Card Sorting For UX

💡Card Sorting and Tree Testing: when and how to use tools Card sorting and tree testing are valuable UX research methods for designing information architecture. 🍎 Card sorting Card sorting helps you understand how users perceive and categorize information. It’s used to create or the structure of a website or app (content organization) Types of card sorting: ✔ Open card sorting: Participants organize cards into groups and name each group. Best for discovering how users think about content and creating initial IA. ✔ Closed card sorting: Participants organize cards into predefined groups. Useful for validating an existing IA or when you have specific categories in mind. ✔ Hybrid card sorting: Combines open and closed methods, where some categories are predefined, and participants can create new ones. When to use card sorting: ✔ To understand users' mental models and terms your target audience uses. ✔ When designing or redesigning a website or app's navigation system. ✔ Early in the design process, to gather insights for structuring content. Tools for card sorting: Maze OptimalSort UserZoom 🍏 Tree testing Tree testing evaluates the findability of topics in your website or app's information architecture. It’s used to validate and refine the IA by seeing how well users can navigate through a text-only version of the site structure. Participants are given tasks to find specific items or information. Success rates, time taken, and paths taken are analyzed to identify problem areas. When to use tree testing: ✔ After creating an initial IA through card sorting. ✔ To test and refine the effectiveness of your navigation labels and structure. ✔ Before fully implementing a new or revised IA. Tools for tree testing: Treejack (by Optimal Workshop) UserZoom UserTesting Common workflow with card sorting & tree testing: 1️⃣ Initial UX research and card sorting ✔ Start with user research to understand your audience and create a user persona.  ✔ Conduct open card sorting to gather insights on how users categorize information. ✔ Analyze results to create an initial draft of the IA. 2️⃣ Refine IA draft with tree testing ✔ Use tree testing to evaluate the draft IA ✔ Identify areas where users struggle to find information. ✔ Iterate on the IA based on tree testing results. 3️⃣ Validation and implementation ✔ Implement the finalized IA in the website or app. ✔ Continuously monitor user feedback and behavior to make iterative improvements. ✔ Conduct additional rounds of tree testing if necessary to refine the IA. 📖 Guides: ✔ Card sorting in product design (+ video tutorial) https://lnkd.in/dFdsWPea ✔ Tree testing: a complete guide(+ video tutorial) https://lnkd.in/dPwB96-y ✔ Information architecture design: step by step https://lnkd.in/dT92ExhC 🖼 Card sorting vs tree testing by Maze #UX #design #IA

What’s the right way to interpret card sorting with LOTS of cards? An empirical study with 160 participants. Card sorting is a quintessential UX technique for understanding how users mentally organize content. But what if there’s just too much content to organize? Imagine that all content from a moderately-sized solution was turned into cards — you would arrive at more than 50 cards in no time flat. Participants sorting all of that would quickly run out of steam. Cognitive complexity must be kept in check. A common approach — proposed by one article over 20 years ago — limits the number of cards seen by individual participants. Everybody sorts a slightly different subset from the full list of cards. Then, you aggregate the results. According to the original article, this is supposed to give you approximately the same results as all the cards being sorted together. But is this really true? In a recent study by UXtweak® Research (link in the comments), we uncovered some key discrepancies. On the surface level, the results appear as similar (comparable patterns in similarity matrices). But it’s not so straightforward. Themes and language found in category names can be affected when everyone sees slightly different cards. This is important, since category labels provide key information for building user-friendly labels. Our study showed that with random subset sorting, consensus became less clear. The frequency of some categories jumped or dipped based on which cards the individuals saw. More participants are also needed to account for increased variability. To obtain stable results, about 25-35 are needed, depending on study complexity (for comparison, standard sorting required 10-15). Our research supports randomized subset card sorting as a practical technique for organizing large volumes of content. However, for greater nuance, the differences from standard card sorting need to be accounted for during recruitment and data analysis. What are your thoughts on the outcomes of our study? Do you use alternative strategies to achieve a manageable number of cards in your own card sorting? Shoot me your feedback or questions in the comments.

Unlocking Better UX with Card Sorting 🚀🔥 Ever struggled to organize content in a way that makes sense to your users? That’s where Card Sorting comes in! 🔹 What is Card Sorting? It’s a simple yet powerful UX research technique where participants categorize content into groups that feel natural to them. This helps teams design intuitive navigation and structure for websites, apps, or products. 🔹 Why It Works: ✅ Reveals how users think about information ✅ Improves IA (Information Architecture) ✅ Reduces friction in user journeys ✅ Data-driven decision-making for content organization 🔹 How to Run a Card Sorting Workshop: 1️⃣ Define your goal (e.g., improve website navigation) 2️⃣ Prepare a set of cards with key topics or features 3️⃣ Let participants sort them into categories 4️⃣ Analyze patterns and refine your structure 💡 Whether you’re designing a new product or revamping an existing one, card sorting is a game-changer for creating user-friendly experiences. #UXDesign #UserResearch #CardSorting #InformationArchitecture #DesignThinking

In a recent UX project, focused on a collaborative productivity platform, card sorting played a pivotal role in understanding how users in B2B scenarios mentally organize content. The idea was to identify navigation patterns that align with the task flow expectations of users. During card sorting sessions with a diverse set of B2B users, labeled cards representing various toolsets were organized based on individual criteria. This process unveiled insights into how professionals in B2B settings naturally group content, whether it be task-oriented or feature-centric. However, relying solely on card sorting posed challenges in determining a definitive navigation structure. Given the varied preferences in content grouping among B2B users, additional research and critical analysis were essential to finalize the design. Following the insights gained from card sorting, tree testing was employed to assess the proposed navigation structure on a wireframe level, especially crucial for B2B contexts. Using tools like Treejack, participants navigated through a simplified text-based structure to locate specific features, helping to confirm the proposed structure's effectiveness. In B2B scenarios, where task flow efficiency is critical, tree testing may yield areas for refinement, ensuring ease of findability for professionals using the product/ service/ feature. Employing both card sorting and tree testing provided a comprehensive understanding of how to structure content for B2B users: a) Card sorting acted as a discovery tool, generating ideas for content organization based on the mental models of professionals. b) Tree testing rigorously evaluated the proposed navigation structure's effectiveness in a controlled environment, ensuring it aligns with B2B workflow expectations. When applying these methods to B2C scenarios, such as a consumer-facing e-commerce platform, card sorting becomes valuable for uncovering how individual consumers mentally organize products and features. The focus shifts towards user preferences and ease of use, aiming for an intuitive structure that resonates with a broader audience. In startups, where agility and responsiveness are crucial, card sorting can be instrumental in quickly understanding user expectations. Startups may leverage the insights gained to iterate rapidly and align their product with evolving market demands. On the other hand, MNCs, with their established user base and complex product suites, benefit from tree testing to evaluate navigation structures. This helps ensure that the proposed design meets the expectations of a diverse user demographic within a multinational setting. Would love to hear out diverse POVs, which were actually the trigger in the first place that inspired me to put this piece together. Image Courtesy: NN Group

One of the biggest CRO opportunities isn't always your product page or checkout. Sometimes it's your navigation. We saw this with Hoffmann, a premium cookware brand. Customers were clicking ads, remembering the brand, and later returning directly to the website. But once they arrived... They couldn't find the product they originally wanted. Instead of redesigning the menu based on assumptions, we used card sorting. We asked members of Hoffmann's community to organise every menu item into categories that felt most intuitive to them, offering a small discount as an incentive to participate. After 20-40 responses, clear patterns began to emerge. Using those insights, we completely restructured the navigation around the way customers naturally searched for products - not the way the business internally organised them. The result? - +3.2% revenue - More than €100,000 in additional sales - Without increasing ad spend. The biggest lesson from this experiment? Your navigation shouldn't reflect your internal company structure. It should reflect how your customers think. If visitors can't find the product they're looking for within a couple of clicks, they'll rarely keep searching. They'll simply leave. Follow me for more CRO psychology, e-Commerce strategy, A/B testing, and experimentation insights.