The Missing Link of Influence: The Potential to Influence, and Be Influenced
In my previous posts, I defined influence and discussed why brands don’t seem to understand digital influence. Today, we are ready to talk about the missing link in the influence industry. This article builds on the previous two, so I would recommend reviewing the following posts if you missed them earlier:
- What is Influence, Really? – No Carrots, No Sticks, No Annoyance, No Tricks
- Why Brands STILL don't Understand Digital Influence?
What’s Wrong with the Current State of Influence Analytics?
Last time we explained why nobody can actually measures real influence on social media. So influence vendors must build models that predict someone’s influence in order to compute their influence score. The problem is that most of these influence models focuses on the influencer. Nearly all models are focused on estimating the influencer’s social capital. Therefore an influence score is merely a prediction on the influencer’s potential to influence.
However, we also know that no actual influence occurs until the influencees produce a measurable action. Without the influencees, there is simply no influence. This is what is surprising and mind boggling to me. If real influence depends so much on the influencees, why are most vendors still so focused so much on the influencers?
It is partly because there are significantly fewer influencers than influencees, so it is much easier to collect, analyze, model, and compute the activity data from the influencer than from the influencees. It is not difficult to spot an influencer, because they are the ones that typically stand out right in front of your eyes when you look at their activity data. However, it is much harder to identify the influencees. They are pervasive. They easily outnumber the influencers by hundreds or thousands of times. Influencees are also inconspicuous despite their abundance. Their activity data are weak and typically masked below the noise level.
However, a valid influence model must consider the influencees. In fact, the accuracy and effectiveness of any influence scoring algorithm depends on how much influencee attributes it considers and how close these attributes are to the influencees. But again, no one measures whether an influencee’s is actually being influenced (i.e. real influence data on social media do not exist). So these influencee attributes can only help predict the influencees’ potential to be influenced.
The Big Missing Link of Influence
So when does real influence take place? To be absolutely rigorous and correct, no one knows for sure. But we do know when influence will occur with high probability. That is when the influencer’s potential to influence is aligned with the influencee’s potential to be influenced.
Because all that influence vendors have is a score that is indicative of the influencer’s potential to influence, there is a missing link in the influence industry today, and it is the influencee’s capacity to be influence. A couple years ago when I introduced a simple influence model, I found four categories of attributes that characterize the influencees’ likelihood to be influenced:
- content relevance
- timing (or temporal relevance)
- channel alignment
- confidence (a.k.a. trust)
Today, several influence vendors have implemented timing and channel alignment, and to a much lesser extent relevance. However, they have implemented these as attributes of an influencer, whereas they should be attributes of the influencee. For example, influence vendors treat timing as an influencer attribute, which indicates when the influencer’s frequency of communication changes. But the timing that I talk about is an attribute of the influencees, and it characterizes the temporal window within which the influencees are susceptible to being influenced.
No wonder, brands don’t get influence, because I don’t even think the influence vendors get influence. In reality, an influencer’s potential to influence has little correlation with his frequency of communication.
For example, President Obama’s potential to influence is the same regardless of whether he communicates or not. His actual influence (i.e. how many people he actually influenced) does change depending on how much he communicates. But influence vendors do not measure actual influence; they can only estimate someone’s potential to influence. By including the timing factor as an attribute of the influencers just tells me that even the influence vendors do not understand the difference between the potential to influence and real influence.
- Most influence vendors focus on measuring the social capital of the influencer, but real influence occurs when a change in thought or behavior is produced in the influencee (i.e. person being influenced)
- This is the missing link of influence: the link from the “potential to influence” to the “potential to be influenced.” Real influence can only occur when there is an alignment between these two. In fact, this is the minimum required state
Influencer marketing has huge potential. But as an industry we are far from realizing this potential. Partly is because influence is a challenging concept that involves much more than just the influencers themselves. Moreover, there are many big data and analytics challenges in accurately estimating someone’s potential to influence. But first, influence vendors need to start incorporating more attributes of the influencees into their model in order to improve the accuracy of their influence score.
Next time, let’s dig deeper into the algorithms that score influence. In the meantime, if you also do research on digital influence, I’d be happy to discuss your findings here.
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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