Whether you’re designing a new website or redesigning an existing one, card sorting is a quick, reliable and inexpensive research tool that can significantly improve your information architecture. By improving your information architecture, you’re giving yourself the best chance at delivering a product that is accessible, usable and relevant.

So, what exactly is card sorting? In short, card sorting is a user research technique that helps you discover how people understand and categorize information. Since great information architecture is built on the premise of organizing and categorizing information, card sorting is a secret weapon for website and digital product designers around the world. Actually, the tool is super common, and for good reason.

In this article we’ll help you prepare and conduct card sorting research. We’ll also help you make sense of the data you find and how to apply it to design great information architecture.

Planning and Preparation

Card sorting delivers the best results when you clearly define your goals. The narrower your scope, the more insightful and practical your results will be. It’s important to focus on one goal at a time when planning a card sorting study. What part of your information structure do you need clarification on? Organizing FAQ’s, product categories in an online store, or submenus, are common examples of card sorting projects.

Next, how best can you feasibly recruit participants? Depending on your situation, you may prefer conducting remote card sorts or in-person. Card sorts in person allow you to read body language and you may be more comfortable asking qualitative “why” questions of your participants. Whereas the benefits of remote card sorts, like OptimalSort, is that you aren’t constrained by location or time - just set it up, share the link with participants, then quickly analyze the results. In either case, be sure to recruit participants that represent the demographics of your intended users.

The next step is to prepare the cards themselves. The cards will represent the elements/topics that you wish to organize. Typically, you should aim for between 30 and 50 cards in order to get enough useful data. It also forces you to include only the most relevant cards. Additionally, they should also be on the same conceptual level to avoid confusion and ambiguity.

Finally, decide if you’re asking participants to group the cards based on categories that you decide (closed card sorting), or if participants will be able to create their own groups for cards (open card sorting). You can also facilitate hybrid card sorting which starts off as a closed card sort, but gives participants the option to create additional categories themselves. When you’re deciding, think about your task list (how you’re asking using to sort the cards) and how open-ended you’re prepared for the answers to be. Closed card sorting will narrow your results, whereas open will broaden your results.

Conducting the Session

Now that you’ve done the preparation, it’s time for the fun part! How involved you’ll be depends on whether you’re conducting remote or in-person sessions. We’ll discuss in-person card sorting first, then we’ll point out how remote card sorting differs.

An overview of conducting in-person card sorts:

1. First, shuffle the cards and give them to your participant(s). Ask them to look at each card, then direct them to either organize into groups on their own (open) or into the groups you have provided (closed). It’s important to emphasize to the participant that there are no right or wrong answers. Remember, you’re looking for a real, unfiltered insight into how people organize your information. You can even ask them to think out loud while they’re sorting the cards to gain additional, qualitative insight. One benefit of group sessions is that they usually do this anyway via natural discussion.
2. Then, if you’re running an open card sort, ask your participant(s) to name the groups they have organized. This will help you to understand the rationale behind their decisions and will give you some pointers when you come to labeling information architecture.
3. Once the session is complete, ask participants some open questions. Did you find any cards difficult to place? Did some overlap? Were any left out entirely? This sort of questioning, along with your notes throughout the session, will prove invaluable when you come to analyze the results.
4. Carefully collect the cards and make a record of the groups - there’s nothing worse than clearing the table and messing up the cards before you do this!

Remote card sorts differ from remote sessions in that once you’ve set up the cards in a tool like OptimalSort, you’re good to go. No printing, no shuffling, no resetting. You simply send a link to your participants and ask them to complete the task within a defined timeframe. Online card sorts are generally quicker and less time consuming in this respect, and they may allow you to find more participants and therefore more data.

There are two key things to highlight when running a remote card sort session. Firstly, ensure your instructions are clear and concise. Unlike an in-person session you won’t get the opportunity to clarify any misunderstandings. Secondly, you may consider a follow up questionnaire to gather additional qualitative insights. Check out this facilitation guide for more pointers on remote card sorting.

Analyzing and Interpreting the Results

Now that you’ve got all of your juicy data, it’s time to analyze it! If you ran a remote card sort, there will be some manual processing of your results (usually translating data to excel) which can be time consuming, whereas online tools will generally have analysis tools built right in. This is great for getting quick insights and quick development of information architecture.

When analyzing results, you’re really looking for patterns by identifying similar groups and labels. Using a tool like OptimalSort, for example, you’ll be provided with a few reports that will help you identify patterns and themes:

• Participants Table: Review all of the people who took part in your card sort and segment or exclude them.
• Participant-Centric Analysis (PCA): See the most popular grouping strategies as well as the alternatives among those people who disagreed with the first strategy.
• Dendrograms: Quickly spot popular groups of cards and get a sense of how similar or different your participant’s card sorts were.

Strong patterns or themes that emerge from the data tell us that participants understood categories in a similar way. On the flipside, different or disperse patterns tell us that there was no clear consensus on how information should be categorized. Both insights support effective design of information architecture. The goal is to find common ground in order to create seamless user experiences.

So far we’ve discussed statistical analysis which is all about the hard numbers. But it’s important to infuse some of the qualitative data into your reporting too. If you find that there is confusion within your results or no clear themes, you need to understand why. This is where the interpretation of questionnaire feedback or notes from in-person sessions become so valuable.

Using a combination of your data and your insights, it’s helpful to pull a summary together of your findings in a report. This can be shared with the wider team who have influence on the design of information architecture. Check out this analysis guide for more information on interpreting your results.

Conclusion

Card sorting is a fairly quick and straightforward way to inform information architecture design. It allows us to put the user at the center of our decisions surrounding the categorisation and grouping of information. Why is this important? Because as designers we can often assume how things should be organized. It’s too easy to be influenced by internal factors, like organization structures and the status quo. Don’t fall into this trap - use card sorting to gather clear, unbiased feedback on your information architecture.

Effective card sorting has clear objectives and is best suited to answering specific, information-related questions. We recommend using it when you need clarification around specific information structure, such navigation, menus and product categorisation.

As we’ve discussed, there are a few different approaches to card sorting research. They all have their place, so hoose which one best suits your needs. There’s a lot of resources available if you want to learn more. A good place to start is our card sorting 101 article. Good luck and happy researching!

Your cards have been sorted, and now you have lots of amazing data and insight to help improve your information architecture. So how do you interpret the results? 

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Never fear, our product ninjas Alex and Aidan are here to help. In our latest live training session they take you on a walk-through of card sort analysis using OptimalSort.
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What they cover:

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• Use cases for open, closed and hybrid card sort methodologies
• How, when and why to standardize categories
• How to interpret 3D cluster views, dendrograms, and similarity matrix
• Tips on turning those results into actionable insights

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We all know that building a robust information architecture (IA) can make or break your product. And getting it right can rely on robust user research. Especially when it comes to creating human-centered, intuitive products that deliver outstanding user experiences.

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But what are the best methods to test your information architecture? To make sure that your focus is on building an information architecture that is truly based on what your users want, and need.

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What is user research? 🗣️🧑🏻💻

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With all the will in the world, your product (or website or mobile app) may work perfectly and be as intuitive as possible. But, if it is only built on information from your internal organizational perspective, it may not measure up in the eyes of your user. Often, organizations make major design decisions without fully considering their users. User research (UX) backs up decisions with data, helping to make sure that design decisions are strategic decisions. 

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Testing your information architecture can also help establish the structure for a better product from the ground up. And ultimately, the performance of your product. User experience research focuses your design on understanding your user expectations, behaviors, needs, and motivations. It is an essential part of creating, building, and maintaining great products. 

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Taking the time to understand your users through research can be incredibly rewarding with the insights and data-backed information that can alter your product for the better. But what are the key user research methods for your information architecture? Let’s take a look.

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Research methods for information architecture ⚒️

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There is more than one way to test your IA. And testing with one method is good, but with more than one is even better. And, of course, the more often you test, especially when there are major additions or changes, you can tweak and update your IA to improve and delight your user’s experience.

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Card Sorting 🃏

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Card sorting is a user research method that allows you to discover how users understand and categorize information. It’s particularly useful when you are starting the planning process of your information architecture or at any stage you notice issues or are making changes. Putting the power into your users’ hands and asking how they would intuitively sort the information. In a card sort, participants sort cards containing different items into labeled groups. You can use the results of a card sort to figure out how to group and label the information in a way that makes the most sense to your audience. 

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There are a number of techniques and methods that can be applied to a card sort. Take a look here if you’d like to know more.

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Card sorting has many applications. It’s as useful for figuring out how content should be grouped on a website or in an app as it is for figuring out how to arrange the items in a retail store.You can also run a card sort in person, using physical cards, or remotely with online tools such as OptimalSort.

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Tree Testing 🌲

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Taking a look at your information architecture from the other side can also be valuable. Tree testing is a usability method for evaluating the findability of topics on a product. Testing is done on a simplified text version of your site structure without the influence of navigation aids and visual design.

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Tree testing tells you how easily people can find information on your product and exactly where people get lost. Your users rely on your information architecture – how you label and organize your content – to get things done.

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Tree testing can answer questions like:

• Do my labels make sense to people?
• Is my content grouped logically to people?
• Can people find the information they want easily and quickly? If not, what’s stopping them?

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Treejack is our tree testing tool and is designed to make it easy to test your information architecture. Running a tree test isn’t actually that difficult, especially if you’re using the right tool. You’ll  learn how to set useful objectives, how to build your tree, write your tasks, recruit participants, and measure results.

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Combining information architecture research methods 🏗

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If you are wanting a fully rounded view of your information architecture, it can be useful to combine your research methods.

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Tree testing and card sorting, along with usability testing, can give you insights into your users and audience. How do they think? How do they find their way through your product? And how do they want to see things labeled, organized, and sorted? 

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If you want to get fully into the comparison of tree testing and card sorting, take a look at our article here, which compares the options and explains which is best and when. 

A great information architecture (IA) is essential for a great user experience (UX). And testing your website or app’s information architecture is necessary to get it right.

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Card sorting and tree testing are the very best UX research methods for exactly this. But the big question is always: which one should you use, and when? Very possibly you need both. Let’s find out with this quick summary.

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What is card sorting and tree testing? 🧐

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Card sorting is used to test the information architecture of a website or app. Participants group individual labels (cards) into different categories according to  criteria that makes best sense to them. Each label represents an item that needs to be categorized. The results provide deep insights to guide decisions needed to create an intuitive navigation, comprehensive labeling and content that is organized in a user-friendly way.

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Tree testing is also used to test the information architecture of a website or app. When using tree testing participants are presented with a site structure and a set of tasks they need to complete. The goal for participants is to find their way through the site and complete their task. The test shows whether the structure of your website corresponds to what users expect and how easily (or not) they can navigate and complete their tasks.

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What are the differences? 🂱 👉🌴

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Card sorting is a UX research method which helps to gather insights about your content categorization. It focuses on creating an information architecture that responds intuitively to the users’ expectations. Things like which items go best together, the best options for labeling, what categories users expect to find on each menu.

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Doing a simple card sort can give you all those pieces of information and so much more. You start understanding your user’s thoughts and expectations. Gathering enough insights and information to enable you to develop several information architecture options.

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Tree testing is a UX research method that is almost a card sort in reverse. Tree testing is used to evaluate an information architecture structure and simply allows you to see what works and what doesn’t. 

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Using tree testing will provide insights around whether your information architecture is intuitive to navigate, the labels easy to follow and ultimately if your items are categorized in a place that makes sense. Conversely it will also show where your users get lost and how.

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What method should you use? 🤷

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You’ve got this far and fine-tuning your information architecture should be a priority. An intuitive IA is an integral component of a user-friendly product. Creating a product that is usable and an experience users will come back for.

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If you are still wondering which method you should use - tree testing or card sorting. The answer is pretty simple - use both.

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Just like many great things, these methods work best together. They complement each other, allowing you to get much deeper insights and a rounded view of how your IA performs and where to make improvements than when used separately. We cover more reasons why card sorting loves tree testing in our article which dives deeper into why to use both.

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Ok, I'm using both, but which comes first? 🐓🥚

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Wanting full, rounded insights into your information architecture is great. And we know that tree testing and card sorting work well together. But is there an order you should do the testing in? It really depends on the particular context of your research - what you’re trying to achieve and your situation. 

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Tree testing is a great tool to use when you have a product that is already up and running. By running a tree test first you can quickly establish where there may be issues, or snags. Places where users get caught and need help. From there you can try and solve potential issues by moving on to a card sort. 

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Card sorting is a super useful method that can be instigated at any stage of the design process, from planning to development and beyond.  As long as there is an IA structure that can be tested again. Testing against an already existing website navigation can be informative. Or testing a reorganization of items (new or existing) can ensure the organization can align with what users expect.

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However, when you decide to implement both of the methods in your research, where possible, tree testing should come before card sorting. If you want a little more on the issue have a read of our article here.

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Check out our OptimalSort and Treejack tools - we can help you with your research and the best way forward. Wherever you might be in the process.

Taxonomy is one of those terms that can feel intimidating at first, especially when most definitions relate to classifying plants and animals. But in UX and information architecture, taxonomy has a very different – and incredibly important – meaning. Whether the concept is new to you or you’re looking to strengthen your IA foundations, this guide will demystify taxonomy and show how it supports better navigation, clearer labeling, and more intuitive user experiences.

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What is a taxonomy in information architecture?

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In information architecture, taxonomy refers to how information is grouped, classified and labeled within a shared information environment. The overarching structure of that shared information environment is the information architecture (IA) and we find our way around it using the navigation. Think of an IA as a house. The taxonomy determines which pieces of furniture belong in each room and we navigate around the house via doorways and hallways. It all fits together to create one shared environment.

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For a website architecture example, think of an online shoe store. The shoes might be organized and labelled by color, size, style, season or collection – that’s the taxonomy. The overall picture of where those groups of shoes live is the IA and in our pursuit of new shoes, we might navigate that structure via a navigation bar at the top of the page. In the amazing Grand Taxonomy of Rap Names visualization below, we can see how the information is categorized, connected and labeled through the lines and the colors. There’s no structure or hierarchy to it yet; that would be the next step in the process to build the IA.

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Creating a taxonomy

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There are so many different ways to carve information up into a taxonomy and the key drivers for determining that are your content and, of course, your users. Your taxonomy needs to make sense to your users.You may be starting from scratch with a new website or you may have inherited a taxonomy that for whatever reason just isn’t fit for purpose. The first step when creating an initial taxonomy is to do a comprehensive audit of your content. Ask yourself, is your content relevant? Is it up-to-date? Is it all necessary? Are there opportunities to delete or condense content? Once you have your content sorted, you’re ready to move on to the next step of running a card sort with users. Running a card sort early in your taxonomy creation process will allow you to build it up from an evidence based foundation. There’s no point guessing then testing and potentially going back to square one, when you can co-create with your users and then test that informed approach to validate and further evolve your thinking.When you’re designing your card sort, you’ll need to decide if you’re going to do an open, closed or hybrid sort. Here’s a very high level look at what each type involves:

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• Open: participants sort cards into groups and name their own categories
• Closed: participants sort cards into categories determined by you
• Hybrid: participants sort cards into categories determined by you AND they can also make up their own.

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This early in the taxonomy creation process, it’s best to start out with an open card sort. Not only will this tell you how your users expect your content to be grouped, but will also provide insight into the language and labels that they would expect that content to be associated with. You never know, an open card sort may even surface something you hadn’t considered. At this stage of the process, it’s important to be open to ideas and new possibilities and an open card sort will do just that. Once you’ve settled on the type of card sort you’ll be running, you’ll need to test which can be done through a tool such as Optimal's Card Sorting. It enables you to run unmoderated card sorts remotely (or print out cards for a moderated/in-person card sort!). After your participants have completed your card sort, you can access the benefits of Optimal's powerful result analysis functions.

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Learn more about running a card sort and more through our 101 guide.

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After you’ve run your initial open card sort with users, you should have everything you need to create the first iteration of your taxonomy. Consider everything you learned during the card sort and cross reference that with your business goals and any tech constraints you might be facing. Don’t stress too much about nailing it this time around, remember this is the first iteration and as you test more and learn more, you can make changes. Build out your taxonomy in Post-it notes with a team and then whack it into a spreadsheet to make future testing and iteration activities easier.

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How to test a taxonomy

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Now that you have the first iteration of your taxonomy, it’s time to have a go at structuring those groups into an IA and running a tree test. A tree test works like a card sort but in reverse – it allows you to test your thinking by working backwards. Optimal’s Tree Testing is an online tree testing tool that helps you assess the findability of your content without any visual design elements. All you need are clear objectives for what you’d like to learn more about and a spreadsheet version of your draft IA (told you it would come in handy!).

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Learn more about Tree Testing and tree testing through our equally handy 101 guide.

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Another way to test your taxonomy thinking is to run another card sort. However this time, a hybrid or a closed card sort might be more suitable. A closed card sort would be useful if you’ve got evidence to suggest that your group labels are making sense to users but you’re not 100% sure what belongs in each group. A hybrid sort will let you go one step further and tell you if your content does in fact fit within those labels and if not you’ll also pick up some new ideas to iterate your taxonomy further.
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Final Takeaway: How Thoughtful Taxonomy Improves Information Architecture

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Developing a taxonomy is much like any other design process: involve your users early, test your assumptions, and continuously refine your structure as your product and content evolve. Taxonomies aren’t limited to digital experiences – we encounter them everywhere, from libraries to supermarkets – and they all serve the same purpose: helping people find what they need quickly and understand how information relates.

In information architecture, it’s the role of UX designers and information architects to shape these systems so they feel intuitive, scalable, and aligned with real user mental models. When grounded in user research and iterated over time, a strong taxonomy becomes the foundation of clear navigation, meaningful labels, and a more discoverable, user-friendly experience.

Qualitative user research is about exploration. Exploration is about the journey, not only the destination (or outcome). Gaining information and insights about your users through interviews, usability testing, contextual, observations and diary entries. Using these qualitative research methods to not only answer your direct queries, but to uncover and unravel your users ‘why’.

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It can be important to use qualitative research to really dig deep, get to know your users and get inside their heads, and their reasons. Creating intuitive and engaging products that deliver the best user experience. 

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What is qualitative research? 🔎

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The term ‘qualitative’ refers to things that cannot be measured numerically and qualitative user research is no exception. Qualitative research is primarily an exploratory research method that is typically done early in the design process and is useful for uncovering insights into people’s thoughts, opinions, and motivations. It allows us to gain a deeper understanding of problems and provides answers to questions we didn’t know we needed to ask. 

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Qualitative research could be considered the ‘why’. Where quantitative user research uncovers the how or the what users want. Qualitative user research will uncover why they make decisions (and possibly much more).

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Priorities ⚡⚡⚡⚡

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When undertaking user research it is great to do a mix of quantitative and qualitative research. Which will round out the numbers with human driven insights.

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Quantitative user research methods, such as card sorting or tree testing, will answer the ‘what’ your users want, and provide data to support this. These insights are number driven and are based on testing direct interaction with your product. This is super valuable to report to stakeholders. Hard data is difficult to argue what changes need to be made to how your information architecture (IA) is ordered, sorted or designed. To find out more about the quantitative research options, take a read.

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Qualitative user research, on the other hand, may uncover a deeper understanding of ‘why’ your users want the IA ordered, sorted or designed a certain way.  The devil is in the detail afterall and great user insights are discoverable. 

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Priorities for your qualitative research needs to be less about the numbers, and more on discovering your users ‘why’. Observing, listening, questioning and looking at reasons for users decisions will provide valuable insights for product design and ultimately improve user experience.

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Usability Testing - this research method is used to evaluate how easy and intuitive a product is to use.  Observing, noting and watching the participant complete tasks without interference or questions can uncover a lot of insights that data alone can’t give. This method can be done in a couple of ways, moderated or unmoderated. While it can be quicker to do unmoderated and easier to arrange, the deep insights will come out of moderated testing. 

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Observational - with this qualitative research method your insights will be uncovered from observing and noting what the participant is doing, paying particular attention to their non-verbal communication. Where do they demonstrate frustration, or turn away from the task, or change their approach? Factual note taking, meaning there shouldn’t be any opinions attached to what is being observed, is important to keep the insights unbiased.

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Contextual - paying attention to the context in which the interview or testing is done is important. Is it hot, loud, cold or is the screen of their laptop covered in post-its that make it difficult to see? Or do they struggle with navigating using the laptop tracker? All of this noted, in a factual manner, without personal inferring or added opinion based observations can give a window into why the participant struggled or was frustrated at any point.

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These research methods can be done as purely observational research (you don’t interview or converse with your participant) and noting how they interact (more interested in the process than the outcome of their product interaction). Or, these qualitative research methods can be coupled with an

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Interview - a series of questions asked around a particular task or product. Careful note taking around what the participant says as well as noting any observations. This method should allow a conversation to flow. Whilst the interviewer should be prepared with a list of questions around their topic, remain flexible enough to dig deeper where there might be details or insights of interest. An interviewer that is comfortable in getting to know their participants unpicks reservations and allows a flow of conversation, and generates amazing insights.

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With an interview it can be of use to have a second person in the room to act as the note taker. This can free up the interviewer to engage with the participant and unpick the insights.

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Using a great note taking side kick, like our Reframer, can take the pain out of recording all these juicy and deep insights. Time-stamping, audio or video recordings and notes all stored in one place. Easily accessed by the team, reviewed, reports generated and stored for later.

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Let’s consider 🤔

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You’re creating a new app to support your gym and it’s website. You’re looking to generate personal training bookings, allow members to book classes or have updates and personalise communication for your members. But before investing in final development it needs to be tested. How do your users interact with it? Why would they want to? Does it behave in a way that improves the user experience? Or does it simply not deliver? But why?

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First off, using quantitative research like Chalkmark would show how the interface is working. Where are users clicking, where do they go after that. Is it simple to use? You now have direct data that supports your questions, or possibly suggests a change of design to support quicker task completion, or further engagement.

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While all of this is great data for the design, does it dig deep enough to really get an understanding of why your users are frustrated? Do they find what they need quickly? Or get completely lost? Finding out these insights and improving on them can make the most of your users’ experience.

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When quantitative research is coupled with robust qualitative research that prioritizes an in-depth understanding of what your users need, ultimately the app can make the most of your users’ experience.

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Using moderated usability testing for your gym app, observations can be made about how the participant interacts with the interface. Where do they struggle, get lost, or where do they complete a task quickly and simply. This type of research enhances the quantitative data and gives insight into where and why the app is or isn't performing.

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Then interviewing participants about why they make decisions on the app, how they use it and why they would use it. These focussed questions, with some free flow conversation will round out your research. Giving valuable insights that can be reviewed, analyzed and reported to the product team and key stakeholders. Focussing the outcome, and designing a product that delivers on not just what users need, but in-depth understand of why. 

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Wrap Up 🥙

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Quantitative and qualitative user research do work hand in hand, each offering a side to the same coin. Hard number driven data with quantitative user research will deliver the what needs to be addressed. With focussed quantitative research it is possible to really get a handle on why your users interact with your product in a certain way, and how. 

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The Optimal Workshop platform has all the tools, research methods and even the note taking tools you need to get started with your user research, now, not next week! See you soon.