Data-Driven UX Design

“𝗨𝘀𝗲𝗿𝘀 𝗱𝗼𝗻’𝘁 𝗰𝗮𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗿𝗲𝗽𝗼𝗿𝘁 𝗱𝗲𝘀𝗶𝗴𝗻.” You sure? Because every major study on usability, adoption, and information design says otherwise. Poor design slows decision-making, hides critical insights, and erodes trust. Good design reduces time to value - and makes the difference between used and ignored reports. Let’s talk specifics. These aren’t opinions - they’re proven UX principles, backed by decades of research: 𝗝𝗮𝗸𝗼𝗯’𝘀 𝗟𝗮𝘄 – 𝗨𝘀𝗲𝗿𝘀 𝘀𝗽𝗲𝗻𝗱 𝗺𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗲𝗶𝗿 𝘁𝗶𝗺𝗲 𝘂𝘀𝗶𝗻𝗴 𝗼𝘁𝗵𝗲𝗿 𝘁𝗼𝗼𝗹𝘀. 𝘚𝘰 𝘸𝘩𝘦𝘯 𝘗𝘰𝘸𝘦𝘳 𝘉𝘐 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘣𝘦𝘩𝘢𝘷𝘦 𝘭𝘪𝘬𝘦 𝘵𝘩𝘦 𝘸𝘦𝘣 𝘢𝘱𝘱𝘴 𝘵𝘩𝘦𝘺 𝘬𝘯𝘰𝘸, 𝘪𝘵 𝘧𝘦𝘦𝘭𝘴 𝘣𝘳𝘰𝘬𝘦𝘯. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Use clear navigation, clickable affordances, and common interaction patterns. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Place slicers where users expect filters - top-left or directly above visuals. 𝗟𝗮𝘄 𝗼𝗳 𝗖𝗼𝗺𝗺𝗼𝗻 𝗥𝗲𝗴𝗶𝗼𝗻 – 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀 𝘄𝗶𝘁𝗵𝗶𝗻 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝘆 𝗮𝗿𝗲 𝘀𝗲𝗲𝗻 𝗮𝘀 𝗮 𝗴𝗿𝗼𝘂𝗽. 𝘛𝘩𝘪𝘴 𝘩𝘦𝘭𝘱𝘴 𝘶𝘴𝘦𝘳𝘴 𝘴𝘤𝘢𝘯 𝘢𝘯𝘥 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯 𝘧𝘢𝘴𝘵𝘦𝘳. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Use whitespace or cards to visually group KPIs, charts, and filters. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Group related metrics like Revenue, Margin, and YoY% into a single visual region. 𝗔𝗲𝘀𝘁𝗵𝗲𝘁𝗶𝗰-𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗘𝗳𝗳𝗲𝗰𝘁 – 𝗔𝘁𝘁𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝘁𝗵𝗶𝗻𝗴𝘀 𝗮𝗿𝗲 𝗽𝗲𝗿𝗰𝗲𝗶𝘃𝗲𝗱 𝗮𝘀 𝗲𝗮𝘀𝗶𝗲𝗿 𝘁𝗼 𝘂𝘀𝗲. 𝘌𝘷𝘦𝘯 𝘪𝘧 𝘵𝘩𝘦 𝘣𝘢𝘤𝘬𝘦𝘯𝘥 𝘭𝘰𝘨𝘪𝘤 𝘪𝘴 𝘤𝘰𝘮𝘱𝘭𝘦𝘹, 𝘢 𝘤𝘭𝘦𝘢𝘯 𝘜𝘐 𝘣𝘶𝘪𝘭𝘥𝘴 𝘵𝘳𝘶𝘴𝘵 𝘢𝘯𝘥 𝘳𝘦𝘥𝘶𝘤𝘦𝘴 𝘶𝘴𝘦𝘳 𝘧𝘳𝘶𝘴𝘵𝘳𝘢𝘵𝘪𝘰𝘯. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Typography, spacing, and alignment aren’t fluff - they’re functional. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: A well-spaced, readable KPI section increases scan speed and comprehension. 𝗠𝗶𝗹𝗹𝗲𝗿’𝘀 𝗟𝗮𝘄 – 𝗧𝗵𝗲 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝗰𝗮𝗻 𝗵𝗼𝗹𝗱 7 ± 2 𝗶𝘁𝗲𝗺𝘀 𝗶𝗻 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗺𝗲𝗺𝗼𝗿𝘆. 𝘠𝘦𝘵 𝘮𝘢𝘯𝘺 𝘳𝘦𝘱𝘰𝘳𝘵𝘴 𝘰𝘷𝘦𝘳𝘸𝘩𝘦𝘭𝘮 𝘶𝘴𝘦𝘳𝘴 𝘸𝘪𝘵𝘩 20+ 𝘤𝘩𝘢𝘳𝘵𝘴 𝘰𝘯 𝘢 𝘴𝘪𝘯𝘨𝘭𝘦 𝘱𝘢𝘨𝘦. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Prioritize. Show what matters first. Use drill-through or navigation to reveal detail. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Use a landing page with 3–5 high-value metrics and actions. Design is not just decoration. It’s how users understand your data. It’s what makes insights actionable. And it’s the difference between adoption and abandonment. If users don’t care about report design, it’s probably because they’ve never seen what good design can do. #PowerBI #DataViz #UIUX

Design based on facts, not vibes. Here’s why UX research matters ↓ Skipping UX research when designing a website is like assembling IKEA furniture without the instructions. Sure, you might end up with a chair, but will it hold your weight—or will it wobble until it collapses? UX research isn’t just another box to check. It’s the foundation that keeps everything from falling apart. Without UX research, you’re designing based on vibes, not facts. And that’s how “cool” designs end up confusing users, tanking conversions, and turning into “oh no” moments after launch. So, what does UX research actually do? → Spot user pain points before they become your pain points. → Prioritize features and designs using real data instead of educated guesses. → Create experiences users love, not just tolerate. → Boost key metrics like engagement and conversions (because let’s be honest, that’s the end goal). So, how do you make UX research happen? By staying curious, asking great questions, and using the right tools: 𝗨𝘀𝗲𝗿 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 Talk to real humans—ask them what’s frustrating, what’s working, and what they need. You’ll learn more in one conversation than you will from staring at analytics. 𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 Put your design in front of users early. Watch where they click, hesitate, or get stuck. Sure, it’s humbling—but it’s also how you fix things before they become disasters. 𝗦𝘂𝗿𝘃𝗲𝘆𝘀 Fast, efficient, and a great way to confirm (or shatter) your assumptions. 𝗛𝗲𝗮𝘁𝗺𝗮𝗽𝘀 Find out where users click, scroll, and hover. They’ll tell you exactly where your design nails it or falls flat. 𝗔/𝗕 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 When you can’t decide between two options, let users vote with their actions. Data > opinions. 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 No, it’s not copying—it’s learning what works in your industry and where you can stand out. 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝗺𝗮𝗽𝗽𝗶𝗻𝗴 Walk in your users’ shoes. Every step of the way. From discovery to conversion, figure out where they’re thrilled and where they’re frustrated. Here’s the bottom line: Fixing problems post-launch is a headache you don’t need. UX research saves you time, money, and the embarrassment of explaining why users can’t figure out your shiny new design. Build websites that don’t just look good—build ones that work for your users and your business. --- Follow Jeff Gapinski for more content like this. ♻️ Share this to help someone else out with their UX research today #UX #webdesign #marketing

User research is great, but what if you do not have the time or budget for it........ In an ideal world, you would test and validate every design decision. But, that is not always the reality. Sometimes you do not have the time, access, or budget to run full research studies. So how do you bridge the gap between guessing and making informed decisions? These are some of my favorites: 1️⃣ Analyze drop-off points: Where users abandon a flow tells you a lot. Are they getting stuck on an input field? Hesitating at the payment step? Running into bugs? These patterns reveal key problem areas. 2️⃣ Identify high-friction areas: Where users spend the most time can be good or bad. If a simple action is taking too long, that might signal confusion or inefficiency in the flow. 3️⃣ Watch real user behavior: Tools like Hotjar | by Contentsquare or PostHog let you record user sessions and see how people actually interact with your product. This exposes where users struggle in real time. 4️⃣ Talk to customer support: They hear customer frustrations daily. What are the most common complaints? What issues keep coming up? This feedback is gold for improving UX. 5️⃣ Leverage account managers: They are constantly talking to customers and solving their pain points, often without looping in the product team. Ask them what they are hearing. They will gladly share everything. 6️⃣ Use survey data: A simple Google Forms, Typeform, or Tally survey can collect direct feedback on user experience and pain points. 6️⃣ Reference industry leaders: Look at existing apps or products with similar features to what you are designing. Use them as inspiration to simplify your design decisions. Many foundational patterns have already been solved, there is no need to reinvent the wheel. I have used all of these methods throughout my career, but the trick is knowing when to use each one and when to push for proper user research. This comes with time. That said, not every feature or flow needs research. Some areas of a product are so well understood that testing does not add much value. What unconventional methods have you used to gather user feedback outside of traditional testing? _______ 👋🏻 I’m Wyatt—designer turned founder, building in public & sharing what I learn. Follow for more content like this!

I've always championed a product-based approach to data management. A decade ago, this perspective was often dismissed, as products were merely seen as buttons on a digital interface. However, that view is shifting as AI initiatives now place data at the forefront. The cornerstone of data product design is user centricity. Today's users could be agents or humans, but the principle remains the same. Do you have clear user profiles? Are you analyzing queries in your environment like we do with clickstream data on websites? How can you improve data navigation? Is your data model intuitive? Are you providing the right aggregations for frequently used queries? Do you have a searchable catalog that helps users find data? Are your data models designed for easier visualization? There are many simple questions to consider. My first step, even as an executive, is to examine the data model and see if I can write a query on the first day. If you're not SQL-savvy, ask your analyst to show you one of theirs. If table names require a PhD to decipher (like a schema name such as ABCD12345), it's clear that the data isn't user-friendly; it’s designed as a technical output. We can all work towards making data more accessible. It just takes a bit of observation, active listening, and thoughtful analysis.

During meetings with stakeholders, we often hear about 𝒓𝒆𝒅𝒖𝒄𝒊𝒏𝒈 𝒃𝒐𝒖𝒏𝒄𝒆 𝒓𝒂𝒕𝒆𝒔, 𝒊𝒏𝒄𝒓𝒆𝒂𝒔𝒊𝒏𝒈 𝒓𝒆𝒕𝒆𝒏𝒕𝒊𝒐𝒏, 𝒂𝒏𝒅 𝒐𝒑𝒕𝒊𝒎𝒊𝒛𝒊𝒏𝒈 𝒄𝒐𝒏𝒗𝒆𝒓𝒔𝒊𝒐𝒏 𝒇𝒖𝒏𝒏𝒆𝒍𝒔. If you're feeling confused and overwhelmed about how to do all of this, you're not alone. Here's something for those new to the world of metric-driven design. Trust me, your designs can make a real difference :) 𝗙𝗶𝗿𝘀𝘁 𝘁𝗵𝗶𝗻𝗴𝘀 𝗳𝗶𝗿𝘀𝘁, 𝗴𝗲𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲𝗿𝘀 𝗔𝗡𝗗 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 → Talk to real users. Understand their pain points. But also, grab coffee with the marketing team. Learn what those metrics mean. You'd be surprised how often a simple chat can clarify things. 𝗠𝗮𝗽 𝗼𝘂𝘁 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝗳𝗹𝗼𝘄 → Sketch it out, literally. Where are users dropping off? Where are they getting stuck? This visual approach can reveal problems you might miss otherwise and which screens you need to tackle. 𝗞𝗲𝗲𝗽 𝗶𝘁 𝘀𝗶𝗺𝗽𝗹𝗲, 𝘀𝘁𝘂𝗽𝗶𝗱 (𝗞𝗜𝗦𝗦)→ We've all heard this before, but it's true. A clean, intuitive interface can work wonders for conversion rates. If a user can't figure out what to do in 5 seconds, you might need to simplify. 𝗕𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗱𝗲𝘀𝗶𝗴𝗻 → Trust isn't built by security badges alone. It's about creating an overall feeling of reliability. Clear communication, consistent branding, and transparency go a long way. 𝗠𝗮𝗸𝗲 𝗶𝘁 𝗲𝗻𝗴𝗮𝗴𝗶𝗻𝗴 → Transform mundane tasks into engaging experiences. Progress bars, thoughtful micro-animations, or even well-placed humor can keep users moving forward instead of bouncing off. Remember, engaged users are more likely to convert and return, directly impacting your key metrics. 𝗧𝗲𝘀𝘁, 𝗹𝗲𝗮𝗿𝗻, 𝗿𝗲𝗽𝗲𝗮𝘁 → Set up usability tests to validate your design decisions. Start small - even minor changes in copy or button placement can yield significant results. The key is to keep iterating based on real data, not assumptions. This approach improves your metrics and also sharpens your design intuition over time. 𝗗𝗼𝗻'𝘁 𝗿𝗲𝗶𝗻𝘃𝗲𝗻𝘁 𝘁𝗵𝗲 𝘄𝗵𝗲𝗲𝗹 → While it's tempting to create something totally new, users often prefer familiar patterns. Research industry standards and find data around successful interaction models, then adapt them to address your specific challenges. This approach combines fresh ideas with proven conventions, enhancing user comfort and adoption. Metric-driven design isn't about sacrificing creativity for numbers. It's about using data to inform and elevate your design decisions. By bridging the gap between user needs and business goals.

AI doesn’t fail because of intelligence - it fails because of misalignment. Designing human-centric AI means understanding that systems learn from patterns, not meaning, and that people interpret those patterns through trust, context, and purpose. An AI system is essentially an agent interacting with an environment: it senses (data), decides (policy), and acts (output). The challenge for designers is to shape these loops so that what the system optimizes aligns with what the user values. Every interaction is part of a probabilistic chain of inference. AI doesn’t say, “this is true,” it says, “this is 87% likely to be true.” That means interfaces must expose uncertainty and design around error tolerance, not perfection. The goal isn’t to make AI seem flawless, but to make it understandable when it fails - and recover gracefully. Feedback loops are critical here. Whether explicit (a correction) or implicit (a click, a pause), every behavior reshapes the model. Designers must plan how this feedback is collected, weighted, and surfaced so that learning feels visible and reciprocal. Trust isn’t achieved through good visuals; it’s achieved through transparency of reasoning. Users need to see why a recommendation, prediction, or decision occurred. Tools like confidence indicators, natural-language rationales, or example-based explanations can reveal the system’s thinking process. Trust calibration becomes a design problem: too little information and users overtrust; too much and they disengage. Ethics in AI design is not a checklist - it’s an architectural constraint. Fairness, privacy, and accountability must be embedded in how data is handled, how models are trained, and how decisions are logged. Human-in-the-loop design is not about control; it’s about responsibility. Each feedback point or override is a governance node in a socio-technical system. Prototyping intelligent behavior means simulating cognition, not just interaction. Before the model even works, designers can model system reasoning: what inputs it listens to, how it weighs them, and how it communicates uncertainty. That’s how you prototype explainability early-before accuracy takes over the agenda. In practice, the best AI teams combine technical literacy with behavioral empathy. Data scientists understand distributions; designers understand interpretation. Together, they build systems that not only learn from data but learn from people. Human-centric AI doesn’t just optimize performance - it aligns cognition, decision, and design around human meaning. That’s what makes intelligence truly useful.

Data doesn’t have to define your design process. But failing to use it is a big mistake. In our process, we use data from the beginning to draw inspiration, then use data to guide our prototyping decisions, and eventually make more data-driven choices. The process is more flexible than people often think. The goal isn’t to use data–it’s to make more informed decisions that ultimately improve user and business outcomes. Here’s how: → Data-Inspired Design (Frame the Challenge) We use data to inspire and shape our understanding of the design problem. The aim is to find insights that lead to creative solutions while considering what users need, how they behave, and why they act in specific ways. We find up to 100 opportunities to create lift in a design initiative. Helio UX metrics help us gather early user feedback or signals, highlighting where users struggle or where new opportunities lie. We can set a clear direction for the design process by using these early insights and proxy metrics. We also do interviews. Our team focuses on collecting these early signals to understand the reasons behind user actions. → Data-Informed Design (Assess the Potential) We weigh the benefits and risks of different ideas. Data helps guide the design process, but intuition and insights are just as important as measurable factors. In more significant engagements, we collect answers from up to 30,000 participants in this phase. Helio is handy here, as it allows teams to test early prototypes on a large scale, gathering UX metrics crucial for evaluating design choices. Data storytelling and analyzing user research turn insights into practical feedback. Collaboration across teams also ensures that the design meets user and business needs. We gather feedback through usability tests and measure task completion rates, helping link early design ideas to clear success criteria. → Data-Driven Design (Finalize the Choices) Data helps us make decisions that align with business and user goals. The focus is refining the design using feedback and data to make it as effective as possible. Once the design is live, we connect early metrics with analytics. Helio helps us collect data, such as success rates, user satisfaction, and task completion. These figures provide the confidence needed to finalize design decisions. We align UX metrics with business goals, focusing on clear outcomes like improved usability, higher feature adoption, or revenue growth. Design KPIs and early signals play a role, guiding us in making final decisions based on how well the product performs against these success metrics. —–––––– Data can be applied differently throughout the design process—from an initial source of inspiration to a guiding force in assessing potential and ultimately as the driver of final decisions. We use data differently in each design phase, balancing creativity and analysis. Interested? DM me. #productdesign #productdiscovery #userresearch #uxresearch

Recent debate in the world of design finds ourselves confused between design as personal choice or the product of a well calculated UX strategy. It’s tempting to lean on aesthetics that feel “right” or ideas that align with personal taste. But when designing for business, it's crucial to look beyond what we like and focus on what works. Here’s why aligning design choices with KPIs and UX metrics drives results. Imagine designing a user interface based purely on color schemes we love or animations that feel fun. While personal style brings creativity to the table, it often lacks a strategic focus. For example, a designer might feel that an intricate navigation system looks sleek. But if UX metrics reveal high abandonment rates at navigation points, that “cool” design is clearly not resonating with users. Here, usability should trump aesthetics every time. KPIs (Key Performance Indicators) and UX metrics – like conversion rates, task success rates, or time-on-task – are not just data points. They’re our users’ voices, telling us what they need and expect. When a design aligns with these metrics, it speaks directly to user behavior and business objectives. This is where real value is created. Let’s prioritize intuitive, data-driven design that serves the user and meets business goals. Personal taste may spark inspiration, but data is what drives sustainable impact. Design that’s user-centered, measurable, and flexible isn’t just visually appealing; it’s strategically valuable. So, next time you face a design decision, ask yourself: Is this about personal taste, or does it align with key metrics? The answer might just change the way you design. 💡 #DesignThinking #UserExperience #UXMetrics #KPIs #ProductDesign

What users say isn't always what they think. This gap can mess up your design decisions. Here's why it happens: → Social desirability bias. → Fear of judgment. → Cognitive dissonance. → Lack of self-awareness. → Simple politeness. These factors lead to misinterpretation of user needs. Designers might miss critical usability issues. Products could fail to meet user expectations. Accurate feedback becomes hard to get. Biased data affects design choices. To overcome this, try these strategies: 1. Create a comfortable environment: Make users feel at ease. Comfort encourages honesty. 2. Encourage thinking aloud: Ask users to verbalize thoughts. This reveals their true feelings. 3. Use indirect questions: Avoid direct queries. Indirect questions uncover hidden truths. 4. Observe non-verbal cues: Watch body language. It often tells more than words. 5. Triangulate data: Use multiple data sources. This ensures a complete picture. 6. Foster honest feedback: Build trust with users. Trust leads to genuine responses. 7. Analyze discrepancies: Compare what users say and do. Identify and understand the gaps. 8. Iterate based on findings: Refine your design. Continuous improvement is key. 9. Stay aware of biases: Recognize potential biases. Work to minimize their impact. 10. Keep testing: Regular testing ensures alignment. Stay connected with user needs. By following these steps, designers can bridge the gap between user thoughts and statements. This leads to better products and happier users.

UX design without data is like driving blindfolded. But at the same time, data alone won't tell you the whole story. Here’s how we balance both for stellar results at UN/COMMON: ↓ 1️⃣ Start with well-tested strategies After building hundreds of eCommerce funnels, we’ve seen certain UX approaches consistently perform well. We focus on designs that: -> Keep users moving down the funnel -> Guide them smoothly from home page to checkout …this sets the foundation. 2️⃣ Dig into the numbers Leveraging data platforms like Triple Whale and GA4 allow us to understand consumer behavior in a funnel at a micro level. They let us analyze every step of the user journey. We use them to: -> Find winning patterns -> Spot conversion roadblocks -> Make data-backed UX decisions From home page to the “thank you” page, we leave no stone unturned. 3️⃣ Get inside customers’ heads Numbers tell a story… …but they don’t tell the *whole* story. So, we put ourselves in the shopper’s shoes and ask: -> How does this design make them feel? -> What motivates them to keep clicking? -> Where might they get stuck or confused? To make conversions, we don’t only analyze behavior— We decode the human behind every click. Because at the end of the day, we’re all consumers— We shop. We browse. We buy. …and the best UX taps into that shared experience. 4️⃣ Balance quant and qual Magic happens when we combine hard data with human insight. This dual approach helps us: -> Validate our hunches with numbers -> Explain our numbers with real user feedback The result? ↳ UX that’s both data-driven *and* user-centric 5️⃣ Keep learning and applying Every project and partnership is a chance to get better— We take lessons from each client and apply them to the next. This constant evolution means: -> Our designs keep improving -> Our strategies stay current -> Our results get stronger At UN/COMMON, we’re never satisfied with “good enough.” The bottom line? Great UX is where quantitative analysis intersects with human psychology. It's not just about data or design. It's about decoding human behavior at scale— That's how we create experiences that convert.