New feature for OptimalSort: 3D cluster view
Our card sorting tool OptimalSort has long been one of the best ways to figure out how people think the content on your website should be organized. In the 10+ years since we first launched this tool, researchers and designers from some of the biggest organizations around the world have run countless card sorts, gathering useful, actionable data to build websites, intranets and other digital products.
One of the biggest strengths of OptimalSort (and the other tools in the Optimal Workshop platform) is the analysis functionality. This has always set our products apart and elevated their usefulness. The similarity matrix allows you to view the strongest card relationships, the dendrograms provide a means to visualize content grouping and suggested category labels, and the participant-centric analysis helps you to view the most popular participant grouping strategies. OptimalSort has always made it easy to gather data, but it’s the ability to draw insights out of that data that’s set the tool apart.
Today, we’re adding a novel yet powerful new analysis feature to OptimalSort, one that’s never been seen before and will give you a totally new perspective on your data. Say hello to the 3D cluster view (3DCV).
What exactly is 3DCV?
While the addition of ‘3D’ may throw off red flags of being a gimmick, it’s actually entirely appropriate. As you’ll see from the screenshots below, this new analysis functionality is indeed three dimensional.
The 3DCV basically allows you to visualize the similarity between cards as three-dimensional spatial relationships. Each point in the 3D visualization represents one of the cards from your original sort. Cards that are closer together were more frequently sorted into the same category. Likewise, when you see 2 cards that are quite far apart, they weren’t sorted together as frequently.
3D polygons are also overlaid on groups of cards that are clustered together. These polygon groups can be interpreted as potential categories for your information architecture. You’ll also see suggested labels for each group, which we generate by assessing how many participants created similar categories to a particular group and comparing the most common labels that participants gave these categories.
If you want a detailed rundown of how the 3DCV works, take a look at our knowledge base article here.
You can also learn about how the 3DCV stacks up to our similarity matrix and dendrogram visualizations here.
We’ve got more exciting updates on the way for the tools in the Optimal Workshop platform – stay tuned to the newsfeed and our blog. You can also read about some of the other updates we’ve launched recently right here.