UX Metrics And KPIs

💾 How To Break Down Complexity With Task Analysis In UX (https://lnkd.in/d9sujQ7m). A practical step-by-step guide on how to study user goals, map user’s workflows, understand top tasks and then apply them to shape design decisions ↓ 🚫 Good UX isn’t just high completion rates for user's tasks. 🤔 Better: high accuracy, low task on time, high completion rates. ✅ Best: high decision quality, low error recovery cost, high completion. ✅ Task analysis breaks down user tasks to understand user goals. ✅ Tasks are goal-oriented user actions (start → end point → success). ✅ First, collect data: users, what they try to do and how they do it. ✅ Refine your task list with stakeholders, then get users to vote. ✅ Translate each top task into goals, starting point and end point. ✅ Break down: user’s goal → sub-goals; sub-goal → single steps. ✅ For non-linear/circular steps: mark alternate paths as branches. ✅ Usually presented as a tree (hierarchical task-analysis diagram, HTA). ✅ Scrutinize every single step for errors, efficiency, opportunities. ✅ Attach design improvements as sticky notes to each step. ✅ Complex systems and non-linear flows → consider OOUX. 🚫 Don’t lose track in small tasks: come back to the big picture. – When we are dealing with complex flows, the most common approach is to break it down into smaller parts. It doesn’t sound too surprising: we try to map out a whole picture of what is in front of us and understand what it's composed of. That's how we learn user goals, key features, frequent steps, processes — but also dependencies and constraints. These smaller parts can be workflows, tasks or specific actions — but they all lead to a desired user's outcome. And because they are smaller, they are more isolated and hence more manageable, with clear start and end points that give us a clearer scope to work with. Now, it sounds quite straightforward in theory, but in practice it quickly becomes an exhausting challenge. We need to gather requirements, reach stakeholders, tip-toe around legacy workflows and puzzle together conflicting priorities, needs and goals. It's difficult to get anywhere without a systematic approach. Let’s explore ways to achieve that. Full article: https://lnkd.in/d9sujQ7m – 🌻 My friendly, practical UX guides (15% off with 🎟 LINKEDIN): Smart Design Patterns → https://smashed.by/smart Design Patterns For AI → https://smashed.by/ai-ux Measure UX & Design Impact → https://measure-ux.com Happy designing, everyone — and thank you so much for reading! 🎉🥳 #ux #design ↓

As companies look to scale their GenAI initiatives, a significant hurdle is emerging: the cost of scaling the infrastructure, particularly in managing tokens for paid Large Language Models (LLMs) and the surrounding infrastructure. Here's what companies need to know: a) Token-based pricing, the standard for most LLM providers, presents a significant cost management challenge due to the wide cost variations between models. For instance, GPT-4 can be ten times more expensive than GPT-3.5-turbo. b) Infrastructure costs go beyond just the LLM fees. For every $1 spent on developing a model, companies may need to pay $100 to $1,000 on infrastructure to run it effectively. c) Run costs typically exceed build costs for GenAI applications, with model usage and labor being the most significant drivers. Optimizing costs is an ongoing process, and the following best practices would help reduce the costs significantly: a) Techniques, like preloading embeddings, can reduce query costs from a dollar to less than a penny. b) Optimizing prompts to reduce token usage c) Using task-specific, smaller models where appropriate d) Implementing caching and batching of requests e) Utilizing model quantization and distillation techniques f) A flexible API system can help avoid vendor lock-in and allow quick adaptation as technology evolves. Investments in GenAI should be tied to ROI. Not all AI interactions need the same level of responsiveness (and cost). Leaders must focus on sustainable, cost-effective scaling strategies as we transition from GenAI's 'honeymoon phase'. The key is to balance innovation and financial prudence, ensuring long-term success in the AI-driven future. #GenerativeAI #AIScaling #TechLeadership #InnovationCosts #GenAI

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

Most teams I meet can tell me how many tickets their AI bot handled last month. Very few can tell me what one of those tickets actually costs. Here’s how I look at it. Take the total run cost and divide it by the number of completed cases. If your bot spends $1,680 to close 2,400 tickets, that is $0.70 per resolution. If it spends $4,000 to close 1,200, that is $3.33. At that point, you are paying more than your human queue. Vodafone proved this math scales. Their bot TOBi handles 45 million conversations a month and trims average hold times by over a minute. The impressive part is the discipline of tying every interaction back to cost per resolution and deflection rates. That same discipline works whether you run 500 cases or 50 million. Set your baseline first. - What does one resolved ticket cost with people in the loop? - Write it down and give the agent a number it has to beat for 30 days straight. - For most teams, under $2 per resolution is a solid target. I outlined the whole framework and a one-pager you can take into your next ops review in this week’s Simform newsletter. Link is in the bio.

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

Most apps lose users because their workflows frustrate, confuse, or overwhelm them. Avoid these 5 pitfalls, and you’ll retain more users and boost satisfaction. 1. Cluttered Home Screen 🚫 Overwhelms users with too many choices upfront. ✅ Do this instead: Prioritize the most critical actions for users. Apply the “Fewer Choices Principle” to guide attention effectively. 2. Confusing Navigation 🚫 Users can’t find what they need quickly. ✅ Do this instead: Use universally recognized labels and icons. Organize content into clear, logical categories. 3. Lengthy Processes 🚫 Every additional step increases drop-offs. ✅ Do this instead: Conduct task analysis to reduce unnecessary steps. Implement features like autofill and a one-click checkout. 4. Slow Loading Times 🚫 1-second delay = 7% fewer conversions. ✅ Do this instead: Compress assets (images, videos). Leverage CDNs and lazy loading to speed up performance. 5. Poor Mobile Optimization 🚫 70% of users will abandon apps with poor mobile UX. ✅ Do this instead: Design for touch gestures like swiping and tapping. Test usability across screen sizes and operating systems. A seamless user flow isn’t just good design; it’s a growth strategy. By prioritizing simplicity and usability, you create apps that users want to return to. Have thoughts or questions? Drop them below or message me, let’s simplify user experiences together!

In procurement, the biggest mistake mid-level professionals make is focusing only on the quoted price. The reality? Hidden costs can derail your entire project budget and timeline. A proper cost evaluation goes beyond comparing numbers—it’s about understanding the true financial impact of a purchase over its lifecycle. 1. Break Down the Cost Components When reviewing supplier quotes, look beyond the unit price. Include: ✅️Material Costs: Breakdown price of goods or services. ✅️Payment Terms: Cost impact due to extend credit period. ✅️Warranty/DLP: Warranty period for the product or the Defect Liability Period. ✅️Brand/Make: Cost impact due to Brand/Make or the Country of origin. ✅️Freight & Logistics: Shipping, handling, customs duties. ✅️Taxes & Duties: VAT, import/export tariffs. ✅️Insurance: Coverage for transit and project risks. ✅️Installation & Commissioning: Labor and equipment setup. Tip: Always request a detailed cost breakdown from suppliers to avoid surprises. 2. Consider Lifecycle Costs (TCO) Total Cost of Ownership (TCO) includes: 📌Maintenance & Spare Parts 📌Energy Consumption 📌Training Costs for Staff 📌Disposal or Decommissioning Example: A machine priced at AED 50,000 might cost AED 80,000 over 5 years due to maintenance and operational cost. 3. Identify Hidden Risks That Add Cost 🔸️Currency Fluctuations: For international purchases. 🔸️Delay Penalties: Late delivery can trigger liquidated damages. 🔸️Compliance Failures: Missing certifications can lead to fines. Tip: Factor these risks into your cost evaluation model. 4. Use Cost Evaluation Tools 🔹️Weighted Scoring Models: Balance technical and commercial factors. 🔹️Risk-Adjusted Cost Analysis: For high-value or critical projects. “Always calculate Total Cost of Ownership (TCO) before awarding a contract. A single overlooked cost can wipe out your savings.” Cost evaluation is not just a financial exercise—it’s a risk management tool. By considering all cost components, lifecycle expenses, and hidden risks, procurement professionals can protect their projects from budget overruns and compliance failures. Share the insights with your network 🤝 #Procurement #CostManagement #SupplyChain #ProjectManagement #RiskMitigation #TCO

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?

How to make UX visible to people who think in spreadsheets: Step 1: Find the friction before you pitch the solution ↳ Sit with users. Count actual steps in a real workflow. Don't estimate. Don't average. Count. One specific task. One specific user. Write it down. Step 2: Translate friction into time ↳ If a task takes 18 minutes and happens 40 times a week per user, that's 12 hours per user per week. Multiply by headcount. That's a number your finance team recognizes. Step 3: Connect time to money (or risk) ↳ Labor cost of those hours + cost of errors + cost of delayed decisions. Keep it conservative. One solid number beats a range every time. Step 4: Show the before and the after ↳ Not wireframes. Not prototypes. A side-by-side of current state vs proposed state, in numbers. 18 steps → 6 steps. 18 minutes → 4 minutes. Step 5: Name the risk of doing nothing ↳ What's the cost of keeping the current system for another year? Another three? Quantify it. That's your business case. Result: You stop arguing about UX maturity and start talking about operational performance. What's the hardest part of making this case in your organization? #NavigatingComplexity #EnterpriseUX #EUX

In Anthropic's Agentic Coding Trends Report, they mention "perhaps the most valuable capability developments in 2026 will be agents learning when to ask for help, rather than blindly attempting every task, and humans stepping into the loop only when required." That's why we are releasing our latest research at Scale AI: Long Horizon Augmented Workflows (LHAW). LHAW is a synthetic data generation pipeline for creating underspecification on *any* dataset and evaluating how agents react. LHAW transforms well-specified long-horizon tasks into controllably underspecified variants using a three-phase pipeline: segment extraction, candidate generation and empirical validation. We generate & validate 285 ambiguous task variants across MCP-Atlas, TAC, and SWE-Bench Pro Finding #1: Clarification recovers meaningful performance, but not fully. Access to a simulated user significantly improves success on underspecified tasks (+31% Pass@3 for Opus4.5 on MCP-Atlas), yet agents are not able to fully recover original performance. Finding #2: Models vary widely in clarification strategy: GPT-5.2 spams, Gemini models underask. Some models extract high value information per question. Others ask far more frequently, achieving gains but with lower value per interaction. We measure this with with Gain/Question Finding #3: Clarification behavior adapts to cost. As expected, when interaction is “cheap”, agents ask more but gain less per question. When interaction is “expensive”, agents ask less but extract more value per question at higher risk of failure. Finding #4: Clarification failure-modes vary from widespread to model-specific. Certain failure-modes like poor question quality, underclarification, and question targeting apply across models. Some models show particularly bad tendencies to overclarify or misinterpret a response. As agents take on longer tasks, we want to know how they act under uncertainty and how much they burden us with their questions :) LHAW provides a way to create these tasks, evaluate clarification strategies, and (soon) train agents for reliability under real-world ambiguity. This work was led by George Pu and Mike Lee with contributions from Udari Madhushani Sehwag, David Lee, Bryan Zhu, Yash Maurya, Mohit Raghavendra, and Yuan (Emily) Xue Blog: https://lnkd.in/gp768At9 Full Paper: https://lnkd.in/gVTjemmv Dataset: Hugging Face https://lnkd.in/gTjVrszU