Card Sorting in information architecture
Running a Card Sort
You can run these tests online using an online tool like Optimal Sort or UserZoom, or you can keep it simple and just use physical cards from squares of paper.
Step 1: Ask users to sort the cards into categories.
To encourage them to think from the bottom up (specific to general), you might ask, “Which of these items make sense together as a group?”
To encourage them to think top down (general to specific), you might ask, “What major questions would you have about Salesforce as a company? Which of these cards seem to answer your main questions?”
If you already have a set of categories (your navigation menu items) in mind, you can ask users to arrange all the cards under one of your preset headings. The cards they struggle to categorize will give you insights into whether your choices follow what people expect. Listen and take notes as they talk through their thinking during the exercise.
Step 2: Ask users to prioritize and label the categories.
Once the users have sorted the cards into clusters, ask what they would call each of the groups. Take notes recording the user’s rationale and any open questions.
Again, if you already have a set of categories (your navigation menu items) in mind, you can show users your list and ask whether they agree with your category choices.
Step 3: Ask users to prioritize the list of cards within each category
Ask users to arrange the product cards from most important to least important.
Keep in mind that prioritization requires a level of knowledge your users might not have. They’ll only feel comfortable with this last step if they are already familiar with all of the items they’re asked to prioritize.
For our Salesforce example, that would mean a user would need to be at least somewhat familiar with all of the products that Salesforce offers.
Apple’s top level site navigation
High-level Search Data
As an alternative to asking users to do a card sort, you can also look at high-level search data. This method gives you insights into what users are looking for with the greatest frequency. You can then develop an info architecture that mimics the highest frequency searches. Apple’s navigation looks like it might have been derived from search data.
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