Whether Influencer Listers are egoists or not, the comments on Peter Kim’s post last week questioning the self-promoting nature of said lists raised some interesting assumptions about what we expect from the construct of Influence.
To sum up the commentary:
• Influencer lists provide no value because they’re simply popularity lists.
• Influence is undefined and ambiguous.
• Influencer marketing is ineffective, or diluted because of size and the ambiguity of lists
I’ve blogged before about my dislike of measuring something for the sake of measurement. Specifically, I’ve been pretty harsh when people make magic formulas combining a hodgepodge of variables then call it Influence. In my opinion, these approaches go wrong for 3 reasons.
• Lack objectivity-- arbitrarily involve variables simply because they’re available (e.g. # friends)
• Lack reliability - incorporate variables that measure the same thing multiple times (e.g. friends on Facebook + followers on Twitter + connections on LinkedIn)
• Lack Validity - fail to show that they predict a meaningful behavior (e.g. “real influence,” sales, good content, etc.)
Without going into detail on psychometrics, I think others would agree there’s an abundance of digital breadcrumbs available to us… we have to start to show how they relate to meaningful constructs; influence, arguably, being one of them.
I think the call to arms today is mainly about validity: we need evidence that people are measuring what they’re trying to measure—that “influence” algorithms predict something meaningful (e.g. widget adoption?).
To be clear, our expectations for influence, influencers and influencer lists probably vary as widely as the ways they are being measured. Transparency will be key.
All this, and I still haven’t come down on the practice of influencer marketing…