“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Chris O’Hara, co-founder and chief revenue officer of Bionic Advertising Systems.
Despite years of online targeting, the idea of having a holistic “360 degree view” of the consumer has been somewhat of a unicorn. Today’s new data management platform landscape and cross-device identification technologies are starting to come close, but they are missing a key piece: the ability to marry key social affinity data.
In this chart, you can see that online consumers tell us about themselves in a number of ways:
Viewing affinities: We can see where they go online and what they like to look at, providing strong signals of their interests. Nielsen, comScore, Arbitron and others have great viewership and listenership data that is strong on demographics, so we can get a great sense of the type of folks a certain website attracts. This is great, but brands still struggle to align demographic qualities with brand engagement. Thirty-four year old men should like ESPN, but they could easily love Cooking.com more.
Conversational affinities: What about the things 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 snapshot of sentiment. But this data on its own lacks the lens of behavior to give it actionable context.
Social behavioral affinities: 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, such as 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 engages with.
Adding social affinity data to the mix to understand a consumer can be a powerful way to understand how brands relate to the people’s many interests, such as teams, books, websites, celebrities or musicians. 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 hire a research firm to 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 to deliver relevance because of the scale effect. In other words, I like Beyonce, and so do my wife and daughter. 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),” he said. “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 the noise in social networks to surface meaningful relationships.”
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.
• 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 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. If “Teen Wolf” fans spend more time on Twitter than Facebook, the show’s marketing team can increase tweets and post more questions that lead to more retweets and replies.
• Keyword buying is also interesting. Probing the mutual affinities between brands and celebrities, shows or bands can yield long tail suggested keyword targets for Google, Bing/Yahoo and Facebook that are less expensive and provide more reach than those automatically suggested. For 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.” Nobody else bid 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.