Using stats to build trust in UX design

⏱️ How To Measure UX (https://lnkd.in/e5ueDtZY), a practical guide on how to use UX benchmarking, SUS, SUPR-Q, UMUX-LITE, CES 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 (fully explained in videos → https://measure-ux.com). Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < Xs (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 a 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 > 30% (usage of a new feature per user) 10. Feature retention rate > 40% (after 90 days) 11. Time to pricing quote < 2 weeks (for B2B systems) 12. Application processing time < 2 weeks (online banking) 13. Default settings correction < 10% (quality of defaults) 14. Relevance of top 100 search requests > 80% (for top 5 results) 15. Service desk inquiries < 35/week (poor design → more inquiries) 16. Form input accuracy ≈ 100% (user input in forms) 17. Frequency of errors < 3/visit (mistaps, double-clicks) 18. Password recovery frequency < 5% per user (for auth) 19. Fake email addresses < 5% (newsletters) 20. Helpdesk follow-up rate < 4% (quality of service desk replies) 21. “Turn-around” score < 1 week (frustrated users -> happy users) 22. Environmental impact < 0.3g/page request (sustainability) 23. Frustration score < 10% (AUS + SUS/SUPR-Q) 24. System Usability Scale > 75 (usability) 25. Accessible Usability Scale (AUS) > 75 (accessibility) 26. Core Web Vitals ≈ 100% (performance) Each team works with 3–4 design KPIs that reflect the impact of their work. 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. [Useful tools in comments ↓]

When I talk with UX researchers and designers, I often hear regression models described as “just another stats test.” In reality, regression is one of the most powerful ways to connect user behavior, design choices, and business outcomes. It is not only a math exercise. It is a method for linking evidence to decisions. Here is why regression matters so much in UX research: 1. Explaining relationships UX data is complex. Task completion time, error rates, satisfaction scores, prior experience, and demographic factors can all influence one another. Regression helps us untangle these influences. For example, does satisfaction decrease because a flow takes too long, or because the interface is confusing? A regression model shows how much each factor contributes to the outcome, giving us explanations that go beyond surface-level observations. 2. Controlling for confounds A major risk in UX research is misattributing cause and effect. Imagine experienced users finishing tasks faster. Is that because of a new design or because of their prior knowledge? Regression allows us to hold prior knowledge constant and see the unique contribution of the design. This ability to separate signal from noise makes regression far more reliable than looking at simple averages or raw correlations. 3. Testing hypotheses UX teams often work with specific hypotheses. For example, “This new onboarding flow will reduce drop-off” or “A clearer button label will increase clicks.” Regression provides a formal way to test these claims. Instead of relying on instinct or anecdotal observations, we can provide evidence that has been statistically checked. This does not mean blindly chasing significance, but it does mean giving structure and rigor to the claims we make. 4. Making predictions Sometimes explanation is not enough. Teams need to forecast outcomes. Regression models allow us to ask practical questions such as: If usability scores increase by one point, how much retention can we expect to gain? Or, if error rates increase by five percent, how much will that reduce satisfaction? These predictive insights help product teams prioritize design work based on the likely size of impact. 5. Quantifying uncertainty and effect sizes Regression also makes us transparent about uncertainty. UX research often involves noisy data, especially when sample sizes are limited. A regression model does not just indicate whether an effect exists. It tells us how strong the effect is and how confident we can be in that estimate. Sharing effect sizes together with confidence or credible intervals builds trust. Stakeholders see that we are not just saying “this works.” We are showing the strength and reliability of our findings. Regression is not an academic luxury. It is a cornerstone of evidence-based UX. It helps us explain what is happening, isolate the effect of design choices, test whether changes are meaningful, forecast future outcomes, and communicate with transparency.

Design metrics guide designers to tell better stories. Perhaps paradoxically, a data-informed process makes designers better storytellers. Engaging an audience, especially in business, takes years to master, as stakeholders can be critical. However, staying focused on the value created during the process keeps stakeholders engaged and more forgiving of presentation issues. While presentation is essential, it's the use of concrete measurements to explain decisions that genuinely builds trust and credibility. Why? Using metrics in design critiques builds trust by making the process transparent and relatable. It provides measurable impact, showing how good design influences actions and economic outcomes. Additionally, it forces simplicity and clarity, allowing designers to communicate effectively with short, impactful, easily understood sentences. Here’s the surprising part. Even poor results can help create a compelling story. Using metrics allows designers to find value even when making poor decisions. Benchmarking these decisions helps everyone learn from potential problems and guides designers to better solutions. Once designers see their role as guiding the team to better outcomes rather than creating perfect solutions, storytelling can help bring everyone along in the design process. Using data in continuous research and iterative design can be complex, but it boils down to two main points in a presentation: 1. Hunch: How will a design concept improve the user experience and business results? We call this a hunch. 2. Measurement: How does a concept perform compared to other iterations? We use UX metrics as leading indicators. ↓ In our design process, we use rapid iteration to capture UX metrics using Helio. Here’s an example: → Point 1: How will a design concept improve the user experience and business results? Redesigning a university’s degree page with a guide and better search functionality can enhance user experience and increase successful applications. This hunch sets a clear focus for the presentation on expected positive outcomes. → Point 2: How does a concept perform compared to other iterations? Multiple versions of the registration page are tested for user satisfaction and task completion rates. Using Helio for rapid testing helps identify the best design, adding credibility to the presentation by showcasing data-informed decisions and measurable improvements. Combining these points into a cohesive narrative helps our design team tell a compelling story. This builds confidence in the process and demonstrates the tangible benefits and data-informed decisions that lead to a better user experience.

Traditional usability tests often treat user experience factors in isolation, as if different factors like usability, trust, and satisfaction are independent of each other. But in reality, they are deeply interconnected. By analyzing each factor separately, we miss the big picture - how these elements interact and shape user behavior. This is where Structural Equation Modeling (SEM) can be incredibly helpful. Instead of looking at single data points, SEM maps out the relationships between key UX variables, showing how they influence each other. It helps UX teams move beyond surface-level insights and truly understand what drives engagement. For example, usability might directly impact trust, which in turn boosts satisfaction and leads to higher engagement. Traditional methods might capture these factors separately, but SEM reveals the full story by quantifying their connections. SEM also enhances predictive modeling. By integrating techniques like Artificial Neural Networks (ANN), it helps forecast how users will react to design changes before they are implemented. Instead of relying on intuition, teams can test different scenarios and choose the most effective approach. Another advantage is mediation and moderation analysis. UX researchers often know that certain factors influence engagement, but SEM explains how and why. Does trust increase retention, or is it satisfaction that plays the bigger role? These insights help prioritize what really matters. Finally, SEM combined with Necessary Condition Analysis (NCA) identifies UX elements that are absolutely essential for engagement. This ensures that teams focus resources on factors that truly move the needle rather than making small, isolated tweaks with minimal impact.