Marketers are increasingly turning to social platform data to understand their customers, and how to tap 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. I recently sat down with Sree Nagarajan, the CEO of Colligent, a social data platform that measures “social affinity” to find out more about what marketers should be measuring, and how important the social graph really is when it comes to audience targeting.
The relatively recent availability of social data has given marketers the ability to enrich their customer data like never before, so they can understand their audience composition. How important is having access to social data for the brand marketer?
Sree Nagarajan (SN): It is very important for the following reasons. Firstly, it means having access to a large virtual panel: Instead of doing research on few thousand consumers, they get access to tens of thousands, even millions, of consumers. Such research using traditional methods would cost them a lot of time and money to obtain. Secondly, it is Specific to their brand; traditional syndicated research or datasets were usually media specific, not brand specific. In contrast, social data can be highly specific to their brand.
Are today’s current “social signals” strong enough to base large enterprise decisions on? In other words, are “likes” driving valid marketing activity, or are many brands just buying “likes” for their own sake?
SN: 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.
Is understanding a person’s friends interests (or “social graph”) important?
SN: Yes. Such an “interest” or “affinity” graph is complementary to a social graph. Each have their own strengths and weaknesses. While the social graph facilitates viral marketing, in practice it’s been harder to pull this off on a large scale consistently. This is because most social graphs are very heterogeneous, making it noisy and less actionable (think about the diversity of your friends and their interests). In contrast, an interest graph, when properly assembled, can be very actionable since it targets a virtual group of people that come together around their shared interests. A relevant analogy may be fans in a Lady Gaga concert. They are not connected by social graph but, rather, connected by a common interest. Interest graphs can provide much larger reach than social graph in many instances, because they can be media agnostic. For example, if Pepsi fans are interested in Nicki Minaj, Pepsi can get access to the Nicki Minaj audience by buying ads in digital, TV or doing sponsorships. A social graph is only actionable within the social network it’s part of, providing less opportunity for reach expansion.
Many 3rd party data providers use contextual or behavioral “signals” as a proxy for understanding consumer intent. What is the best data for really getting to a consumer’s purchase intent? Search? Membership in a social group/page?
SN: The best digital data sets for understanding purchase behaviors are search and social engagement. The value of search is already proven in bottom of funnel. However, social engagement drives the mid-funnel audience who are highly representative of your buyers. A correlation study done between album sales and engaged fans of hundreds of music artists in social networks revealed a 90% or better correlation in most cases.
With hundreds of millions of rich, deep customer profiles, how relevant are 3rd party segment providers anymore? Why shouldn’t marketers just “one stop shop” on Facebook for audience targeting (or use FB profile IDs to cookie target on the exchanges)?
SN: This is not practical for the following reasons. First off, you need to filter casual affinity (likes or follows) from true engagement. While engagement is predictive of purchase, “likes” may not be. Third parties can provide such filters. Secondly, each of the social networks capture only a subset of consumer’s engagement dimensions. Think about your own behavior: When you want to engage with your friends or some brands you probably go to Facebook. However, when you want to be up to date on favorite sports personality or celebrity, you do that much more on Twitter. Thus, no single network has all the contexts. Integrating different contexts from multiple networks is the value a 3rd party segment provider can offer. Thus, you can understand what celebrities capture Pepsi fans’ imagination in Twitter, and armed with that intelligence, can better target those celebrities’ fans in Facebook to expand your audience in a targeted way.
What is social “affinity” data? Why is this better than “likes”?
SN: Social affinity data measures true engagement. Such engagement is defined by the social network. For Facebook, it’s the people making comments, posting photos or videos, liking comments, or sharing them. On Twitter, it’s re-tweets, hashes, replies and talks beyond simple following of the brand. 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.
How far can you take social affinity data (beyond digital)?
SN: Because there so many people participating in social networks, and the measurement of their engagement is done passively by measurement companies, this data set represents the true behavior of brand’s consumers. In other words, social becomes a “looking glass” for mapping real world affinities. Think about your Facebook profile: How many of the likes you have there are driven by real world influences that you had from outside any social network (e.g., favorite artists, TV shows, celebs, etc.?) Thus it can be applied beyond digital. Affinity data can be used for traditional television media planning (i.e., buying the shows, networks, and radio stations that my fans are engaged with); public relations (focusing on the media outlets my fans are engaged with); and sponsorships (using affinity data to determine which celebrity should be a brand’s spokesperson, for example).
You mentioned a distinction between “brand” vs. “media” audiences Talk a little bit about your concept of and why its important to be aware of.
SN: 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). 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.
This interview, among many others, appears in EConsultancy’s recently published Best Practices in Data Management by Chris O’Hara. Chris is an ad technology executive, the author of Best Practices in Digital Display Media, a frequent contributor to a number of trade publications, and a blogger.