Usability Heuristics Explained

⏱️ How To Measure UX (https://lnkd.in/e5ueDtZY), a practical guide on how to use UX benchmarking, SUS, SUPR-Q, UMUX-LITE, CES, UEQ to eliminate bias and gather statistically reliable results — with useful templates and resources. By Roman Videnov. Measuring UX is mostly about showing cause and effect. Of course, management wants to do more of what has already worked — and it typically wants to see ROI > 5%. But the return is more than just increased revenue. It’s also reduced costs, expenses and mitigated risk. And UX is an incredibly affordable yet impactful way to achieve it. Good design decisions are intentional. They aren’t guesses or personal preferences. They are deliberate and measurable. Over the last years, I’ve been setting ups design KPIs in teams to inform and guide design decisions. Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < 60s (for critical tasks) 3. Time to first success < 90s (for onboarding) 4. Time to candidates < 120s (nav + filtering in eCommerce) 5. Time to top candidate < 120s (for feature comparison) 6. Time to hit the limit of free tier < 7d (for upgrades) 7. Presets/templates usage > 80% per user (to boost efficiency) 8. Filters used per session > 5 per user (quality of filtering) 9. Feature adoption rate > 80% (usage of a new feature per user) 10. Time to pricing quote < 2 weeks (for B2B systems) 11. Application processing time < 2 weeks (online banking) 12. Default settings correction < 10% (quality of defaults) 13. Search results quality > 80% (for top 100 most popular queries) 14. Service desk inquiries < 35/week (poor design → more inquiries) 15. Form input accuracy ≈ 100% (user input in forms) 16. Time to final price < 45s (for eCommerce) 17. Password recovery frequency < 5% per user (for auth) 18. Fake email frequency < 2% (for email newsletters) 19. First contact resolution < 85% (quality of service desk replies) 20. “Turn-around” score < 1 week (frustrated users → happy users) 21. Environmental impact < 0.3g/page request (sustainability) 22. Frustration score < 5% (AUS + SUS/SUPR-Q + Lighthouse) 23. System Usability Scale > 75 (overall usability) 24. Accessible Usability Scale (AUS) > 75 (accessibility) 25. Core Web Vitals ≈ 100% (performance) Each team works with 3–4 local design KPIs that reflects the impact of their work, and 3–4 global design KPIs mapped against touchpoints in a customer journey. Search team works with search quality score, onboarding team works with time to success, authentication team works with password recovery rate. What gets measured, gets better. And it gives you the data you need to monitor and visualize the impact of your design work. Once it becomes a second nature of your process, not only will you have an easier time for getting buy-in, but also build enough trust to boost UX in a company with low UX maturity. [more in the comments ↓] #ux #metrics

💡 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.

💎 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

Compare designs to show improvement and build trust. Design is about understanding and managing change for users and stakeholders. If you change something too much, it might overwhelm users or lead to negative feedback. If you only slightly change an underperforming screen or page, the improvement might not generate the lift stakeholders seek. In the past, understanding a stakeholder’s needs was often enough to add value to design. But now, with established design patterns and increased specialization, designers need to answer a more specific question: How much did this design improve? Lately, I’ve been posting a lot about measuring design. Measuring design helps build trust and transparency in the process, but it’s only helpful if you have something to compare your design. Here are 11 ways to compare your work, also known as UX benchmarking. We use Helio to test. 1. Competitors See how your metrics compare to similar features in competing products. This will show you where you’re strong and where you can improve. 2. Iterations Track metrics across design versions to see if changes make the user experience better or worse. 3. Timeline Look at metrics over time to find patterns, like seasonal changes or long-term trends. 4. Segments Break down metrics by user groups (like age or location) to understand different experiences and make targeted improvements. 5. Journeys Check metrics at each user journey stage to see where users get the most value or run into issues. 6. Platforms/Devices Compare across devices (like mobile vs. desktop) to spot and fix issues specific to each platform. 7. User Goals/Tasks Focus on specific tasks (like completing a task vs. exploring) to see if the product supports what users want to do. 8. Feature Usage Review metrics for individual features to prioritize improvements for high-value or underperforming areas. 9. Geographies Compare by region to see if user experience differs in various parts of the world. 10. User Lifecycle Look at new vs. experienced users to understand adoption patterns and loyalty. 11. Behavioral Triggers Examine how specific actions (like seeing a tutorial) affect user satisfaction and behavior. If these ideas excite you, DM me–we’re focused on finalizing Glare, our open UX metrics framework, for its public 1.0 release (https://glare.helio.app/). We've been refining the ways to benchmark UX design work to support individual product designers and teams. #productdesign #productdiscovery #userresearch #uxresearch

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).

Benchmarks are a valuable tool for understanding the relative strengths and weaknesses of a user experience. However, they can be challenging to plan and execute, especially for those new to the method. By being aware of common pitfalls, a researcher can increase their chances of setting up an effective baseline benchmark that can flourish into a successful, recurring program. Key points to follow: 1) Involve stakeholders from cross-functional teams to promote buy-in and capitalize on shared information & resources when planning benchmarks 2) Collect a sufficient sample size to get precise and meaningful estimates of your metrics 3) Prioritize a set of 5-10 representative user tasks 4) Be selective in the metrics you collect & analyze; pick metrics to capture effectiveness, efficiency, and satisfaction 5) Contextualize your findings, why they matter, and how to act on them in your report 6) Document your methods, tasks, metrics, and analyses to make replication easier https://buff.ly/3QUk3zw