Understanding User Experience

⏱️ 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

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

Here’s a study that will change the way you look at public space. Literally. Researchers from Brigham Young University used heat maps to visualize something many women already know: walking home at night is a fundamentally different experience depending on your gender. Researchers asked participants to imagine walking through different nighttime environments and click on what drew their attention. The results? 👀 Men looked straight ahead - toward the path or destination. 👀 Women scanned the edges - the bushes, the shadows, the places where someone could be hiding. The visual difference is a reminder that design isn’t gender neutral. It either acknowledges fear or ignores it. This kind of data makes visible what is often invisible: the quiet vigilance, the habitual threat assessment, the constant environmental awareness so many women carry in public space. Learned this during a fascinating webinar with Elise Moeskops. She passed a decision at the Amsterdam city council that calls for better, equitable design of public space: "Young women are not called the otters of the public space for nothing – when they are somewhere, you know things are good." Research by Robert Chaney, Alyssa Baer & Ida Tovar [Links to the research & Amsterdam's decision in the comments.]

The perfect crypto wallet doesn't exist yet. But we finally figured out why. After talking to hundreds of users, the same three complaints kept surfacing: 1) Buying crypto is still harder than it should be Centralized exchanges make purchasing simple. Wallets make you jump through many platforms, bridges, and somehow you still end up with the wrong token on the wrong chain… 2) The gas fee maze "Wait, I need ETH to send USDC?" - every new user hits this wall. You want to use your tokens but first need different tokens just to move them. 3) The privacy + password nightmare Seed phrases = one lost paper means lifetime savings gone. Plus, every transaction is public. Phishers can see your entire portfolio and target you accordingly. 4) The paradox of choice Open any wallet and face 10,000 tokens, 50 protocols, 20 bridges. Users don't want options - they want solutions. "What should I actually use for X problem?" remains unanswered. We're building something different. A wallet that handles gas in the background. Where losing your password doesn't mean losing everything. Where your wallet balance is your business, not for others to pry. That actually recommends what's useful instead of showing you everything. The crypto industry built wallets for crypto natives. We're building for the crypto curious - and for all the places where users just want things to work and to solve their daily issues. The industry spent the last ~15 years making wallets more powerful. Maybe it's time we made them more human. Want to test what we're building? Join the waitlist - link in comments. Limited spots for early testers who've felt these exact frustrations. ♻️ Share this with someone who gave up on crypto because of bad UX

Influencing people and attracting customers with technology is all about leveraging digital tools, platforms, and strategies to build engagement, trust, and value. What can you learn from this bird? - **Personalization and Data Analytics:** - Create Personalized Experiences: Utilize data analytics and AI to tailor customer experiences, such as personalized product recommendations and custom content, fostering a sense of relevance and connection. - Gain Customer Insights: Analyze customer behavior to understand preferences and needs, enabling businesses to refine their offerings and messaging effectively. - **Social Media and Digital Presence:** - Boost Social Media Engagement: Platforms like Instagram and LinkedIn facilitate direct customer engagement through interactive content creation and community building around your brand. - Collaborate with Influencers: Partnering with influencers extends your reach by leveraging their follower base and credibility. - **Automation and AI-Driven Marketing:** - Implement Chatbots and AI Support: AI-driven chatbots enhance customer support responsiveness and service quality, while automation tools streamline communication through email marketing and CRM systems. - Leverage Predictive Marketing: Use AI to anticipate customer needs, ensuring satisfaction and loyalty. - **Interactive Technology:** - Offer AR/VR Experiences: Immersive AR/VR experiences enable customers to virtually try products, enhancing the buying process and engagement. - Develop Interactive Websites and Apps: Intuitive platforms boost customer satisfaction, driving longer interaction times and increased conversion rates. - **Trust through Transparency and Security:** - Ensure Blockchain and Secure Transactions: Utilize blockchain and encrypted payment systems to foster trust and ensure secure transactions. - Showcase Reviews and Testimonials: Use technology to display user reviews, ratings, and case studies, building trust through social proof. - **Innovative Product Features:** - Integrate AI and IoT: Products incorporating AI or IoT attract customers seeking cutting-edge solutions. - Offer Mobile Apps and Tools: Complement your product with apps or digital tools like fitness trackers and others. #ai #technology #marketing via @ferarrigophoto

We care a lot about user experience at Duolingo and monitor it via a number of app performance metrics. App performance is especially a challenge on Android because of the breadth of the ecosystem of devices. In 2021, we ran a cross-company Android reboot effort to improve the code architecture and improve latency. We then set latency and performance guardrails to prevent new changes from slowing down the app. Despite our best efforts, though, latency crept up. Early in 2024, one of our data scientists, Daniel Distler, was able to demonstrate that improving latency in some key parts of the user journey would drive solid increases in DAUs (daily active users), one of our main company metrics. This was the nudge we needed to re-invest in the effort. We created a cross-company tiger team to work on improving Android performance. Throughout the year, 20 software engineers participated. In 2024, the team ran 200+ A/B tests on Android performance and delivered remarkable results: - Entry-level device app open conversion jumped from 91% to 94.7% - Entry-level device users experiencing 5+ second app open latency dropped from 39% to just 8% - Hundreds of thousands of DAU gains were directly attributable to these performance enhancements and we expect the actual long-term impact was even larger What work proved most impactful? - Almost half of our DAU impact came from improving code efficiency - Another 20% of impact came from optimizing network requests  - Another chunk came from deferring non-critical work to happen later in key flows - Baseline profiles took a lot of time to get right, but sped up application start-up by 30% Want to learn more? Check out Chenglai Huang and Michael Huang’s blog post: https://lnkd.in/dni58Hez #engineering

15 activities to test mobile accessibility In the last 15 years, the internet has gone mobile. Every major platform — from news to shopping to social media — has invested in sleek mobile versions because that’s where people spend their time. 📊 In fact, more than 60% of web traffic now comes from mobile devices (the source: https://lnkd.in/eeSrdHx4) We optimized for speed, performance, and design. But there’s one area where many mobile experiences still fall short: accessibility. And yet, mobile accessibility isn’t a niche concern. It affects everyone — whether you’re navigating with one hand while holding a coffee, trying to read in bright sunlight, or relying on a screen reader every single day. The good news is that you don’t need special tools to understand these challenges: your phone is already the perfect testing lab. That’s why I put together 15 quick activities to test mobile accessibility. Each one reveals how real people experience barriers and how small design choices can make a huge difference. Try these activities: 1. Turn on VoiceOver (iOS) or TalkBack (Android) → Navigate your favorite app. Every unlabeled button or image will suddenly become invisible. Study: Screen Reader User Survey 9 – WebAIM shows that over 70% of users rely on mobile screen readers daily (the study: https://lnkd.in/e9JeHsMx). 2. Increase text size to maximum in settings → Does your layout adjust gracefully? Do words overlap and buttons disappear? WCAG criterion: 1.4.4 Resize text (the link: https://lnkd.in/eDaYZ8wS) 3. Test color contrast outdoors → Step into bright sunlight. Can you still read the buttons? Fact: poor contrast is one of the most common accessibility issues 4. Switch your phone to grayscale → Do instructions still make sense without color cues (“Click the green button” won’t work). Study by WHO: around 300 million people worldwide have some form of color vision deficiency (the study: https://lnkd.in/eD9PkQk7) 5. Try captions on videos → Turn sound off. Are captions accurate, synced, and complete? Fact: 80% of caption users are not deaf or hard of hearing 6. Enable Dark Mode → Is content still clear, or do logos/icons disappear into the background? 7. Try high-contrast mode (Android) or Smart Invert (iOS) → Does the app break visually? 8. Test with one hand only → Can you still reach all main actions (especially on large phones)? 9. Rotate the phone (portrait ↔ landscape) → Does the app adapt, or do important features vanish? 10. Check hit targets → Can you tap small buttons without misclicking? WCAG requires minimum 44×44px target size (the link: https://lnkd.in/eNuZidir) Accessibility on mobile isn’t about edge cases, it’s about real-world design for real-world humans. #WebAccessibility #Inclusion #a11y #MobileAccessibility #WCAG

"Start with why". Some of my users recently texted me to say they preferred using our app to a larger, more established competitor. The reasons they gave surprised me. I previously shared how we took an unconventional approach to onboarding each of our customers at Peek individually. It's slow, cumbersome, and uncomfortable. But what we learned from that process has been invaluable. People told us that other portfolio tracking and personal finance apps felt like they were designed for CFOs, or portfolio managers at asset management companies. Sure, the graphs and charts were comprehensive and pretty, but they didn't have the slightest clue about what to do next. It also helped us understand the social context around investing. People invest to make money, but the real "jobs-to-be-done" are concrete life goals. Most apps also weren't designed to help them visualize their progress towards personal goals; like saving for a retirement number, or a house! To build what people want, don't start from thinking about cool features, designing an impressive tech stack, or even how to get eyeballs on the product. Talk to users. Every single one of them. For as many and as long as you can. You'll be surprised how much of an edge it could give you over the competition!

Brains aren’t calculators (they really aren’t). People compare, not score, so why do we keep asking for numbers when their minds work in stories and snapshots? I used to rely heavily on rating questions in UX studies. You’ve probably used them too. Rate the ease of a task from 1 to 7 or indicate satisfaction on a scale from 1 to 10. These questions feel measurable and look neat in reports, but after running enough sessions, I started noticing a pattern. A participant would finish a task and pause when asked for a score. They’d hesitate, look unsure, and eventually say something like, “Maybe a six?” followed by, “I’m not really sure what that means.” That hesitation is not about the experience itself. It’s about the format of the question. Most people do not evaluate their experiences using numbers. They judge by comparing, whether against other apps, past expectations, or familiar interactions. When I started asking questions like “How did that compare to what you’re used to?” or “Was that easier or harder than expected?” the responses became clearer and more useful. Participants shared what stood out, what surprised them, and what felt better or worse. Their answers were grounded in real impressions, not guesses. This shift from rating questions to comparison questions changed how I run research. Rating scales flatten experiences into abstract numbers. Comparison questions surface preference, context, and emotion. They help users express themselves in the way they naturally reflect on experiences. And they help researchers hear the parts of the experience that actually drive behavior. There is strong support for this in cognitive science. Tversky’s Elimination by Aspects model shows that people decide by gradually filtering out options that lack something important. Prototype theory explains that we judge how well something matches our internal image of what “good” looks like. Both models show that people think in relative terms, not fixed scores. Even heuristic evaluation in usability relies on comparing designs to expected norms and mental shortcuts, not isolated measurement. These models all point to the same idea. People understand and evaluate experiences through contrast. Asking them to rate something on a scale often hides what they really feel. Asking them to compare helps them express it. I still use quantitative data when needed. It helps with tracking and reporting. But when I want to understand why something works or fails, I ask comparison questions. Because users don’t think in scores. They think in reference points, in expectations, and in choices. That is what we should be listening to.

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.