Data-Driven UX Design

📌 Dashboard Design Principles 101 (What Every Company Needs to Know) Dashboards are one of the most powerful tools we have to make data useful. When they are built right, they give leaders and teams: ⤷ A clear view of performance ⤷ Highlight where action is needed ⤷ And ultimately enable better decisions. But here’s the reality: most dashboards fail to deliver on this promise. And it’s not because the data is wrong or the tool is limited. They fail because of poor design choices that make them confusing, overwhelming, or simply irrelevant to the people who are supposed to use them. If you want to build dashboards that actually drive adoption and influence decisions, there are three design principles you need to follow 1️⃣ 𝐃𝐨 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐁𝐞𝐟𝐨𝐫𝐞 𝐘𝐨𝐮 𝐃𝐞𝐬𝐢𝐠𝐧 Every dashboard starts with a purpose. Without it, you’re just arranging charts on a canvas. Ask yourself simple but critical questions: → Who exactly will use this dashboard? → What business decisions should it support? → Which insights and KPIs are truly essential? This is where most projects go wrong. Instead of focusing on the end user, dashboards get built around the data that happens to be "available" or the KPIs that someone thought might look good. The result? A nice-looking report that nobody actually uses. A strong dashboard is user-centric and decision-driven. It exists to answer questions and reduce uncertainty. Not to display every data point you’ve collected (a very common mistake). 2️⃣ 𝐆𝐮𝐢𝐝𝐞 𝐭𝐡𝐞 𝐔𝐬𝐞𝐫 𝐰𝐢𝐭𝐡 𝐚 𝐂𝐥𝐞𝐚𝐫 𝐅𝐥𝐨𝐰 Good design is invisible. A user should glance at the dashboard and instantly know where to focus. That means creating a logical flow of information that follows natural reading patterns (top left to bottom right) and keep the number of visuals under control (5 to 7 is usually the sweet spot) The goal is not to impress people with how much data you can show. It’s to guide them toward the insight that matters most. If you want to go deeper, I highly recommend exploring Nicholas Lea-Trengrouse’s work on UX/UI principles for dashboard design. 3️⃣ 𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐒𝐭𝐨𝐫𝐲 Data visualization is not decoration. It’s communication. The chart type you choose can completely change how your data is interpreted. The wrong choice creates confusion. The right choice makes the insight obvious, even for someone seeing it for the first time. Always think in terms of clarity: does this chart highlight the story I want the data to tell? At the end of the day, dashboards are about clarity, usability, and decision-making. If a dashboard doesn’t tell a story, guide the user, and present insights in a way that is easy to interpret, it will fail. No matter how advanced your tool or how clean your data. 📥 Save this framework. Share it with your team. And keep it in mind before your next build. #BusinessIntelligence #DashboardDesign

If you're a UX researcher working with open-ended surveys, interviews, or usability session notes, you probably know the challenge: qualitative data is rich - but messy. Traditional coding is time-consuming, sentiment tools feel shallow, and it's easy to miss the deeper patterns hiding in user feedback. These days, we're seeing new ways to scale thematic analysis without losing nuance. These aren’t just tweaks to old methods - they offer genuinely better ways to understand what users are saying and feeling. Emotion-based sentiment analysis moves past generic “positive” or “negative” tags. It surfaces real emotional signals (like frustration, confusion, delight, or relief) that help explain user behaviors such as feature abandonment or repeated errors. Theme co-occurrence heatmaps go beyond listing top issues and show how problems cluster together, helping you trace root causes and map out entire UX pain chains. Topic modeling, especially using LDA, automatically identifies recurring themes without needing predefined categories - perfect for processing hundreds of open-ended survey responses fast. And MDS (multidimensional scaling) lets you visualize how similar or different users are in how they think or speak, making it easy to spot shared mindsets, outliers, or cohort patterns. These methods are a game-changer. They don’t replace deep research, they make it faster, clearer, and more actionable. I’ve been building these into my own workflow using R, and they’ve made a big difference in how I approach qualitative data. If you're working in UX research or service design and want to level up your analysis, these are worth trying.

Most UX teams have been there: standing in front of a wall of sticky notes, surrounded by user quotes and caffeine, trying to decide if “Goal Oriented Greg” and “Curious Carla” are genuinely different people or just the same imaginary user with better handwriting. Persona discovery sessions like this often feel productive, the colors, the discussions, the post-its forming patterns, but deep down, we know something is off... The process is usually more art than science, more consensus building than discovery. It produces personas that sound nice in presentations but rarely hold up when real users start behaving unpredictably. Good news?! There is a more rigorous way to approach this, one that turns persona creation from a creative exercise into an analytical process grounded in evidence. Instead of guessing who your users are, you can identify them empirically by examining their real behaviors, motivations, and characteristics across your datasets. This is where clustering analysis becomes invaluable, allowing your data to uncover the story of your users on its own. Clustering uses statistical algorithms to uncover patterns and similarities across multiple dimensions of user data, revealing natural groups that exist beneath the surface. These are not personas invented in a meeting; they are personas discovered in the data. Here is how it works in practice. You begin by gathering rich, multidimensional data, including behavioral metrics. After cleaning and preparing your data, you apply a clustering algorithm such as K Means, Hierarchical Clustering, or Gaussian Mixture Models. These methods analyze the combined patterns across all features and group users who are statistically similar into clusters. Each cluster represents a group of people who share distinctive traits, perhaps they are highly efficient but disengaged, or slower but deeply curious. From there, you interpret and label these clusters in human terms. The data gives you the structure, and your UX insight gives it meaning. You might visualize the results, examine which variables most differentiate each group, and build out personas that reflect the real diversity within your audience. These personas are no longer fictional composites; they are data backed archetypes that show how meaningful subgroups actually behave, think, and feel. The benefits are substantial. Clustering eliminates much of the bias that comes from relying on small samples or internal intuition. It exposes hidden user types that might never emerge from interviews alone, such as a quiet but influential group of users whose needs are consistently overlooked. It also creates alignment across teams because the evidence is transparent and reproducible. When you present personas derived from clustering, you can trace every insight back to data, not opinion. #PersonaDiscovery #UXResearch #DataDrivenDesign #CustomerSegmentation #ProductStrategy #UserExperience #QuantitativeUX

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

Most product failures aren’t engineering failures. They’re empathy failures. Teams ship what they think customers want… …and then wonder why adoption stalls, churn climbs, and the roadmap turns into a graveyard of “nice features.” Here’s the shift that changes everything: Customer-centric design isn’t a UX phase — it’s an operating system. It means building around real user needs, behaviors, and outcomes (not internal opinions). And in the last few years, AI has raised the bar: Customers expect relevance and ease (not generic journeys) Personalization is now table-stakes — but trust is fragile The winners will be the teams who pair speed with human-centered design The customer-centric loop (that actually works) 1) Learn deeply Talk to customers weekly. Mine tickets, reviews, churn reasons, behavior data. 2) Map reality Personas + journeys that expose friction, emotion, and drop-off points. 3) Design for outcomes Less effort. More clarity. Better defaults. Faster “time to value.” 4) Prototype + test fast Small tests beat big debates. 5) Measure + iterate Track experience and behavior (activation, retention, task success, effort). Where AI fits (and where it breaks) Use AI to accelerate: Synthesizing feedback Finding patterns Generating variations and prototypes But design AI like a relationship: Set expectations Provide controls (“undo,” preferences, corrections) Fail gracefully Escalate when confidence is low Customer-centric design is the advantage that compounds. Because when you build what people truly need, growth stops being a fight. Question: What’s one customer insight you learned recently that changed how you build? iQor we take customer centric design to the next level with InsightsIQ, hit me up with questions. #CustomerExperience #ProductManagement #UXDesign #ProductDesign #AI #HumanCenteredDesign #Leadership

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!

🚀 **Defining the UX Process: From Research to Insightful Design!** 🚀 Excited to share my latest presentation, where I walk through a structured, user-centered approach to UX design that I use to uncover insights and drive impactful design decisions. Here’s a quick snapshot of the core steps: 1️⃣ **User Research**: Starting with a survey to gather quantitative data and understand users’ needs, behaviors, and motivations. 2️⃣ **Persona Development**: Transforming survey insights into realistic personas that embody our users’ goals and challenges. 3️⃣ **Customer Journey Mapping**: Visualizing the journey to pinpoint moments of delight and frustration, ensuring we address all user touchpoints. 4️⃣ **Competitive Analysis**: Evaluating competitors to understand the market landscape and identify areas for differentiation. 5️⃣ **Affinity Mapping**: Organizing and synthesizing insights from qualitative data to identify trends and patterns. Each of these steps brings us closer to creating a product experience that’s both user-friendly and impactful. ✨ Check out the presentation on Canva [insert link] for a deeper dive into each phase! Looking forward to hearing your thoughts and connecting with other UX enthusiasts! #UXDesign #UserExperience #CustomerJourney #UXResearch #CompetitiveAnalysis #AffinityMapping #Persona #UXProcess #DesignThinking #LinkedInPresentations #Canva https://lnkd.in/dKaiY2Ky

Atomic UX Research Cheatsheet Turn user data into product decisions that actually drive results Where UX research often breaks down 👇 Teams collect data… But fail to turn it into clear actions That’s where impact is lost Step 1: Experiments Start with the right inputs • User interviews & usability tests • Surveys, reviews, feedback loops • Analytics & behavioral data Capture real user signals, not assumptions Step 2: Facts Document what actually happened • Quotes → What users say • Observations → What users do • Metrics → What data proves  Focus on objective evidence only Step 3: Insight Translate data into understanding • Context → Where the issue happens • Cause → Why users struggle • Effect → What it leads to  Turn information into clear problem clarity Step 4: Recommendation Convert insight into action • Action → What to improve • Audience → Who it impacts • Outcome → Expected result • Measurement → How to track success Make every insight decision-ready Data alone doesn’t improve UX Interpretation does If insights aren’t actionable, They're just noise Experiment → Fact → Insight → Action This is how strong teams: • Reduce friction • Improve usability • Increase conversions Build products users actually understand. 🔄 Repost to share this with your team and network! For next, Follow Subash Chandra for UX strategies that drive growth 

Strong signals bring user needs into focus. Over the years, I’ve worked with many teams that create user personas, giving them names like “Cindy” and saying things like “She needs to find this feature” to guide their design decisions. That’s a good start. But user needs are more complex than a few traits or surface-level goals. They include emotions, behaviors, and deeper motivations that aren’t always visible. That’s why we’re building Glare, our open framework for data-informed design. We've learned a lot using Helio. It helps teams create clear, measurable signals around user needs. UX metrics help turn user needs into real data: → What users think → What users do → What users feel → What users say When you define the right audience traits and pick the helpful research methods, you can turn vague assumptions into specific, actionable signals. Let’s take a common persona example: Your team says, “Cindy can’t find the new dashboard feature.” Instead of stopping there, create signals using UX metrics to define usefulness better: → Attitudinal Metrics (how Cindy feels) Usefulness ↳ 42% of users say the dashboard doesn’t help them complete their tasks Sentiment ↳ Users overwhelmingly selected: Confused, Frustrated, Overwhelmed Only 12% chose Clear or Confident Post-Task Satisfaction ↳ 52% of people are satisfied after completing key actions → Behavioral Metrics (what Cindy does) Frequency ↳ Only 18% of users revisit the dashboard weekly, down from 35% last quarter → Performance Metrics (how the product supports Cindy) Helpfulness ↳ 60% of users say they needed help materials to complete a task, suggesting the experience is unclear With UX data like this, your team can stop guessing and start aligning around the real needs of users. UX metrics turn assumptions into signals… leading to better product decisions. Reach out to me if you want to learn how to incorporate UX metrics into your team workflows. #productdesign #productdiscovery #userresearch #uxresearch

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.