Social Marketing Analytics Framework Review #5 of 10 – Measuring Advocate Influence

Ok, here we go again, wrote about Share of VoiceAudience EngagementConversation Reach Key Performance Indicators and measuring Active Advocates fostering dialog according to Altimeter Research and Web Analytics Demystified previously.  Have to admit, half way though this series I’m getting tired of it – but will finish it as sheer act of will.

Besides the point, there is some good stuff in Altimeter‘s/WAD’s laundry list – but I’ve concluded the real problem here is the people who formulate the formulas don’t appear to be hands actually pulling this data .. so they don’t understand what they suggesting people do … nor how realistic it is to accomplish.

Hell, I’m a dreamer (and have nothing against a good dream……) so maybe it’s ok to muse about what it would take to come up with a “relative percentage of influence for an individual advocate across one or more social media channels by setting expectations for how far and wide each advocates message will travel using an advocacy influence calculation”.   All I can say is …  join the club.     I don’t think anyone has figured this KPI out or how to effectively do it.   At least, I’d like to see some examples of where a calculation such as this one was successfully applied  next time someone comes up with a bright idea like this.

Having said that – I am curious to know if any one has cracked this one – so I searched around.

First, I reasoned the formula could be rephrased partially, at least, to say “how do I calculate the influence of an influencer” which is more or less the same thing as calculating an Advocates’ influence – except I won’t split hairs.   With that in mind  I came to a post on the Fermentation blog on calculating the influence of Wine blogs.  The author of the post writes …

….. I’m unaware of any absolutely objective way to measure influence. Yet, I know who is influential. I know what publications are influential. How can I KNOW this? That’s what I’ve been wondering.

Turns out he figured out how to know who was influential…

That which has the greatest influence will most positively or negatively affects your bottom line when they speak or write of you or your service/product.

…. When calculating “influence” the first thing to know is who is your primary customer. The second thing to know is where are the bulk of those customers likely to point their eyeballs? Know these two things and you will know exactly who the “influencers” are.

So answering the question for this KPI sorta depends, at least, based on the above, on figuring out who your audience really is in the first place.  Certainly, your advocates are likely to be among them.

We can always go back to Social Monitoring – for example, take a look at Radian6‘s Influencer Widget where for The Havana Central profile I set up in Radian6, @amyvernon , @citynosh and @cecipf (she’s the community strategist / manager) are more influential than I am.

But, if I go and talk about Havana Central to “Havana Central’s” audience, or to people ]who could be their audience but don’t know it yet, how influential will either of us be … that will probably depend on our respective “Advocate Influence” score.

Unless we want to be totally arbitrary about it – you’d almost have to be if your going to apply this KPI to your data set -in order to calculate a score for Advocate Influence you first might need to have in place:

  1. audience segmentation – know as much as you can on how audiences are organized throughout the web.
  2. Your audience – how it’s organized and where it resides.
  3. a way of observing how much sway an advocate has across audiences in different segments than your own audience – then calculate some offset that accounts
  4. for the differences.

The closest thing I could find to what the Advocate Influence Score could be is using Eric T. Peterson’s Twitalyzer and looking at the IMPACT metric  – see

Impact, as defined by Twitalyzer, is a combination of the following factors:

  • The number of followers a user has
  • The number of unique references and citations of the user in Twitter
  • The frequency at which the user is uniquely retweeted
  • The frequency at which the user is uniquely retweeting other people
  • The relative frequency at which the user posts updates

I guess if we can figure out a factor that works  – maybe that will use it as the Advocate Influencer Score.

The lesson here – if you go though the trouble of defining a formula like Altimeter Group and Web Analytics Demystified did, also include the information you will need to apply the formulas – Altimeter group forgot that part.

I am glad they wrote and published their ebook on Social Marketing Metrics as it has given me a whole topic to write about.

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