UX Design with Statistical Analysis

⏱️ 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

I've been getting a lot of questions about how to really use Bayesian methods in UX studies, especially how to come up with a solid prior distribution for your analysis. This issue is important because the prior distribution forms the foundation of Bayesian analysis and shapes how you update your beliefs as new data comes in. Many researchers find it challenging to establish these initial assumptions systematically, which is why I'm writing this post. Before diving into how qualitative data can help, let me explain briefly what Bayesian methods are and why the prior is so important. Bayesian methods let you update your understanding as you gather new data. You begin with a prior distribution, which represents your initial assumptions about a parameter, and then you combine it with observed data using the likelihood function to produce an updated view called the posterior. This process effectively blends what you already know with new information. Now, let me explain how qualitative data can contribute to setting your prior. For example, if you conduct interviews or focus groups and notice that many users mention having trouble navigating a feature, you can count how often this concern arises and translate that frequency into a numerical estimate for your prior. Similarly, if usability tests reveal that users stumble on a specific interaction in about one third of the sessions, you can use that frequency as an initial estimate. Expert opinions are valuable too; if experienced UX professionals suggest that a design flaw might affect roughly 20 percent of users, that percentage can serve as your starting point. Even thematic coding from qualitative data can guide you; if one theme emerges as significantly more prevalent than others, you can assign a higher probability to outcomes related to that theme. These examples illustrate how you can turn rich qualitative insights into concrete numbers that inform your prior distribution. Integrating qualitative insights into Bayesian analysis is a powerful strategy because it grounds your models in real-world user experiences. In UX research, you are not merely relying on abstract numbers; you are capturing the nuances of user behavior and refining your models as new data becomes available. This iterative process leads to a deeper understanding of how users interact with a product and ultimately informs better design decisions. In short, using qualitative data to set your prior distributions is a practical and effective approach. It leverages rich, contextual insights and combines them with the rigorous updating process of Bayesian methods, resulting in more informed and responsive design decisions that truly reflect the user experience.

Ever looked at a UX survey and thought: “Okay… but what’s really going on here?” Same. I’ve been digging into how factor analysis can turn messy survey responses into meaningful insights. Not just to clean up the data - but to actually uncover the deeper psychological patterns underneath the numbers. Instead of just asking “Is this usable?”, we can ask: What makes it feel usable? Which moments in the experience build trust? Are we measuring the same idea in slightly different ways? These are the kinds of questions that factor analysis helps answer - by identifying latent constructs like satisfaction, ease, or emotional clarity that sit beneath the surface of our metrics. You don’t need hundreds of responses or a big-budget team to get started. With the right methods, even small UX teams can design sharper surveys and uncover deeper insights. EFA (exploratory factor analysis) helps uncover patterns you didn’t know to look for - great for new or evolving research. CFA (confirmatory factor analysis) lets you test whether your idea of a UX concept (say, trust or usability) holds up in the real data. And SEM (structural equation modeling) maps how those factors connect - like how ease of use builds trust, which in turn drives satisfaction and intent to return. What makes this even more accessible now are modern techniques like Bayesian CFA (ideal when you’re working with small datasets or want to include expert assumptions), non-linear modeling (to better capture how people actually behave), and robust estimation (to keep results stable even when the data’s messy or skewed). These methods aren’t just for academics - they’re practical, powerful tools that help UX teams design better experiences, grounded in real data.

Is there room in UX research for complicated statistics like mixed-effects models, mediation analysis, factor analysis, etc? 🏁 Yes! This is often confused with the question: should I show my stakeholders all of the work that went into my complicated statistics? 🚩 No! Advanced modeling can be extremely useful in a UX researcher's toolkit to help show things like causality or grouping/clustering (just to name two examples). Stakeholders, like product managers, often want to know if X causes Y or how certain groups of features or users are similar to each other. You've probably heard that we need to ditch our academic statistics because no one cares and they're too slow. 🧐 No one cares? On one hand, this is true. Nerd out with your UXR friends about the cool model, stow it in the appendix, and then put the simple insight/recommendation out front for your stakeholders. Some of my most impactful findings have been distilled into a short sentence, but they're sitting on a deep regression analysis that led to the insight. You can run an entire regression without needing to show one chart and sometimes be more effective as a researcher. ⌛ It's too slow? It might be if you're using excel or SPSS, doing it manually each time. Using things like R or Python you can pre-code your analysis with dummy or partial data while a survey fields. Then you're ready to go the moment you have all of your data. You can even reuse that code after the fact for fast analysis in similar projects. Quantitative analysis takes time, but so does any qualitative theming (just ask any qualitative UXR). It doesn't have to take that more *more* time than other methods. Bottom line: you don't need to forget all of your statistics you learned in school, just implement in a fashion and timeline that meets the expectations of a product team.

Are you practicing 'Safe UX'? If you don't know what a p value is, then probably not...... I inadvertently caused a bit of a stir with my last post, so here’s a follow up to explain more…. For USABILITY TESTING (flagging up usability issues) – Nielsen & Landuar’s theory of 5 participants is completely sound in most cases But, for conducting OTHER KINDS of UX Research – Exercise caution. You will probably need more. If you’re a UX Designer carrying out UX Research, then it’s highly likely that most of the UX Research you do is usability testing. However, sometimes you may produce more than one Design and your questions may become ‘Which design would users prefer to use?’ and ‘Which design are users more likely to successfully complete a task on?’ This is no longer usability testing. You are now carrying out other categories of UX Research (in this example, preference testing and measurement of a KPI). If you want to practice ‘Safe UX’ you need carry out a test of statistical significance before drawing any conclusions. If you don’t get a statically significant result (typically, a p value of <.05) then there’s no firm indicator that users prefer one design over the other, or that they are more likely to successfully complete the task on that design. With a sample size of only 10, you would need 9 out of the 10 to show a preference for one design in order to produce a statistically significant finding. If you see that in moderated testing, then great! If not, then try running a simpler unmoderated test on a larger sample to see the bigger picture. With a sample size of 100, a preference of 60/100 or more will return a statistically significant p value The trap I’ve seen many fall in to is assuming a preference of just 7 /10 (in moderated interviews or think aloud studies) is a ‘significant’ preference. It’s not! If you make this assumption you may be in for disappointment when the A/B test launches on the live site!