Social intelligence, if done well, promises a full and true understanding of your consumer. If you can understand your consumer, the story goes, you can create meaningful relationships that then lead to stronger customer loyalty and greater market share. This is a lofty goal, of course. For that reason, let’s break down one component of social intelligence: audience analytics.
What is audience analytics?
Put simply, social-based audience analytics is looking at social media data to better understand who your audience is, its size, what individuals think, and how to target them. Of all the types of social listening, audience analytics is one of the most essential. While mainly intended to inform campaign strategies, audience analysis impacts everything from product development to customer experience.
Today, we’re examining two critical principles of an effective audience analysis strategy, both of which focus on data.
Observational or Passive Data?
‘Naturalistic observation’ is a concept in market research that involves observing people’s behavior in which it typically occurs. In the world of social media, those who comment or tweet may not always represent your audience. In many cases, they are either on one side of the spectrum, or their social comments are prompted by you, the brand. Thus, capturing this set of consumers, while essential, is not sufficient.
Now consider yourself in the shoes of a social media user. You might be on a number of platforms, but are you actively posting, tweeting and commenting? Now, imagine your consumer audience. There are millions of people who quietly leave behind a social footprint of follows, likes and shares, rather than comments and tweets. In other words, this audience offers a form of ‘naturalistic observation’. Combined, this quiet majority offers a deep reservoir of behavioral data that can inform your content and channel strategy, among many things. This data is larger in number and serves as an important counterbalance to the data and insights you find from those who are actively commenting and tweeting.
In order to tap into this quiet behavioral data, you will need software. When evaluating different software programs, be mindful of the type of data that is captured and assess whether it considers this quiet majority.
Consider the data source
Not all social media sources are the same. That’s not an earth-shattering insight. But think more deeply about how they are different. Let’s assume you are running a simple, independent, qualitative study. This sort of thinking can help you focus your time on the right resources, depending on your questions and use-case.
It’s helpful to think about this data across two axes: (1) whether the mentions are more emotional or more functional and (2) whether the platform is more interactive or more declarative.
For example, if you are looking to vet the size of your audience and inform product development, consider starting with consumer opinion pages and forums, like Reddit. Across these sources, you’ll find more objective, fact-based insights. This will help you create some boundaries around your investigation, like where are there unmet needs or existing product weaknesses.
Another dimension you could consider is timeliness and, as mentioned, emotion. Let’s assume you are considering a new campaign and you have a short turnaround. You would like the campaign to be highly topical and thought-provoking. You may turn to Twitter, as it reveals in-moment experiences. But you also should consider comments under news articles or blog posts. These sources are great for relevant topics and emotional reactions.
Applying Audience Analytics in Real Life
Under the big umbrella of social listening, audience analytics is the largest and most fruitful component. Before you begin unearthing audience analytics, make sure that the strategy you’re using to understand your audience is appropriate and fitting for your current goals. Read our newest use case story about how a global energy drink company applied audience analytics here.
