Interface Design Benchmarking

⏱️ 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

Benchmarking is one of the most direct ways to answer a question every UX team faces at some point: is the design meeting expectations or just looking good by chance? A benchmark might be an industry standard like a System Usability Scale score of 68 or higher, an internal performance target such as a 90 percent task completion rate, or the performance of a previous product version that you are trying to improve upon. The way you compare your data to that benchmark depends on the type of metric you have and the size of your sample. Getting that match right matters because the wrong method can give you either false confidence or unwarranted doubt. If your metric is binary such as pass or fail, yes or no, completed or not completed, and your sample size is small, you should be using an exact binomial test. This calculates the exact probability of seeing your result if the true rate was exactly equal to your benchmark, without relying on large-sample assumptions. For example, if seven out of eight users succeed at a task and your benchmark is 70 percent, the exact binomial test will tell you if that observed 87.5 percent is statistically above your target. When you have binary data with a large sample, you can switch to a z-test for proportions. This uses the normal distribution to compare your observed proportion to the benchmark, and it works well when you expect at least five successes and five failures. In practice, you might have 820 completions out of 1000 attempts and want to know if that 82 percent is higher than an 80 percent target. For continuous measures such as task times, SUS scores, or satisfaction ratings, the right approach is a one-sample t-test. This compares your sample mean to the benchmark mean while taking into account the variation in your data. For example, you might have a SUS score of 75 and want to see if it is significantly higher than the benchmark of 68. Some continuous measures, like task times, come with their own challenge. Time data are often right-skewed: most people finish quickly but a few take much longer, pulling the average up. If you run a t-test on the raw times, these extreme values can distort your conclusion. One fix is to log-transform the times, run the t-test on the transformed data, and then exponentiate the mean to get the geometric mean. This gives a more realistic “typical” time. Another fix is to use the median instead of the mean and compare it to the benchmark using a confidence interval for the median, which is robust to extreme outliers. There are also cases where you start with continuous data but really want to compare proportions. For example, you might collect ratings on a 5-point scale but your reporting goal is to know whether at least 75 percent of users agreed or strongly agreed with a statement. In this case, you set a cut-off score, recode the ratings into agree versus not agree, and then use an exact binomial or z-test for proportions.

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