Almost every marketing plan starts with a sentence like "our target audience is the HR manager at a mid-sized company". Then the budget goes to channels and content, and hardly anyone still knows what that HR manager is looking for, asking or comparing. A target audience analysis is meant to close that gap. Because a list of characteristics is not yet an analysis: a good analysis helps you make real decisions.

What is a target audience analysis?

A target audience analysis is the systematic study of the people or organisations an organisation wants to reach: who they are, what they want to achieve and how they look for information and make decisions. The result is a description of one or more segments, for example in the form of personas, to which message, channels and content are tailored. A target audience analysis is not a one-off exercise: the audience, its questions and the places where it looks for answers change along with the market.

It helps to keep a few terms apart. A target audience is the group you want to reach. A persona describes one representative type within that group. And the target audience analysis is the work that leads from the first to the second, and that keeps testing the persona against what is happening in the market.

A four-step model

You don't need a heavy framework. Four steps are enough, and each step produces something you can build on in the next.

  • Define. Decide which group you are studying and why. Choose one goal, for example more enquiries from mid-sized companies in Flanders, and write down which decision the analysis needs to support. Without that decision, you end up with a nice document nobody does anything with.
  • Collect. Combine what you already know with what you still need to learn. Internally, that means customer conversations, questions that come in at sales and support, and your own website data. Externally, it means the questions your audience asks online, the terms it searches with and the sources it trusts.
  • Segment. Divide the group by behaviour and need, not just by age or sector. Two buyers from the same sector can have very different questions: one wants to solve a problem, the other wants to replace a supplier. Work out each segment as a persona with a name, a description, an intent and demographic characteristics.
  • Test. Translate each persona into search terms and questions, and measure whether you are visible there. If you can't find a segment in what people search for or ask an AI model, it is probably too vague or too small.

That last step is where most analyses drop off. A pity, because only when search terms and questions are attached to a persona does that persona really steer anything. As long as it only lives on a slide, not much happens with it.

An example

Take a fictional Flemish software company, ExampleSoft, that supplies scheduling software to technical service providers. It wants to understand why enquiries from installation companies are lagging behind.

  • Define. The goal: more enquiries from installation companies with 20 to 100 employees. The decision: what the team writes content about over the next two quarters.
  • Collect. Conversations with sales show that managing directors mainly want to know how to avoid double bookings in their schedules, while planners ask whether the software links to their existing accounting package.
  • Segment. That produces two personas. The managing director wants fewer errors and less overtime. The planner wants an integration and as little switching effort as possible.
  • Test. For the managing director, the team writes questions like "how do I prevent double scheduling in my installation company?". For the planner: "scheduling software with an accounting integration". The team then looks at which answers and search results ExampleSoft appears in, and where a competitor shows up.

What the team is left with is a working list: for each persona, the questions where the company is missing, in the order in which it wants to tackle them.

How personas make the analysis measurable

A persona only becomes measurable when you link it to something you can track. In GripIQ, that happens within a project. That is where you track your own Brand and your competitors, and where your personas, Keywords and Prompts belong. A persona has a name, a description, an intent and demographic characteristics, and is linked to Prompts and Keywords. So for each persona, you see how your brand performs on the search terms and in the AI answers that belong to that persona. You find the personas in the menu under Personas. They are managed by GripIQ, together with your account manager.

In concrete terms: for the managing director from the example, you record the Keywords and Prompts that match their questions. The Keywords give you your position in search results and your Share of Voice. The Prompts show whether and how your brand appears in the answers of AI models. If you are strong for one persona and barely visible for the other, you know where the analysis needs to lead to action.

The personas also feed into the weekly Insights. The name, description, intent and demographics of each persona are passed on as context, so the recommendations match your audience. If you haven't set up any personas, the Insights simply stay as they were.

One caveat: GripIQ does not replace conversations with your customers. The personas contain what you and your account manager put into them. What the platform adds is the test from step four, over and over again and on a larger scale.

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