UX Metrics And KPIs

𝗖𝘂𝗹𝘁𝘂𝗿𝗲 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 𝘄𝗵𝗲𝗿𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗹𝗼𝗼𝗸. 𝗕𝗶𝗼𝗹𝗼𝗴𝘆 𝗱𝗼𝗲𝘀. 𝘞𝘩𝘢𝘵 𝘢 𝘯𝘦𝘸 7-𝘤𝘰𝘶𝘯𝘵𝘳𝘺 𝘦𝘺𝘦-𝘵𝘳𝘢𝘤𝘬𝘪𝘯𝘨 𝘴𝘵𝘶𝘥𝘺 𝘮𝘦𝘢𝘯𝘴 𝘧𝘰𝘳 𝘨𝘭𝘰𝘣𝘢𝘭 𝘮𝘢𝘳𝘬𝘦𝘵𝘪𝘯𝘨 For years, the following assumption has shaped how global campaigns get built: • Western audiences focus on the product. • Eastern audiences scan the context. The cure: Adapt your visuals. Localize everything. The cultural differences are too big to ignore. But that was in theory. New studies tell a very different story! A study just published in Scientific Reports actually tested this. Jiří Čeněk and colleagues tracked eye movements across 408 participants from 7 cultural samples spanning Africa, East Asia, Europe, and the Near East. Real-world scenes. Controlled conditions. 𝙏𝙝𝙚 𝙘𝙪𝙡𝙩𝙪𝙧𝙖𝙡 𝙨𝙩𝙚𝙧𝙚𝙤𝙩𝙮𝙥𝙚𝙨 𝙙𝙞𝙙𝙣'𝙩 𝙝𝙤𝙡𝙙 𝙪𝙥. Germany and Czechia showed no significant difference from Taiwan. Ghana and Turkey -- expected to sit somewhere in the middle -- turned out to be the most object-focused of everyone tested. The predicted order was either absent or completely inverted. You can find the study here: https://lnkd.in/e2PZ3M4v We ran into the same thing at Neurons. Our own global eye-tracking study covered Denmark, USA, Brazil, Guatemala, and Iran. Across all five, attention to brand, product, and text was remarkably consistent. 𝗡𝗼 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝗰𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗶𝗻 𝘄𝗵𝗲𝗿𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗹𝗼𝗼𝗸. The visual attention system follows biological rules, not cultural ones. This matters if you're running global campaigns: • Attention models trained in one market generalize to others • You don't need separate eye-tracking studies in every geography • Creative optimization at the attention level scales across borders The things that do vary across cultures — emotional tone, trust signals, social norms in imagery — are real. But they operate downstream, after attention has already landed. The brain's first move is universal. Make sure your creative earns it. Full methodology and heatmaps from our study here: https://lnkd.in/eX5pigQE #neuromarketing #eyetracking #attentionscience #globalmarketing #consumerneuroscience

A user can finish a task quickly and still be mentally overloaded, stressed, or frustrated in ways they never report. Multimodal UX research tries to close that gap by combining traditional UX data with physiological signals like eye movements, heart rate, skin conductance, facial expressions, voice tone, and sometimes EEG. When these signals are aligned on the same timeline as interaction data, we can see not just what users did, but what it cost them cognitively and emotionally to do it. This matters because many UX decisions are made on incomplete evidence. Time on task or success rates can look fine while biometrics quietly show elevated stress or sustained cognitive strain. Eye tracking can reveal that long fixations are not clarity but confusion. GSR spikes can point to moments of frustration users never mention. Heart rate and variability can show mental effort building across a workflow. EEG can highlight designs that are harder to process even when performance looks identical. When these signals are integrated, UX teams gain access to latent experience states that are otherwise invisible. Multimodal UX is about supporting decisions with more diagnostic evidence, especially in complex systems like enterprise software, games, AR and VR, automotive interfaces, accessibility research, and voice based experiences. The goal is to reduce blind spots. Used carefully and ethically, multimodal data helps teams design experiences that are not just usable, but cognitively lighter, emotionally safer, and more humane.

I'm excited to share insights from a comprehensive analysis of 19 studies on the use of eye-tracking technology in medical education. Our research lab is equipped with state-of-the-art eye-tracking devices, which have been instrumental in exploring its diverse applications—from decoding clinical vignettes to enhancing radiological expertise. This technology provides deep insights into cognitive processes by measuring visual attention and cognitive load and offers data-driven enhancements to medical training. Moreover, its adaptation to remote learning through innovative webcam-based solutions is revolutionizing online education. Are you curious to learn more or interested in discussing how these technologies can be integrated into healthcare training programs? Let’s connect and explore eye-tracking technology's possibilities for advancing medical education! #MedicalEducation #EyeTracking #HealthcareTechnology #MedTech

Recap: Gaze + Pen UI Study Recently Apple Vision Pro started support of gaze+pen UI with the Logitech Muse stylus. Earlier this year, we conducted a study to better understand its performance and usability trade-offs. With Meta Quest Pro's eye-tracking and the stylus-grip controllers, we evaluated 4 object movement techniques in a shape point translation task: ✏️Direct Pen: selects the object directly, then drags it directly ✏️Raypointing: selects via the pen’s forward ray, then drags indirectly ✏️👀Gaze + Pen: selects with gaze, drags with pen indirectly ✏️👀👀Gaze + Snap: selects with gaze, drags with gaze using target-snapping* Results: ⏱️Gaze + Snap fastest overall (≈2.5s), compared to other techniques (3.4-3.6s) ❌Higher error rate for Gaze+Snap (2.6%), others (0.5–1.2%) ⏱️Raypointing ~10% faster for initial selection but ~16% slower during dragging compared to Direct Pen 💪Gaze + Snap lowest perceived hand fatigue but highest eye fatigue 🧠TLX workload and overall user preference favored Gaze+Snap In sum, more integrated use of gaze can be beneficial to performance. Compared to our similar study last year using hand+gaze, a key new finding is that the snapping approach not only reduces hand fatigue but also improves time by ~30%, at the cost of ~2% additional errors. This makes it a useful alternative to current Gaze+Pinch / Gaze+Pen UIs in tasks where snapping is possible. The paper was led by Uta Wagner (Universität Konstanz) and Jeremy Wu (KTH Royal Institute of Technology), with Qiushi Zhou and myself (Department of Computer Science, Aarhus University / Pioneer Centre for AI (P1)), in collaboration with Jinwook Kim (KAIST), Mario Romero (Linköping University), Alessandro Iop (KTH Royal Institute of Technology), and Tiare Feuchtner (Universität Konstanz). Presented at #ISMAR 2025 in Seoul, Korea. Links: 📄 Paper: https://lnkd.in/d9QQVWjF 🎥 Video: https://lnkd.in/eAywyUNi - Last year's object movement study: https://lnkd.in/eWiRP_YZ * This technique requires target-knowledge, enabling the target-snapping. Based on Vildan Tanriverdi and Rob Jacob's early work https://lnkd.in/eMtiTTKZ

I was reading a magazine on a Sunday morning in 2014 when I stumbled over a word. Ouagadougou. The capital of Burkina Faso. My eyes paused. Dwelled longer. Went back to re-read it. The article explained: Eye movement reveals cognitive understanding in real-time. When you read fluently, your eyes flow smoothly. When you encounter something unfamiliar, they pause, return, hesitate. That pause is measurable. It reveals cognitive load. That’s when I had a thought I couldn’t shake: If eye movement shows cognitive friction during reading… what would it show during airline operations control decisions? I was a product manager for ops and crew systems. Controllers would tell me: “The system works fine.” But I’d see the hunting. The clicking back and forth. The frustration. They’d adapted so completely to dysfunction they couldn’t articulate what was wrong. Within a week, I found an eye-tracking partner in Brandenburg, Germany. We tracked controllers through 12-hour shifts. Controllers said: “System works fine.” Their eyes said: Cognitive chaos. → 47-second hunt for information that should be immediate → Repeated returns to same screen (context loss) → Extended dwell time revealing confusion, not comprehension One ops controller watched her video: “I didn’t realize how much I was searching. I thought I was working. I was just… hunting.” She’d been doing the job for 12 years. Eye-tracking made the invisible visible. Here’s what haunts me: That Brandenburg partner? Acquired by Apple. The same technology is now in every iPhone. On every controller’s desk. Right now. Millions use it daily. But airline operations systems haven’t adopted it. Not because it doesn’t work. Because it’s “not proven in aviation.” And here’s what we’re missing: Eye-tracking isn’t just UX research. It’s the ultimate AI performance metric. Everyone’s deploying “AI-powered” systems. But how do you know if AI actually helps? Current metrics: Accuracy, speed, error rates Missing metric: Does it reduce cognitive burden? Eye-tracking reveals this objectively: If AI works: Eyes move smoothly, less dwelling, directed patterns (like reading fluent text) If AI fails: Extended dwell time, anxious scanning, more returns (like stumbling over Ouagadougou) You can’t fake eye patterns. Controllers can say “AI is helpful” while eyes reveal anxiety. The technology exists. The capability sits on controllers’ desks. We’re just not measuring what matters. The question isn’t whether AI produces right answers. The question is: Does it make decision-making feel like reading fluent text, or stumbling over Ouagadougou? I wrote about how a Sunday morning magazine led to measuring cognitive loa, and why eye-tracking should be the standard for AI performance. Operations professionals: When you use “AI assistance,” does it feel like it’s reading your mind, or like you’re validating everything it does? Technology teams: Are you measuring cognitive load, or just accuracy?

The smallest design tweaks can have the biggest impact. Take the “View All” link for product swatches on Nordstrom's website. When it’s grouped directly within the grid of options, it feels natural. It’s part of the same flow. Users don’t have to hunt for extra colors or sizes. They discover it effortlessly because it’s right where they’re already looking. Research backs this up. Nielsen Norman Group’s “10 Usability Heuristics” highlights the principle of “Recognition rather than Recall.” When “View all” is grouped with the color swatches, users recognize it as the way to access additional shades. If the link is separate, they have to remember to look for extra options somewhere else—often missing it. Baymard Institute’s 2022 Product Page Usability Study likewise found that up to 31% of desktop users missed extended color or size variations if the “More Colors/See More” link was placed out of the immediate scanning area. Keeping “View all” in the same chunk as the other swatches raised discoverability and lowered user frustration. What’s the result of better placement? → Higher discoverability. → Lower frustration. → A seamless shopping experience. If you’re designing for usability, remember this: Every detail counts. Putting options where users expect them isn't just a best practice—it’s a boost to your bottom line.

A designer's survival guide to proving impact... Every design decision we make has ripple effects, but if we can't communicate that impact, we're leaving career opportunities on the table. Reality check! 💥 Most of us struggle to get any business metrics. We can't prove our design changed anything. Frustrating? Absolutely. Career-limiting? Not if you know how to pivot! Let's do a mindset shift: The impact isn't just about metrics. It comes in many forms. (𝘐 𝘬𝘯𝘰𝘸 𝘴𝘰𝘮𝘦 𝘰𝘧 𝘵𝘩𝘦𝘮 𝘤𝘢𝘯 𝘴𝘵𝘪𝘭𝘭 𝘣𝘦 𝘩𝘢𝘳𝘥 𝘵𝘰 𝘨𝘦𝘵, 𝘣𝘶𝘵 𝘪𝘵 𝘮𝘪𝘨𝘩𝘵 𝘣𝘦 𝘦𝘢𝘴𝘪𝘦𝘳 𝘵𝘩𝘢𝘯 𝘤𝘰𝘯𝘷𝘦𝘳𝘴𝘪𝘰𝘯 𝘰𝘳 𝘳𝘦𝘷𝘦𝘯𝘶𝘦) → User-centric indicators • Reduction in user errors • Time saved per user flow • Decreased learning curve • User satisfaction scores from testing → Client relationship wins • Positive feedback in client meetings • Extended contracts/repeat business • Client referrals • Stakeholder testimonials • Increased trust (shown through autonomous decision-making) → Team efficiency gains • Faster design iteration cycles • Reduced revision rounds • Improved developer handoff efficiency • Better cross-functional collaboration • Streamlined documentation process → Brand & market impact • Positive social media mentions • Industry recognition • Design awards • Competitor analysis advantages • Brand consistency improvements Impact isn't just about numbers - it's about telling a compelling story of transformation through design. Start collecting "micro-wins" in every project. The client team's excitement, developer feedback, user testing insights. These stories became more powerful than any conversion rate could be. Remember: Lack of metrics isn't a roadblock. It's an invitation to tell a richer story! P.S. How do you showcase impact without direct access to metrics? Share your strategies below!

Last week, during a design review, a Fortune 500 client asked me: "Your design is beautiful. But where's the revenue impact?" That question always hit hard. For 16 years at NetBramha - Global UX Design Studio, I've seen this shift: Design alone isn't enough anymore. Business will save design. Here's why 👇 Fact 1 - The reality of design today → Beautiful UIs don't drive growth → User research needs business context → Design must impact revenue → Aesthetics alone won't save budgets How we turned it around: An edtech client wanted a new redesigned website, we studied their user personas first: → Customer drop offs & conversion rates → User research to identify real motivation  → Our design increased the conversions on the website by 534% → ROI: Through the roof! Fact 2 - The reality of design tomorrow → Design must speak the business language → Metrics matter more than mockups → Strategy will beat pure creativity → Impact outweighs inspiration Fact 3 - How business will save us → Forces us to measure the real impact → Makes design accountable for results → Aligns creativity with market needs → Transforms design from cost to investment After designing digital experiences for 1B+ people, here's what I know: Business isn't killing design. It's making design stronger. More purposeful. More impactful. The future belongs to designers who understand this: business will save design. It was never the other way around. What's your take on this? Have you seen business expertise elevate design work? #DesignStrategy #BusinessOfDesign #DesignLeadership

This is what bad interfaces do to your eyes We usually talk about bad UX as an inconvenience, but what if it’s something more physical than that? Having my Master degree in biomedical engineering I was really curious about how our body actually respond to digital inaccessibility. When a user lands on a page with: • low contrast text • dense blocks of content • unclear structure • too many competing elements they don’t just feel “annoyed”, their brain has to work harder to process what’s in front of them. Also, when people concentrate harder, they blink significantly less and blinking is what keeps the eye hydrated and protected. Here’s what science tells us in depth about it: 1️⃣ People blink less when interfaces are harder to process Higher cognitive load → lower blink rate → more eye strain 🔗Multiple usability studies show that difficult interfaces reduce blinking and increase attention effort (https://lnkd.in/ec79UN77) 2️⃣ Pupils dilate when users struggle Your brain signals effort → your pupils physically expand 🔗 Pupil dilation is a well-established biological marker of mental effort in cognitive tasks (https://lnkd.in/e5Ztu9NG) 3️⃣ Eyes stay fixed longer on confusing content Users stare longer, re-read, and search more 🔗 Eye-tracking research shows increased fixation duration with higher task difficulty and poor usability (https://lnkd.in/eWzhNCJV) 4️⃣ Eye behavior reflects cognitive stress in real time Blink rate, pupil size, gaze patterns = measurable stress signals 🔗Eye tracking is widely used to objectively measure cognitive load and user effort (https://lnkd.in/e_GTNJtm) 5️⃣ These signals are directly tied to how hard the interface is Not the user. Not their skills. 🔗Studies confirm eye metrics (blink rate, pupil dilation, fixations) are reliable indicators of interface difficulty and mental workload (https://lnkd.in/euRPJpNF) What’s interesting is that most studies don’t even look at “accessibility”, instead they look at cognitive load, which is exactly what accessibility issues increase. ➡️ A wall of text is not just “bad design” - it forces the eyes to work harder to track lines ➡️ Low contrast is not just “non-compliant” - it increases effort for basic perception ➡️ Unclear buttons are not just “confusing” - they keep users in a constant state of micro-decision-making So accessibility is not only about inclusion, it’s about respecting the limits of the human body. Have you ever noticed certain websites making your eyes tired almost instantly? #CognitiveLoad #HumanCenteredDesign #DigitalHealth #DesignResearch #Usability #A11y #ProductDesign

Eye-gaze measurement is becoming an advanced metric to understand audience engagement. Here’s how it’s typically done: 1. Camera-based Eye-Tracking Technology • How it works: Cameras (usually discreet and privacy-compliant) are installed near the DOOH screen. They capture facial landmarks and estimate where the viewer’s eyes are directed. • AI processing: Algorithms analyze head position, gaze direction, and dwell time to determine if and how long a person looked at the screen. • Metrics collected: Impressions, gaze duration, number of engaged viewers, heatmaps of attention. 2. Computer Vision + Sensors • Uses a combination of video analytics and depth sensors to track movement and attention. • Can differentiate between those who simply pass by and those who actively look at the screen. 3. Eye-Tracking Panels or Wearables (Less Common) • Research panels may use glasses or mobile devices with eye-tracking sensors to record gaze data in controlled studies, providing insights into how much attention DOOH ads receive. 4. Privacy & Compliance • Data is anonymized and aggregated (no facial recognition for identity). • GDPR and local privacy laws require clear policies and sometimes opt-in mechanisms. Why Measure Eye-Gaze in DOOH? • Proof of engagement: Beyond reach, it measures attention quality. • Creative optimization: Which content holds attention longest. • Media value: Can help justify premium pricing for high-engagement sites.