Identifying influencers is an important part of social marketing, and becoming big business for some companies like Klout . Yet, influencer identification and analysis isn’t well understood, or easy to optimize. That’s because no two influence analysis efforts are the same. Like fingerprints or snowflakes, an influence analysis program is unique, specific to the goals and priorities of the organization executing it.
To date, automating influence analysis has relied heavily on quantitative measures (example: like followers/friends, RTs, mentions/replies). While these metrics can be valuable inputs into determining whether or not someone is influential, they aren’t worth much at all without their counterpart, qualitative metrics. If influence is a coin, then the 2 sides are quantitative and qualitative. Focusing purely on quantitative metrics tells you if someone is popular, not influential.
A recent and fitting example of this is the news of celebrity Charlie Sheen joining Twitter. Sheen amassed 1 million followers in approximately 24 hours. An impressive feat, no? Is he popular? Definitely. Is he influential? Well, according to Klout he is. Sheen currently has a Klout score of 88 (higher than most people, including industry thought leaders like Chris Brogan (81), Jason Falls (72), and Brian Clark a.k.a Copy Blogger (73). Does this mean Sheen is more influential than any of those 3? It depends on the topic.
Enter relative influence. Relevance brings the qualitative perspective necessary to complete the influence picture. Without it, you cannot be sure that you have an actionable list of influencers. The 3 individuals mentioned above are respected, trusted voices on topics such as marketing, social media and blogging. If you were to take a simple quantitative approach, and rely on Klout’s score, you could make the argument that Sheen is the more valuable influencer to focus on. Would you take his advice on any of those topics over Brogan, Falls, Clark? Absolutely not! Relative influence should be the focus on influencer analysis. You’re looking for individuals with deep, strong ties to the community on specific topics, not individuals with weak ties to many communities. Don’t confuse popularity with influence, they aren’t the same.
In addition to using automated tools to improve the effectiveness of your quantitative influencer analysis, here are some tips to gain a more robust qualitative view and gain a balanced perspective.
Finally, if you’re interested in a rich, in-depth perspective on influencer analysis, take the time to read this eBook, authored by @chuckhemann and @radian6. It will enlighten you on the finer points of influencer analysis, including specific methods and metrics to consider in yours.





