In the past couple years, a number of players have come to prominence in the Online Influence space. Some are providing free influence rankings and tying into third party applications (like Klout with Hootsuite), whereas others are offering paid services or bundling an influence component into existing solutions (like Lithium’s sCRM).
In this post, I’ll attempt to briefly describe the methodology of a few of the players and to consider the question of who’s doing it right/best.
Klout
Klout has been a dominant player on the influencer identification and ranking scene for some time now. Their fundamental ranking consists of three factors which they describe as True Reach, Amplification Probability, and Network Score. True Reach takes a stab at “engaged followers” which is a step ahead of merely counting followers and following. Amplification is derived by second level activity such as mentions, retweets, and likes (since Klout recently added Facebook integration to its predominantly twitter based system). By their own description, the score is weighted towards “clicks, comments, and retweets.”
Klout has won some significant battles in establishing connection with 3rd party software like HootSuite to include their influence ranking within the context of the software. Ultimately, I think this is where tremendous usefulness lies – as with anything in Social Media, you need to bring in in line with existing business processes so that people don’t have to go out of their way to use it.
For more on Klout’s ranking – see their description.
PeerIndex
PeerIndex makes an important distinction in their determination of online influence and authority. They recognize that online authority is contextual – i.e. being an authority on one topic has a very low correlation to authority on another topic. So, PeerIndex built their system to determine authority within a given category. This is certainly a step in the right direction.
Their core components on these topics include “authority, activity, and audience.” They base authority on content shared (whether created by the individual or not…) and do a comparison against others sharing on the same topic. This is another important factor as influence is relative. They look at your following for their audience score but attempt to normalize that often deceptive metric by looking for spam, gamed, and inactive accounts and pulling them from the overall audience metric.
Their activity score is based on volume for a topic – too high or too low relative to others on that topic is bad and being the first to mention something is good.
For more on how PeerIndex looks at influence – see their description.
Traackr
Traackr is a little bit more secretive than the first two in how they calculate influence. Their scoring system centers around three components – Reach, Resonance, and Relevance. Looks like a good start, but it’s hard to decipher what’s going into those scores other than “normalized metrics put through a rules engine” – which would basically describe any influence ranking. This is hardly surprising though as Traackr is one of the “closed door” influence systems with a paywall coming before you see any output or data on their system. It’s certainly a different approach than the freemium models adopted by Klout and PeerIndex.
Click here to see more on how Traackr looks at influence.
TunkRank
TunkRank is one of the more transparent systems for calculating online influence. I’ll let them speak for themselves to some extent – there are three key assumptions they’re making:
- Influence(X) = Expected number of people who will read a tweet that Xtweets, including all retweets of that tweet. For simplicity, we assume that, if a person reads the same message twice (because of retweets), both readings count.
- If X is a member of Followers(Y), then there is a 1/||Following(X)||probability that X will read a tweet posted by Y, where Following(X) is the set of people that X follows.
- If X reads a tweet from Y, there’s a constant probability p that X will retweet it.
These assumptions are then brought together in this equation:
TunkRank compares its methodology to the PageRank Algorithm which is heavily based on Eigenvector Centrality (for more on Centrality, click here) – and so is looking at the relationship dynamics behind influence. This is a key differentiator for TunkRank.
To lay out their approach more simply they claim:
- The amount of attention you can give is spread out among all those you follow. The more you follow, the less attention you can give each one.
- Your influence depends on the amount of attention your followers can give you.
For more on TunkRank check out this page and this blog.
Lithium
Though not solely an influence ranking provider, I have to give mention to Lithium in this discussion. Their principal scientist Michael Wu (@mich8elwu on Twitter) has done some of the most impressive and comprehensive work I’ve seen to date on online influence. Lithium builds their influence ranking into the S(ocial) CRM solutions and hosted community solutions they provide.
So that will conclude this discussion – thanks for reading all the way to the end. There’s plenty more to say about online influence and I know there are players I missed. What’s your take? Let me know in the comments.
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