We have all had this experience: you land on a new website, and you feel instantly lost. You are looking for pricing, but it is hidden under a “Solutions” tab. You want to find “Support,” but it is buried in a footer link labeled “Knowledgebase.” You click “Products,” but what you need is inexplicably located under “Services.”
This is not a failure of visual design; it is a failure of Information Architecture (IA). A broken IA is a critical, conversion-killing problem. It is the direct result of a company organizing its website based on its internal departments rather than its users’ goals. For complex platforms—large e-commerce sites, multi-faceted SaaS products, or sprawling entertainment hubs—a confusing IA is a multi-million dollar problem. Card sorting is the single most effective, user-centric research method to fix it.
The “Curse of Knowledge”: Why Your Current Navigation Is Failing
The root cause of a bad IA is a psychological bias known as the “Curse of Knowledge.” As a product owner, marketer, or developer, you are so deeply familiar with your own product, jargon, and internal structure that you are incapable of seeing it from a beginner’s perspective.
The result is a navigation menu that mirrors your company’s internal org chart. This is a fundamental E-E-A-T failure. It shows a complete lack of Experience with the actual customer journey, which is especially critical on high-stakes platforms. For example, a user attempting to navigate the NV casino app is not thinking about internal departments; they are thinking, “How do I check my bonus and find a slot?”
Card sorting is the antidote. It is a research method that forces you to abandon your internal assumptions and build an IA based on your users’ actual mental models. Choosing the right method for this diagnosis is the crucial next step.
The Card Sorting Toolkit: Open, Closed, and Hybrid Methods
At its simplest, card sorting is a UX research method where you ask real users to group topics (written on digital or physical “cards”) into categories that make sense to them. This reveals their natural vocabulary and logical groupings.
Open Card Sorting (For Discovery)
Open card sorting is the most generative method. Users are given a stack of topics (e.g., “My Account,” “Forgot Password,” “Slot Games,” “Promotions”) and are asked to sort them into groups that feel logical to them. Then, they are asked to create their own names for these groups. Open sorting is best for building a new IA from scratch or completely rethinking an old one. While open sorting is great for discovery, you often need a method to validate specific labels and test user consensus.
Closed Card Sorting (For Validation)
Closed card sorting helps confirm existing ideas. Users are given a stack of cards and a set of pre-defined categories (e.g., “Games,” “Support,” “My Profile”). Their task is to sort the cards into the categories you provide. This method is ideal for validating an existing IA or testing if new content fits logically into your current structure. Often, the most insightful approach is a mix of both, which gives users more flexibility while maintaining structure.
Hybrid Card Sorting (The Best of Both Worlds)
Hybrid card sorting is a form of closed sorting, but with a crucial exception: users are given pre-defined categories but also have the option to create their own if they feel nothing fits. This method is the most flexible and often most insightful, as it validates your structure while also identifying its hidden gaps. Running the test is just the data collection phase. The real value is found in analyzing the results to find the hidden patterns in your users’ logic.
From Chaos to Clarity: Analyzing Card Sorting Results
After running a card sort with 15–20 users, you will have a large, messy dataset. The goal is to find consensus and translate that consensus into a logical menu structure. The analysis involves standardizing labels and identifying clusters. The data collected through the card sort is most powerfully visualized in a similarity matrix, which highlights the percentage of users who grouped specific items together.
| Card A: Promotions | Card B: My Account | Card C: Help Center | |
| Card A: Promotions | 100% | 15% (Low Agreement) | 5% (Low Agreement) |
| Card B: My Account | 15% (Low Agreement) | 100% | 85% (High Agreement) |
| Card C: Help Center | 5% (Low Agreement) | 85% (High Agreement) | 100% |
This table provides an immediate, actionable insight: 85% of users believe “Help Center” belongs inside “My Account,” a finding that directly informs the structure of the app menu. This kind of data-driven insight is essential for building a user-centric structure.
The User-Centric IA and the Continuous Process
The card sort data allows us to build a final, user-centric menu that aligns the platform’s structure with the user’s logic, dramatically improving the user experience. The following list shows how raw data translates into a high-E-E-A-T menu structure:
- Games (parent category for Slots, Live Games, etc.)
- Promotions (includes: All Bonuses, Loyalty Program, Bonus T&Cs)
- Tournaments (leaderboards, upcoming events)
- My account (includes: Wallet/Banking, Profile Settings, Support/Help Center, Responsible Gaming)
This new IA, driven by user logic, dramatically improves the user experience. It demonstrates high E-E-A-T (Experience) by showing the user that the platform was designed with their journey in mind, not the company’s org chart.
Your platform will evolve. You will add new features, products, and services. As you do, your IA must evolve with it. If you simply “tack on” new features to your old menu, you will recreate the very clutter you sought to fix.
Card sorting is not a one-time project; it is a tool for continuous improvement. It is the most effective way to fight the “Curse of Knowledge” and ensure your platform remains intuitive, trustworthy, and easy to navigate.
