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
• 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
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
• Lack Validity – fail to show that they predict a meaningful behavior (e.g. “real influence,” sales, good content, etc.)
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?).
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…