Understanding 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, which 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 10-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 percent of Ford's socially active fan base 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, is 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. As he said, "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 a brand's true audience. True engagement as measured by comments, photo posts, retweets, hashes, etc. became much better indicators of brand affinity and engagement."
Colligent data recently convinced Pepsi to choose Nicki Minaj as its 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 is 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:
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 companies become more aggressive aligning their CRM databases with social data.
Social Branding image on home page via Shutterstock.
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Chris O'Hara is an ad technology executive, and the author of "Best Practices in Digital Display Media," a contributor to ClickZ, and the author of the new whitepaper "Best Practices in Data Management." He can be reached through his blog at www.chrisohara.com
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