The navigation of a digital product is usually designed in one of two ways.
The second route offers, among other things, card sorting. For a long time, this type of research has been conducted offline, based on physical cards. Hence the name, which today also applies to the more modern and popular online form.
Card sorting is about checking how users categorize and prioritize information, what criteria are most important to them and what structure their knowledge has. This is where the user’s mental model comes from, i.e. a set of subjective beliefs about the product and its use. The navigation and information architecture need to be adjusted to this model.
The sorting is based on labeled tabs that contain the names of the navigation items and categories. The test can be performed in one of several ways:
Our researchers recommend combining card sorting with tree testing.
While moderated tests with paper cards are still organized, online tests are becoming more and more popular. Carried out via dedicated drag-drop software – Optimal Workshop, UserBit, UserZoom, Maze.co, UXTweak, Userlytics. As it is a question of finding a realistic and consistent sorting pattern, 15-40 subjects are generally recruited.
Depending on the choice – moderated or not moderated survey; online or offline; variant (list above) – the organization and course of card sorting will differ. The following process can be a starting point for creating your own study.
The end of the research is analysis. In the material, you need to find duplicate names of the stacks; the content and topics that were grouped together most often; as well as the cards most often put away without assigning them.
These formulas, together with the answers of the respondents after the sorting phase, will tell you where the problems are and why. Perhaps some of the topics are not associated with each other due to insufficient knowledge of users or specific names have not been understood because users do not use them, while the card deck lacks a matching synonym.
Reverse card sorting, on the other hand, compares search paths and looks for patterns to match the tree in the future with the thinking and action sequence of users.