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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Social Affinity

Is your media measurement as dated as this 1970s den?

Is your media measurement as dated as this 1970s den?

The New Panel-Based Audience Measurement for Brands

With the prevalence of social data, yesterday’s panel-based measurement for digital campaigns is starting to look like the wood paneling in your grandmother’s den: A bit out of fashion. Marketers have been trained to buy media based on demographics, and it is natural to want your ads to be where you think your customers are. For BMW’s new entry level sedan, that might mean finding the media that males aged 26-34, who are earning 75,000 or more a year, consume. That makes a lot of sense, but it also means that your paid media will always compete alongside ads for your competitors. That is a big win for websites with premium inventory that fits your demographic, because it means scarcity and high prices for marketers.

What if there was another way to measure what audiences are right for brands? And what if that data were based on a panel of a few hundred million people, rather than a few thousand? Well, thanks to Facebook and Twitter, we have just such a web-based panel of consumers, and they are always eager to share their opinions in the forms of “likes,” “follows,” and (more importantly) engagement. Social listening platforms have been able to tell brands what people think about them directionally, and measure how certain marketing efforts move the social needle. Listening is great, but how do you get to hear what to buy?

A company called Colligent has been going beyond listening, by measuring what people actually do on Twitter, Facebook, and other social sites. “Liking” is not enough (when me, my 10-year old daughter, and my mom “like” Lady Gaga, the audience I am a part of gets too broad to target against). What matters is when people express true affinity by sharing videos, tweeting, and commenting. When people who are nuts about a certain celebrity are also nuts about a certain brand—and that relationship over-indexes against normal affinity, you have struck real social gold: Data that can make a difference. Pepsi recently used such data to choose Nicki Minaj as a spokesperson over dozens of other choices.

What about other media? Nielsen defines television, Arbitron measures radio, and MRI defines magazine audiences by demographics. Now, for the first time, marketers can use social data—gathered from panels nearly as large as the buying population—to define audiences by their own brand and category terms. That’s a world in which Pepsi can purchase “Pepsi GRPs” across all media, rather than GRPs in a specific media.

This is the way brands will buy in the future.

[This post originally appeared on 2/21/13 in The CMO Site, a United Business Media publication]

Can you Buy “Brand?”

SreeUnderstanding Social Affinity Data

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

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

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

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

Colligent data recently convinced Pepsi to choose Nicki Minaj as their spokesperson, since the data revealed a strong correlation between socially activated Pepsi and Minaj fans. Think about that for a second. For years, major brands have used softer, panel-based data (think “Q Score”) to decide what celebrities are most recognizable, and capture the right brand attributes. Now, getting hard metrics around the type of people who adore your brand are just a query away.  Digital marketers have been talking about “engagement” for years, and have developed a lexicon around measurement including “time spent” and “bounce rate.” Social affinity data goes deeper, measuring true engagement. For Nagarajan, “In order for the engagement to be truly effective, it needs to be measured from both sides (mutual engagement). The parallel is a real-world relationship. It’s not enough for me to like you, but you have to like me for us to have a relationship. Mapped to the brand affinity world, it’s not enough for Pepsi fans to engage with Nicki Minaj; enough Nicki fans have to engage with Pepsi (more than the population average on both sides) to make this relationship truly meaningful and thus actionable. When true engagement is married with such mutual engagement, the result is intelligence that filters the noise in social networks to surface meaningful relationships.”

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

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

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

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

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

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