UX Metrics And KPIs

⏱️ 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

The DER SPIEGEL team set out to use audience research to discover which editorial and product features lead to regular and deep engagement. The goal: to be able to prioritize which features should be promoted and explained during onboarding, and incorporated into product design considerations Results: All features are mapped on the usage-preference matrix below using two axes. The Y-axis shows the observed usage values from our website tracking tool, while the X-axis shows the preferences that fans indicated in the survey. 8 features can be identified as engagement boosters... > Apps & push > News & update > Opinion > Debate > Quiz > Video > Thematic Entry > Recommendation boxes What do they plan to do with this information? 1 - Increase the number of engaged readers by strategically nudging those with lower usage towards key engagement drivers and support the adaptation of ritualized interactions with these features 2 - Enhance the visibility and ease of access to these engagement drivers on the homepage More detail on the methodology and results in this brilliant article by Alex Held and Angelika Zajac on The Audiencers: https://lnkd.in/e6kD4QkY

💎 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

How do you figure out what truly matters to users when you’ve got a long list of features, benefits, or design options - but only a limited sample size and even less time? A lot of UX researchers use Best-Worst Scaling (or MaxDiff) to tackle this. It’s a great method: simple for participants, easy to analyze, and far better than traditional rating scales. But when the research question goes beyond basic prioritization - like understanding user segments, handling optional features, factoring in pricing, or capturing uncertainty - MaxDiff starts to show its limits. That’s when more advanced methods come in, and they’re often more accessible than people think. For example, Anchored MaxDiff adds a must-have vs. nice-to-have dimension that turns relative rankings into more actionable insights. Adaptive Choice-Based Conjoint goes further by learning what matters most to each respondent and adapting the questions accordingly - ideal when you're juggling 10+ attributes. Menu-Based Conjoint works especially well for products with flexible options or bundles, like SaaS platforms or modular hardware, helping you see what users are likely to select together. If you suspect different mental models among your users, Latent Class Models can uncover hidden segments by clustering users based on their underlying choice patterns. TURF analysis is a lifesaver when you need to pick a few features that will have the widest reach across your audience, often used in roadmap planning. And if you're trying to account for how confident or honest people are in their responses, Bayesian Truth Serum adds a layer of statistical correction that can help de-bias sensitive data. Want to tie preferences to price? Gabor-Granger techniques and price-anchored conjoint models give you insight into willingness-to-pay without running a full pricing study. These methods all work well with small-to-medium sample sizes, especially when paired with Hierarchical Bayes or latent class estimation, making them a perfect fit for fast-paced UX environments where stakes are high and clarity matters.

So many product teams work on new features they believe will be a game-changer for users. But how do you really know if a feature will be adopted by users? This is where UX research comes in. As UX researchers, we can help identify the probability of feature adoption by digging deep into user needs, behaviors, and expectations. Here are some ways we measure and predict feature adoption: 1. User Interviews and Surveys: By speaking directly to users, we can gauge their interest in a new feature. Through surveys or interviews, we explore how they might use the feature, what problems it would solve for them, and how it fits into their current workflows. These qualitative insights give us an early understanding of potential adoption barriers. 2. Usability Testing: A feature may seem like a great idea on paper, but how do users actually interact with it? Conducting usability tests on prototypes allows us to see whether users understand the feature, how intuitive it is, and where they might get stuck. If the feature feels cumbersome, adoption rates will likely be lower. 3. Task Success Rate: This metric allows us to measure how easily users can complete tasks using the new feature. A low success rate indicates friction, and users are less likely to adopt a feature if it doesn’t make their experience easier. 4. User Journey Mapping: By mapping out the user journey, we can see where the new feature fits into the overall user experience. Does it make sense within the flow of their tasks? Are there unnecessary steps or points of confusion? A smooth, integrated feature is more likely to be adopted. 5. A/B Testing: Once a feature is live, we can run A/B tests to see if it’s driving the desired behavior. Does the feature increase engagement or task completion compared to the previous version? These quantitative insights allow us to measure real-world adoption and refine the feature based on user interactions. 6. Feature Feedback: After a feature is released, gathering feedback is key. By monitoring user comments, satisfaction scores, and support tickets, we can understand how users feel about the feature. Are they using it as intended? Are there any pain points that need addressing? As UX researchers, our role is to validate whether a feature truly meets user needs and fits within their daily tasks. We can predict adoption rates, identify potential issues early, and help product teams make informed decisions before launching a feature. How do you measure feature adoption in your research?

Struggles of doing data science in the real world 🤦: What do you do when there’s no A/B test but you still need insights? I recently faced that challenge (again): 👉 The growth team asked me to evaluate the impact of a new mobile app feature on conversions (a week after it launched) In the real world, data is messy, and A/B tests aren’t always an option. As a Data Scientist, you need to learn to be resourceful Here’s how I approached it: 1️⃣ Segmented analysis: I created pre- and post-launch groups based on user signup dates. 2️⃣ Exploratory data analysis (EDA): Visualized conversion trends, layering in cohort and seasonal comparisons. 3️⃣ Statistical testing: Ran an independent t-test to validate observed changes, carefully checking assumptions like normality and variance equality. Result? A clear signal of increased conversions on iOS, while Android showed minimal impact. 💡 Key takeaway: T-tests (or similar methods) can still deliver actionable insights outside traditional A/B testing, but validating assumptions and adding context is critical to making reliable conclusions. I broke down my full workflow and the lessons learned in my latest newsletter article (If you’re curious, check the link in the comments👇) What’s your go-to method for analyzing feature impacts without a perfect experimental setup?

1. 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲: Measures how often users make mistakes while interacting with a design, such as clicking the wrong button or entering incorrect information. 2. 𝗧𝗶𝗺𝗲 𝗼𝗻 𝗧𝗮𝘀𝗸: Tracks the time users take to complete a specific task within the interface, reflecting usability efficiency. 3. 𝗠𝗶𝘀𝗰𝗹𝗶𝗰𝗸 𝗥𝗮𝘁𝗲: Indicates how often users unintentionally click on incorrect elements, showing potential design misguidance. 4. Response Time: The time it takes for the system to respond after a user takes an action, such as clicking a button or loading a page. 5. Time on Screen: Monitors how long users spend on specific screens, revealing engagement or confusion levels. 6. Session Duration: Tracks the total time a user spends during a single session on the website or app. 7. Task Success Rate: The percentage of users who successfully complete a task as intended, measuring design clarity. 8. User Path Analysis: Evaluates the paths users take to complete tasks, identifying if they follow the intended workflow. 9. Task Completion Rate: Measures the proportion of users who can finish a given task within the interface without errors. 10. Test Level Satisfaction: Reflects users' overall satisfaction with a design after completing usability testing. 11. Task Level Satisfaction: Assesses user satisfaction for specific tasks, offering detailed insights into usability bottlenecks. 12. Time-Based Efficiency: Combines task success with time on task, analyzing how efficiently users can complete tasks. 13. User Feedback Surveys: Gathers direct feedback from users to understand their opinions, pain points, and suggestions. 14. Heatmaps and Click Maps: Visualizes user interactions, showing where users click, scroll, or hover the most on a screen. 15. Accessibility Audit Scores: Assess how well the design complies with accessibility standards, ensuring usability for all. 16. Single Ease Question (SEQ): A one-question survey asking users to rate how easy a task was to complete, providing immediate feedback. 17. Use of Search vs. Navigation: Compares how often users rely on search functionality instead of navigating through menus. 18. System Usability Scale (SUS): A standardized questionnaire measuring the overall usability of a system. 19. User Satisfaction Score (CSAT): Measures user happiness with a specific interaction or overall experience through ratings. 20. Mobile Responsiveness Metrics: Evaluates how well the design adapts to various screen sizes and mobile devices. 21. Subjective Mental Effort Questionnaire: Measures how mentally taxing a task feels to users, highlighting design complexity. #UX #UI #UserExperience #UsabilityTesting #AccessibilityMatters #UserSatisfaction #DesignMetrics #InteractionDesign #TaskEfficiency #UIUXMetrics #DigitalDesign #Heatmap #TimeOnTask #SystemUsability #UserFeedback #UIAnalytics #DataDrivenDesign

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!

Last week, during a design review, a Fortune 500 client asked me: "Your design is beautiful. But where's the revenue impact?" That question always hit hard. For 16 years at NetBramha - Global UX Design Studio, I've seen this shift: Design alone isn't enough anymore. Business will save design. Here's why 👇 Fact 1 - The reality of design today → Beautiful UIs don't drive growth → User research needs business context → Design must impact revenue → Aesthetics alone won't save budgets How we turned it around: An edtech client wanted a new redesigned website, we studied their user personas first: → Customer drop offs & conversion rates → User research to identify real motivation  → Our design increased the conversions on the website by 534% → ROI: Through the roof! Fact 2 - The reality of design tomorrow → Design must speak the business language → Metrics matter more than mockups → Strategy will beat pure creativity → Impact outweighs inspiration Fact 3 - How business will save us → Forces us to measure the real impact → Makes design accountable for results → Aligns creativity with market needs → Transforms design from cost to investment After designing digital experiences for 1B+ people, here's what I know: Business isn't killing design. It's making design stronger. More purposeful. More impactful. The future belongs to designers who understand this: business will save design. It was never the other way around. What's your take on this? Have you seen business expertise elevate design work? #DesignStrategy #BusinessOfDesign #DesignLeadership