User Experience Metrics for Success

✅ How To Run Task Analysis In UX (https://lnkd.in/e_s_TG3a), a practical step-by-step guide on how to study user goals, map user’s workflows, understand top tasks and then use them to inform and shape design decisions. Neatly put together by Thomas Stokes. 🚫 Good UX isn’t just high completion rates for top tasks. 🤔 Better: high accuracy, low task on time, high completion rates. ✅ Task analysis breaks down user tasks to understand user goals. ✅ Tasks are goal-oriented user actions (start → end point → success). ✅ Usually presented as a tree (hierarchical task-analysis diagram, HTA). ✅ 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. ✅ Scrutinize every single step for errors, efficiency, opportunities. ✅ Attach design improvements as sticky notes to each step. 🚫 Don’t lose track in small tasks: come back to the big picture. Personally, I've been relying on top task analysis for years now, kindly introduced by Gerry McGovern. Of all the techniques to capture the essence of user experience, it’s a reliable way to do so. Bring it together with task completion rates and task completion times, and you have a reliable metric to track your UX performance over time. Once you identify 10–12 representative tasks and get them approved by stakeholders, we can track how well a product is performing over time. Refine the task wording and recruit the right participants. Then give these tasks to 15–18 actual users and track success rates, time on task and accuracy of input. That gives you an objective measure of success for your design efforts. And you can repeat it every 4–8 months, depending on velocity of the team. It’s remarkably easy to establish and run, but also has high visibility and impact — especially if it tracks the heart of what the product is about. Useful resources: Task Analysis: Support Users in Achieving Their Goals (attached image), by Maria Rosala https://lnkd.in/ePmARap3 What Really Matters: Focusing on Top Tasks, by Gerry McGovern https://lnkd.in/eWBXpCQp How To Make Sense Of Any Mess (free book), by Abby Covert https://lnkd.in/enxMMhMe How We Did It: Task Analysis (Case Study), by Jacob Filipp https://lnkd.in/edKYU6xE How To Optimize UX and Improve Task Efficiency, by Ella Webber https://lnkd.in/eKdKNtsR How to Conduct a Top Task Analysis, by Jeff Sauro https://lnkd.in/eqWp_RNG [continues in the comments below ↓]

Most teams pick metrics that sound smart… But under the hood, they’re just noisy, slow, misleading, or biased. But today, I'm giving you a framework to avoid that trap. It’s called STEDII and it’s how to choose metrics you can actually trust: — ONE: S — Sensitivity Your metric should be able to detect small but meaningful changes Most good features don’t move numbers by 50%. They move them by 2–5%. If your metric can’t pick up those subtle shifts , you’ll miss real wins. Rule of thumb: - Basic metrics detect 10% changes - Good ones detect 5% - Great ones? 2% The better your metric, the smaller the lift it can detect. But that also means needing more users and better experimental design. — TWO: T — Trustworthiness Ever launch a clearly better feature… but the metric goes down? Happens all the time. Users find what they need faster → Time on site drops Checkout becomes smoother → Session length declines A good metric should reflect actual product value, not just surface-level activity. If metrics move in the opposite direction of user experience, they’re not trustworthy. — THREE: E — Efficiency In experimentation, speed of learning = speed of shipping. Some metrics take months to show signal (LTV, retention curves). Others like Day 2 retention or funnel completion give you insight within days. If your team is waiting weeks to know whether something worked, you're already behind. Use CUPED or proxy metrics to speed up testing windows without sacrificing signal. — FOUR: D — Debuggability A number that moves is nice. A number you can explain why something worked? That’s gold. Break down conversion into funnel steps. Segment by user type, device, geography. A 5% drop means nothing if you don’t know whether it’s: → A mobile bug → A pricing issue → Or just one country behaving differently Debuggability turns your metrics into actual insight. — FIVE: I — Interpretability Your whole team should know what your metric means... And what to do when it changes. If your metric looks like this: Engagement Score = (0.3×PageViews + 0.2×Clicks - 0.1×Bounces + 0.25×ReturnRate)^0.5 You’re not driving action. You’re driving confusion. Keep it simple: Conversion drops → Check checkout flow Bounce rate spikes → Review messaging or speed Retention dips → Fix the week-one experience — SIX: I — Inclusivity Averages lie. Segments tell the truth. A metric that’s “up 5%” could still be hiding this: → Power users: +30% → New users (60% of base): -5% → Mobile users: -10% Look for Simpson’s Paradox. Make sure your “win” isn’t actually a loss for the majority. — To learn all the details, check out my deep dive with Ronny Kohavi, the legend himself: https://lnkd.in/eDWT5bDN

💡 System Usability Scale (SUS): A Simple Yet Powerful Tool for Measuring Usability The System Usability Scale (SUS) is a quick, efficient, and cost-effective method for evaluating product usability from the user's perspective. Developed by John Brooke in 1986, SUS has been extensively tested for nearly 30 years and remains a trusted industry standard for assessing user experience (UX) across various systems.  1️⃣ Collecting user feedback Collect responses from users who have interacted with your product to gain meaningful insights using the SUS questionnaire, which consists of 10 alternating positive and negative statements, each rated on a 5-point Likert scale from "Strongly Disagree" (1) to "Strongly Agree" (5). 📌 Important: The SUS questionnaire can be customised, but whether it should be is a debated topic. The IxDF - Interaction Design Foundation suggests customisation to better fit specific contexts, while NNGroup recommends using the standard version, as research supports its validity, reliability, and sensitivity. 2️⃣ Calculation To calculate the SUS score for each respondent:   • For positive (odd-numbered) statements, subtract 1 from the user’s response.   • For negative (even-numbered) statements, subtract the response from 5.   • Sum all scores and multiply by 2.5 to convert them to a 0-100 scale.  3️⃣ Interpreting the Results • Scores above 85 indicate an excellent usability, • Scores above 70 - good usability, • Scores below 68 may suggest potential usability issues that need to be addressed. 🔎 Pros & Cons of Using SUS ✳️ Advantages:  • Valid & Reliable – it provides consistent results across studies, even with small samples, and is valid because it accurately measures perceived usability.  • Quick & Easy – requires no complex setup, takes only 1-2 minutes to complete. • Correlates with Other Metrics – works alongside NPS and other UX measures.    • Widely respected and used - a trusted usability metric since 1986, backed by research, industry benchmarks, and extensive real-world application across various domains. ❌ Disadvantages: • SUS was not intended to diagnose usability problems – it provides only a single overall score, which may not give enough insight into specific aspects of the interface or user interaction. • Subjective User Perception – it measures how users subjectively feel about a system's ease of use and overall experience, rather than objective performance metrics.  • Interpretation Challenges – If users haven’t interacted with the product for long enough, their perception may be inaccurate or limited. • Cultural and language biases can affect SUS results, as users from different backgrounds may interpret questions differently or have varying levels of familiarity with the system, influencing their responses. 💬 What are your thoughts? Check references in the comments! 👇  #UX #metrics #uxdesign #productdesign #SUS

A design can pass a usability test and still feel exhausting. That is why 𝗡𝗔𝗦𝗔-𝗧𝗟𝗫 is useful in UX research. NASA-TLX, the Task Load Index, is a post-task rating method for measuring perceived workload. Instead of asking only whether users succeeded, it asks what success cost them. The scale looks at six dimensions: 🧠 𝗠𝗲𝗻𝘁𝗮𝗹 demand: How much thinking, remembering, deciding, or searching was required? 💪 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 demand: How much physical action was required? ⏱️ 𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗹 demand: How rushed or time-pressured did the task feel? 🎯 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: How successful did the user feel? 🔥 𝗘𝗳𝗳𝗼𝗿𝘁: How hard did the user have to work to reach that result? 😤 𝗙𝗿𝘂𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: How insecure, annoyed, discouraged, or stressed did the user feel? In a UX study, the usual pattern is simple. Users complete a task, then rate these dimensions. Researchers compare ratings across tasks, prototypes, user groups, or design alternatives. The result is not just a score. The real value is often in the workload profile: where the burden appears and what kind of burden it is. This matters because ease of use is not the same as low workload. A product can be learnable but draining. A checkout can be fast but stressful. A dashboard can be powerful but mentally expensive. A workflow can produce few errors while forcing users to hold too much in memory. NASA-TLX helps teams see these hidden costs. It turns subjective strain into a structured design signal. For UX designers, that signal is practical: 🔍 Reveal cognitive friction that observation alone may miss. 📊 Compare competing designs beyond task time and completion rate. ⚠️ Explain why users make errors, hesitate, or abandon a flow. ✅ Encourage interfaces that are not only efficient, but sustainable to use. Good UX reduces the work the interface adds to the work users already came to do.

💡Design System Metrics Design system brings two main benefits: Consistency and Efficiency. It helps minimize usability issues and maintain design consistency. However, without metrics, it can be hard to tell how well the system performs. That’s why it’s recommended to define metrics up front when establishing a foundation for your design system. Here are some popular design system metrics: Product design process: ✔ Adoption rate. What % of products use the design system? The more the design system is used, the more time is saved. ✔ Average task completion time. The time designers spend on completing the task (for example, designing a new user flow). Compare before/after the design system. ✔ Design to development time. Design system should speed up the handoff process from designers to developers. ✔ Component usage. The number of components used across products vs the total number of components available in the design system. Compare the usage of components in design (Figma) and code (Github). This will help you identify unused components.  ✔ Effect on code. Measure code complexity and how much code developers change with each release. ✔ Number of component detachments (Figma). If some components are often detached, you won’t have the right picture of how effective the design system is. Design output quality: ✔ User interface design consistency. # of visual inconsistencies in a final design.  ✔ Error rates and usability issues. Whether the design system reduces error rates and usability issues. ✔ Design documentation state. % of outdated docs. Outdated docs increase the risk of releasing inconsistent design. ✔ Accessibility score. How the design system improves accessibility (e.g., WCAG score) Business: ✔ Return on Investment (ROI). ROI is a key metric that stakeholders analyze to understand if the investment in DS is paying off. ✔ Team satisfaction score. How do team members feel about the design system? Collect feedback to understand what problems team members face using a design system. ✔ Tech debt. After having the design system in place, there should be less tech debt. ✔ Average time to market. The time the product team spends on releasing a new feature/scenario. Compare before/after the DS. ✔ Company scalability. How does workload capacity change after having the design system? ✔ Brand consistency. There should be less work required to fix visual differences because the design system drives repeat usage. 📖 Guides and tools: ✔ Measuring DS success (by Nathan Curtis) https://lnkd.in/gA25QK73 ✔ Measuring the impact of a design system (by Cristiano Rastelli)  https://lnkd.in/dx5YMWta ✔ Design system metrics collection, checklist for Figma (by Romina Kavcic)  https://lnkd.in/gAeN_sfk 🖼 Design system adoption by Stylebit #designsystem #designsystems

UX design is not art, and it can and should be measured. Some methods, though, like NPS, are not fit for purpose. Its popularity amongst designers is due to its simplicity rather than its effectiveness. In this article — Design KPIs and UX Metrics https://lnkd.in/dZfH_RYH — Vitaly Friedman outlines a much more comprehensive approach to measuring effective UX design with 20 metrics to track with suggested benchmarks for success. We're in a confusing era in UX design. Design influencers claim design can't be measured, while hiring managers are laser-focused on seeing impact in every corner of your CV and portfolio. Design can and should be measured and the metrics are multi-layered. Some you will impact directly, such as "time to complete". Other metrics will be lagging indicators that you won't be able to correlate directly to your designs (such as retention rates), but demonstrating awareness of them as overarching goals will improve your influence. It's true that not everything can be measured, and design can create an intangible emotional response, both good and bad, but most digital products badly need basic UX improvements. Arguments against design being measurable only weaken the influence designers have, reinforcing the idea that we just had a shiny coat of paint at the end of the process.

Most companies see business, customer, and UX metrics as separate stories. I had Bruno M. (JP Morgan Chase, HealthEquity), who led the journey-centric transformation to make these separate layers work together. I love the simplicity of the approach, when every job to be done or journey get structured with 3 layers of metrics. That way, every level of the journey framework is consistent: 1️⃣ Business Layer (Top Layer) This layer focuses on traditional KPIs that matter most to executives — the metrics that indicate how the journey contributes to overall business performance. Examples include: - Revenue - Conversion rates - Cost savings (e.g., shorter average handle time) - Retention / Churn rates These help executives and general managers see how customer experience links directly to financial and operational performance. 2️⃣ Customer Experience Layer (Middle Layer) Here, Bruno connects business KPIs to customer sentiment using metrics like: - NPS (Net Promoter Score) - CSAT (Customer Satisfaction) While he’s critical of NPS (“hard to know what’s really broken just from NPS”), he acknowledges it remains a key business-facing metric that helps secure buy-in from leadership. However, he stresses that NPS alone is meaningless — its value emerges only when overlaid with other measures like completion rates or drop-off data. 3️⃣ UX / Behavioral Layer (Bottom Layer) The third layer goes deeper into the user experience where the actual friction or success of the journey can be observed. Examples include: - Task completion rates - Time on task - Error rates - Drop-offs or conversion funnels These granular metrics help teams act quickly and connect customer behaviors directly to business outcomes. 🤝 How It All Connects Bruno envisions a single dashboard where you can: - Click into a “job to be done” or journey. - See the KPI layer, CX layer, and UX layer all linked together. This way: - Executives can see how journeys drive business. - CX teams can track satisfaction and loyalty. - Product and design teams can pinpoint usability and behavioral issues. He calls this layered approach the core of accountability in journey management. Making sure everyone from the CEO to the UX designer looks at the same truth through their own lens. Check out the Episode for a deep dive, this one is 🔥🔥🔥

In UX, we talk a lot about what users think, but we rarely study how their attitudes actually change over time. Most research still relies on one-time surveys like SUS, NPS, or post-test ratings. These snapshots are useful, but they tell us almost nothing about how trust grows, how frustration accumulates, or how confidence rises and collapses after a single confusing update. Attitudes are not steady states. They are trajectories shaped by experience. There are scientific ways to track those trajectories. Continuous-Time SEM lets researchers measure how satisfaction or trust evolves in real time, even if we collect feedback at irregular moments. A streaming app can trigger a question after each session and see exactly when enjoyment starts to drop, so recommendations can intervene before disengagement sets in. Latent Transition Analysis helps us understand how people move between hidden states such as novice, intermediate, competent, or stuck. Instead of guessing who needs help in onboarding, we can calculate the probability a user will progress or remain frustrated and then redesign tutorials to move them forward. Bayesian Hierarchical Models solve a common UX problem. What if we do not have huge samples like consumer apps do? With twenty or thirty enterprise users, traditional statistics break down, but Bayesian methods still model growth and decline in attitudes. They can reveal that confidence improves for new employees but decreases for experts after a redesign, a pattern that would otherwise remain invisible. Joint Modeling goes further by connecting attitude trends with real outcomes such as churn. It can show that a drop in usability or motivation predicts cancellation two weeks before users actually leave, turning measurement into prevention. One of the most powerful and practical tools is Hidden Markov Modeling. Instead of relying on surveys, it infers emotional states from behavior like hesitation, rage clicks, repeated backtracking, or abandoned tasks. It detects frustration even when people are silent, revealing emotional shifts that traditional surveys fail to capture. If you want to go deeper into these methods and see more concrete examples, I put together a full breakdown on the blog. You can read it here: https://lnkd.in/eY_Nwme2

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

Too often UX teams celebrate a “significant” p-value without asking what it actually means. A tiny p-value can come from a huge sample even if the effect itself is trivial. This is a common mistake across research teams, assuming statistical significance automatically implies practical significance. Without looking at effect size, you have no way of knowing if the difference you found actually matters for users or if it’s just a statistical artifact. This is why effect sizes matter. They show how big or meaningful an observed difference or relationship really is. Instead of just reporting that a change is “significant,” you can show whether it’s large enough to influence design decisions, prioritize features, or guide stakeholders. For example, understanding whether a new onboarding flow improved task completion by a small or large margin gives product teams actionable insight, not just a p-value. Reporting effect sizes helps translate data into clear, practical recommendations that drive user-centered decisions. The following table shows the most common effect sizes used in UX research. ______________ I’m Mohsen Rafiei, Ph.D., an Assistant Professor of Cognitive Psychology, and Quant UX lead at Perceptual User Experience Lab, where I study how people think, feel, and behave through rigorous, evidence-based research.