One of the basic principles of content marketing is “Know Thy Audience.” The principle is mentioned in almost every “how to get started” article on content marketing. Earlier this year, Ben Russell, a senior insights analyst at LinkedIn, had a particularly interesting take on the issue in an article entitled, “On Content Marketing – Know Your Audience”.

In trying to explain why content marketers struggle with developing relevant, engaging content, even when they know more about their audience than ever before, Russell writes…

I believe the problem is data. I believe we’re inundated with data to a point where we’ve lost focus, we don’t know what to ask for, and we’re overwhelmed by the sheer amounts at our disposal. I don’t blame anyone for feeling this way. We’ve got a lust for data, but no real data strategy.

The solution to this problem though – the answer to the question ‘what makes my audience tick’, naturally, is also found in data. Audiences send us signals – we need to be tuned into the right frequencies to pick these signals up .. we need to cut data in the right way to extract insights .. we need to look outside the scope of our own content in order to identify themes and topics that our audience has an affinity with.

We agree with Russell. However, picking up and deciphering the signals being sent by your audience is easier said than done. Your audience lives in different channels – Facebook, Twitter, LinkedIn, etc. And in each channel, users behave differently – what they read, how they interact with others, etc. Keeping track of it all is very difficult.

This is why we developed the audience graph. The audience graph is a unique collection of billions of data points from different channels and it consists of:

  • User-related information that can be used to define audiences.
  • Demographic and geographic data. Age, sex, location, etc.– data that are typically found in user profiles.
  • Psychographic data. Interests, activities, opinions, etc. – data found in a variety of sources – user profiles, posts made by users, search history, etc.
  • Phrases associated with topics. Typically related to the psychographic data about users, these phrases are words that are commonly found in posts and comments related to a particular topic.

Our Keywee solution uses the audience graph and predictive modeling capabilities to determine what sets of users – or “audiences” – would read what types of content. This gives content marketers the insights they need to not only know what content their audience is interested in, but also how the content will perform.

So, instead of the common approach where content marketers develop content and then figure out how to distribute it, the Keywee solution turns the process on its head. It can recommend content based on understanding a brand or publisher’s target audience. We call this “audience-driven content marketing” and we will be writing more about this topic in the future.