Monday, April 30

Influencer Measurement: What does it mean?

Over the past couple of weeks, I have noted an increasing fever pitch of discussion on Klout, Kred and PeerIndex - and how the concepts of measuring influence is both good and bad, the potential downfall of society, an enviable outcome of a more open, transparent community (great one Nick!) and so on.

Wired's article goes into the history of Klout and how it came about (Joe and his three-month speaking diaengagement), Kred's own story is borne out of their tool for social media analytics and monitoring and PeerIndex was borne out of an initial desire to find the best topic curator using crowdsourcing to provide the most interesting content being published at the time.

But the real question is: what are these companies doing?  What is the big deal about "influence marketing"?  Why do I get a score of 47 at Peerindex, 54 at Klout and 721(!!!) at Kred?

http://www.peerindex.com/sanfordhttp://www.klout.com/sanfordhttp://www.kred.com/sanford

What We Measure is One Thing, What Our Clients Want Can Be Another


One of my favorite discussions in the past 15 months often consists of either an agency marketing person, or a brand director or other publication entrepreneurs working to understand what can "influence marketing" do for them?  In a previous post, I describe what I believe to be the rebranding of word-of-mouth marketing to influence marketing.  The difference is that in the new era of social networks, APIs for "exhaust data" and Big Data systems to process all of this data into actionable information brings about a new belief that we can "measure" word-of-mouth.  I would argue, like Nathan Gilliat did, that there is no such thing as a "unit of influence".  All of us - Klout, PeerIndex and Kred - are building models that become our own "truth" for determining actions.

And yes, these are models - not specifically measurement on influence if that was possible.  We use models to extrapolate what is happening - and all models have biases that are built around hypotheses.  As Nathan says:
Models reflect the opinion of the modeler and the objectives they support. Because apparently simple concepts might be used for different purposes by different specialists, we end up with diverse models using the same labels. In essence, we talk about the labels, because they represent familiar ideas (influence, et al), but the models represent what we really care about (such as positive word of mouth, leads, and sales).   
If you understand that the label is just a convenient shorthand for a model that takes too many words to describe in conversation, it's not a problem. If the model generates useful information, it's doing its job. Just don't assume that any one usage of the label is the correct usage. Modeling requires judgment, interpretation, and prioritization in context, which are incompatible with standardization.
So, if you understand that the models are our shorthand for creating meaning out of measurement, the real question is, what are our clients looking for?  And to be clear, by clients usually mean the ones who pay for our services.

Agencies and Brands are suffering.  They are awash in data and dashboards and monitoring and listening and so on - trying to get a grip on this world we call social media marketing.  Years ago, when I spoke on politics and social media , the ideas of "social media" was scoffed at.  Today, it will be the "biggest" area of growth in all businesses.  Shades of the Cluetrain Manifesto, anyone? 

So, when the clients are asking for help, what are they doing?  The same thing they have been asking for years on end - how do I get the message out to the right people to move the needle on my goal?  The problem is - what do you need to measure to be able to determine return-on-investment (ROI)?  And that is where the problem and the puzzle exists.

Sunday, April 8

Politics in a World of Social Data - who knows what about whom?

This past month, I have been reading Eli Parser's "The Filter Bubble" on my iPad Kindle app - and I must say, Eli has done a bang up job of discussing the impact the Filter Bubble can and will have on our political and intellectual discourse.

As I continue to work on the problem of "influence marketing", it brings back concepts of control theory to mind, and how tuning the inputs to a system can probabilistically ensure a bounds on response.  And, as you begin to learn the "modes" of the "system", you are able to generate more desirable performance from the "system" overall.

Eli uses the phrase "persuasion styles" (from Dean Eckles at Stanford and Facebook) which discuss the modes on how a person can be influenced to perform an act/action based on how the request is made, not simply by the content.  And with so much Social Action data around on the web (and purchasable in large batches from companies like GNIP and DataSift), the ability to sift through the voting records, demographic records and Social Action records will be a powerful mix.

Political Influencer Marketing

So what is "influencer marketing"?  As I described in an earlier post, some circles consider the measure of influence corresponds to your perceived expertise in a particular topics or arena.  It harkens back to the old E.F. Hutton commercial, "When E.F. Hutton talks, people listen."  But in today's world of social media focused on driving action - and advertising dollars being spent to drive purchases or brand impressions, influence marketing becomes more of a word-of-mouth advocacy concept - who is able to drive actions from whom by creating reciprocal or responsive actions to the original action.

In Control Theory, we would call the original Social Action an "impulse" and then see what "modes" get "excited" (e.g., vibrate, resonate).  This processes is known as System Identification.  And while most "systems" are considered from a single-input/single-output (SISO) model, we know that we are bombarded by "impulses" from all sorts of sources (e.g., TV, adverts, Facebook shares, retweets, LinkedIn updates).