Measuring User Experience Metrics That Matter

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 👇

As UX researchers, we often rely on survey totals. We sum up Likert scale responses across a few items and call it a metric - satisfaction, usability, engagement, trust. It’s fast, familiar, and widely accepted. But if you’ve ever questioned whether a survey is truly capturing what matters, that’s where Item Response Theory (IRT) steps in. IRT is more than just a statistical model - it’s a smarter way to design, evaluate, and optimize questionnaires. While total scores give you a general snapshot, IRT gives you the diagnostic toolkit. It shifts your focus from just what the total score is to how each question behaves across different user types. Instead of treating every item as equally valuable, IRT assumes that each question has its own characteristics - its own difficulty level, its ability to discriminate between users with different trait levels (like low vs. high satisfaction), and even its tendency to generate noise. It mathematically models the likelihood of a particular response based on the person’s underlying trait (e.g., engagement) and the specific properties of that item. This lets you see which items are doing real work - and which ones are just adding bloat. Let’s say you’re trying to measure perceived product enjoyment. You include five questions. One of them - "I enjoy using this product" - is endorsed by nearly everyone. Another one - "This product makes me feel inspired" - gets more varied responses. Under IRT, the first item would be flagged as too easy; it doesn’t help you separate highly engaged users from moderately engaged ones. The second item, if it cleanly differentiates users with different enjoyment levels, would be seen as high in discrimination power. That’s the kind of insight you won’t get from a simple average. One of the biggest advantages of IRT is that it allows you to assess not just people’s responses, but the quality of the items themselves. You can identify and remove redundant or low-informative questions, focus your surveys to measure what matters most, and retain high precision with fewer items. This is a huge win for both survey respondents and UX researchers, especially when you're working in product environments where every question has to earn its place. IRT also enables more advanced applications. You can build adaptive surveys- ones that tailor themselves in real time to each participant. You can create item banks that offer equivalent measurement across time or populations. And you can track individual-level changes in UX perceptions over time more reliably, which is something traditional scoring methods often miss. I use IRT models to analyze UX questionnaires in my own work, especially when I want to make sure each item is pulling its weight. It also leads to clearer communication with designers, PMs, and engineers, because I can show why a certain item matters or doesn’t, backed by data that makes sense.

AI changes how we measure UX. We’ve been thinking and iterating on how we track user experiences with AI. In our open Glare framework, we use a mix of attitudinal, behavioral, and performance metrics. AI tools open the door to customizing metrics based on how people use each experience. I’d love to hear who else is exploring this. To measure UX in AI tools, it helps to follow the user journey and match the right metrics to each step. Here's a simple way to break it down: 1. Before using the tool Start by understanding what users expect and how confident they feel. This gives you a sense of their goals and trust levels. 2. While prompting  Track how easily users explain what they want. Look at how much effort it takes and whether the first result is useful. 3. While refining the output Measure how smoothly users improve or adjust the results. Count retries, check how well they understand the output, and watch for moments when the tool really surprises or delights them. 4. After seeing the results Check if the result is actually helpful. Time-to-value and satisfaction ratings show whether the tool delivered on its promise. 5. After the session ends See what users do next. Do they leave, return, or keep using it? This helps you understand the lasting value of the experience. We need sharper ways to measure how people use AI. Clicks can’t tell the whole story. But getting this data is not easy. What matters is whether the experience builds trust, sparks creativity, and delivers something users feel good about. These are the signals that show us if the tool is working, not just technically, but emotionally and practically. How are you thinking about this? #productdesign #uxmetrics #productdiscovery #uxresearch

I wish someone taught me this in my first year as a PM. It would’ve saved years of chasing the wrong goals and wasting my team's time: "Choosing the right metric is more important than choosing the right feature." Here are 4 metrics mistakes even billion-dollar companies have made and what to do instead with Ron Kohavi: 1. Vanity Metrics They look good. Until they don’t. A social platform he worked with kept showing rising page views… While revenue quietly declined. The dashboard looked great. The business? Not so much. Always track active usage tied to user value, not surface-level vanity. 2. Insensitive Metrics They move too slowly to be useful. At Microsoft, Ronny Kohavi’s team tried using LTV in experiments. but saw zero significant movement for over 9 months. The problem is you can’t build momentum on data that’s stuck in the future. So, use proxy metrics that respond faster but still reflect long-term value. 3. Lagging Indicators They confirm success after it’s too late to act. At a subscription company, churn finally spiked… but by then, 30% of impacted users were already gone. Great for storytelling but let's be honest, it's useless for decision-making. You can solve it by pairing lagging indicators with predictive signals. (Things you can act on now.) 4. Misaligned Incentives They push teams in the wrong direction. One media outlet optimized for clicks and everything was looking good until it wasn't. They watched their trust drop as clickbait headlines took over. The metric had worked. They might had "more MRR". But the product suffered in the long run. It's cliche but use metrics that align user value with business success. Because Here's The Real Cost of Bad Metrics - 80% of team energy wasted optimizing what doesn’t matter - Companies with mature metrics see 3–4× stronger alignment between experiments and outcomes - High-performing teams run more tests but measure fewer, better things Before you trust any metric, ask: - Can it detect meaningful change in faster? - Does it map to real user or business value? - Is it sensitive enough for experimentation? - Can my team interpret and act on it? - Does it balance short-term momentum and long-term goals? If the answer is no, it’s not a metric worth using. — If you liked this, you’ll love the deep dive: https://lnkd.in/ea8sWSsS

Try this if you struggle with defining and writing design outcomes: Map your solutions to proven UX Metrics Let's start small. Learn the Google HEART framework H - Happiness: How do users feel about your product? 📈 Metrics: Net Promotor Score, App Rating E - Engagement : Are users engaging with your app? 📈 Metrics: # of Conversions, Session Length A - Adoption: Are you getting new users? 📈 Metrics: Download Rate, Sign Up Rate R - Retention Are users returning and staying loyal? 📈 Metrics: Churn Rate, Subscription Renewal T - Task Success Can users complete goals quickly? 📈 Metrics: Error Rates, Task Completion Rate These are all bridges between design and business goals. HEART can be used for the whole app or specific features. 👉 Let's tie it to an example case study problem: Students studying overseas need to know what recipes can be made with ingredients available at home, as eating out regularly is too expensive and unhealthy. ✅ Outcome Example: While the app didn't launch, to track success and impact, I would have monitored the following: - Elevated app ratings and positive feedback, indicating students found the app enjoyable and useful - Increased app usage, implying more students frequently cooking at home - Growth in new sign-ups, reflecting more students discovering the app - Lower attrition rates and more subscription renewals, showing the app's continued value - Decrease in incomplete recipe attempts, suggesting the app was successful in helping students achieve their cooking goals. The HEART framework is a perfect tracker of how well the design solved or could solve the stated business problem. 💡Remember: Without data, design is directionless. We are solving real business problems. ------------------------------------------- 🔔 Follow: Mollie Cox ♻ Repost to help others 💾 Save it for future use