Online influence metrics are literally everywhere at the moment. People are even getting refused jobs because their Klout score is too low – it’s both a bit scary and a lot stupid, but, there has to be a reason that these metrics are so popular. The fact of the matter is that they save an awful lot of time and effort in terms of working out who to bother with, and whilst that may be unpalatable as a concept, the basic economics of the argument are hard to deny.
The problem with these metrics is two-fold. Firstly, simplification – when you try to condense large amounts of mostly unstructured data into a single number you necessarily factor-out almost any attempt at nuance. Secondly, it’s a lack of transparency. It’s almost impossible to understand how this simplification happens, what nuance sacrifices are being made for the sake of simplification and, because of this, it’s very difficult to use the metrics effectively (or to ethically recommend their use to clients).
Into this mix comes Kred – Peoplebrowsr’s version of Klout and PeerIndex. On the surface it’s very similar (in-fact I’d say it’s headline number is slightly less accurate than that of it’s competition), however, underneath the surface it has one very distinct advantage – transparency. Users can see an activity log that shows exactly how someones score is being affected and, agree or disagree with how it calculates influence, at least you can understand it.
So, over the last few days we’ve thrown together an experiment designed to see if our understanding of Kred has allowed us to create something useful to people. The tool is designed to help you get a big picture of who you’re followers are – it allows you to download a spreadsheet of all your followers with separate tabs for the key influencers in different communities of interest. The idea is that once you’ve done it you should be able to isolate key groups of people within your followers who have a particular area of influence and cater to them accordingly.
We use Kred’s global and community influence scores, set minimum numbers for both and then look at the ratio between influence on a given community and overall influence. The result should be that people who are quite influential on the overall ratings but particularly influential within specific communities get ranked highly on the tabs for that community. People who are just uber influential on everything (think Stephen Fry / Hootsuite) should come out lower down as they are really only ranking highly because they have a bazillion followers. People who are influential overall but not particularly influential within a given community get heavily penalised so that when we outline a community, you get people who are specialised in that particular area.
Sorry if that sounds complicated but, it turns out, nuance is important.
We’d love it if you would check out www.followrz.com and let us know whether the results match your expectations. It’s a free tool and we’re limited by Kred’s api so please be patient if it takes a while to get your results to you.