User Experience for Non-Technical Users

✅ Survey Design Cheatsheet (PNG/PDF). With practical techniques to reduce bias, increase completion and get reliable insights ↓ 🚫 Most surveys are biased, misleading and not actionable. 🤔 People often don’t give true answers, or can’t answer truthfully. 🤔 What people answer, think and feel are often very different things. 🤔 Average scores don’t speak to individual differences. ✅ Good questions, scale and sample avoid poor insights at scale. ✅ Industry confidence level: 95%, margin of error 4–5%. ✅ With 10.000 users, you need ≥567 answers to reduce sample bias. ✅ Randomize the order of options to minimize primacy bias. ✅ Allow testers to skip questions, or save and exit to reduce noise. 🚫 Don’t ask multiple questions at once in one single question. 🤔 For long surveys, users regress to neutral or positive answers. 🚫 The more questions, the less time users spend answering them. ✅ Shorter is better: after 7–8 mins completion rates drop by 5–20%. ✅ Pre-test your survey in a pilot run with at least 3 customers. 🚫 Avoid 1–10 scales as there is more variance in larger scales. 🚫 Never ask people about their behavior: observe them. 🚫 Don’t ask what people like/dislike: it rarely matches behavior. 🚫 Asking a question directly is the worst way to get insights. 🚫 Don’t make key decisions based on survey results alone. Surveys aim to uncover what many people think or feel. But often it’s what many people *think* they think or feel. In practice, they aren’t very helpful to learn how users behave, what they actually do, if a product is usable or learn specific user needs. However, they do help to learn where users struggle, what user’s expectations are, if a feature is helpful and to better understand user’s perception or view. But: designing surveys is difficult. The results are often hard to interpret and we always need to verify them by listening to and observing users. Pre-test surveys before sending out. Check if users can answer truthfully. Review the sample size. Define what you want to know first. And, most importantly, what decisions you will and will not make based on the answers you receive. --- ✤ Useful resources: Survey Design Cheatsheet (PNG, PDF), by yours truly https://lnkd.in/ez9XQAk3 A Big Guide To Survey Design, by H Locke https://lnkd.in/eJWRnDRi How to Write (Better) Survey Questions, by Nikki Anderson, MA https://lnkd.in/eHpzr-Q6 Survey Design Guide, by Maze https://lnkd.in/e4cMp5g5 Why Surveys Are Problematic, by Erika Hall https://lnkd.in/eqTd-7xM --- ✤ Books ⦿ Just Enough Research, by Erika Hall ⦿ Designing Surveys That Work, by Caroline Jarrett ⦿ Designing Quality Survey Questions, by Sheila B. Robinson #ux #surveys

Most UX folks are missing the one skill that could save their careers. For a long time, many UXers have been laser-focused on the craft. Understanding users. Testing ideas. Perfecting pixels. But here’s the reality. Companies are cutting those folks everywhere, because they don’t connect their work to hard, actual, tangible $$$$$. So it’s viewed as a luxury. A nice-to-have. My 2 cents.. If you can’t tie your decisions to how it helps the business make or save money, you’re at risk. Full stop. But I have good news. You can quantify your $$ impact using basic financial modeling. Here’s a quick example.. Imagine you’re working on a tool that employees use every day. Let’s say the current experience requires 8 hours a week for each employee to complete a task. By improving the usability of the tool, you cut that time by three hours. Let’s break it down. If the average employee makes $100K annually (roughly $50/hr), and 100 employees use the tool, that’s $15K saved each week. Over a year, that’s $780K in savings.. just by shaving 3 hours off a process. Now take it a step further. What if those employees use those extra 3 hours to create more value for customers? What’s the potential revenue upside? This is the kind of thinking that sets a designer apart. It’s time for UXers to stop treating customer sentiment or usability test results as the final metric. Instea learn how your company makes or saves money and model the financial impact of your UX changes. Align your work with tangible metrics like operational efficiency, customer retention, or lifetime value. The best part? This isn’t hard. Basic math and a simple framework can help you communicate your value in ways the business understands. Your prototype or design file doesn’t need to be perfect. But your ability to show how it drives business outcomes? That does. — If you enjoyed this post, join hundreds of others and subscribe to my weekly newsletter — Building Great Experiences https://lnkd.in/edqxnPAY

Remember that bad survey you wrote? The one that resulted in responses filled with blatant bias and caused you to doubt whether your respondents even understood the questions? Creating a survey may seem like a simple task, but even minor errors can result in biased results and unreliable data. If this has happened to you before, it's likely due to one or more of these common mistakes in your survey design: 1. Ambiguous Questions: Vague wording like “often” or “regularly” leads to varied interpretations among respondents. Be specific—use clear options like “daily,” “weekly,” or “monthly” to ensure consistent and accurate responses. 2. Double-Barreled Questions: Combining two questions into one, such as “Do you find our website attractive and easy to navigate?” can confuse respondents and lead to unclear answers. Break these into separate questions to get precise, actionable feedback. 3. Leading/Loaded Questions: Questions that push respondents toward a specific answer, like “Do you agree that responsible citizens should support local businesses?” can introduce bias. Keep your questions neutral to gather unbiased, genuine opinions. 4. Assumptions: Assuming respondents have certain knowledge or opinions can skew results. For example, “Are you in favor of a balanced budget?” assumes understanding of its implications. Provide necessary context to ensure respondents fully grasp the question. 5. Burdensome Questions: Asking complex or detail-heavy questions, such as “How many times have you dined out in the last six months?” can overwhelm respondents and lead to inaccurate answers. Simplify these questions or offer multiple-choice options to make them easier to answer. 6. Handling Sensitive Topics: Sensitive questions, like those about personal habits or finances, need to be phrased carefully to avoid discomfort. Use neutral language, provide options to skip or anonymize answers, or employ tactics like Randomized Response Survey (RRS) to encourage honest, accurate responses. By being aware of and avoiding these potential mistakes, you can create surveys that produce precise, dependable, and useful information. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Imagine this: you’re filling out a survey and come across a question instructing you to answer 1 for Yes and 0 for No. As if that wasn't bad enough, the instructions are at the top of the page, and when you scroll to answer some of the questions, you’ve lost sight of what 1 and 0 means. Why is this an accessibility fail? Memory Burden: Not everyone can remember instructions after scrolling, especially those with cognitive disabilities or short-term memory challenges. Screen Readers: For people using assistive technologies, the separation between the instructions and the input field creates confusion. By the time they navigate to the input, the context might be lost. Universal Design: It’s frustrating and time-consuming to repeatedly scroll up and down to confirm what the numbers mean. You can improve this type of survey by: 1. Placing clear labels next to each input (e.g., "1 = Yes, 0 = No"). 2. Better yet, use intuitive design and replace numbers with a combo box or radio buttons labeled "Yes" and "No." 3. Group the questions by topic. 4. Use headers and field groups to break them up for screen reader users. 5. Only display five or six at a time so people don't get overwhelmed and bail out. 6. Ensure instructions remain visible or are repeated near the question for easy reference. Accessibility isn’t just a "nice to have." It’s critical to ensure everyone can participate. Don’t let bad design create barriers and invalidate your survey results. Alt: A screen shot of a survey containing numerous questions with an instructing you to answer 1 for Yes and 0 for No. The instruction is written at the top and it gets lost when you scroll down to answer other questions. #AccessibilityFailFriday #AccessibilityMatters #InclusiveDesign #UXBestPractices #DigitalAccessibility

I ran my survey past five respondents before a single human saw it. None of them are real of course. I've been designing a staff survey to understand how people across an organisation actually use AI in their day-to-day work. Before it went anywhere near an inbox, I wanted to know where it would trip people up. So I asked Claude to become my respondents. We built five personas — a sceptic who isn't convinced AI is useful for his work, a non-technical operations person, a technical colleague who builds AI tools rather than just using them, a cautious professional worried about confidentiality, and a busy generalist. Then I had each of them "take" the survey and tell me where they hesitated, what they misread, and what felt like a test they might fail. The edges are where the problems live. A few things it surfaced that I'd missed as word blindness sets in: → Two personas couldn't tell whether Microsoft Copilot counted as an "AI assistant" or as "AI built into my tools" — because it's genuinely both. A section split was wobbling on a distinction real people couldn't reliably make. → The sceptic flagged that one question quietly assumed everyone was already using AI. He had nowhere honest to say "I tried it and stopped." → The non-technical persona tripped over jargon — words that are obvious inside the industry and opaque everywhere else. 📣 Persona testing doesn't replace testing on real humans. 📣 But it catches the easy stuff for free — before you've spent anyone's goodwill — so the actual humans get a cleaner draft and you get better data. Two rounds of this and the survey was meaningfully better. Cost me an afternoon, not a field study. If you're building anything people have to read and respond to — a survey, a form, an onboarding flow — have you tried letting AI role-play your hardest users first? (Part of a short series on how I'm actually using Claude day to day — learn out loud.)

I walked up to what looked like the entrance and hit a familiar problem: the only communication option was a speaker. No text. No visual cue. No other way to understand what to do. The screenshot shows the speaker as the only communication method. If you couldn't hear it like me, you were stuck. It was not loud. It was not chaotic. It was just the two of us trying to find the right door. A hearing person was with me and could listen. If I had been alone, I would have had to walk back around and hope I found the correct entrance. That is the part people overlook. Audio only fails in the simplest situations. This is not a disability issue. It is a usability and business issue. - People are Deaf or hard of hearing. - People do not always hear clearly, even in quiet spaces. - People are focused on directions, schedules, or safety. - People understand information faster when they can see it. Relying on one communication mode creates barriers for many. It slows people down, increases confusion, and forces staff to step in and explain what the system should have made clear. I see this across industries because I work with organizations on communication access and inclusive customer experience. The same gap shows up in kiosks, apps, events, and support flows: one mode, one channel, one assumption about how people receive information. When teams design for multiple communication options, the experience becomes smoother, faster, and more inclusive. That is not just good accessibility. It is operational efficiency and a better customer experience. If your organization is investing in technology or customer touchpoints, ask one more question: how many ways can a person understand what to do here? The more options you give people, the stronger the results. #CustomerExperience

Question: If you had a bad experience with a company or product, would you buy from them again? The answer is "no" right? For disabled people, 75-80% of customer experiences are failures. That means that 75-80% of transactions for our community aren't repeated. That's pretty bad right? The impact of a negative experience resonates far beyond a single transaction. It can influence a customer's decision-making process and brand loyalty for the long term. In striving for improvement, businesses must recognise the importance of inclusivity and accessibility. By investing in accessible design, empathetic customer service, and continuous feedback loops, we can create an environment where every customer feels valued and understood. Here are some actionable steps to enhance the customer experience for everyone: * Prioritise accessibility: Ensure your physical and digital spaces are accessible to disabled people. This includes wheelchair ramps, accessible websites, and accommodating customer service practices. * Educate your team: Educate your staff to the diverse needs of customers. Training programmes that emphasise empathy and understanding can go a long way in fostering a positive and inclusive customer experience. * Feedback mechanisms: Establish channels for customers to provide feedback easily. Actively seek input from disabled people to understand our unique challenges and implement necessary improvements. * Adopt universal design: From product packaging to online interfaces, adopt a design philosophy that considers the diverse needs of all customers. Universal design benefits everyone and creates a more positive overall experience. * Transparent communication: Be transparent about your commitment to inclusivity. Communicate the steps you are taking to improve accessibility, both internally and externally. This fosters trust and demonstrates your dedication to positive customer experiences. Remember, creating a truly inclusive business environment not only improves the lives of disabled people but also enhances the overall customer experience for everyone. It's a win-win strategy that builds lasting connections and fosters brand loyalty. #InclusiveBusiness #CustomerExperience #AccessibilityMatters

Lyft knew they had a problem. Only 5.6% of its users are over 65, and those users are 57% more likely to miss the ride they ordered. So, Lyft created Silver – a special app version for seniors. But why create a separate app when these improvements would benefit all users? The curb-cut effect is real. Features designed for wheelchair users ended up helping parents with strollers, travelers with luggage, and delivery workers with carts. The features in Lyft's senior-friendly app wouldn't only benefit older riders: 💡The 1.4x larger font option? Great for bright sunlight, rough rides. 💡Simplified interface? Less cognitive load for all of us. 💡Live help operators? Great for anyone when there's a problem. 💡Select preference for easy entry/exit vehicles? Not everyone likes pickup trucks. What started as an accommodation should became a universal improvement. The most powerful insight? Designing for seniors forced Lyft to prioritize what truly matters: simplicity and ease of use. Will they leverage this for all their users? The next time someone suggests adding another button to your interface or feature to your product, consider this approach instead: sometimes the most innovative design is the one that works for everyone. Rather than creating separate "accessible" versions, what if we just built our core products to be usable by all? This is the paradox of inclusive design - what works better for some almost always works better for all. What "accessibility" feature have you encountered that actually made life better for all users? #UniversalDesign #ProductThinking #CustomerExperience

🙈🙊🙉 Inclusive Design = Better UX Did you know that over 1 billion people worldwide live with a disability? That is about 16% of the global population. 😱 Yet, 98% of websites still fail basic accessibility standards. Even more concerning, 68% of users with disabilities leave websites because of design barriers. Microsoft Inclusive Design https://lnkd.in/gxx_CXp9 💡 What is Inclusive Design? Inclusive design is a methodology that considers the full range of human diversity. Abilities, languages, cultures, genders, ages, and different life situations. It is not only about accessibility for people with disabilities. It is about creating better experiences for everyone. 🛠️ Examples of inclusive design in real life: 🚪 Automatic doors Originally created for wheelchair users. Now convenient for everyone. 📃 Subtitles Essential for deaf and hard of hearing users. Also useful in noisy places or when sound is off. 🎮 Xbox Adaptive Controller Designed for gamers with limited mobility. Fully customizable for many user needs. 🎯 Why does it matter? When we design for diverse needs, we build products that work for a wider audience. ✅ How to get started: 1️⃣ Explore Microsoft’s Inclusive Design toolkit https://lnkd.in/gxx_CXp9 2️⃣ Use accessibility checklists W3C Web Accessibility Guidelines https://lnkd.in/dAjpu_8R 3️⃣ Apply WCAG principles in your projects https://lnkd.in/dSVbFjXi 4️⃣ Involve users with different needs in your design process. Let’s design products that are accessible, inclusive, and useful for all. #InclusiveDesign #UXDesign #Accessibility #DesignForAll #MicrosoftDesign

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.