[ Update : Klout changed their algorithms, I’m back to being a specialist – just enforces my post ]
Post Written for B2B Marketing Mag Nov / Dec Issue
On the day I jumped from a mere 52 specialist to a 60 broadcaster in Klout I thought it might be time to discuss social media influence. 

I guess by now most people have heard of the 1,9,90 rule, for those who have not: 1% of your community create and drive large amounts of activity, 9% are editors who modify content or add opinion around created content and the other 90% who tend to read and observe but rarely contribute to the community. So your primary influencers are the 1% of the community important to remember that a community does not just exist within the closed wall of a linkedin group or facebook page, they proliferate their content and opinions across more than one channel.

There have been many posts about Klout and Peer Index (the two big influencer measurement tools) most of them have been pretty negative as a whole claiming massive inaccuracies due to their method of measurement. To be honest I don’t blame people for slamming these services when the editor of mashable can have a higher influence than the President of the United States (argue-ably the most influential person in the world) it does start to pose a few questions. Personally I think both services have a few flaws but then they have a massive task of attempting to turn what is essentially qualitative relationship based interactions into hard qualitative statistics.

When they start banding around one liners such as “the standard for influence” is probably not helping their cause, but for me they still have value and I will keep using them to hunt down influencers for any given campaign I may be working on at any one time, this said they are by no means the only method I use, finding real influencers is no easy task and there should be a manual process, after all your looking for that magic 1%!!  Even some of the some of the more expensive options out there such as Meltwater Buzz cannot capture it all, it really does take you looking across all their social networks, where they are commenting and engaging to gain a comprehensive view of what that user is doing, how many people are acting upon information they are receiving from said user and to what extent.

Another issue these influence metrics have is they can be ‘gamed’, they are pretty easy to break unfortunately you’ll find a raft blog posts out their showing how easy it is to do, that’s why using as many tools as possible and then putting in some manual graft will get you closer to the influencers you need to find.

We like to work up a social mood board of influencers we have identified by benchmarking in solutions such as klout, from tweets to forum comments to blog posts and the list goes on, this gives us a good picture of that person to work from and helps us think about tactics we can use for the best possible response, and chances are even after all of that, they still may not want to pick up on the content or incentives we are feeding them to filter through their networks. Working with humans in this social environment is tough and working out influencers and sentiment cannot be solely given to ‘nifty’ algorithm, perhaps in a few years when AI is a little more advanced, but for now this needs to be a manual process.

I hope this article has not sounded like I’m trying to bash these tools, personally I’m a fan as they work well as a bench marking tool to begin the discovery process, and will only get better over time.

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