Maximizing User Comprehension

Most IFUs don’t fail at compliance. They fail at comprehension. Today, you get my full clarity playbook. The same one QA/RA teams use to fix IFUs before regulators fix them. If you’re in MedTech and you write for users, this is for you ↓ (Save this post. And share with your favorite RA person ♻️) 1. Start with this mindset: Assume the user knows nothing. (Not dumb. Just new.) → No medical background → No device knowledge → No acronyms → No prior training 2. Structure your instructions like this: → One idea per step → Max 3 logically linked actions → In clear, logical order Before showing steps, say: "This section contains 5 steps." Yes, people skip less that way. 3. Each step should say: → What to do → How to do it → What to expect → What could go wrong 4. Keep steps on one page. Don’t make people scroll mid-action. Ever. 5. Never send people on a scavenger hunt. Avoid cross-referencing or make it clear. No “Go back to the mid-page 82”. 6. Don’t be clever with headings. Use short, obvious titles. And only one topic per heading. 7. Discuss user errors. Proactively. Anticipate misuse. Call it out. Help them correct it. 8. Now... sentence construction 101 ↓ → Similar ideas = similar form → Use active voice → Use verbs, not noun-ified verbs → Ditch parentheses for must-read info → Use consistent terms for device parts → No vague fluff like "ensure proper connection" 9. Acronyms and jargon? Use with care. → Define them once → Use lay language for lay users → Keep definitions short + clear → If you wouldn't use the word in a coffee shop, find another one 10. Final clarity test: Ask someone to read your IFU out loud. If they stumble → rewrite. If they need to re-read → rewrite. Clarity isn't a style. It’s risk control. Especially if you play the “Information for Safety” card as a risk control measure (cf. ISO 14971). Want the full advices + examples? Grab the full guide here → https://lnkd.in/dHXgc37y

Most users don’t read interfaces. They scan for clues, patterns, and signals. That is why layouts control understanding more than colors or graphics. F-pattern layouts work for text-heavy pages. Users read a line across the top, then move down the left side, scanning for keywords and headings. Blogs, articles, and news platforms rely on it because 69% of viewing time stays on the left half of the screen. Z-pattern layouts suit simple pages with clear hierarchy. The eye moves from top-left, to top-right, then diagonally to the bottom-left, and ends at the bottom-right. This makes it effective for landing pages and ads where you want users to reach a call-to-action without friction. The Gutenberg Diagram shows how a page naturally breaks into four visual zones. The top-left gains instant focus. The bottom-right becomes the terminal point. The center areas are weaker, so clutter in those zones makes users skip instead of engage. Visual hierarchy, spacing, and typography decide what gets noticed first and what gets ignored. Large text, bold weight, contrasting color, and strategic white space guide the user without saying a word. Poor layout forces the brain to work harder, which is when users leave. These are not theories. They come from eye-tracking studies and real data. Better layouts lead to faster comprehension and higher conversions. Designing for how the human eye actually moves is what makes an interface usable, readable, and trustworthy.

🔎 How To Redesign Complex Navigation: How We Restructured Intercom’s IA (https://lnkd.in/ezbHUYyU), a practical case study on how the Intercom team fixed the maze of features, settings, workflows and navigation labels. Neatly put together by Pranava Tandra. 🚫 Customers can’t use features they can’t discover. ✅ Simplifying is about bringing order to complexity. ✅ First, map out the flow of customers and their needs. ✅ Study how people navigate and where they get stuck. ✅ Spot recurring friction points that resonate across tasks. 🚫 Don’t group features based on how they are built. ✅ Group features based on how users think and work. ✅ Bring similar things together (e.g. Help, Knowledge). ✅ Establish dedicated hubs for key parts of the product. ✅ Relocate low-priority features to workflows/settings. 🤔 People don’t use products in predictable ways. 🤔 Users often struggle with cryptic icons and labels. ✅ Show labels in a collapsible nav drawer, not on hover. ✅ Use content testing to track if users understand icons. ✅ Allow users to pin/unpin items in their navigation drawer. One of the helpful ways to prioritize sections in navigation is by layering customer journeys on top of each other to identify most frequent areas of use. The busy “hubs” of user interactions typically require faster and easier access across the product. Instead of using AI or designer’s mental model to reorganize navigation, invite users and run a card sorting session with them. People are usually not very good at naming things, but very good at grouping and organizing them. And once you have a new navigation, test and refine it with tree testing. As Pranava writes, real people don’t use products in perfectly predictable ways. They come in with an infinite variety of needs, assumptions, and goals. Our job is to address friction points for their realities — by reducing confusion and maximizing clarity. Good IA work and UX research can do just that. [Useful resources in the comments ↓] #ux #IA

“𝗨𝘀𝗲𝗿𝘀 𝗱𝗼𝗻’𝘁 𝗰𝗮𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗿𝗲𝗽𝗼𝗿𝘁 𝗱𝗲𝘀𝗶𝗴𝗻.” You sure? Because every major study on usability, adoption, and information design says otherwise. Poor design slows decision-making, hides critical insights, and erodes trust. Good design reduces time to value - and makes the difference between used and ignored reports. Let’s talk specifics. These aren’t opinions - they’re proven UX principles, backed by decades of research: 𝗝𝗮𝗸𝗼𝗯’𝘀 𝗟𝗮𝘄 – 𝗨𝘀𝗲𝗿𝘀 𝘀𝗽𝗲𝗻𝗱 𝗺𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗲𝗶𝗿 𝘁𝗶𝗺𝗲 𝘂𝘀𝗶𝗻𝗴 𝗼𝘁𝗵𝗲𝗿 𝘁𝗼𝗼𝗹𝘀. 𝘚𝘰 𝘸𝘩𝘦𝘯 𝘗𝘰𝘸𝘦𝘳 𝘉𝘐 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘣𝘦𝘩𝘢𝘷𝘦 𝘭𝘪𝘬𝘦 𝘵𝘩𝘦 𝘸𝘦𝘣 𝘢𝘱𝘱𝘴 𝘵𝘩𝘦𝘺 𝘬𝘯𝘰𝘸, 𝘪𝘵 𝘧𝘦𝘦𝘭𝘴 𝘣𝘳𝘰𝘬𝘦𝘯. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Use clear navigation, clickable affordances, and common interaction patterns. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Place slicers where users expect filters - top-left or directly above visuals. 𝗟𝗮𝘄 𝗼𝗳 𝗖𝗼𝗺𝗺𝗼𝗻 𝗥𝗲𝗴𝗶𝗼𝗻 – 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀 𝘄𝗶𝘁𝗵𝗶𝗻 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝘆 𝗮𝗿𝗲 𝘀𝗲𝗲𝗻 𝗮𝘀 𝗮 𝗴𝗿𝗼𝘂𝗽. 𝘛𝘩𝘪𝘴 𝘩𝘦𝘭𝘱𝘴 𝘶𝘴𝘦𝘳𝘴 𝘴𝘤𝘢𝘯 𝘢𝘯𝘥 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯 𝘧𝘢𝘴𝘵𝘦𝘳. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Use whitespace or cards to visually group KPIs, charts, and filters. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Group related metrics like Revenue, Margin, and YoY% into a single visual region. 𝗔𝗲𝘀𝘁𝗵𝗲𝘁𝗶𝗰-𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗘𝗳𝗳𝗲𝗰𝘁 – 𝗔𝘁𝘁𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝘁𝗵𝗶𝗻𝗴𝘀 𝗮𝗿𝗲 𝗽𝗲𝗿𝗰𝗲𝗶𝘃𝗲𝗱 𝗮𝘀 𝗲𝗮𝘀𝗶𝗲𝗿 𝘁𝗼 𝘂𝘀𝗲. 𝘌𝘷𝘦𝘯 𝘪𝘧 𝘵𝘩𝘦 𝘣𝘢𝘤𝘬𝘦𝘯𝘥 𝘭𝘰𝘨𝘪𝘤 𝘪𝘴 𝘤𝘰𝘮𝘱𝘭𝘦𝘹, 𝘢 𝘤𝘭𝘦𝘢𝘯 𝘜𝘐 𝘣𝘶𝘪𝘭𝘥𝘴 𝘵𝘳𝘶𝘴𝘵 𝘢𝘯𝘥 𝘳𝘦𝘥𝘶𝘤𝘦𝘴 𝘶𝘴𝘦𝘳 𝘧𝘳𝘶𝘴𝘵𝘳𝘢𝘵𝘪𝘰𝘯. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Typography, spacing, and alignment aren’t fluff - they’re functional. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: A well-spaced, readable KPI section increases scan speed and comprehension. 𝗠𝗶𝗹𝗹𝗲𝗿’𝘀 𝗟𝗮𝘄 – 𝗧𝗵𝗲 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝗰𝗮𝗻 𝗵𝗼𝗹𝗱 7 ± 2 𝗶𝘁𝗲𝗺𝘀 𝗶𝗻 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗺𝗲𝗺𝗼𝗿𝘆. 𝘠𝘦𝘵 𝘮𝘢𝘯𝘺 𝘳𝘦𝘱𝘰𝘳𝘵𝘴 𝘰𝘷𝘦𝘳𝘸𝘩𝘦𝘭𝘮 𝘶𝘴𝘦𝘳𝘴 𝘸𝘪𝘵𝘩 20+ 𝘤𝘩𝘢𝘳𝘵𝘴 𝘰𝘯 𝘢 𝘴𝘪𝘯𝘨𝘭𝘦 𝘱𝘢𝘨𝘦. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Prioritize. Show what matters first. Use drill-through or navigation to reveal detail. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Use a landing page with 3–5 high-value metrics and actions. Design is not just decoration. It’s how users understand your data. It’s what makes insights actionable. And it’s the difference between adoption and abandonment. If users don’t care about report design, it’s probably because they’ve never seen what good design can do. #PowerBI #DataViz #UIUX

Designing effective surveys is not just about asking questions. It is about understanding how people think, remember, decide, and respond. Cognitive science offers powerful models that help researchers structure surveys in ways that align with mental processes. The foundational work by Tourangeau and colleagues provides a four-stage model of the survey response process: comprehension, retrieval, judgment, and response selection. Each step introduces potential for cognitive error, especially when questions are ambiguous or memory is taxed. The CASM model -Cognitive Aspects of Survey Methodology- builds on this by treating survey responses as cognitive tasks. It incorporates working memory limits, motivational factors, and heuristics, emphasizing that poorly designed surveys increase error due to cognitive overload. Designers must recognize that the brain is a limited system and build accordingly Dual-process theory adds another important layer. People shift between fast, automatic responses (System 1) and slower, more effortful reasoning (System 2). Whether a user relies on one or the other depends heavily on question complexity, scale design, and contextual framing. Higher cognitive load often pushes users into heuristic-driven responses, undermining validity. The Elaboration Likelihood Model explains how people process survey content: either centrally (focused on argument quality) or peripherally (relying on surface cues). Users may answer based on the wording of the question, the branding of the survey, or even the visual aesthetics rather than the actual content unless design intentionally promotes central processing. Cognitive Load Theory offers tools for managing effort during survey completion. It distinguishes intrinsic load (task difficulty), extraneous load (poor design), and germane load (productive effort). Reducing the unnecessary load enhances both data quality and engagement. Attention models and eye-tracking reveal how layout and visual hierarchy shape where users focus or disengage. Surveys must guide attention without overwhelming it. Similarly, the models of satisficing vs. optimizing explain when people give thoughtful responses and when they default to good-enough answers because of fatigue, time pressure, or poor UX. Satisficing increases sharply in long, cognitively demanding surveys. The heuristics and biases framework from cognitive psychology rounds out this picture. Respondents fall prey to anchoring effects, recency bias, confirmation bias, and more. These are not user errors, but expected outcomes of how cognition operates. Addressing them through randomized response order and balanced framing reduces systematic error. Finally, modeling approaches like like cognitive interviewing, drift diffusion models, and item response theory allow researchers to identify hesitation points, weak items, and response biases. These tools refine and validate surveys far beyond surface-level fixes.

We often assume that testing our UX designs is a time-consuming process because usability testing usually involves detailed prototypes and extensive sessions. But there’s a faster way: comprehension-based usability testing. This method focuses on validating whether users understand the information on the screen without requiring a fully interactive prototype. It’s all about testing if your design communicates effectively. By engaging real users and asking open-ended questions about your prototype, you can quickly identify misunderstandings and address assumptions you might have made as a designer. The key is to focus on qualitative feedback from unbiased users—people who haven’t seen the design before. This helps you spot areas where the design may fail to communicate as intended, all without the need for exhaustive testing. It’s a lean, practical way to ensure your design speaks clearly to your audience.

Most interviews stay surface-level. You get polite answers. You get user stories. But you don’t get the kind of thinking that changes roadmaps. Here’s what most researchers miss: Every question triggers a type of thinking. If your question is basic, the answer will be too. Bloom’s Taxonomy breaks cognitive effort into 6 levels and the best UXRs shape their questions around them. Here’s how to spot which one you’re aiming for (and what to ask instead): 𝗪𝗮𝗻𝘁 𝗿𝗲𝗰𝗮𝗹𝗹? 𝗔𝘀𝗸 𝗳𝗼𝗿 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲. Great for timelines and habits. Not insight. ↳ “When did you last use it?” ↳ “Who was involved?” ↳ “Where were you?” Use when you’re mapping the what, not the why. 𝗪𝗮𝗻𝘁 𝗰𝗹𝗮𝗿𝗶𝘁𝘆? 𝗔𝘀𝗸 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝗼𝗻. Perfect for mental models, first-use tests, confusion points. ↳ “Walk me through how you would explain this to a teammate” ↳ “Explain what you think this does.” Use when you want to catch mismatched expectations. 𝗪𝗮𝗻𝘁 𝘁𝗼 𝘁𝗲𝘀𝘁 𝘂𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆? 𝗔𝘀𝗸 𝗳𝗼𝗿 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻. This is real-world behavior, not theory. ↳ “Show me how you’d complete this.” ↳ “Tell me about what you did the last time X happened.” Use when you need to see the friction. 𝗪𝗮𝗻𝘁 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 𝗶𝗻𝘁𝗼 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴? 𝗔𝘀𝗸 𝗳𝗼𝗿 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀. This is where users break things down. ↳ “Describe what matters most when deciding.” ↳ “Explain where you get stuck.” Use when you’re mapping criteria and tradeoffs. 𝗪𝗮𝗻𝘁 𝘁𝗼 𝘀𝗽𝗮𝗿𝗸 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆? 𝗔𝘀𝗸 𝗳𝗼𝗿 𝘀𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘀. (my fave!!!) Co-creation starts here. ↳ "Tell me about a time when a tool or product really impressed you. Describe what made it stand out.” ↳ “If you’ve ever hacked together your own version of this, walk me through what you did.” Use when you’re in the generative zone. 𝗪𝗮𝗻𝘁 𝘁𝗼 𝘂𝗻𝗰𝗼𝘃𝗲𝗿 𝘃𝗮𝗹𝘂𝗲𝘀? 𝗔𝘀𝗸 𝗳𝗼𝗿 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻. This is judgment, reasoning, preference. ↳ “Talk me through what you would recommend.” ↳ “Explain why you chose that one?” Use when you’re surfacing what matters most. If your research isn’t getting deep enough, it’s not the user, it’s the question. Want to rewrite your interview guide using Bloom’s? I’ve made a cheat sheet with prompts for every domain: https://lnkd.in/eSuNdv_V

📊 Power BI is not just about data – it's about user experience Overloaded dashboards. Fonts you can barely read. Slicers scattered everywhere. You open the report and... you're lost. You don’t know where to look or what the report is really trying to say. A good report is not just technically correct — it’s a visual narrative that guides the user and supports better decisions. 🧭 Here are 10 rules I follow in my daily Power BI work – inspired by Storytelling with Data by Cole Nussbaumer Knaflic: 1️⃣ Readable fonts first Stick to simple, clean fonts — ideally monospace or sans-serif. Style matters, but clarity wins. 2️⃣ Use color sparingly Color should highlight, not decorate. The fewer colors, the stronger their impact. Example: Use a single accent color (e.g., red) only to highlight key KPIs — everything else in grayscale. 3️⃣ Leverage brand colors (wisely) Branded color palettes increase consistency and trust — but don’t let them overpower your message. 4️⃣ Remove visual clutter If it doesn’t add meaning, it’s noise. Gridlines, axis lines, 3D effects? Get rid of them. 5️⃣ Minimize data More data ≠ more clarity. Sometimes just showing the minimum and maximum is enough. Example: Instead of labeling every bar in a chart, display only the highest and lowest values. 6️⃣ Choose visuals carefully Pie charts? Rarely useful. 3D pie charts? Never. They distort perception and kill comprehension. Example: Replace a 3D pie chart with a horizontal bar chart to compare categories more accurately. 7️⃣ Design with layout in mind Top-left is where the eye goes first. Put the most important insights there. Example: Place key KPIs (e.g., revenue YTD) in the top-left corner, not at the bottom. 8️⃣ Go from overview to detail Start with a KPI, then trends, then a detailed table for those who want to dive deeper. 9️⃣ Visually differentiate importance Line thickness, solid vs dashed, or color intensity — all help communicate meaning. Example: Show actuals with a bold solid line, and forecasts with a thinner dashed line. 🔟 Limit slicers to essentials 2–3 relevant filters are usually enough. More than that overwhelms the user. 📌 And finally – the most important rule: Test with real users. Do they know what they’re looking at? If not, you’re not done. 💬 What’s your #1 tip for creating great user experience in Power BI? Let me know in the comments 👇 #PowerBI #UXDesign #DataStorytelling #StorytellingWithData #ColeNussbaumerKnaflic #DashboardDesign #BI #DataVisualization #PowerPlatform #UserExperience  

We can all agree benefits and unique selling points are crucial for SaaS success. Yet many companies struggle to effectively communicate them. I've seen countless products with amazing features fail to gain traction. Why? Users simply didn't understand the value. Your digital experience must clearly convey what sets you apart. At every touchpoint in the customer journey, reinforce your key differentiators. This goes beyond listing features. Break down exactly how you solve user pain points. Show the tangible impact on their daily work. Consider tactics like interactive product tours, comparison charts, or benefit-focused messaging. The goal is to make your value proposition crystal clear. Don't assume users intuitively grasp your product's strengths. Guide them to that understanding through intentional design and messaging. By optimizing your digital experience around benefits and USPs, you remove friction from the decision-making process. Then, users gain confidence faster. This translates directly to improved metrics – from initial signups to long-term retention and referrals. Take a hard look at your current approach. Are you truly highlighting what makes your product unique and valuable? If not, you're likely leaving conversions on the table.

Find the shape of your design decision. Fix the constraint, then scale the problem space. Most teams argue about design because they don’t know what kind of UX problem they’re in. Once you can read the UX metric stack, the next decision becomes obvious. In many of my work sessions with customers, we’re trying to make a call using constraints and UX metrics to understand where users actually are. These meetings can be groups of five to ten people, and there’s rarely time for deep analysis. Teams often need orientation to ideate. Not a full answer, but clarity on what the design signals mean and how to move forward. Iteration can always come later. In the moment, people want to know: what does this tell us, and what should we do next? Over time, I started noticing a pattern in how I frame design decisions and recommendations. With a small set of UX metrics in a stack, you can orient a team in about 30 seconds. You can quickly see which problems matter most. This is where design leverage starts. You cannot earn trust if people are not clear on what you are presenting to them. I often sit at the front end of fast, million dollar decisions at ZURB that compound over the life of an initiative. These decisions tend to fall into the same few shapes. Think of UX metrics as four layers that sit on top of each other. 1. Clarity Do people understand what this is and what to do next? 2. Ability Can they do it quickly and without mistakes? 3. Confidence Do they feel safe, in control, and willing to continue? 4. Commitment Do they come back, adopt it, recommend it, and rely on it? Here are the four common shapes of design problems. These patterns show up again and again. Each one tells you what kind of problem you actually have. → Confusion Trap Clarity is low. Everything above it becomes noisy or misleading. Users are not oriented. They guess, hesitate, and click around. The job here is to fix comprehension before touching polish, new features, or conversion tactics. →  Friction Wall Clarity is solid, but ability is low. Time is high, errors increase, and drop-offs appear. Users get it, they just cannot do it. The move is to remove steps, simplify flows, reduce cognitive load, tighten IA, and improve affordances. → Trust Gap Clarity and ability look fine, but confidence is low. Doubt, anxiety, perceived risk. The experience works, but it does not feel safe. This is common in fintech, healthcare, and AI. Teams need to focus on building reassurance. Transparency, guardrails, explanations, error prevention, and clear feedback about what happens next. → Adoption Leak The top of the stack is healthy, but commitment is weak. The design works, but there is no habit. The move is to focus on the value loop. Activation timing, defaults, reminders, integrations, onboarding sequence, and ongoing use cases. Finding the shape of your decision helps orient everyone on a team. You fix the lowest broken layer…and everything above it gets easier!