User Experience Performance Indicators

Everyone’s excited to launch AI agents. Almost no one knows how to measure if they’re actually working. Over the last year, we’ve seen brands launch everything from GenAI assistants to support bots to creative copilots but the post-launch metrics often look like this: • Number of chats • Average latency • Session duration • Daily active users Useful? Yes. But sufficient? Not even close. At ALTRD, we’ve worked on AI agents for enterprises and if there’s one lesson it’s this: Speed and usage mean nothing if the agent isn’t solving the actual problem. The real performance indicators are far more nuanced. Here’s what we’ve learned to track instead: 🔹 Task Completion Rate — Can the AI go beyond answering a question and actually complete a workflow? 🔹 User Trust — Do people come back? Do they feel confident relying on the agent again? 🔹 Conversation Depth — Is the agent handling complex, multi-turn exchanges with consistency? 🔹 Context Retention — Can it remember prior interactions and respond accordingly? 🔹 Cost per Successful Interaction — Not just cost per query, but cost per outcome. Massive difference. One of our clients initially celebrated their bot’s 1 million+ sessions - until we uncovered that less than 8% of users actually got what they came for. That 8% wasn’t a usage issue. It was a design and evaluation issue. They had optimized for traffic. Not trust. Not success. Not satisfaction. So we rebuilt the evaluation framework - adding feedback loops, success markers, and goal-completion metrics. The results? CSAT up by 34% Drop-off down by 40% Same infra cost, 3x more value delivered The takeaway: Don’t just measure what’s easy. Measure what matters. AI agents aren’t just tools - they’re touchpoints. They represent your brand, shape user experience, and influence business outcomes. P.S. What’s one underrated metric you’ve used to evaluate AI performance? Curious to learn what others are tracking.

If your site is slow, you’re leaving traffic and revenue on the table. Core Web Vitals are no longer optional. Google has made them a ranking factor, meaning publishers that ignore them risk losing visibility, traffic, and user trust. For those of us working in SEO and digital publishing, the message is clear: speed, stability, and responsiveness directly affect performance. Core Web Vitals focus on three measurable aspects of user experience: → Largest Contentful Paint (LCP): How quickly the main content loads. Target: under 2.5 seconds. → First Input Delay (FID) / Interaction to Next Paint (INP): How quickly the page responds when a user interacts. Target: under 200 milliseconds. → Cumulative Layout Shift (CLS): How visually stable a page is. Target: less than 0.1. These metrics are designed to capture the “real” experience of a visitor, not just what a developer or SEO sees on their end. Why publishers can't ignore CWV in 2025 1. SEO & Trust: Only ~47% of sites pass CWV assessments, presenting a competitive edge for publishers who optimize now. 2. Page performance pays off: A 1-second improvement can boost conversions by ~7% and reduce bounce rates—benefits seen across industries 3. User expectations have tightened: In 2025, anything slower than 3 seconds feels “slow” to most users—under 1 s is becoming the new gold standard, especially on mobile devices. 4. Real-world wins: a. Economic Times cut LCP by 80%, CLS by 250%, and slashed bounce rates by 43%. b. Agrofy improved LCP by 70%, and load abandonment fell from 3.8% to 0.9%. c. Yahoo! JAPAN saw session durations rise 13% and bounce rates drop after CLS fixes. Practical steps for improvement • Measure regularly: Use lab and field data to monitor Core Web Vitals across templates and devices. • Prioritize technical quick wins: Image compression, proper caching, and removing render-blocking scripts can deliver immediate improvements. • Stabilize layouts: Define media dimensions and manage ad slots to reduce layout shifts. • Invest in long-term fixes: Optimizing server response times and modernizing templates can help sustain improvements. Here are the key takeaways ✅ Core Web Vitals are measurable, actionable, and tied directly to SEO performance. ✅ Faster, more stable sites not only rank better but also improve engagement, ad revenue, and subscriptions. ✅ Publishers that treat Core Web Vitals as ongoing maintenance, not one-time fixes will see compounding benefits over time. Have you optimized your site for Core Web Vitals? Share your results and tips in the comments, your insights may help other publishers make meaningful improvements. #SEO #DigitalPublishing #CoreWebVitals #PageSpeed #UserExperience #SearchRanking

How well does your product actually work for users? That’s not a rhetorical question, it’s a measurement challenge. No matter the interface, users interact with it to achieve something. Maybe it’s booking a flight, formatting a document, or just heating up dinner. These interactions aren’t random. They’re purposeful. And every purposeful action gives you a chance to measure how well the product supports the user’s goal. This is the heart of performance metrics in UX. Performance metrics give structure to usability research. They show what works, what doesn’t, and how painful the gaps really are. Here are five you should be using: - Task Success This one’s foundational. Can users complete their intended tasks? It sounds simple, but defining success upfront is essential. You can track it in binary form (yes or no), or include gradations like partial success or help-needed. That nuance matters when making design decisions. - Time-on-Task Time is a powerful, ratio-level metric - but only if measured and interpreted correctly. Use consistent methods (screen recording, auto-logging, etc.) and always report medians and ranges. A task that looks fast on average may hide serious usability issues if some users take much longer. - Errors Errors tell you where users stumble, misread, or misunderstand. But not all errors are equal. Classify them by type and severity. This helps identify whether they’re minor annoyances or critical failures. Be intentional about what counts as an error and how it’s tracked. - Efficiency Usability isn’t just about outcomes - it’s also about effort. Combine success with time and steps taken to calculate task efficiency. This reveals friction points that raw success metrics might miss and helps you compare across designs or user segments. - Learnability Some tasks become easier with repetition. If your product is complex or used repeatedly, measure how performance improves over time. Do users get faster, make fewer errors, or retain how to use features after a break? Learnability is often overlooked - but it’s key for onboarding and retention. The value of performance metrics is not just in the data itself, but in how it informs your decisions. These metrics help you prioritize fixes, forecast impact, and communicate usability clearly to stakeholders. But don’t stop at the numbers. Performance data tells you what happened. Pair it with observational and qualitative insights to understand why - and what to do about it. That’s how you move from assumptions to evidence. From usability intuition to usability impact. Adapted from Measuring the User Experience: Collecting, Analyzing, and Presenting UX Metrics by Bill Albert and Tom Tullis (2022).

When we think about improving an online shopping experience, we often jump to new features or a better design. But behind the scenes, web performance—how fast and smoothly a site loads and responds—is one of the biggest drivers of user satisfaction and business impact. In a recent blog post, Walmart Global Tech’s Engineering team shared their journey to improve their site’s performance systematically, and the lessons are highly relevant for anyone building digital products today. Their journey began with choosing the right metrics. Instead of relying solely on backend or infrastructure-level indicators, they shifted toward Core Web Vitals, a set of user-centric metrics that reflect the real customer experience. These include how quickly content loads (Largest Contentful Paint), when the page becomes interactive (Interaction to Next Paint), and how stable the layout is during loading (Cumulative Layout Shift). By anchoring their efforts in these three metrics, the team ensured that any optimization directly improved what customers actually felt. From there, they focused on how to move these metrics across a massive user base meaningfully. The team set goals based on the 75th percentile, ensuring that improvements benefited most users and weren’t overly influenced by outliers. They also embedded web performance into company-wide decision-making: Core Web Vitals are integrated into Walmart’s experimentation platform, incorporated into the release review process, and included in leadership discussions. In other words, performance isn’t just an engineering KPI—Walmart turned it into a shared organizational priority. This work is a great reminder that improving performance isn’t just an engineering task; it’s about building a culture where user experience is measurable, visible, and owned by everyone. Their approach shows that when a company aligns around the right metrics and integrates them into everyday workflows, even small performance gains can compound into meaningful business results. #DataScience #Analytics #Metrics #CoreWebVitals #Optimization #WebPerformance #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gqsfix-p

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!

When reporting on your impact as a UX Researcher, here are the best → worst metrics to tie your work to: 𝟭. 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 Every company is chasing revenue growth. This is especially true in tech. Tying your work to new (or retained) revenue is the strongest way to show the value that you’re bringing to the organization and make the case for leaders to invest more in research. Examples: - Research insights → new pricing tier(s) → $X - Research insights → X changes to CSM playbook → Y% reduction in churn → $Z 𝟮. 𝗞𝗲𝘆 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 This might not be possible for many UXRs, but if you can, showing how your work contributed to key decisions (especially if those decisions affect dozens or hundreds of employees) is another way to stand out. Examples: - Research insights → new ideal customer profile → X changes across Sales / Marketing / Product affecting Y employees' work - Research insights → refined product vision → X changes to the roadmap affecting Y employees' work 𝟯. 𝗡𝗼𝗿𝘁𝗵 𝘀𝘁𝗮𝗿 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 If you can’t directly attribute your work to revenue, that’s ok! The majority of research is too far removed from revenue to measure the value in dollars. The next best thing is to tie your work to core user engagement metrics (e.g. “watch time” for Netflix, “time spent listening” for Spotify). These metrics are north star metrics because they’re strong predictors of future revenue. Examples: - Research insights → X changes to onboarding flow → Y% increase in successfully activated users - Research insights → X new product features → Y% increase in time spent in app 𝟰. 𝗖𝗼𝘀𝘁 𝘀𝗮𝘃𝗶𝗻𝗴𝘀 For tech companies, a dollar saved is usually less exciting than a dollar of new (or retained) revenue. This is because tech companies’ valuations are primarily driven by future revenue growth, not profitability. That being said, cost savings prove that your research is having a real / tangible impact. 𝟱. 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻’𝘁 𝗯𝗲 𝘁𝗿𝗮𝗰𝗲𝗱 𝘁𝗼 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗮𝗯𝗼𝘃𝗲 Hot take: The biggest trap for researchers (and product folks generally) is focusing on user experience improvements that do not clearly lead to more engagement or more revenue. At most companies, it is nearly impossible to justify investments (including research!) solely on the basis of improving the user experience. Reporting on user experience improvements without tying them to any of the metrics above will make your research look like an expendable cost center instead of a critical revenue driver. — TL;DR: Businesses are driven by their top line (revenue) and bottom line (profit). If you want executives to appreciate the impact of (your) research, start aligning your reporting to metrics 1-4 above.

From HEART to CASTLE: Why Consumer UX Frameworks Fail for Workplace Platforms Here's the uncomfortable truth about UX frameworks in enterprise software: What works for consumer products often fails spectacularly in workplace environments. Here's why: 1. The Motivation Mismatch Consumer apps? Users choose to engage. Workplace platforms? Users have to engage. This fundamental difference makes frameworks like HEART (Happiness, Engagement, Adoption, Retention, Task success) miss the mark. 2. The Reality of Enterprise UX In workplace settings: - Retention isn't about choice - Engagement isn't optional  - Adoption follows mandates We need metrics that reflect actual workplace dynamics. 3. Enter the CASTLE Framework CASTLE addresses what really matters in workplace UX: C = Cognitive load (mental effort required) A = Advanced feature usage S = Satisfaction T = Task efficiency L = Learnability E = Errors This framework acknowledges a crucial truth: Success isn't about whether users stay - it's about how effectively they work. 4. Why CASTLE Works Better ✅ Measures actual workplace priorities ✅ Focuses on productivity metrics ✅ Accounts for mandatory usage ✅ Tracks learning curves ✅ Identifies friction points 5. Making the Switch Moving from HEART to CASTLE isn't just changing acronyms. It's about: - Reframing success metrics - Understanding different user motivations - Measuring what actually matters Remember: Great workplace UX isn't about making things engaging. It's about making things efficient. 🎯 The goal isn't to make users want to stay. The goal is to help them succeed at their jobs. #UXResearch #EnterpriseUX #UserExperience

💎 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

⚡ UX Metrics Flashcards (https://lnkd.in/dTbwBzJU), a helpful guide on how to help UX teams choose the right metrics, align UX measurement with business goals — and show the impact of their work. Put together by Anna Kaley from NN/g. ⚬ Print-ready PDF: https://lnkd.in/duKJzDyE ⚬ Miro board template: https://lnkd.in/d7_7YGrC ⚬ Design KPIs & UX Metrics: https://lnkd.in/dgbJVEWS ⚬ 70+ UX Metrics (by MeasuringU): https://lnkd.in/dBDNDkNb ⚬ UX KPIs Cheatsheet (by Helio): https://lnkd.in/dXqbySTe --- One point I’d like to raise is that design changes rarely have a clear immediate impact on business. It’s difficult to find a causation between how a change in filters UX has increased conversion or improved retention or reduced churn. Typically we need to measure at 2 levels — locally (if people use filters more efficiently) and globally (how successful people are at their journeys). Also, UX metrics that work well in one environment will not be applicable in others. E.g. Time on Task is difficult to measure in products with non-linear workflows since there are no linear journeys that people take repeatedly. Sometimes retention isn’t particularly useful either as employees can’t choose the product they use for work. There, we need to track retention on the level of features, flows, internal tools we are building — and focus our work on how to dial up success moments and dial down frustrations and mistakes. Still, in many products there are central hubs that a lot of users are going through. In fact, every product is like a city. And so if we can improve the experience across most frequent flows, features and tasks, we can have quite an impact — and drive up business metrics as result (over time). No business can be successful without successful customers. If business goals are fluffy and unclear, we have to build up product value from user needs (task analysis). And a way there is to study what users need to do, what would make them successful and where they currently struggle. Then we make a business case from there — and focus on what matters most to the business. A helpful guide by NN/g to get started, but I would highly recommend to customize the kit for your needs — chances are high that you will need a very different and very specific metrics to track success. Thanks to Anna and colleagues for putting it together! --- And if you’d like to dive deeper, I‘m trying to address many of painful challenges around UX metrics in Measure UX (https://measure-ux.com). I’ve tried my best to keep the pricing affordable. But if it’s still expensive, please send me a message and I’ll do my best to make it work. 👏🏽 #ux #design

"Most apps lose 80% of users before they experience any value." Here's the interesting part: It's not because the product is bad. It's because we're measuring the wrong things. After studying successful onboarding flows, I discovered 3 hidden metrics that actually matter: 1. Time to "Aha!" Not just first value - but first MEANINGFUL value. The psychology behind it: • Users form judgments in seconds • Each extra step builds frustration • Value needs to beat skepticism 2. The "Cliff Points" Those moments where users suddenly vanish. What to watch: • Which screen sees sudden exits • When motivation drops • Where confusion peaks 3. The Patience Threshold Not just how long onboarding takes. But how long users THINK it takes. The counterintuitive truth: A 5-minute onboarding that feels smooth beats a 2-minute one that feels confusing. Want to see exactly how to measure and optimize these metrics? Watch our latest Behind The Feature episode where I break down real examples [Link in comments] The brutal reality? Users don't care about your features. They care about getting to their goal. What's your biggest onboarding challenge? Drop it below 👇 #ProductStrategy #UserExperience #ProductGrowth #BehindTheFeature