Creating the Fabled 360 View of the Consumer

ImageDespite 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]

 

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The RFP is Dead: New Concepts in Audience Discovery

The Programmatic Approach to Media Allocation is Coming Soon to a Platform near You.

Since its inception, advertising has always been about putting the right message in front of the right audience. Back when televisions were really expensive, and people used to gather around them in bars to watch baseball, beer companies started to do a lot of television advertising. While it’s still pretty easy for marketers to find the right beer demographic on sports programming in broadcast, the new world of multiple screens makes finding that audience at scale tougher every day.

The guy who was likely in the pub watching the game back in the 1940s and 1950s is now watching the game at home, but maybe on his iPad. Or perhaps he’s sneaking it in at work on his computer via Slingbox, or following along on his Android phone on the MLB Mobile app. The point is, there’s no easy way to find him, it’s almost impossible to find him at cheaply at scale, and we may have the wrong way of discovering him online.

The traditional method of finding your audience in the digital space is to put together a campaign request for proposal (RFP) that details the nature of your ad campaign, the audience you are looking for, where you want to find them, and the most you expect to pay to reach them. An agency’s trusted inventory suppliers receive and evaluate the RFP, and put together (hopefully) creative strategies that deliver a way to find that audience, and put the agency’s message in front of the user at the right time, in the right place. This approach makes complete sense. Except when it doesn’t.

Here are some ways in which the traditional, single RFP fails:

Multiple Pricing Methodologies: One of the problems in the traditional RFP process is that the agency is often limited to suggesting a single price range they are willing to pay for the media. For example, a typical RFP for a branding campaign looking for contextually relevant, above-the-fold inventory may suggest a price range to publishers of between $8-$12 CPM. This is fine if the proposal is only going to premium publishers with guaranteed inventory. But what if the advertiser is also interested in finding his audience on a cost-per-click basis? Knowing the historical performance of similar past campaigns, he might suggest a range of $1.50 -$3.50 per click. While the agency is comfortable buying using both methodologies (and certainly prefers the latter), the publisher is left wondering how to respond in a way that gives him the best overall price, and best revenue predictability. After evaluating the campaign, he may well decide that he will fare better on the CPC model, but in the absence of the granular past performance data of the demand side client, he will probably opt for the revenue visibility afforded by a CPM campaign.

Markets tend to work most best when both sides of the transaction have access to similar information. That leads to pricing efficiency, which in turn creates long-term sustainable performance results. Unfortunately, the traditional RFP process tends to strongly favor the demand side customer rather than the inventory purveyor. Add in the possibilities of buying on cost-per-lead (CPL) and cost-per-action (CPA), and you have a situation in which the demand side customer has the benefit of greater data visibility, and the supply side opportunity becomes purely speculative, leading to even more pronounced market inequities. These dynamics have largely occurred due to the seemingly unlimited supply of banner inventory (a supply side problem that will be debated in another article), but the fact remains that today’s standard agency RFP process falls far short of accounting for the multiple ways in which digital media can be bought and sold today.

Multiple Buying Methodologies: Along with a new multitude of pricing choices available to both sides, the emergence of real-time-bidding (RTB) makes the traditional RFP process even less relevant in for today’s progressive digital marketer. Say a marketer wants to reach “Upper income men in Connecticut that are in-market for a BMW 5-Series sedan.” That’s a pretty specific target, and I’ll bet that if a marketer could actually identify and find the several dozen guys in Darien, Stamford, and Greenwich that are looking for that specific make and model of car within the time period of the campaign, they might be willing to bid upwards of $500 CPM to reach him. Unfortunately, if you were to restrict the RFP variable to that exact target, you would end up serving a few hundred impressions, and probably fail to even spend $500 altogether. Naturally, the marketer is willing to bid a lot less find all men and women in Connecticut that are in market for a BMW; or just men in Connecticut in market for a car in general; or even just men in Connecticut, whether they need a car or not. Naturally, bids for each segment will vary widely, and can span from single to triple digits. Without a CPM-based pricing cap, it is not uncommon to see bids above $1,000 for certain impressions, although very few of them are won.

Well executed RTB campaigns have multiple segments that bid at different levels, and impressions are won at widely differing prices. While the marketer expects some visibility around what the effective CPM may be for such a campaign, RTB systems work best when agnostic to media cost, and should depend purely on the advertiser’s CPC or CPA goals. While a marketer can be very specific about his ultimate CPA, CPC, or CPL pricing cap, the traditional RFP does not address his tolerance for certain types of risk, his willingness to deploy a large percentage of media budget for data costs, and his willingness to forgo placement and context in exchange for reaching his ultimate demographic targets. This is just one of the reasons that agencies are having difficulty transitioning to the new world of demand side platforms in general.

New Discovery Mechanisms: Finding your audience by creating a well-crafted RFP and working with inventory suppliers to cobble together an effective buying program is still a great way to reach your ultimate goal, mostly because publishers know their audiences really well and have been able to offer new and creative ways to engage them on webpages (and, now, multiple screens). But what if the publisher isn’t really in control of his audience? What if the content an advertiser wants to be associated with migrates and changes constantly, based on user behavior and activity? I am talking, of course, about user generated content. Companies like Buzz Logic measure the “conversational density” around a topic and find where people are talking about, say, “organic food.” You can’t find that audience with a traditional RFP. The prevalence (or downright dominance) of social media outlets has created an explosion of UGC that is creating content almost faster than marketers can discover it. And that those new content areas are highly desirable to advertisers looking to engage consumers in contextually relevant activities. Those audiences are found via technology. How about finding people through the products they own (OwnerIQ) or even based on their occupation (Bizo)?

RTB and data make finding very granular audiences an intriguing option for marketers, but the traditional RFP process makes it hard to describe a marketers willingness to mix traditional, contextual audience buying (finding fantasy football fans on ESPN, for example) from some of the new audience discovery options (finding college students online based on their ownership of mini refrigerators, for example). Both are possible, and probably great to deploy over the course of a single campaign, but the traditional RFP process doesn’t really address this well.

Allocation: In my mind, the most important aspect missing from the traditional RFP process is that it doesn’t bring the demand and supply sides together effectively to suggest proper budget allocation for a campaign. If you have a $100,000 budget, and suggest $10,000 per publisher, every publisher is going to suggest $10,000 in media—regardless of whether or not they have it available. Moreover, you are going to alienate some publishers that may have larger minimums. The real problem is that the traditional RFP process doesn’t easily allow budget allocation across multiple media types (guaranteed display, real-time bidded display, mobile, video, search, and social) or take into account historical performance data. Essentially, the RFP makes a crude guess at budget allocation, with the marketer using his gut and some past performance data (“well, the $40,000 I spent with Pandora last time performed pretty well, so I’ll do that again”). Although the amount of choices today’s digital marketer has have expanded greatly, his form of communicating specific campaign needs is still an essay-length Word document or form-based technology with limited fields that do not capture the breadth of choices available.

So, what is the answer? New platform technologies are helping marketers expand the way they describe their campaign needs-and their willingness to deploy differing pricing and buying methodologies to reach their intended audience. Real time bidding systems are also giving end users hundreds of different levers to control the types of bids they are willing to make, based on the granularity of the audience, and performance of the inventory they purchased. In coming months, technology will not only expand a digital marketer’s ability to better describe his goals, but also use past performance data to suggest more effective media allocations in the beginning—and during—a campaign. Based on granular campaign attributes, knowledge of price points where certain real-time bids are won, and historical campaign performance, systems will be able to tell the marketer: “Allocate this percentage to SEM, this percentage to guaranteed display, and this much to real-time display” while suggesting the most effective bids to place. This three-dimensional discovery technique is where we are headed. While we are getting ready for its arrival, marketers should start thinking outside the traditional RFP box, and begin configuring new ways to ask inventory partners to find their desired audiences.

[This post originally appeared in eConsultancy on 8/19/11]