Analyzing Test Results in UX

⏱️ 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

If you're a UX researcher working with open-ended surveys, interviews, or usability session notes, you probably know the challenge: qualitative data is rich - but messy. Traditional coding is time-consuming, sentiment tools feel shallow, and it's easy to miss the deeper patterns hiding in user feedback. These days, we're seeing new ways to scale thematic analysis without losing nuance. These aren’t just tweaks to old methods - they offer genuinely better ways to understand what users are saying and feeling. Emotion-based sentiment analysis moves past generic “positive” or “negative” tags. It surfaces real emotional signals (like frustration, confusion, delight, or relief) that help explain user behaviors such as feature abandonment or repeated errors. Theme co-occurrence heatmaps go beyond listing top issues and show how problems cluster together, helping you trace root causes and map out entire UX pain chains. Topic modeling, especially using LDA, automatically identifies recurring themes without needing predefined categories - perfect for processing hundreds of open-ended survey responses fast. And MDS (multidimensional scaling) lets you visualize how similar or different users are in how they think or speak, making it easy to spot shared mindsets, outliers, or cohort patterns. These methods are a game-changer. They don’t replace deep research, they make it faster, clearer, and more actionable. I’ve been building these into my own workflow using R, and they’ve made a big difference in how I approach qualitative data. If you're working in UX research or service design and want to level up your analysis, these are worth trying.

I started programming 20 years ago (yes, I was a kid!) with QBasic, where just getting a circle to appear on the screen felt like magic. I spent hours figuring out why a single typo could turn everything into a flashing mess. If you ever wrote a game in QBasic, you probably remember how easy it was to create an infinite loop and crash everything. From there, I moved on to Pascal, Delphi, and eventually Python and MATLAB. But the first language that truly clicked for me was R, and for almost a decade, RStudio has been my go-to tool for data analysis and statistical modeling. As I moved into human-centered studies, I noticed a gap. UX and Human Factors researchers rely on scattered tools, but no all-in-one R package existed for this field. I have used these statistical models in my research, from analyzing attention and behavior to evaluating game experience and cognitive load. Every time, I had to combine functions from multiple packages. I wanted a single, unified library that had everything a UX or HF researcher needs. So, Dr. Jozranjbar (Bahareh Jozranjbar, PhD), and I built one! UXtoolbox is designed to be the first R package made specifically for UX and HF researchers. Whether you are analyzing usability tests, running A/B experiments, or modeling user behavior, it provides all the statistical and analytical tools you need in one place. What Can UXtoolbox Do? 🔻 Bayesian Statistics Run Bayesian ANOVA to compare UX designs Use Bayesian Linear Regression to model relationships and quantify uncertainty Apply Bayesian Mixed-Effects Models for hierarchical UX data Perform Bayesian Survival Analysis to study user retention 🔻 Frequentist Statistics Standard T-tests for comparing groups ANOVA and Mixed-Effects Models for UX experiments Linear and Logistic Regression Survival Analysis for user behavior studies 🔻 Predictive Modeling and Machine Learning Use Mixture Models to cluster users based on behavior Try Structural Equation Modeling (SEM) to analyze UX relationships Run Time-Series Analysis to track engagement over time 🔻 UX-Specific Laws and Metrics Apply Fitts' Law to predict movement time in UI interactions Use Hick’s Law to estimate decision time based on complexity Model perceptual sensitivity with Weber’s Law 🔻 Content and Sentiment Analysis Analyze word frequency in user feedback Perform sentiment analysis on reviews and survey responses Use topic modeling to group and categorize discussions 🔻 Data Visualization for UX Research Generate correlation heatmaps Visualize user behavior trends Create eye-tracking heatmaps We’d love your feedback. If there’s a feature or model you need, let us know, and we’ll add it. If you find any bugs, report them, and I’ll fix them ASAP. A Python version is also in the works, bringing the same powerful UX and HF tools to more users. Stay tuned. Download UXtoolbox from my GitHub (link in the comments), and let’s build the future of data-driven UX research together.

Drive design impact by comparing UX metrics. UX metrics turn raw user data into useful signals. Benchmarketing is what turns these signals into actionable design decisions. Once you know what you're measuring and how you're collecting data, a benchmark helps you measure the differences in user behaviors. Benchmarking helps you answer two questions. • What does this data mean? • What should we do next? Using benchmarks, like a goal, past result, or industry standard, you can see if your design works and what to change. We use Helio with iterative design to create these signals before development begins. Example: 80% of users completed the task after a design iteration—up from 60%. Shifting the call to action and rewriting the copy had an impact. That 20% jump shows that the design change worked. Benchmarking made it clear. Measuring your work is good. Comparing the performance makes it great. #productdesign #productdiscovery #userresearch #uxresearch