This post kicks off a multi-post miniseries on the topic of influencers: how to find them, engage them, and collaborate with them in word-of-mouth (WOM) marketing programs.
Influence marketing today is in a state of experimentation that scientists call the pre-paradigm phase or exploratory phase. During this phase, everyone is trying different approaches based on experience. There are incomplete theories about why some approaches work and others fail, but there is no underlying fundamental principle that explains everything. My approach in this series is to see if we can gain a deeper understanding by analyzing the process of influence from a data analytics perspective, using a simplified model of social media influence.
A Simplified Model of Social Media Influence:
Influence involves two entities, which I will refer to as influencer and target.
1. The influencer's power to influence depends on two factors:
a. Credibility: The influencer's expertise in a specific domain of knowledge.
Please note: There is no such thing as a universal influencer, because no one can possibly be influential in all domains. The best that anyone can hope for is an influencer in a specific domain of knowledge
b. Bandwidth: The influencer's ability to transmit his expert knowledge through a social media channel.
Please note: Active influencers in one channel may not even be present on another channel. So influencers are not only specific to a domain of knowledge, they are specific to social media channels
2. The target's likelihood to be influenced by a specific influencer depends on four factors:
a. Relevance (the right information): How closely the target's information needs coincide with the influencer's expertise. If the information provided by the influencer is not relevant, then it is just spam to the target and will be ignored.
b. Timing (the right time): The ability of the influencer to deliver his expert knowledge to the target at the time when the target needed it. There is only a small time window along the decision trajectory when the target can be influenced. Outside this golden window, even relevant content will be treated as spam because there is no temporal relevance.
c. Alignment (the right place): The amount of channel overlap between the target and the influencer. If the target is on a different social media channel, then the influencer's information either take too long or never reach the target.
d. Confidence (the right person): How much the target trusts the influencer with respect to his information needs. Even if the influencer is credible, the target must have confidence in him. Without trust, any information from the influencer will be downgraded by the target.
This model is very general, and it is intended to be applicable to any social media channel. However, it is by no means complete. I just like to use the principle of Occam's razor and start with a simple model that is consistent with the data out there and see how much it explains. We can always add to the model if it proves to be insufficient. As Albert Einstein once said, "Everything should be made as simple as possible, but not simpler."
Please note that a lot of attention has been focused on influencers, but very little has focused on their targets. Although it is easier to work with the influencers, we must not forget that it is the targets that we want ultimately. I hope this simple model will help you think about social influence from a more balanced perspective, so that even when we are looking for the influencers and working with them, we still have the targets in mind.
Now that you know the basics of how social media influence works, it should not be difficult to diagnose the success or failure of a social media campaign, at least from a data analytics perspective. As shown in the photo above, any broken link between the influencer and the target is enough to break the chain and stall the whole influence process. Next time, I will show you how to take the first step of WOM/influencer marketing: find the influencers.
Dr. Michael Wu was the Chief Scientist at Lithium Technologies from 2008 until 2018, where he applied data-driven methodologies to investigate and understand the social web. Michael developed many predictive social analytics with actionable insights. His R&D work won him the recognition as a 2010 Influential Leader by CRM Magazine. His insights are made accessible through “The Science of Social,” and “The Science of Social 2”—two easy-reading e-books for business audience. Prior to industry, Michael received his Ph.D. from UC Berkeley’s Biophysics program, where he also received his triple major undergraduate degree in Applied Math, Physics, and Molecular & Cell Biology.
You must be a registered user to add a comment. If you've already registered, sign in. Otherwise, register and sign in.