The problem with social influence scores are they always measured an individual within the context of the entire TwitterSphere. Klout, Kred and PeerIndex have traditionally suffered from been unable to score individuals as part of a specific community. Kred, and their CEO Andrew Grill, have always had a slightly different approach and may be about to break from the pack.
After attending a networking event in London last week, there is a glimmer that Kred may be about to pivot in a genuinely interesting direction. Andrew produced an interesting roadmap, which introduced allowing the analysis of proprietary networks defined by a subscribing brand. Whether this goes as far as a dynamic influencer score that recognises the framework of the conversations that the individual is engaged with remains to be seen.
What brands seek to explain the power of social media to the top-table of marketing is ultimately the context of their immediate competitors. For example, it is great that I can identify authorities in automotive but the insight I need is whether the individual authority is more positive towards BMW, Audi or Mercedes-Benz and how this changes over time.
Triangulating audiences by language and dynamic connections is not a new concept; it is one the Cambridge Data has been practising for a number of years. However, where Kred offers new potential is the sheer scale of their indexed data profiles, which top 500m records. Reinventing Kred as a big data analysis engine is a good strategic move and may remove the key objection to influencer scoring that all the providers have suffered since its inception.
I’d be the first in the queue to test the analytics capability promised by Andrew and whether it does indeed produce a measure of social influence in context, dynamically and at scale.