How social media makes all communication better
Graphic shows audience segments from Targeted Audience to Interested Audience to Engaged Audience to Fans. Graphic describes ways to identify who is in which audience and ways to measure movement from one to the next.

Who is in what audience segment, and how do we know when they move from one to the next?

Social media channels, which spew data like a fire hydrant, have helped us learn a lot fast. A broad spectrum of society uses social media. Their actions are recorded and provide good indicators of human reaction to communication stimuli. In just a few short years, we have learned intricate communication/response patterns. With those patterns, we can predict and prescribe more accurately.
That body of knowledge must be brought to bear on all our communication, whether or not we use digital media. True, as the people in our organizations learn and adapt to change they don’t leave as clear a trail as you find in a Google Analytics report. But they exhibit analogous, measurable, and non-ditigal behaviors:
How many show up to a town hall?
How many ask questions?
Are there patterns to the questions asked across the organization?
How many ideas are submitted?
How many volunteer?
How many are seeking new training?
And if your organization is like mine, there is a trove of employee survey data and digital metrics to tap into as well:
Employee satisfaction surveys.
Email open rates.
Website visit data.
Training sessions completed.
Videos watched.
Materials downloaded.
The trick is to identify patterns in all those digital and non-digital data points. For this, I suggest we expand upon the work of Jeffrey K. Rohrs and Michael Wu. Exact Target’s Rohrs published his book Audience this year, and Lithium’s Wu published a white paper, The Science of Social 2, this year as well. Each has established new frontiers in social media analysis, and each has built their theories upon non-digital human patterns of behavior.
Rohrs describes the consumer journey that lives outside social media as well as in it: Consumers start by trying to solve problems or ask questions. They have their tried-and-true sources of information and a means for finding and assessing new sources. Many enjoy sharing what they learn. Some become fans of a good or a service, and then evangelize on behalf of the brand.
Wu’s “Science of Social” emphasizes the degree of interactivity between people. Opinion and behavior change is more likely as interactivity increases.
Synthesizing their work with the traditional communication continuum of awareness-understanding-engagement-action, I have developed a framework I have called Audience Builder. I suggest the audiences we as communicators must cultivate are:
  • Targeted audience: Let’s get clear about the distinct groups we want to influence.
  • Interested audience: Either our communication piques their interest or they already are interested. In either case, we need to know how to tell they are interested and how to feed their curiosity.
  • Engaged audience: They are working with the subject matter. They are touching it, manipulating it, figuring out how it works for them. The ultimate engagement: they act by buying or joining.
  • Fans: These are the people who really make change happen. They not only have bought in, they are turning around to bring their colleagues along. Some have matriculated into this audience through our efforts to cultivate their interest and engagement. Others have been here all along, waiting for us to give them the tools to do their evangelizing.
My Audience Builder framework draws our attention to two things:
    How do we know who is in what audience.
    How do we know someone has moved from one audience to the next.
We look for data points that help us quantify how many people are in a particular audience set (examples: demographics, attendance figures, email lists, forum participants). And we look for data points that indicate movement from one audience set to another (examples: email open rates, questions asked, ideas submitted, networks formed). We observe these data over time and begin to detect patterns that inform our ongoing campaign and the campaigns to follow.
This framework is still very much in beta. My team has used it to spur thinking and action on communication projects large and small. And I am currently trying to use it to refresh our overall internal communication program. I hope that through this process, we develop new communication planning tools and new metrics dashboards that will routinize the framework. I will share those pieces as we go along, and hope communicators in other industries will try them and share what they learn.
Let’s crowd source this thing!

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