created. But nobody wanted to buy $10 CPM banner ads on message board pages, and the “contact us” page. So, in order to “solve” this problem, exchanges popped up and allowed publishers to “monetize” this space by having various parties bid on the inventory. Things got even better when data companies came in, and were able to layer some demographic data atop those impressions, making audience buying possible for the first time. The venture money flowed, as smart young technologists created fast-moving software companies to help marketers exploit this trend as they sought a way to help reduce industry average CPMs from $20 to $2.
Mission accomplished! In the last 10 years, average CPMs have been drastically reduced, 100% of a publishers inventory is being “monetized” (often by 10 or more companies), and you can target an ad down to one’s shoe size. So, what’s the problem? Hasn’t turning advertising from an art into a science worked?
The answer is: Yes, but not for all of the companies on that map. People visit three sites a day, and one of them is Facebook. If you want audience targeting, why not just find exactly what you want from a social network? They are the ones with the real audience data. They are also the ones with the audience scale, having about 5 times as many “profiles” as the next largest data company. The problem with all the companies trying to sell you audience targeting and ad technology is that it only works when you have audience scale (they don’t) and deep audience data (they don’t have that either).
Facebook, Google, and LinkedIn (and the next company that people are willing to share their private information with) are going to win the audience targeting game. When you are talking about audience buying at scale, social media IS digital media.
It’s Still about Art
If you believe that the average web user visits only two sites a day besides Facebook, then you better find them on those sites—and give them a really amazing experience with your banner ad. That thing should play video, games, talk to you, and almost pay you to look at it. Since only three out of every 10,000 people will click on it, you had better make sure the creative really tells a terrific story and gets your brand message across too.
That means standard sized banners that work with exchange-based buying are pretty much irrelevant, since they have a hard time doing any of the above. It also means that context has to accompany placement. It is not enough to reach a “35 year old woman in-market for shoes.” You have to reach her when she is on her favorite fashion site, or otherwise psychologically engaged in shoe consideration. The ad should be in a brand-safe environment that engenders trust—and compliments the creative in question. That sounds suspiciously like premium display advertising…the stuff that was being sold 10 years ago!
In a certain sense, we have almost come back full-circle to guaranteed, premium advertising. And that means an emphasis on the creative itself. If you look at the map, it’s clear that creative isn’t a part of the picture…but it might be the most important thing driving the future of the digital display advertising business.
Even if agencies and advertisers wanted to take advantage of a few of the of companies cluttering the “landscape,” they would need to log into and learn multiple systems. As a marketer looking to reach women, am I really going to log into Blue Kai and bid on demographic “stamps” from Nielsen, log into AppNexus and apply those to a real-time exchange buy, constantly log into my DART account to check ad pacing and performance, periodically log into my Aperture account to download audience data, and then log into my Advantage account every month to bill my clients? Maybe—but that’s exactly the reason why digital media agencies are making 3% margins lately. Most of these technologies are really great on their own, but string together too many of them and you start to get lost in the data, and are unable to react to it.
For digital marketing to be effective, a set of standards need to be created that enables systems to work together and share information. Basic B-school dogma teaches you that effectiveness starts to break down when a manager has more than 5 direct reports. If you believe that, then it’s not hard to imagine the effectiveness of a 22-year old media planner managing 5 logins on behalf of his agency. It’s not just confusing, but impossible.
We have built an industry ripe for aggregation, and the Googles, Adobes, and IBMs of the world will not disappoint us! So, what companies will succeed in this ecosystem?
— Social Scalers: If you agree that all reach advertising targeting audiences will eventually be on social networks, then you should look to work with companies that are making social advertising scale effectively. Doing Facebook advertising is incredibly easy—but doing it right is hard. Doing it properly requires extreme multivariate creative optimization and, more importantly, knowing what to do with the mounds of truly actionable audience data that Facebook and other social networks will hand you. Companies like XA.net that are doing this are EPIC WIN.
— Creative enablers: Since the conversation is coming back to the creative, how can technology help make great creative even better—and help advertisers understand how that creative is being engaged with? The click is a dead metric to most seasoned advertisers, who are spending more time with branding measurement tools (Vizu) and creative ad analytics startups (Moat) that are well positioned to “science-ify” the truly important part of advertising: the creative itself. Companies doing that well are also going to be EPIC WIN.
— Standard Bearers: With all of the logins out there, it is inevitable that one company is going to try and create the technology stack for next generation media buying that puts all the pieces together seamlessly. There are a number of companies trying to do this right now (full disclosure: I work for one of them), and I believe there will be a lot of advertisers and agencies relieved to log into a single platform, and be able to access all of their vendor relationships in one dashboard. This will take some time, but the companies that enable standardization across technology providers will also WIN big.
[This post originally appeared 7/20/11 on eConsultancy blog]