Can we unlock insights in big data using influence?

For the last couple posts, I have focused a lot of energy into trying to illustrate the power of influence on engagement strategies.  With so many voices contributing content, how can we focus on the conversations which are going to impact our brands to the greatest degree?  By knowing who is influential, we can ensure that we are paying the most attention to the people who can influence their own audiences.  This is often about figuring out a better engagement strategy on social media, so that company messaging reaches the largest audiences.  You can also understand what people think about your current products and campaigns, and it’s much better to focus on influencer conversations than wade through a billion conversations.

Companies like Klout and PeerIndex are giving companies the opportunity to see scores of influence for every individual, and they are starting to drill down onto the topics that these individuals influence.  Professional networks like LinkedIn allow brands to find and engage people based on what they do for work, and that’s a great way to tap into interest graphs, especially when it comes to people and topics where big purchases are being made.  And then you have a whole ecosystem of companies like Radian6, eCairn and Traackr who help to filter conversations based on influencer mapping using partner or home-grown methods.

But what’s the next step?  How do we help social media conversations achieve the promise of looking into the future?  I would make the case that influence is the answer.  It is one the primary keys to getting better interest graphs.  And interest graphs will then be the key to making sense of the insights hidden in big data.  So then instead of using influence just to figure out who should be engaged, rewarded and marketed to, we can also begin to use influence to find out where we need to go.

This is more strategic than simply finding out that Taco Bell’s Black Jack taco was a bad idea, because “I hate eating black colored food”.  Once we can accurately measure influence and match it to interest, we can use big data to figure out where thought leaders across the globe think the tablet market is going.  What do influencers in fashion think will be next year’s trends?  Which innovations are CIOs excited about over the next five years, and how can that affect the long term strategy of a Google or Salesforce?

I think these are the really big strategies that we can hope to understand from big data as influence and interest graphs are better understood.  What do you think?

, ,

Related Posts

Leave a Reply