Over the last year, Iâve 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 â like response time or number of users. Thatâs not enough. To create AI systems that actually deliver value, we need ðµð¼ð¹ð¶ððð¶ð°, ðµððºð®ð»-ð°ð²ð»ðð¿ð¶ð° ðºð²ðð¿ð¶ð°ð that reflect: ⢠User trust ⢠Task success ⢠Business impact ⢠Experience quality   This infographic highlights 15 ð¦ð´ð´ð¦ð¯ðµðªð¢ð dimensions to consider: â³ ð¥ð²ðð½ð¼ð»ðð² ðð°ð°ðð¿ð®ð°ð â Are your AI answers actually useful and correct? â³ ð§ð®ðð¸ ðð¼ðºð½ð¹ð²ðð¶ð¼ð» ð¥ð®ðð² â Can the agent complete full workflows, not just answer trivia? â³ ðð®ðð²ð»ð°ð â Response speed still matters, especially in production. â³ ð¨ðð²ð¿ ðð»ð´ð®ð´ð²ðºð²ð»ð â How often are users returning or interacting meaningfully? â³ ð¦ðð°ð°ð²ðð ð¥ð®ðð² â Did the user achieve their goal? This is your north star. â³ ðð¿ð¿ð¼ð¿ ð¥ð®ðð² â Irrelevant or wrong responses? Thatâs friction. â³ ð¦ð²ððð¶ð¼ð» ððð¿ð®ðð¶ð¼ð» â Longer isnât always better â it depends on the goal. â³ ð¨ðð²ð¿ ð¥ð²ðð²ð»ðð¶ð¼ð» â Are users coming back ð¢ð§ðµð¦ð³ the first experience? â³ ðð¼ðð ð½ð²ð¿ ðð»ðð²ð¿ð®ð°ðð¶ð¼ð» â Especially critical at scale. Budget-wise agents win. â³ ðð¼ð»ðð²ð¿ðð®ðð¶ð¼ð» ðð²ð½ððµ â Can the agent handle follow-ups and multi-turn dialogue? â³ ð¨ðð²ð¿ ð¦ð®ðð¶ðð³ð®ð°ðð¶ð¼ð» ð¦ð°ð¼ð¿ð² â Feedback from actual users is gold. â³ ðð¼ð»ðð²ð ððð®ð¹ ð¨ð»ð±ð²ð¿ððð®ð»ð±ð¶ð»ð´ â Can your AI ð³ð¦ð®ð¦ð®ð£ð¦ð³ ð¢ð¯ð¥ ð³ð¦ð§ð¦ð³ to earlier inputs? â³ ð¦ð°ð®ð¹ð®ð¯ð¶ð¹ð¶ðð â Can it handle volume ð¸ðªðµð©ð°ð¶ðµ degrading performance? â³ ðð»ð¼ðð¹ð²ð±ð´ð² ð¥ð²ðð¿ð¶ð²ðð®ð¹ ðð³ð³ð¶ð°ð¶ð²ð»ð°ð â This is key for RAG-based agents. â³ ðð±ð®ð½ðð®ð¯ð¶ð¹ð¶ðð ð¦ð°ð¼ð¿ð² â Is your AI learning and improving over time? If you're building or managing AI agents â bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system â these are the metrics that will shape real-world success. ðð¶ð± ð ðºð¶ðð ð®ð»ð ð°ð¿ð¶ðð¶ð°ð®ð¹ ð¼ð»ð²ð ðð¼ð ððð² ð¶ð» ðð¼ðð¿ ð½ð¿ð¼ð·ð²ð°ðð? Letâs make this list even stronger â drop your thoughts ð
Key User Experience Metrics to Track
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Summary
Key user experience metrics to track are specific measurements that help businesses understand how users interact with digital products and whether those experiences meet their needs. By monitoring these metrics, companies can pinpoint areas where users struggle, feel satisfied, or return to use a product over time.
- Track goal completion: Measure whether users are able to successfully finish important tasks, such as checking out or signing up, so you know if your design supports their needs.
- Monitor engagement levels: Keep an eye on how often users return, how long they stay, and their interaction rates to spot patterns in satisfaction and loyalty.
- Collect user feedback: Use satisfaction scores and effort ratings to gather direct opinions on the experience, helping you understand how users truly feel about your product.
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Everyoneâs excited to launch AI agents. Almost no one knows how to measure if theyâre actually working. Over the last year, weâve 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, weâve worked on AI agents for enterprises and if thereâs one lesson itâs this: Speed and usage mean nothing if the agent isnât solving the actual problem. The real performance indicators are far more nuanced. Hereâs what weâve learned to track instead: ð¹ Task Completion Rate â Can the AI go beyond answering a question and actually complete a workflow? ð¹ User Trust â Do people come back? Do they feel confident relying on the agent again? ð¹ Conversation Depth â Is the agent handling complex, multi-turn exchanges with consistency? ð¹ Context Retention â Can it remember prior interactions and respond accordingly? ð¹ Cost per Successful Interaction â Not just cost per query, but cost per outcome. Massive difference. One of our clients initially celebrated their botâs 1 million+ sessions - until we uncovered that less than 8% of users actually got what they came for. That 8% wasnât 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: Donât just measure whatâs easy. Measure what matters. AI agents arenât just tools - theyâre touchpoints. They represent your brand, shape user experience, and influence business outcomes. P.S. Whatâs one underrated metric youâve used to evaluate AI performance? Curious to learn what others are tracking.
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UX metrics work best when aligned with the right questions. Below are ten common UX scenarios and the metrics that best fit each. 1. Completing a Transaction When the goal is to make processes like checkout, sign-up, or password reset more efficient, focus on task success rates, drop-off points, and error tracking. Self-reported metrics like expectations and likelihood to return can also reveal how users perceive the experience. 2. Comparing Products For benchmarking products or releases, task success and efficiency offer a baseline. Self-reported satisfaction and emotional reactions help capture perceived differences, while comparative metrics provide a broader view of strengths and weaknesses. 3. Frequent Use of the Same Product For tools people use regularly, like internal platforms or messaging apps, task time and learnability are essential. These metrics show how users improve over time and whether effort decreases with experience. Perceived usefulness is also valuable in highlighting which features matter most. 4. Navigation and Information Architecture When the focus is on helping users find what they need, use task success, lostness (extra steps taken), card sorting, and tree testing. These help evaluate whether your content structure is intuitive and discoverable. 5. Increasing Awareness Some studies aim to make features or content more noticeable. Metrics here include interaction rates, recall accuracy, self-reported awareness, and, if available, eye-tracking data. These provide clues about whatâs seen, skipped, or remembered. 6. Problem Discovery For open-ended studies exploring usability issues, issue-based metrics are most useful. Cataloging the frequency and severity of problems allows you to identify pain points, even when tasks or contexts differ across participants. 7. Critical Product Usability Products used in high-stakes contexts (e.g., medical devices, emergency systems) require strict performance evaluation. Focus on binary task success, clear definitions of user error, and time-to-completion. Self-reported impressions are less relevant than observable performance. 8. Designing for Engagement For experiences intended to be emotionally resonant or enjoyable, subjective metrics matter. Expectation vs. outcome, satisfaction, likelihood to recommend, and even physiological data (e.g., skin conductance, facial expressions) can provide insight into how users truly feel. 9. Subtle Design Changes When assessing the impact of minor design tweaks (like layout, font, or copy changes), A/B testing and live-site metrics are often the most effective. With enough users, even small shifts in behavior can reveal meaningful trends. 10. Comparing Alternative Designs In early-stage prototype comparisons, issue severity and preference ratings tend to be more useful than performance metrics. When task-based testing isnât feasible, forced-choice questions and perceived ease or appeal can guide design decisions.
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If your site is slow, youâre 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: â Largest Contentful Paint (LCP): How quickly the main content loads. Target: under 2.5 seconds. â First Input Delay (FID) / Interaction to Next Paint (INP): How quickly the page responds when a user interacts. Target: under 200 milliseconds. â Cumulative Layout Shift (CLS): How visually stable a page is. Target: less than 0.1. These metrics are designed to capture the ârealâ 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 ratesâbenefits seen across industries 3. User expectations have tightened: In 2025, anything slower than 3 seconds feels âslowâ to most usersâunder 1â¯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 â¢Â Measure regularly: Use lab and field data to monitor Core Web Vitals across templates and devices. â¢Â Prioritize technical quick wins: Image compression, proper caching, and removing render-blocking scripts can deliver immediate improvements. â¢Â Stabilize layouts: Define media dimensions and manage ad slots to reduce layout shifts. â¢Â Invest in long-term fixes: Optimizing server response times and modernizing templates can help sustain improvements. Here are the key takeaways â Core Web Vitals are measurable, actionable, and tied directly to SEO performance. â Faster, more stable sites not only rank better but also improve engagement, ad revenue, and subscriptions. â 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
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ð UX Metrics: How to Measure and Optimize User Experience? When we talk about UX, we know that good decisions must be data-driven. But how can we measure something as subjective as user experience? ð¤ Here are some of the key UX metrics that help turn perceptions into actionable insights: ð Experience Metrics: Evaluate user satisfaction and perception. Examples: â NPS (Net Promoter Score) â Measures user loyalty to the brand. â CSAT (Customer Satisfaction Score) â Captures user satisfaction at key moments. â CES (Customer Effort Score) â Assesses the effort needed to complete an action. ð Behavioral Metrics: Analyze how users interact with the product. Examples: ð Conversion Rate â How many users complete the desired action? ð Drop-off Rate â At what stage do users give up? ð Average Task Time â How long does it take to complete an action? ð Adoption and Retention Metrics: Show engagement over time. Examples: ð Active Users â How many people use the product regularly? ð Churn Rate â How many users stop using the service? ð Cohort Retention â What percentage of users remain engaged after a certain period? UX metrics are more than just numbers â they tell the story of how users experience a product. With them, we can identify problems, test hypotheses, and create better experiences! ð¡ð ð¢ What UX metrics do you use in your daily work? Letâs exchange ideas in the comments! ð #UX #UserExperience #UXMetrics #Design #Research #Product
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When we think about improving an online shopping experience, we often jump to new features or a better design. But behind the scenes, web performanceâhow fast and smoothly a site loads and respondsâis one of the biggest drivers of user satisfaction and business impact. In a recent blog post, Walmart Global Techâs Engineering team shared their journey to improve their siteâs 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 werenât overly influenced by outliers. They also embedded web performance into company-wide decision-making: Core Web Vitals are integrated into Walmartâs experimentation platform, incorporated into the release review process, and included in leadership discussions. In other words, performance isnât just an engineering KPIâWalmart turned it into a shared organizational priority. This work is a great reminder that improving performance isnât just an engineering task; itâs 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 â â â 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
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Product metrics provide invaluable insights into the health of a product and business. But it's easy to get stuck measuring vanity metrics that impress but don't inform decisions. Instead, focus on actionable metrics tied to user behaviors. The Pirate Metrics framework tracks users through the funnel of acquisition, activation, retention, referral, and revenue. Following this customer journey exposes where fallout happens so you can improve experiences. Another approach that includes satisfaction metrics is, HEART - Happiness, Engagement, Adoption, Retention, and Task success. These quantify how users interact with and feel about a product. Now you donât need to stick to one framework. Use what makes sense for your product and mix and match. The key is deliberately choosing metrics that: ð Relate to business and product goals ð Expose actionable issues to drive improvement ð Provide leading indicators to guide strategy Avoid output metrics like features shipped that lack customer context. Useful metrics tell a story to focus teams and trigger change. They turn data into insights that prioritize valuable work. What metrics have you found most effective for steering product direction? Are there any surprising metrics that provided unexpectedly useful insights? Let me know key lessons youâve learned using data to guide products. Learn more about #ProductMetrics in our Product Foundations course at https://lnkd.in/gKrpruPW ð #ProductManagement #Metrics #ProductOps #ProductInstitute #ProductOperation