Find Content Gaps Using Radar Charts

Welcome to the first post from our toolbox, where we'll share the techniques, activities, and ideas we're using in our content strategy workshops.

One of my favorite things about strategic analysis is the power of fuzzy ratings —that’s what I call it when we grade things on an imprecise scale like “easy/moderate/hard” or “terrible/needs work/decent/perfect.” In early strategic planning phases, a gap analysis with fuzzy ratings can help us move a project in the right direction without getting caught up on exact tactics and specific implementations. This is especially important when a site or project has a lot of moving parts, distinct audiences, and complex business goals, and you risk important pieces falling through the cracks.

This gap identification exercise uses a radar chart to help find holes—like unserved audiences, or features not tied to larger goals—in the current website and content planning. Also called a star chart, a polar chart, or charmingly, a kiviat graph, these simple diagrams are a great way to visualize a bunch of disparate metrics.

In a classic radar chart, the spokes represent a family of variables, like organizational departments, website audiences, geographical regions, and so on. Each spoke holds a scale to plot data against. In this Wikipedia example chart, the scale is visible so the chart looks like a spiderweb. When I'm making these charts by hand (see below), and to measure fuzzy metrics, it's fine to have the rendering be a little less precise.

Sample radar chart from Wikipedia showing sales data plotted along the axes of the radar.

How to use it

In this example, we're looking at the homepage of a university site to see if there is content targeted to each audience segment.

  1. We start by choosing the metrics represented by the spokes—in our case, audience segments. Where we can, we arrange overlapping segments next to each other around the chart (for example, faculty and prospective faculty, who may have similar needs).

    Choose the fuzzy ratings. The center of the chart represents the poor end of the spectrum (“low interest”, in this case), and the outside edge of each spoke represents the ideal end of the spectrum (“high interest”).

    Graph with 6 spokes radiating out from a central point; each spoke is labeled with a different audience segment. The center of the graph corresponds to low interest, where the outer edge of each spoke indicates high interest.
  2. Create an empty chart for each of the pieces (content, features, and so on) that will be analyzed.

  3. Fill out charts together as a group, or individually if there are lots of different departments and viewpoints represented in the room. The ratings can be based on actual measurements (like site analytics) if that’s appropriate, but often in the early days of a project it’s more about gut feelings.

    6 graphs, labeled with potential homepage features like “Carousel with demographics”, “Sports news”, and so on. Each graph shows different data, indicating each audience segment’s level of interest in the content feature.
  4. Compare all the charts. When you line all the charts up together: are there audiences who are not served by any feature? Are there features that no one is particularly interested in? Are there hidden gems that meet everyone’s needs better than you realized?

    If each person filled out individual graphs, this is a great time to see if there are different assumptions across departments. Did Alex from marketing rate things entirely differently than Erica from development? Talk about that.

In our example, we realized that the “In the Media” was not compelling enough to any audiences to earn a spot on the homepage. We also saw that the Spotlight (an article and video clip about an upcoming event) did a great job of showcasing the college to different audience segments, and deserved a higher priority on the page.

Simple radar charts can be used to measure content plans against business goals, website features against development resources, or almost any set of items against multiple metrics.