Key Metrics For User Experience And Conversion Rates

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

UX metrics work best when aligned with the right questions. Below are ten common UX scenarios and the metrics that best fit each. 1. Completing a Transaction When the goal is to make processes like checkout, sign-up, or password reset more efficient, focus on task success rates, drop-off points, and error tracking. Self-reported metrics like expectations and likelihood to return can also reveal how users perceive the experience. 2. Comparing Products For benchmarking products or releases, task success and efficiency offer a baseline. Self-reported satisfaction and emotional reactions help capture perceived differences, while comparative metrics provide a broader view of strengths and weaknesses. 3. Frequent Use of the Same Product For tools people use regularly, like internal platforms or messaging apps, task time and learnability are essential. These metrics show how users improve over time and whether effort decreases with experience. Perceived usefulness is also valuable in highlighting which features matter most. 4. Navigation and Information Architecture When the focus is on helping users find what they need, use task success, lostness (extra steps taken), card sorting, and tree testing. These help evaluate whether your content structure is intuitive and discoverable. 5. Increasing Awareness Some studies aim to make features or content more noticeable. Metrics here include interaction rates, recall accuracy, self-reported awareness, and, if available, eye-tracking data. These provide clues about what’s seen, skipped, or remembered. 6. Problem Discovery For open-ended studies exploring usability issues, issue-based metrics are most useful. Cataloging the frequency and severity of problems allows you to identify pain points, even when tasks or contexts differ across participants. 7. Critical Product Usability Products used in high-stakes contexts (e.g., medical devices, emergency systems) require strict performance evaluation. Focus on binary task success, clear definitions of user error, and time-to-completion. Self-reported impressions are less relevant than observable performance. 8. Designing for Engagement For experiences intended to be emotionally resonant or enjoyable, subjective metrics matter. Expectation vs. outcome, satisfaction, likelihood to recommend, and even physiological data (e.g., skin conductance, facial expressions) can provide insight into how users truly feel. 9. Subtle Design Changes When assessing the impact of minor design tweaks (like layout, font, or copy changes), A/B testing and live-site metrics are often the most effective. With enough users, even small shifts in behavior can reveal meaningful trends. 10. Comparing Alternative Designs In early-stage prototype comparisons, issue severity and preference ratings tend to be more useful than performance metrics. When task-based testing isn’t feasible, forced-choice questions and perceived ease or appeal can guide design decisions.

What CR doesn’t tell you But 7 components do You fixed the Conversion Rate, but nothing changed. Because CR is just the tip of the iceberg. It doesn’t explain the customers' journey. And definitely not the drop-offs. With Nick Valiotti, PhD we mapped 7 elements of conversion that reveal where your funnel actually leaks. That's what's under the water: 1/ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁 𝗥𝗮𝘁𝗲 = Product page views / Sessions Shows if users are landing on high-interest products or generic pages. 2/ 𝗖𝗮𝗿𝘁-𝘁𝗼-𝗩𝗶𝗲𝘄 𝗥𝗮𝘁𝗲 = Add to carts / Product views Reveals product appeal + pricing clarity. 3/ 𝗖𝗮𝗿𝘁 𝗢𝗽𝗲𝗻 → 𝗖𝗵𝗲𝗰𝗸𝗼𝘂𝘁 𝗦𝘁𝗮𝗿𝘁 = Checkout starts / Carts opened Do people commit after opening the cart? 4/ 𝗦𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗠𝗲𝘁𝗵𝗼𝗱 → 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 = Purchases / Shipping method selected Highlights issues with delivery cost, speed, or trust. 5/ 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗠𝗲𝘁𝗵𝗼𝗱 → 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 = Purchases / Payment method selected Do people quit after choosing how to pay? 6/ 𝗣𝗿𝗼𝗺𝗼 𝗖𝗼𝗱𝗲 → 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 = Purchases / Promo code applied Reveals whether discounts drive actual commitment. 7/ 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲-𝘁𝗼-𝗩𝗶𝗲𝘄 𝗥𝗮𝘁𝗲 = Purchases / Product views The real conversion beyond CR. These metrics tell you why CR changed. Not just that it did. 🤓 Save this if you want to audit your funnel like a pro

Align your UX metrics to the business KPIs. We've been discussing what makes a KPI in our company. A Key Performance Indicator measures how well a person, team, or organization meets goals. It tracks performance so we can make smart decisions. But what’s a Design KPI? Let’s take an example of a design problem. Consider an initiative to launch a new user dashboard to improve user experience, increase product engagement, and drive business growth. Here might be a few Design KPIs with ways to test them: →  Achieve an average usability of 80% within the first three months post-launch. Measurement: Conduct user surveys and collect feedback through the dashboard's feedback feature using the User Satisfaction Score. →  Ensure 90% of users can complete key tasks (e.g., accessing reports, customizing the dashboard) without assistance. Measurement: Conduct usability testing sessions before and after the launch, analyzing task completion rates. →  Reduce the average time to complete key tasks by 20%. Measurement: Use analytics tools to track and compare time spent on tasks before and after implementing the new dashboard. We use Helio to get early signals into UX metrics before coding the dashboard. This helps us find good answers faster and reduces the risk of bad decisions. It's a mix of intuition and ongoing, data-informed processes. What’s a product and business KPI, then? Product KPI: →  Increase MAU (Monthly Active Users) by 15% within six months post-launch. Measurement: Track the number of unique users engaging with the new dashboard monthly through analytics platforms. →  Achieve a 50% feature adoption rate of new dashboard features (e.g., customizable widgets, real-time data updates) within the first quarter. Measurement: Monitor the usage of new features through in-app analytics. Business KPI: → Drive a 5% increase in revenue attributable to the new dashboard within six months. Measurement: Compare revenue figures before and after the dashboard launch, focusing on user subscription and upgrade changes. This isn't always straightforward! I'm curious how you think about these measurements. #uxresearch #productdiscovery #marketresearch #productdesign