The Anti-Social of Social Network Analytics

Today I am sitting at home having a little pity-party as my entire family has succumbed to this season’s cold.

It reminded me of a TED presentation I watched a few years ago – ironically while in a taxi returning from a Cognos forum in the sunny Gold Coast.  Nicholas Christakis was discussing how social networks can be used to predict epidemics.

Essentially he proposed that with the vast amount of information available to us about the friends, families and other members of society they we interact with – through Facebook, Twitter, community groups, schools, doctors etc. – we are able to apply analytics to identify individuals with high social influence and use that information to predict the likely spread of disease.

In theory, had they conducted such analysis on my local community over the past few weeks, they may have been able to prevent the spread of this season’s cold by identifying the people with highest social contact and asking them to…well…stay home!

Then again, social network analytics is so much more than that!  Social influence is no longer constrained to physical interactions – we can influence peoples’ opinions and decisions across the other side of the world through social media.

How does this apply to the world of business?

Imagine if you could identify individuals in your existing or potential customer base that had high social influence, and target marketing campaigns and offers towards those specific individuals?  By providing them with offers and great service, they are likely to spread positive messages amongst their social networks with greater effectiveness than those with less social influence.

And by social influence, I’m not talking about how many twitter followers they have!  High influence can only be identified by analysing the nature of relationships across social domains (both online and in the real world) – who communicates with who, who is asked to make recommendations, whose recommendations are followed up with purchases – insight that can only be derived using technologies that can understand language, semantics and hidden relationships.

So whilst I sit at home waiting for the “wahmbulance”, pondering how social network analytics could have saved me and my family from the common cold, I hope you are sitting in the office planning your use of social network analytics to drive better outcomes for your business.