A good place to start is to ensure your website or app resonates intuitively with its audience. But how can you be confident that how you've structured your content aligns with the mental models of your target users? Card sorting holds the answer. This technique offers a window into the users’ minds, revealing how they categorize and interpret information.
A card sorting exercise helps you gain invaluable insights into your users’ thought processes and expectations. Harnessing these insights allows you to tailor your website’s content organization in an intuitive, user-centric way. This comprehensive guide delves deeper into the intricacies of card sorting, including various card sorting methods and best practices when employing this technique.
Card sorting is a UX research method designed to gain insights into the structure, categorization, and navigation of content for digital platforms, such as websites and apps. This method helps designers and researchers understand how users perceive, relate to, and categorize information. Insights from card sorting can be used to inform the information architecture (IA) of an app or website, ensuring it aligns with users’ expectations.
Participants, often potential users or representatives of the target audience, are presented with a set of cards, each labeled with a piece of content or feature relevant to the project. They are then asked to organize these cards into categories that make sense to them.
By observing and analyzing how participants group and label these cards, you can understand the user's mental model — their internal framework for how they expect content to be organized.
You can then use the results to determine the best way to label or group the information on your website or in your product so that users enjoy a smooth, intuitive experience.
The origins of card sorting are deeply rooted in the broader concept of categorization, dating back to the intellectual pursuits of the ancient Greeks. Renowned thinkers like Aristotle laid the groundwork for modern classification systems, particularly concerning the organization of plants and animals.
Over a century ago, card sorting found its footing in the evolving domain of psychology.
Early experiments utilized playing cards, which soon saw the integration of blank cards inscribed with specific words for subjects to categorize. These endeavors primarily aimed to discern participants' cognitive attributes, from their mental agility and memory prowess to their imaginative capacities.
As the 20th century progressed, card sorting expanded beyond its psychological confines, permeating diverse disciplines such as criminology, market analysis, and semantics, solidifying its stature in the realm of social sciences. The pivotal moment for card sorting's application to digital spaces came with the advent of the World Wide Web in the early 1990s, opening avenues for optimizing information architecture in the digital realm.
Before you begin conducting card sorts yourself, it helps to understand the various benefits of the method. Here are a few of them:
Observing how participants group and label cards gives you invaluable insights into their natural inclinations and thought processes. Understanding these mental models ensures that the information architecture, navigation systems, and overall structure of your website or app align with users' inherent expectations, making the interface more intuitive and user-friendly.
Information architecture is about organizing, structuring, and labeling content in an intuitive way that aligns with user expectations. Card sorting is key to refining this structural design, as it offers direct insights into how users expect to see information grouped and labeled.
With this knowledge, you can make more informed decisions when structuring menus, categorizing content, labeling sections, or even determining the information hierarchy on a website or an application.
When determining how to label sections or categorize content in your product or website, card sorting can be your compass. It offers you a window into your users' minds, revealing how they naturally think about and group similar items or topics. Observing how participants label and cluster cards will help you gather user-centric ideas for naming and categorization to create frictionless experience.
Card sorting is most beneficial when you have the necessary content or information in hand but are uncertain about the best way to structure it. This may include:
Whether you're crafting a brand-new website, adding a fresh section, or refining an existing platform, card sorting can guide your design choices. For instance, if you're building an e-commerce platform for books, card sorting can help you determine whether customers would prefer separate categories for fiction, non-fiction, and children's books or if they'd gravitate towards an overarching 'bestsellers' category.
Are you curious about how your audience anticipates information to be organized on your site?
Card sorting offers clarity.
Consider the scenario of an online fitness portal: through card sorting, you can determine if users would prefer workout routines to be categorized by fitness level, exercise type, or targeted muscle groups.
Card sorting is great for when you need to make difficult choices in sequencing or hierarchy. For instance, if you're curating a digital music library, card sorting can help you uncover if users would prioritize playlists based on mood, artist, genre, or release date. This feedback aids in presenting the most relevant content front and center, optimizing your product for user satisfaction.
When stuck on what to label a particular feature or section, card sorting offers clarity.
Gathering naming suggestions straight from the user’s mouth ensures your terminology resonates with your target audience.
Card sorting is a versatile technique adaptable to a wide range of scenarios. Let's delve into the various types and their applications:
In an open card sort, the moderator provides participants with a set of cards that they define and group into categories that make sense to them. This method allows participants to categorize the cards based on their perspectives, allowing for diverse ways of differentiation and understanding.
The main advantage of open card sorting is that it offers insights into users' natural thought processes and categorization preferences, revealing unanticipated ways they might group information.
However, results can vary widely across participants, making it challenging to identify a common structure. The absence of predefined categories also might overwhelm some participants, leading to potentially skewed or less actionable results.
In a closed card sort, the moderator provides participants with predefined categories into which they are asked to sort a set of cards. Unlike open card sorting where participants create their own categories, closed card sorting asks participants to work within the constraints of the given categories to organize the information.
While closed card sorting doesn't reveal how participants form categories themselves, it does allow you to assess the effectiveness of an established category framework from the user's viewpoint.
Providing predefined categories reduces ambiguity and offers clearer insights into users' alignment with the proposed organizational scheme.
That said, closed card sorting can limit participants' natural inclinations, potentially masking alternative organizational strategies they might have preferred. The predefined categories might also introduce bias, influencing participants to fit content into the given structures rather than suggesting what feels most intuitive to them.
Hybrid card sorting combines elements of both open and closed card sorting methods. You provide participants with a predetermined set of categories, like in closed card sorting, but also allow them to create new categories or suggest changes where they see fit, like in open card sorting.
Hybrid card sorting bridges the gap between open and closed methods.
It allows you to validate your existing categories and discover potential enhancements or alternative groupings simultaneously. However, it can be more complex to analyze since you're dealing with both predefined and spontaneously generated categories, potentially making it more time-consuming.
In a remote card sort, participants engage in the research activity online without the need for physical presence or face-to-face interaction. They utilize specialized software tools (more on these in a bit) to drag and drop digital "cards" into categories from the comfort of their own environment.
Remote card sorting offers the advantage of reaching a wider and more diverse group of participants.
This approach is often more cost-effective and efficient, eliminating the need for physical spaces or travel arrangements. The digital nature of the process allows for streamlined data collection and analysis, with many software tools providing real-time insights.
However, without the in-person context, misunderstandings or confusion might go unnoticed, potentially skewing results.
In-person or paper card sorting involves participants physically organizing printed cards, each containing a piece of content or a concept, into categories in a face-to-face setting. The researcher observes and often engages in discussions with participants, seeking to understand their thought processes and the reasoning behind their choices.
The advantage of this method is that it provides a tangible, hands-on experience, promoting rich insights through direct interaction and immediate feedback.
Conversely, organizing in-person sessions can be logistically challenging and often more time-consuming than digital methods. Manual data analysis from paper card sorts can also be labor-intensive and prone to human error.
Card sorting can be both qualitative and quantitative, depending on how it's conducted and analyzed.
In most cases, you will collect and analyze qualitative and quantitative data to comprehensively understand how users perceive and categorize content.
Card sorting is inherently qualitative when you focus on understanding the "why" behind participants' decisions. This might involve observing participants as they sort the cards, listening to their thought processes, and asking open-ended questions about their choices. The qualitative aspect provides insights into participants' mental models, their understanding of terminologies, and their rationale for grouping items in particular ways.
When analyzing the results of card sorting, especially when using digital tools with a large number of participants, the data becomes quantitative. You can generate statistical data on how often items were grouped together, calculate agreement percentages among participants for certain categories, and generate dendrograms or cluster analyses. The emphasis here is on measuring and counting to identify patterns in the data.
While card sorting and tree testing share some similarities, they serve different purposes and are used at different stages in the design process. As we have discussed, card sorting is used to discover how users group and label information. It's especially useful in the early stages of design or when rethinking the structure of an existing site or product.
On the other hand, tree testing, often called reverse card sorting, evaluates the effectiveness of an existing or proposed information architecture in a website or app. Instead of grouping items, participants are given a hierarchical menu or structure (the "tree"), which is usually an outline of the site or system’s IA. Without the influence of visual design or navigation aids, they're asked to locate particular items or complete specific tasks by navigating through this tree.
Tree testing determines if users can find items effectively within the structure.
It helps you identify which parts of the IA work well and which areas might be confusing or misaligned with user expectations. It's particularly beneficial after you've used card sorting to develop an IA and before you move on to more detailed design and development.
While both methods involve the sorting of cards or data points into groups, card sorting is specifically about understanding user mental models for designing effective digital architectures. In contrast, affinity mapping is a more general tool used for synthesizing and finding patterns in large data sets across various contexts. It involves clustering a large volume of data — such as ideas, user feedback, observations, or any unstructured information — into related groups or themes.
The data points, which could be on post-its, cards, or digital platforms, are then organized on a wall or board based on their natural relationships.
Affinity mapping aims to find patterns and themes in the data, often to identify pain points, opportunities, or solutions.
Now that you're familiar with the benefits and different types of cards sorting, let's take a closer look at what conducting a card-sorting session actually entails. At a high level, this method can broken down into seven steps.
Before you begin, be clear about what you want to achieve with the card sort. Are you looking to design a new information architecture, validate an existing one, or perhaps gather insights for a specific section of your site?
Decide whether you'll conduct open, closed, hybrid, remote, or in-person card sorting based on your needs and available resources.
List out the items you want to test on physical cards, sticky notes, or digital tools designed for card sorting. These could be names of web pages, product features, or any other piece of content you want to categorize. Make sure each card has one item or topic.
Aim for a diverse group that represents your target users. Typically, 15-30 participants are adequate, but this can vary based on the scope and type of card sorting method you choose.
For in-person card sorting, ensure a quiet room with a large table. Provide participants with the cards and, if desired, a recording device. For remote card sorting, use specialized software to ensure your study runs smoothly.
Clearly explain the process to participants. For open card sorting, instruct them to group cards in a way that makes sense to them and then name each group. For closed card sorting, provide them with predefined categories. Watch (or analyze, for remote sessions) how participants group the cards, noting any patterns, confusion, or specific insights.
Identify common ways participants grouped and named categories. Take note of cards that were frequently misplaced or groups that were named differently by participants. Some card sorting software provides dendrograms or cluster analysis to visualize the common groupings.
To ensure your card sorting sessions are productive, you’ll want to keep these best practices in mind throughout:
Avoid leading participants in a particular direction with your instructions. Be clear with them that there's no right or wrong way to categorize or name the groups. To design a user-friendly product, you need to understand how users behave and why, without your influence.
Too many cards can overwhelm participants and lead to cognitive fatigue. Typically, a good range is between 30 and 60 cards. This provides enough breadth without causing decision paralysis.
The wording on each card should be clear and unbiased. Avoid leading words or jargon that might push participants towards a particular grouping. Even though you'll randomize card orders, ensure the initial list isn't biased.
Each participant should receive the cards in a random order. This reduces the chance that the order will unintentionally influence how participants group the cards.
Whether it's open, closed, or hybrid card sorting, choose the method that aligns best with your research objectives. An open sort might be best if you're exploring how users naturally categorize. If you're validating predefined categories, a closed sort is more appropriate.
Each card should be easy to comprehend at a glance. It should represent a single, distinct concept to prevent confusion or overlap in categorization.
Analyzing card sorting data is pivotal in translating user insights into actionable design decisions. When using remote card sorting tools, many come equipped with built-in reporting functionalities, simplifying the process of sorting through the plethora of data at hand. Start your analysis by getting a broad overview of the results.
Scan for any recurring patterns or trends in the way participants have sorted the cards or the category labels they've proposed during open card sorts. If you have used the open card sort method, a crucial step is to review and standardize category labels.
Participants might label categories in slightly different ways, whether it’s varied spellings, different capitalizations, or just alternate phrasing. By standardizing these labels, you'll get a clearer picture of the general consensus among participants.
Identify common themes or groupings that emerge consistently. Take note of unique or outlier categorizations too, as they can provide insights into different user perspectives.
With closed card sorts, focus on how often participants place each card into the predefined categories. Look for patterns that suggest agreement or discord with the predetermined structure.
A similarity matrix can be a valuable tool at this stage. It showcases how frequently items are grouped together by participants, helping identify strongly correlated items. Hierarchical cluster analysis is another method that can visually represent how cards are commonly grouped, forming a dendrogram or tree diagram.
While patterns and clusters provide a structured understanding, also consider qualitative feedback or observations made during the sessions. This holistic approach ensures that the insights derived are both data-driven and grounded in user behavior and perception.
Although pen and paper is always an option, nowadays there’s plenty of powerful remote card sorting tools for you to choose from. Here are five that are worth consideration.
Optimal Workshop’s card sorting tool, OptimalSort, offers a seamless user experience. Whether you opt for unmoderated card sorts that function autonomously or prefer a moderated approach, either remotely or in-person, OptimalSort is versatile enough to accommodate. Beyond its efficient functionality, the tool enables swift analysis with techniques such as dendrograms and the similarity matrix.
Maze lets you implement both open and closed card sorting, empowering you to understand how users categorize information and enhancing your UX strategy. It features a user-friendly drag-and-drop interface, and you can customize instructions, adjust task settings, and even incorporate images or detailed descriptions for comprehensive research. Once your tasks are completed, Maze instantly transforms results into visual analytics, presenting data in formats like agreement rates, a similarity matrix, and an agreement matrix for rapid insights.
UserTesting offers various card sorting options, including closed, open, and hybrid. Utilizing its integrated tool, you can conveniently access your results on the UserTesting platform's metrics tab. The overview tab presents card and category labels, accompanied by figures that detail how contributors aligned labels and categories.
Lyssna (formerly UsabilityHub) offers both open and closed card sorting methods, ensuring effortless setup, recruitment, and analysis. Its user-friendly interface is optimized for desktop and mobile devices, featuring a fluid drag-and-drop mechanism and accessible buttons for alternative sorting methods. With Lyssna, you can delve deep into your results using agreement and similarity matrices, questionnaire feedback, advanced filters, and other analytics.
UXtweak supports open, closed, and hybrid card sorting, allowing participants to either establish their own categories or utilize predefined ones. The tool's robust analytical features, such as the standardization grid, results matrix, and popular placement matrix, illuminate underlying patterns in the results. Additionally, the inclusion of dendrograms and a similarity matrix further aids users in identifying and interpreting patterns.
Card sorting remains a popular and cost-effective tool for understanding user preferences and shaping effective information architecture. Whether you opt for open, closed, hybrid, or any other method, it offers insights into user-centric design and information categorization. It's not a cure-all solution for every website or app challenge, but refining the information structure can address specific usability issues.
Leverage digital card sorting tools for a more seamless experience. By harnessing these tools, you're not just guessing — you're making data-driven decisions to optimize your product or site’s structure and flow.
Jack is the Content Marketing Lead at Great Question, the end-to-end UX research platform for customer-centric teams. Previously, he led content marketing and strategy as the first hire at two insurtech startups, Breeze and LeverageRx. He lives in Omaha, Nebraska.
