Influence is a fairly nebulous term – Phil Sheldrake’s Influence Scorecard is a great step towards making it less so. By using Phil’s variation of Kaplans Balanced Scorecard, there’s opportunity to connect organizational performance indicators to measures of influence, which, looked at independently, can be confusing or misleading.

As an invited contributor, we’re excited about offering nearly 5 years of
learnings to the discussion. Through our analytics platform and advertising
network, we’ve conducted influence measurement not only as a way to rank how
influential online consumers are, but as a new method of predict a site’s
likelihood to perform as an advertiser. It’s clear that UGC is influencing
both consumer perception and buying behavior – it’s by diving into the
characteristics of that influential content and understanding its
connections to other influencers that we’ve been cracking the code to date.
And what we’re finding is that the level of influence a site may have on its readership
can directly correlate to how advertising on that site performs. Pretty
amazing, and I expect a framework like Phil’s will unearth many more success
stories.

We’ve also been thinking a lot about different influence metrics for the “social
web” – and by that I don’t mean specific platforms like Facebook or Twitter.
As marketers, we need to start looking more holistically at the Web to see how
all online media is becoming socially connected. Understanding the intent
and nature of social connections across both UGC and “traditional” content is
one factor to exposing it’s influence. Analyzing the content itself, and the author’s
credibility on the specific topic, is another. It’s important to remember that influence
is highly contextual, so that should be kept in mind as we drive toward working
models and standardization.

This should be a meaningful discussion – one we look forward to helping to
shape in the weeks and months to come.