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

💡How to measure UX research impact Research is an integral part of the design process. It's nearly impossible to imagine a product design team that doesn't practice UX research. Yet, organizations often don't measure how UX research impacts product design. The framework proposed by Karin den Bouwmeester (https://lnkd.in/diA3fDS9) helps to measure UX research impact across 3 levels: 1️⃣ Outcome level Measures the impact on the customer and business outcome. This level is evident since we all want to design user-centered products that align with business goals. User experience: Measure how UX research impacts the user experience. Metrics:  ✔ Task success rate ✔ User engagement (like time spent on a page) ✔ User satisfaction score Business: Determine how UX research influences business performance (impact on business bottom line). Metrics:  ✔ Retention (i.e., returning customers) ✔ Transaction rate ✔ Customer satisfaction score 2️⃣ Organizational level Direct impact research makes on organizational culture. Ideally, research should be baked into the heart of org culture, driving innovation and constant improvement. Organizational learning Evaluate how UX research is influencing strategy and product decisions. Metrics:  ✔ Number of product changes based on research insights ✔ Frequency of referencing research data Engagement: Measure the involvement of different roles within the organization in the research process. Metrics:  ✔ Number of research requests ✔ Number of observers at UX research studies ✔ Stakeholder satisfaction rate 3️⃣ UX Research level Finally, UX research level measures how much effort the org invests in the research itself: what research activities it's doing, how frequently and how many people are involved in it. Structure Assess how formalized the UX research process is within the organization Metrics:  ✔ Dedicated researchers per team ✔ Acceptance of research procedures ✔ Use of research templates. Reach: Measure the extent to which UX research activities are applied across the organization. Metrics:  ✔ Number of research studies per year ✔ Ratio of discovery research to evaluative research 📺 How to measure UX: https://lnkd.in/dhkwy_jN #UX #research #design #productdesign #uxresearch #designresearch

The top challenge I hear from UX researchers who want to quantify their impact 👇 'I don't have access to business and product metrics.' This can be for a few reasons: 1. Your organisation doesn't facilitate access to product/business data. 2. It takes a long time for your work to influence those metrics. 3. You've left the organisation. Whatever the reason, we need strategies to show our impact even when we don't have access to those metrics. Here are 6 ways UX researchers can quantify impact without access to product metrics (with examples in the carousel!) 1. Quantify the number of customers reached by your work 2. Quantify the reach of your work internally 3. Quantify your research activities 4. Quantify your thought leadership 5. Quantify your training efforts 6. Quantify your process improvements How else can we quantify UXR impact without using product or business metrics?

“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

The UX Workflow 𝘐𝘴𝘯’𝘵 𝘓𝘪𝘯𝘦𝘢𝘳. It’s a 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗟𝗼𝗼𝗽 Many people think UX design starts with wireframes and ends with UI screens. In reality, strong user experiences are built through a 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 and 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵-𝗱𝗿𝗶𝘃𝗲𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄. – 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 🔍 𝗔𝗻𝗮𝗹𝘆𝘀𝗲 – 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗕𝗲𝗳𝗼𝗿𝗲 𝗬𝗼𝘂 𝗖𝗿𝗲𝗮𝘁𝗲 This stage focuses on learning the problem deeply. ✔️ Stakeholder Interviews – Align business goals expectations and success metrics ✔️ User Interviews – Understand real user behaviour pain points and motivations ✔️ Field Studies – Observe how users interact with products in real environments Outcome: Clear problem definition and validated insights 🎨 𝗗𝗲𝘀𝗶𝗴𝗻 – 𝗧𝘂𝗿𝗻 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗜𝗻𝘁𝗼 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 Once the research is clear, solution building begins. ✔️ User Journey Mapping – Visualize user emotions actions and touchpoints ✔️ User Stories – Translate needs into actionable design requirements ✔️ Affinity Mapping – Organize research insights into patterns ✔️ User Flow Creation – Define how users move across the product Outcome: Structured experience blueprint ready for visualization 🧪 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 – 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗕𝗲𝗳𝗼𝗿𝗲 𝗟𝗮𝘂𝗻𝗰𝗵 Design without testing is guessing. ✔️ Usability Testing – Identify friction and improve usability ✔️ Analytics – Track behaviour and performance metrics ✔️ Surveys – Collect qualitative feedback from users ✔️ Wireframing Iterations – Refine structure based on insights Outcome: Data-backed design improvements and user-validated experiences 💡 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: UX is not a one-time process. It’s a 𝘤𝘰𝘯𝘵𝘪𝘯𝘶𝘰𝘶𝘴 𝘤𝘺𝘤𝘭𝘦 𝘰𝘧 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘥𝘦𝘴𝘪𝘨𝘯𝘪𝘯𝘨 𝘵𝘦𝘴𝘵𝘪𝘯𝘨 𝘢𝘯𝘥 𝘪𝘮𝘱𝘳𝘰𝘷𝘪𝘯𝘨. Great products are not built on assumptions. They are built on understanding users deeply and validating solutions consistently. How does your team approach UX workflow?  Do you follow a structured process or adapt based on project needs? #UXDesign #UserExperience #ProductDesign #DesignProcess #UserResearch #UsabilityTesting #DesignThinking #UXStrategy #DigitalProductDesign #UXWorkflow

When reporting on your impact as a UX Researcher, here are the best → worst metrics to tie your work to: 𝟭. 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 Every company is chasing revenue growth. This is especially true in tech. Tying your work to new (or retained) revenue is the strongest way to show the value that you’re bringing to the organization and make the case for leaders to invest more in research. Examples: - Research insights → new pricing tier(s) → $X - Research insights → X changes to CSM playbook → Y% reduction in churn → $Z 𝟮. 𝗞𝗲𝘆 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 This might not be possible for many UXRs, but if you can, showing how your work contributed to key decisions (especially if those decisions affect dozens or hundreds of employees) is another way to stand out. Examples: - Research insights → new ideal customer profile → X changes across Sales / Marketing / Product affecting Y employees' work - Research insights → refined product vision → X changes to the roadmap affecting Y employees' work 𝟯. 𝗡𝗼𝗿𝘁𝗵 𝘀𝘁𝗮𝗿 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 If you can’t directly attribute your work to revenue, that’s ok! The majority of research is too far removed from revenue to measure the value in dollars. The next best thing is to tie your work to core user engagement metrics (e.g. “watch time” for Netflix, “time spent listening” for Spotify). These metrics are north star metrics because they’re strong predictors of future revenue. Examples: - Research insights → X changes to onboarding flow → Y% increase in successfully activated users - Research insights → X new product features → Y% increase in time spent in app 𝟰. 𝗖𝗼𝘀𝘁 𝘀𝗮𝘃𝗶𝗻𝗴𝘀 For tech companies, a dollar saved is usually less exciting than a dollar of new (or retained) revenue. This is because tech companies’ valuations are primarily driven by future revenue growth, not profitability. That being said, cost savings prove that your research is having a real / tangible impact. 𝟱. 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻’𝘁 𝗯𝗲 𝘁𝗿𝗮𝗰𝗲𝗱 𝘁𝗼 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗮𝗯𝗼𝘃𝗲 Hot take: The biggest trap for researchers (and product folks generally) is focusing on user experience improvements that do not clearly lead to more engagement or more revenue. At most companies, it is nearly impossible to justify investments (including research!) solely on the basis of improving the user experience. Reporting on user experience improvements without tying them to any of the metrics above will make your research look like an expendable cost center instead of a critical revenue driver. — TL;DR: Businesses are driven by their top line (revenue) and bottom line (profit). If you want executives to appreciate the impact of (your) research, start aligning your reporting to metrics 1-4 above.

Measuring and showcasing the business impact of UX research can be challenging, but it’s crucial for demonstrating the value we bring. To make this more tangible, I’ve built a dashboard that tracks key metrics and insights to help communicate our contribution to the company’s success. Here’s how I’m breaking it down to show impact for the business: 1. Research impact: We categorize each research project by its level of impact: ➔ Low: Provides valuable insights but doesn’t lead to immediate action. ➔ Medium: Validates or refines existing ideas. ➔ High: Directly influences the product roadmap or business strategy. By tracking these metrics, we can demonstrate how many projects directly contribute to strategic decisions and influence the company’s goals. 2. Tied to business KPIs: We tag each research project with the business goals it aligns with, like CSAT, conversion rates, or task completion rates. This allows us to highlight how research contributes to improving key business metrics. By linking research findings to KPIs, we’re able to quantify the value we’re adding. 3. Project complexity and external costs: We categorize projects by complexity (simple, moderate, complex). This helps us see how often we’ve needed external resources because of a lack of internal capacity. Tracking this allows us to highlight the extra costs of outsourcing, helping make the case for expanding the internal research team to reduce expenses. 4. Denied Projects: We track the number of projects we’ve had to deny due to capacity constraints. This is crucial for showing unmet demand for research, which in turn shows the business the need for more resources. Denied projects highlight how much additional impact we could be making if the research team had more capacity. 5. National vs. International: By tagging whether research projects are national or international, we gain insights into where the company is focusing its efforts. This helps ensure that we’re aligning our research efforts with market priorities and identifying gaps where more international or cross-market research could drive business growth. By creating these dashboards, we’re able to quantify and visualize the impact UX research makes for the business, whether it’s driving decisions, contributing to KPIs, or highlighting the need for more capacity to meet growing demand. How are you showing the business value of UX research in your company?

When I talk with UX researchers and designers, I often hear regression models described as “just another stats test.” In reality, regression is one of the most powerful ways to connect user behavior, design choices, and business outcomes. It is not only a math exercise. It is a method for linking evidence to decisions. Here is why regression matters so much in UX research: 1. Explaining relationships UX data is complex. Task completion time, error rates, satisfaction scores, prior experience, and demographic factors can all influence one another. Regression helps us untangle these influences. For example, does satisfaction decrease because a flow takes too long, or because the interface is confusing? A regression model shows how much each factor contributes to the outcome, giving us explanations that go beyond surface-level observations. 2. Controlling for confounds A major risk in UX research is misattributing cause and effect. Imagine experienced users finishing tasks faster. Is that because of a new design or because of their prior knowledge? Regression allows us to hold prior knowledge constant and see the unique contribution of the design. This ability to separate signal from noise makes regression far more reliable than looking at simple averages or raw correlations. 3. Testing hypotheses UX teams often work with specific hypotheses. For example, “This new onboarding flow will reduce drop-off” or “A clearer button label will increase clicks.” Regression provides a formal way to test these claims. Instead of relying on instinct or anecdotal observations, we can provide evidence that has been statistically checked. This does not mean blindly chasing significance, but it does mean giving structure and rigor to the claims we make. 4. Making predictions Sometimes explanation is not enough. Teams need to forecast outcomes. Regression models allow us to ask practical questions such as: If usability scores increase by one point, how much retention can we expect to gain? Or, if error rates increase by five percent, how much will that reduce satisfaction? These predictive insights help product teams prioritize design work based on the likely size of impact. 5. Quantifying uncertainty and effect sizes Regression also makes us transparent about uncertainty. UX research often involves noisy data, especially when sample sizes are limited. A regression model does not just indicate whether an effect exists. It tells us how strong the effect is and how confident we can be in that estimate. Sharing effect sizes together with confidence or credible intervals builds trust. Stakeholders see that we are not just saying “this works.” We are showing the strength and reliability of our findings. Regression is not an academic luxury. It is a cornerstone of evidence-based UX. It helps us explain what is happening, isolate the effect of design choices, test whether changes are meaningful, forecast future outcomes, and communicate with transparency.

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!