Competitive Analysis In UX

⏱️ How To Measure UX (https://lnkd.in/e5ueDtZY), a practical guide on how to use UX benchmarking, SUS, SUPR-Q, UMUX-LITE, CES, UEQ to eliminate bias and gather statistically reliable results — with useful templates and resources. By Roman Videnov. Measuring UX is mostly about showing cause and effect. Of course, management wants to do more of what has already worked — and it typically wants to see ROI > 5%. But the return is more than just increased revenue. It’s also reduced costs, expenses and mitigated risk. And UX is an incredibly affordable yet impactful way to achieve it. Good design decisions are intentional. They aren’t guesses or personal preferences. They are deliberate and measurable. Over the last years, I’ve been setting ups design KPIs in teams to inform and guide design decisions. Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < 60s (for critical tasks) 3. Time to first success < 90s (for onboarding) 4. Time to candidates < 120s (nav + filtering in eCommerce) 5. Time to top candidate < 120s (for feature comparison) 6. Time to hit the limit of free tier < 7d (for upgrades) 7. Presets/templates usage > 80% per user (to boost efficiency) 8. Filters used per session > 5 per user (quality of filtering) 9. Feature adoption rate > 80% (usage of a new feature per user) 10. Time to pricing quote < 2 weeks (for B2B systems) 11. Application processing time < 2 weeks (online banking) 12. Default settings correction < 10% (quality of defaults) 13. Search results quality > 80% (for top 100 most popular queries) 14. Service desk inquiries < 35/week (poor design → more inquiries) 15. Form input accuracy ≈ 100% (user input in forms) 16. Time to final price < 45s (for eCommerce) 17. Password recovery frequency < 5% per user (for auth) 18. Fake email frequency < 2% (for email newsletters) 19. First contact resolution < 85% (quality of service desk replies) 20. “Turn-around” score < 1 week (frustrated users → happy users) 21. Environmental impact < 0.3g/page request (sustainability) 22. Frustration score < 5% (AUS + SUS/SUPR-Q + Lighthouse) 23. System Usability Scale > 75 (overall usability) 24. Accessible Usability Scale (AUS) > 75 (accessibility) 25. Core Web Vitals ≈ 100% (performance) Each team works with 3–4 local design KPIs that reflects the impact of their work, and 3–4 global design KPIs mapped against touchpoints in a customer journey. Search team works with search quality score, onboarding team works with time to success, authentication team works with password recovery rate. What gets measured, gets better. And it gives you the data you need to monitor and visualize the impact of your design work. Once it becomes a second nature of your process, not only will you have an easier time for getting buy-in, but also build enough trust to boost UX in a company with low UX maturity. [more in the comments ↓] #ux #metrics

By now, most of us use AI tools daily. As an experience designer, here is my observation: the shift from task-based to intent-based design is fundamentally changing our discipline. The Interface Paradox Look at any conversational AI, ChatGPT, Claude, Grok, Gemini and more. They’re nearly identical. A text input field. A waiting state. An output response. Yet we have clear preferences. We favor one over another. Why? It’s not the visual design. It’s the quality of output. This is the critical insight: in AI-driven experiences, we’re no longer designing for tasks. We’re designing for intent and outcome. The GUI elements between input and output are minimal, almost invisible. What matters is relevance and accuracy. The Responsibility Gap Users rarely acknowledge poor prompts. When results disappoint, they blame the tool. “This AI sucks.” Never “My prompt sucked.” This is human nature, user psychology 101. The user is never wrong, the system always is. Whether deterministic or non-deterministic, we designers must account for this. We build padding around human error and input quality issues because that’s our job. The New Design Imperative Stop obsessing over visual representation. Start obsessing over output quality. In the age of AI, the experience isn’t what users see between input and output. It’s what they get as a result. That’s where differentiation lives. That’s where user experience is won or lost. #ai #design

UX hiring is quietly changing. And if you blink, you’ll miss it. Earlier, companies hired “UX Designers.” Now they’re hiring: UX Designer – Agentic AI UX Designer – Cybersecurity UX Designer – FinTech / BFSI UX Designer – HealthTech UX Designer – DevTools / SaaS Infra This is not fancy titling. This is a signal. What’s happening is domain-specialized UX hiring. Products today are no longer just screens and flows. They are: decision systems risk-heavy environments regulated ecosystems AI-driven workflows A general UX skillset alone is not enough when the product: can auto-act on behalf of users (Agentic AI) deals with threats, alerts, and false positives (Cybersecurity) involves money, compliance, and trust (FinTech) affects real human lives (HealthTech) So companies hire designers who already have domain judgment, not just design skills. Now let’s address the uncomfortable part. Does this mean generalist UX designers are useless? No. But it does mean “I can design anything” is too vague in 2026. Generalists are struggling not because they lack skill, but because they lack positioning. Here’s how generalists actually win today: - A strong generalist is not someone who knows everything. - A strong generalist is someone who: has solid UX fundamentals - understands systems, not just interfaces AND has gone deep in at least one domain Think of it like this: You keep your UX core broad, but your value spike comes from specialization. Examples: General UX + AI mental models = Agentic UX Designer General UX + risk & compliance thinking = Cybersecurity UX General UX + workflows & tooling = DevTools UX General UX + data & metrics = Growth / Product UX Specialization does not mean boxing yourself forever. It means giving hiring managers a clear reason to trust you fast. The market is not rejecting generalists. It’s rejecting vague designers. If you’re a UX designer today, the move is simple: Keep your fundamentals sharp. Pick a domain. Build depth. Learn the language of that industry. That’s how UX careers stay relevant while products get more complex. Design is evolving. So should our positioning.

I use my personal GitHub pages website as a testing ground for AltCSS. It's built purely using HTML, with AltCSS directly applying the native styles. So, it's super lightweight, embraces native HTML with zero overhead. The metrics reflect the same. Some trivia for frontend engineers. What do these metrics in the second screenshot actually mean? In short they are what we refer to as "Core Web Vitals". 1. First Contentful Paint (FCP): The time it takes for the first piece of content (like text, image, SVG, etc.) to appear on the screen after the page starts loading. The lower it is, the better UX and fast page loading. 2. Largest Contentful Paint (LCP): Time it takes for the largest visible element (like a big image, heading, or video) to render on the screen. Always make sure to check what's your largest element and what optimizations can be done. For example, if you are loading a big image, you can think of loading it in WebP format for supported browsers as it would load faster, decreasing the LCP value. 3. Total Blocking Time (TBT): The total time during which the main thread is blocked and the browser can't respond to user input (e.g., clicks, typing). Interestingly even the ChatGPT website suffers from a TBT issue where the input chat box is unresponsive on page load instance and can reload or empty out the instantaneous text you type. Shows how even a top traffic websites with the best engineers are not able to get it right. 4. Cumulative Layout Shift (CLS): Measures how much visible elements shift around as the page loads. This is not always a bad thing depending on your usecase. For example, I have added an artificial layout shift on the TechFlix landing page to a nice transition of loading and animation for better UX. Alternatively CLS is often very poor in most news websites due to "ads" and unnecessary popups that block content or shift the layout. 5. Speed Index: A score that represents how quickly visible content is populated during page load. This can be terrible for data intensive applications. A neat trick is to always statically serve the first batch of data that is to be shown on the screen. After the page loads and the user performs an action, you can update the data dynamically. For example if you have a table where you want to show the first 10 records on page load, don't make an API call. Instead, serve the first 10 records alone as direct static data. Only when the user goes to page 2, fetch from the API. If the user comes back to page 1, you can do a prefetch and cache that data (to ensure its fresh). Bottomline: Landing pages, Pricing pages, and Pages with key calls to actions and important data widgets must be optimized for the best web vitals to ensure better search indexing and UX for the end user. This often translates to a step percentage increase in CTA clicks, time spent on websites, etc.

AI is killing the UX Design role as we know it. Designers who adapt will evolve into Strategic Experience Architects who will be in high demand. While traditional designers are "pixel-pushing," a new set of designers is emerging.  They're using AI to fast-track design ideas and turning prototypes into working code. A lot of what UX designers are doing manually today is exactly what AI tools are getting good at: • Rapid wireframing concepts • UI component creation • Basic user research • Persona development • Usability testing automation The ability to automate some UX tasks is already here. We have to assume that the technology will only advance quickly. I recently spoke with several Product Managers who are already replacing basic UX tasks with AI tools. When PMs can generate, iterate, and validate designs using AI, what happens to the traditional UX role? Simple products and startups will streamline. PMs with AI will be able to handle the basics. We're already seeing this shift. However, there's a big opportunity here as well. AI has a critical blind spot: it can't grasp the nuanced psychology of human behavior. It can't navigate complex stakeholder dynamics. It can't translate business objectives into meaningful user experiences. This is where the evolution happens. The future belongs to Strategic Experience Architects who: ✦ Define the right problems to solve ✦ Extract insights from human complexity ✦ Align teams around user value ✦ Guide AI with human context The market is splitting: → Basic products: UX roles blend into other roles on the team → Complex enterprises: Strategic UX roles become critical Fortunately, most valuable products are complex and human-centered. Want to stay relevant? Here's what to consider. 1. Master AI design tools   But don't just use them, learn to orchestrate them 2. Evolve from maker to strategist   Your value is in thinking, not in pushing pixels (AI will eventually handle this) 3. Develop business intelligence   Connect user needs to revenue 4. Study human psychology    This is your moat against AI 5. Learn systems thinking Focus on developing repeatable systems in your daily work The UX industry isn't dead, but it is transforming. -- ♻️ Share if you think this will help others ➕ Follow Jason Moccia for more insights on AI and Product Design

Something feels off in UX right now 🥺 My 2026 UX Design predictions Over the last months, I’ve had the same conversations again and again. With designers. With teams. With leaders. “Should I learn AI?” “Is my role still relevant?” “Am I falling behind… or overreacting?” Here’s what I’m seeing for UX in 2026 👇 1️⃣ Interfaces are dissolving With Generative UI, interfaces are created on demand. No more fixed screens. UX shifts from designing flows to defining systems, constraints, and intent. 2️⃣ UX research is scaling radically AI can analyze thousands of open-ended responses, run deep research, and surface patterns in minutes. The role of designers and researchers is changing: less execution, more sense-making, validation, and decision-making. 3️⃣ Designing for AI is no longer optional AI products learn from users. That makes them powerful and incredibly confusing if UX is missing. Trust, explainability, recovery UX, and mental models are now core design work. “Just type something” is not a UX strategy. 4️⃣ AI agents change how systems behave AI doesn’t just respond anymore. It plans and acts on behalf of users. This breaks traditional UX patterns. The key question becomes: When can a system act on its own and when must it stop? 5️⃣ Vibe coding removes technical barriers Ideas turn into prototypes and products at near-zero cost. The real bottleneck is no longer code, but judgment. UX shifts even further toward problem framing, direction, and quality control. 6️⃣ Roles matter less. Ownership matters more. Job titles lose their edge. Execution becomes cheap. What matters is who takes responsibility, who makes decisions, and who asks the uncomfortable questions. 7️⃣ Personalization is entering uncomfortable territory AI systems are building long-term memory about users. Helpful at first. Creepy the moment people realize what the system knows. UX must make data use visible, adjustable, and understandable. Not more data. More choice. My biggest takeaway: 2026 is not about designing faster. It’s about deciding better. UX is moving away from interface design toward intent, systems thinking, and responsibility. If you work in UX and ignore AI, you won’t be replaced. You’ll slowly become irrelevant. If you stay curious, critical, and intentional about how you use AI, this is one of the biggest opportunities UX has ever had. P.S.: I also recorded a podcast episode (link in the comments) where I go much deeper into these shifts, with concrete examples and reflections from my own work. If you want the longer version beyond a LinkedIn post, feel free to give it a listen.

A design can pass a usability test and still feel exhausting. That is why 𝗡𝗔𝗦𝗔-𝗧𝗟𝗫 is useful in UX research. NASA-TLX, the Task Load Index, is a post-task rating method for measuring perceived workload. Instead of asking only whether users succeeded, it asks what success cost them. The scale looks at six dimensions: 🧠 𝗠𝗲𝗻𝘁𝗮𝗹 demand: How much thinking, remembering, deciding, or searching was required? 💪 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 demand: How much physical action was required? ⏱️ 𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗹 demand: How rushed or time-pressured did the task feel? 🎯 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: How successful did the user feel? 🔥 𝗘𝗳𝗳𝗼𝗿𝘁: How hard did the user have to work to reach that result? 😤 𝗙𝗿𝘂𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: How insecure, annoyed, discouraged, or stressed did the user feel? In a UX study, the usual pattern is simple. Users complete a task, then rate these dimensions. Researchers compare ratings across tasks, prototypes, user groups, or design alternatives. The result is not just a score. The real value is often in the workload profile: where the burden appears and what kind of burden it is. This matters because ease of use is not the same as low workload. A product can be learnable but draining. A checkout can be fast but stressful. A dashboard can be powerful but mentally expensive. A workflow can produce few errors while forcing users to hold too much in memory. NASA-TLX helps teams see these hidden costs. It turns subjective strain into a structured design signal. For UX designers, that signal is practical: 🔍 Reveal cognitive friction that observation alone may miss. 📊 Compare competing designs beyond task time and completion rate. ⚠️ Explain why users make errors, hesitate, or abandon a flow. ✅ Encourage interfaces that are not only efficient, but sustainable to use. Good UX reduces the work the interface adds to the work users already came to do.

💎 Overview of 70+ UX Metrics Struggling to choose the right metric for your UX task at hand? MeasuringU maps out 70+ UX metrics across task and study levels — from time-on-task and SUS to eye tracking and NPS (https://lnkd.in/dhw6Sh8u) 1️⃣ Task-Level Metrics Focus: Directly measure how users perform tasks (actions + perceptions during task execution). Use Case: Usability testing, feature validation, UX benchmarking. 🟢 Objective Task-Based Action Metrics These measure user performance outcomes. Effectiveness: Completion, Findability, Errors Efficiency: Time on Task, Clicks / Interactions 🟢 Behavioral & Physiological Metrics These reflect user attention, emotion, and mental load, often measured via sensors or tracking tools. Visual Attention: Eye Tracking Dwell Time, Fixation Count, Time to First Fixation Emotional Reaction: Facial Coding, HR (heart rate), EEG (brainwave activity) Mental Effort: Tapping (as proxy for cognitive load) 2️⃣ Task-Level Attitudinal Metrics Focus: How users feel during or after a task. Use Case: Post-task questionnaires, usability labs, perception analysis. 🟢 Ease / Perception: Single Ease Question (SEQ), After Scenario Questionnaire (ASQ), Ease scale 🟢 Confidence: Self-reported Confidence score 🟢 Workload / Mental Effort: NASA Task Load Index (TLX), Subjective Mental Effort Questionnaire (SMEQ) 3️⃣ Combined Task-Level Metrics Focus: Composite metrics that combine efficiency, effectiveness, and ease. Use Case: Comparative usability studies, dashboards, standardized testing. Efficiency × Effectiveness → Efficiency Ratio Efficiency × Effectiveness × Ease → Single Usability Metric (SUM) Confidence × Effectiveness → Disaster Metric 4️⃣ Study-Level Attitudinal Metrics Focus: User attitudes about a product after use or across time. Use Case: Surveys, product-market fit tests, satisfaction tracking. 🟢 Satisfaction Metrics: Overall Satisfaction, Customer Experience Index (CXi) 🟢 Loyalty Metrics: Net Promoter Score (NPS), Likelihood to Recommend, Product-Market Fit (PMF) 🟢 Awareness / Brand Perception: Brand Awareness, Favorability, Brand Trust 🟢 Usability / Usefulness: System Usability Scale (SUS) 5️⃣ Delight & Trust Metrics Focus: Measure positive emotions and confidence in the interface. Use Case: Branding, premium experiences, trust validation. Top-Two Box (e.g. “Very Satisfied” or “Very Likely to Recommend”) SUPR-Q Trust Modified System Trust Scale (MST) 6️⃣ Visual Branding Metrics Focus: How users perceive visual design and layout. Use Case: UI testing, branding studies. SUPR-Q Appearance Perceived Website Clutter 7️⃣ Special-Purpose Study-Level Metrics Focus: Custom metrics tailored to specific domains or platforms. Use Case: Gaming, mobile apps, customer support. 🟢 Customer Service: Customer Effort Score (CES), SERVQUAL (Service Quality) 🟢 Gaming: GUESS (Game User Experience Satisfaction Scale) #UX #design #productdesign #measure

🥖 Fresh-baked UX jobs data analysis from the always-brilliant Jeff Sauro and James R. Lewis of MeasuringU. I've been waiting for this: A historical, in-depth analysis of UX job market data they've been collecting with UXPA International for over a decade. Key findings for UX professionals: 1️⃣ 2023-2024 was rough. 35% of organizations reported reducing UX staff — twice the rate we've seen over recent years. From 2022 to 2024, net UX jobs (% added - % lost) dropped from +38% to 0%. It was even slightly worse than 2009 (post-financial crisis). 🫣 👉 No, you're not crazy — the job market has really, really sucked lately. If you've been job hunting without result, it really wasn't your fault. 2️⃣ It wasn't just us. This contraction was related to a broader tech downturn. Macroeconomic factors (like higher interest rates) have hit our industry hard — especially startups that don't have a heavy AI focus. 👉 We aren't the only roles struggling. However, I still think it's time for UX to look inward and reflect on how our approaches need to change. (Sarah Gibbons and I are working on an article on this right now for Nielsen Norman Group, coming soon.) 3️⃣ Things might improve this year. 70% of hiring managers plan to hire 1+ UX people in 2025. It took us about 2 years to recover after 2009, so that might be the case now. But with AI in the mix, the future outlook is unclear. 👉 Job hunters take heart — this could be your year. 🔎 Check out the study: https://lnkd.in/eE_DwJRm

For a long time, the golden rule in UX research was simple: just test 5 users, and you will catch 80 percent of usability issues. It made sense in early usability testing when the goal was catching obvious bugs or severe blockers. But today, UX research often asks bigger questions. We explore subtle user preferences, test multiple design variations, predict market behavior, or validate critical flows in products where mistakes can cost millions. Suddenly, 5 users do not seem enough anymore. As UX research matured, so did the need for smarter ways to plan sample sizes. Recent years have brought more advanced methods that help researchers move beyond rough estimates. For instance, adapted statistical power analysis for UX allows us to calculate sample size based on expected effect sizes, even when working with small or noisy samples. Bayesian approaches are gaining traction too, offering flexible sample planning that updates based on incoming data, letting you stop early when enough certainty is reached. Sequential and adaptive sampling strategies are another exciting development, especially for usability studies or preference tests. Instead of setting a fixed number in advance, you continue collecting data until you achieve a desired confidence level, making studies faster and more cost-effective. Risk-based models are also changing how researchers think about participant numbers. Instead of focusing only on detecting problems, they consider the business or design risk of making a wrong decision, adjusting the sample size based on how much uncertainty you can afford. Another growing trend is mixing qualitative and quantitative sizing in adaptive ways. Some frameworks now combine early qualitative saturation analysis with quantitative validation stages, offering a dynamic approach where the study evolves based on what you learn. All of these methods offer something critical that the old "5 users" rule does not: they match the sample size to the research goal, the risk involved, and the complexity of the product. If you are running a simple early discovery study, small samples still work well. But if you are testing pricing sensitivity, final designs, or behavioral metrics that inform big decisions, modern UX research demands more. It is an exciting time because we now have access to Bayesian calculators, sequential stopping rules, risk modeling tools, and mixed-methods planning guides that make our studies not just bigger, but smarter.