Why Brands STILL don't Understand Digital Influence?
The topic on influence and influencers is very complex and continues to baffle the industry. In an attempt to clarify the intricacy of this subject, I presented the foundation and defined what influence really means last time. I also showed how the simplistic definition is insufficient. Moreover, we’ve put this definition to use and explained why the follower count metric shouldn’t contribute to someone’s influence. However, there are still much misunderstanding about influence and how it works.
When I started writing about influence and influencer two plus years ago, it was primarily because of two reasons:
- I was developing an influencer scoring algorithm on our community platform based on Social Network Analysis (SNA)
- I see a lot of misconceptions about influencers and how influence works in real life as well as on social media
But two years later, despite thousands of articles and dozens of good whitepapers written on this topic, brands still don’t understand digital influence. The interesting question is why?
No One Has Any Data on Real Influence
One of the reasons that brands don’t understand digital influence is because they don’t seem to realize that nobody actually has “data” on influence (i.e. data that says precisely who influenced who, when, where, how, etc.). Just because someone has a number, it doesn’t mean that number is measured. In the case of influence score, it is computed via some algorithms. These influence scoring algorithms are called black box models in statistics. They take inputs, which are typically activity and interactivity data of a user plus some environmental data on various social channels (e.g. posting messages, connecting with others, retweeting, etc.). Then they crank out an output score (via their black box) that supposedly predicts that user’s influence.
But where do influence vendors get those inputs? They certainly don’t own the user interaction data on various social channels. Vendors typically get their input from the respective social media platforms: either through public APIs, special data feeds through partnerships, or they buy the data.
However, social media were never designed to measure influence either, so they are not able to track explicit data on who influenced who. Social media were designed to let users connect, communicate, and interact in novel ways, so they only have data about connection, communication, and interaction between users. Social media platforms don’t have influence data either. Therefore, all influence scores are computed based on some models. As such, these scores are merely a prediction of someone’s influence, not his actual influence. Those who don’t understand this difference between real measured influence vs. predicted potential to influence are probably wasting their valuable dollars.
Real Influence Depends on the Influencees
Another aspect of influence that brands don’t seem to understand is that influence is a concept involving two parties: the influencer and the influencee (i.e. the target audience being influenced). Moreover, the definition of influence inherently depends on the effects produced on the influencees, not the influencers. So influence only occurs when there is a change in the influencees’ thought or behavior. An influencer can only try to cause that change, but ultimately it is the influencees who determine when real influence actually took place.
This is also why it is so hard to measure true influence, because we will need to track when a desired change has taken place among the influencees, then tally up how many of these are caused by specific influencers. As you can see, to measure real influence, we need to measure the influencees, not the influencers. That is why no one actually has this data, because no one can measure it. The influence score that is computed (not measured) from someone’s social media activities is merely his potential to influence.
Alright, back to the influencees. These influencees are actually implicit in every definition of influence I’ve seen. For example, let’s take the definition from “The Rise of Digital Influence” by Brian Solis: “The ability to cause effect, change behavior, and drive measurable outcomes online.” Cause effect on whom? Change who’s behavior? And drive measureable outcome from whom? If I recommended the new iPhone 5, and you (the influencee) did just what I said, that is influence. If I went and bought the iPhone 5 myself, is that still influence? Obviously not!
Remember, it is the influencees’ reaction to the influencer’s action that constitutes real influence. Without these influencees, there is no influence at all! Regardless how influential an influencer might be, without the influencees, the influencer cannot produce any influence in isolation.
Real influence is a difficult concept for brands to grasp, and there are two reasons for that:
- Brands don’t seem to realize that nobody has influence data (i.e. explicit data that says precisely who influenced who, when, where, under what context, on what subject matter, etc.). Therefore all influence scores are really influence estimation, which quantifies someone’s potential to influence
- Real influence depends on the influencees. No actual influence took place until the desired change of thought or behavior is carried out by the influencees. Influencers only have the potential to influence. They can’t actually influence anything without the influencees.
OK, now we understand why brands misunderstand influence, next time we will address the missing link in the influence industry. Stay tuned for the exciting revelation... In the meantime, I’m happy to discuss further any misunderstanding you might have about influence.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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