The Value Of Usability Metrics In UX Design Reviews

Most teams pick metrics that sound smart… But under the hood, they’re just noisy, slow, misleading, or biased. But today, I'm giving you a framework to avoid that trap. It’s called STEDII and it’s how to choose metrics you can actually trust: — ONE: S — Sensitivity Your metric should be able to detect small but meaningful changes Most good features don’t move numbers by 50%. They move them by 2–5%. If your metric can’t pick up those subtle shifts , you’ll miss real wins. Rule of thumb: - Basic metrics detect 10% changes - Good ones detect 5% - Great ones? 2% The better your metric, the smaller the lift it can detect. But that also means needing more users and better experimental design. — TWO: T — Trustworthiness Ever launch a clearly better feature… but the metric goes down? Happens all the time. Users find what they need faster → Time on site drops Checkout becomes smoother → Session length declines A good metric should reflect actual product value, not just surface-level activity. If metrics move in the opposite direction of user experience, they’re not trustworthy. — THREE: E — Efficiency In experimentation, speed of learning = speed of shipping. Some metrics take months to show signal (LTV, retention curves). Others like Day 2 retention or funnel completion give you insight within days. If your team is waiting weeks to know whether something worked, you're already behind. Use CUPED or proxy metrics to speed up testing windows without sacrificing signal. — FOUR: D — Debuggability A number that moves is nice. A number you can explain why something worked? That’s gold. Break down conversion into funnel steps. Segment by user type, device, geography. A 5% drop means nothing if you don’t know whether it’s: → A mobile bug → A pricing issue → Or just one country behaving differently Debuggability turns your metrics into actual insight. — FIVE: I — Interpretability Your whole team should know what your metric means... And what to do when it changes. If your metric looks like this: Engagement Score = (0.3×PageViews + 0.2×Clicks - 0.1×Bounces + 0.25×ReturnRate)^0.5 You’re not driving action. You’re driving confusion. Keep it simple: Conversion drops → Check checkout flow Bounce rate spikes → Review messaging or speed Retention dips → Fix the week-one experience — SIX: I — Inclusivity Averages lie. Segments tell the truth. A metric that’s “up 5%” could still be hiding this: → Power users: +30% → New users (60% of base): -5% → Mobile users: -10% Look for Simpson’s Paradox. Make sure your “win” isn’t actually a loss for the majority. — To learn all the details, check out my deep dive with Ronny Kohavi, the legend himself: https://lnkd.in/eDWT5bDN

You run a usability test. The results seem straightforward - most users complete the task in about 10 seconds. But when you look closer, something feels off. Some users fly through in five seconds, while others take over 20. Same interface, same task, wildly different experiences. Traditional UX analysis might smooth this out by reporting the average time or success rate. But that average hides a crucial insight: not all users are the same. Maybe experienced users follow intuitive shortcuts while beginners hesitate at every step. Maybe some users perform better in certain conditions than others. If you only look at the averages, you’ll never see the full picture. This is where mixed-effects models come in. Instead of treating all users as if they behave the same way, these models recognize that individual differences matter. They help uncover patterns that traditional methods - like t-tests and ANOVA - tend to overlook. Mixed-effects models help UX researchers move beyond broad generalizations and get to what really matters: understanding why users behave the way they do. So next time you're analyzing UX data, ask yourself - are you just looking at averages, or are you really seeing your users?

Recently, someone shared results from a UX test they were proud of. A new onboarding flow had reduced task time, based on a very small handful of users per variant. The result wasn’t statistically significant, but they were already drafting rollout plans and asked what I thought of their “victory.” I wasn’t sure whether to critique the method or send flowers for the funeral of statistical rigor. Here’s the issue. With such a small sample, the numbers are swimming in noise. A couple of fast users, one slow device, someone who clicked through by accident... any of these can distort the outcome. Sampling variability means each group tells a slightly different story. That’s normal. But basing decisions on a single, underpowered test skips an important step: asking whether the effect is strong enough to trust. This is where statistical significance comes in. It helps you judge whether a difference is likely to reflect something real or whether it could have happened by chance. But even before that, there’s a more basic question to ask: does the difference matter? This is the role of Minimum Detectable Effect, or MDE. MDE is the smallest change you would consider meaningful, something worth acting on. It draws the line between what is interesting and what is useful. If a design change reduces task time by half a second but has no impact on satisfaction or behavior, then it does not meet that bar. If it noticeably improves user experience or moves key metrics, it might. Defining your MDE before running the test ensures that your study is built to detect changes that actually matter. MDE also helps you plan your sample size. Small effects require more data. If you skip this step, you risk running a study that cannot answer the question you care about, no matter how clean the execution looks. If you are running UX tests, begin with clarity. Define what kind of difference would justify action. Set your MDE. Plan your sample size accordingly. When the test is done, report the effect size, the uncertainty, and whether the result is both statistically and practically meaningful. And if it is not, accept that. Call it a maybe, not a win. Then refine your approach and try again with sharper focus.

Track customer UX metrics during design to improve business results. Relying only on analytics to guide your design decisions is a missed opportunity to truly understand your customers. Analytics only show what customers did, not why they did it. Tracking customer interactions throughout the product lifecycle helps businesses measure and understand how customers engage with their products before and after launch. The goal is to ensure the design meets customer needs and achieves desired outcomes before building. By dividing the process into three key stages—customer understanding (attitudinal metrics), customer behavior (behavioral metrics), and customer activity (performance metrics)—you get a clearer picture of customer needs and how your design addresses them. → Customer Understanding In the pre-market phase, gathering insights about how well customers get your product’s value guides your design decisions. Attitudinal metrics collected through surveys or interviews help gauge preferences, needs, and expectations. The goal is to understand how potential customers feel about the product concept. → Customer Behavior Tracking how customers interact with prototype screens or products shows whether the design is effective. Behavioral metrics like click-through rates and session times provide insights into how users engage with the design. This phase bridges the pre-market and post-market stages and helps identify any friction points in the design. →  Customer Activity After launch, post-market performance metrics like task completion and error rates measure how customers use the product in real-world scenarios. These insights help determine if the product meets its goals and how well it supports user needs. Designers should take a data-informed approach by collecting and analyzing data at each stage to make sure the product continues evolving to meet customer needs and business goals. #productdesign #productdiscovery #userresearch #uxresearch

Most UX folks are missing the one skill that could save their careers. For a long time, many UXers have been laser-focused on the craft. Understanding users. Testing ideas. Perfecting pixels. But here’s the reality. Companies are cutting those folks everywhere, because they don’t connect their work to hard, actual, tangible $$$$$. So it’s viewed as a luxury. A nice-to-have. My 2 cents.. If you can’t tie your decisions to how it helps the business make or save money, you’re at risk. Full stop. But I have good news. You can quantify your $$ impact using basic financial modeling. Here’s a quick example.. Imagine you’re working on a tool that employees use every day. Let’s say the current experience requires 8 hours a week for each employee to complete a task. By improving the usability of the tool, you cut that time by three hours. Let’s break it down. If the average employee makes $100K annually (roughly $50/hr), and 100 employees use the tool, that’s $15K saved each week. Over a year, that’s $780K in savings.. just by shaving 3 hours off a process. Now take it a step further. What if those employees use those extra 3 hours to create more value for customers? What’s the potential revenue upside? This is the kind of thinking that sets a designer apart. It’s time for UXers to stop treating customer sentiment or usability test results as the final metric. Instea learn how your company makes or saves money and model the financial impact of your UX changes. Align your work with tangible metrics like operational efficiency, customer retention, or lifetime value. The best part? This isn’t hard. Basic math and a simple framework can help you communicate your value in ways the business understands. Your prototype or design file doesn’t need to be perfect. But your ability to show how it drives business outcomes? That does. — If you enjoyed this post, join hundreds of others and subscribe to my weekly newsletter — Building Great Experiences https://lnkd.in/edqxnPAY