Digital Insights

Part I: Social Intelligence—BI Meets Social Media Data


Published: May 29, 2012

Ok, you’ve got the intelligence, so now what are you going to do with it?  Is it actionable?

Your company has been mining the nearly inexhaustible treasure trove of social media data to be found in Facebook posts, Twitter microblogs, Yelp reviews, social blogs, social bookmarks, YouTube videos, wikis, podcasts, Internet forums, etc.  You’ve created and published your own content designed to entice an audience into engaging with your brand.  And you’re diligently collecting, analyzing, integrating and distributing relevant social media data about current and future (hopefully) customers.

But can you act on what you’ve learned?  Can you correctly and effectively calculate and implement business decisions and marketing programs based on all that rigorously extracted and  hard-won social knowledge?

Actionable Insights

“It’s all about actionable insights,” according to Chase McMichael, CEO of Palo Alto, Calif.-based InfiniGraph (“the Ultimate Source of Social Intelligence”).  InfiniGraph tracks social network use to measure the strength of user connections to brands, friends and followers, as well as to help brands identify and analyze influencer segmentation, brand affinity and social activity hubs.

“Brands using social media today are demanding that their marketers go beyond the Facebook ‘Like’ and show some tangible results such as a click to a call-to-action or some level of traffic or conversion,” said McMichael.  “As a result, it’s now all about measuring and organizing consumer intent at the large scale and making it work in the social platforms.”

Predictive Audience Targeting

Sree Nagarajan is founder and CEO of Colligent, Inc., Redmond, Wash., a social network data collection, research and analysis company.  Colligent’s MeQ™ (Mutual Engagement Quotient™) system is a predictive audience targeting tool used for analyzing “affinities”—shared likings for comparable consumer items or brands that create a similarity of characteristics suggesting a relationship. These affinities can be revealed in online social network (e.g., Facebook, MySpace, etc.) activity. Nagarajan estimates that Colligent is able to produce one trillion+ data points showing how consumers engage with brands.

“Social intelligence is really a ‘looking glass’ of consumer behavior across media,” said Nagarajan.  “For example, people watch TV shows and then ‘comment/like’ on social networks. This makes social intelligence an ideal foundation to build all media/marketing applications, not just digital. The ability to apply this intelligence across media is where the next big opportunity lies for Social Intelligence.”

‘Beavis and Butt-head Redux’

Nagarajan knows whereof he speaks.  In 2011, his company used a meetup of cable TV, Google and Facebook to help MTV revive “Beavis and Butt-head,” television’s notorious ‘90s cartoon teen slackers.

The music network was looking for ways to expand awareness and interest in “Mike Judge’s Beavis and Butt-head,” MTV’s relaunch of the once-popular animated comedy.  MTV execs initiated a multi-media marketing campaign that included online contextual advertising buys in Google AdSense using a list of keywords researched by them and suggested by the Google AdPlanner tool. This brought the network certain audience reach, but execs wanted more.

MTV turned to Colligent’s MeQ™ system.  Colligent’s targeting identified specific but “non-obvious” TV show (Breaking Bad) and music artist (Kid Cudi) keywords that “Beavis” fans engaged with, and vice versa.  As a result, MTV created a new Ad Group (container for keywords in a Google search marketing campaign) with Colligent’s proposed keywords to buy additional contextual advertising.

In comparison to the Google-MTV-generated in-house Ad Group, the Colligent-recommended Ad Group increased reach by 177 percent with an 11 percent higher click-thru rate (CTR) and a cost per click (CPC) that was slightly lower.

On Oct. 27, 2011, almost 3.3 million Americans tuned in for the premiere episode of “Mike Judge’s Beavis and Butt-head,” and the show scored a 2.6 rating in the 12-34 year-old age group.  This was great news for the network, which had targeted just a 2.0 rating but instead gained an audience increase of 30 percent – 750,000 actual viewers.  Unsurprisingly, the series performed best with a male audience, as two-thirds of its viewers were men.

“We got keyword recommendations from Google, but Colligent brought up some things we hadn’t seen or expected—reality shows, dramas, the Lonely Island comedy troupe, hip-hop groups, even older bands like Pantera,” said Jon Ward, SEM/SEO Coordinator and a digital marketing specialist for MTV Networks.

“We were able to generate huge buzz for our series while targeting ads to the people we believed would be most interested,” said Ward.

Part II: Social Intelligence—Gathering Social Media Data

Part III: Social Intelligence Drives the Shaping of Online Content

Part IV: Social Intelligence—Content Curation a Key Filter