Tips for Enhancing User Research Practices

Your research findings are useless if they don't drive decisions. After watching countless brilliant insights disappear into the void, I developed 5 practical templates I use to transform research into action: 1. Decision-Driven Journey Map Standard journey maps look nice but often collect dust. My Decision-Driven Journey Map directly connects user pain points to specific product decisions with clear ownership. Key components: - User journey stages with actions - Pain points with severity ratings (1-5) - Required product decisions for each pain - Decision owner assignment - Implementation timeline This structure creates immediate accountability and turns abstract user problems into concrete action items. 2. Stakeholder Belief Audit Workshop Many product decisions happen based on untested assumptions. This workshop template helps you document and systematically test stakeholder beliefs about users. The four-step process: - Document stakeholder beliefs + confidence level - Prioritize which beliefs to test (impact vs. confidence) - Select appropriate testing methods - Create an action plan with owners and timelines When stakeholders participate in this process, they're far more likely to act on the results. 3. Insight-Action Workshop Guide Research without decisions is just expensive trivia. This workshop template provides a structured 90-minute framework to turn insights into product decisions. Workshop flow: - Research recap (15min) - Insight mapping (15min) - Decision matrix (15min) - Action planning (30min) - Wrap-up and commitments (15min) The decision matrix helps prioritize actions based on user value and implementation effort, ensuring resources are allocated effectively. 4. Five-Minute Video Insights Stakeholders rarely read full research reports. These bite-sized video templates drive decisions better than documents by making insights impossible to ignore. Video structure: - 30 sec: Key finding - 3 min: Supporting user clips - 1 min: Implications - 30 sec: Recommended next steps Pro tip: Create a library of these videos organized by product area for easy reference during planning sessions. 5. Progressive Disclosure Testing Protocol Standard usability testing tries to cover too much. This protocol focuses on how users process information over time to reveal deeper UX issues. Testing phases: - First 5-second impression - Initial scanning behavior - First meaningful action - Information discovery pattern - Task completion approach This approach reveals how users actually build mental models of your product, leading to more impactful interface decisions. Stop letting your hard-earned research insights collect dust. I’m dropping the first 3 templates below, & I’d love to hear which decision-making hurdle is currently blocking your research from making an impact! (The data in the templates is just an example, let me know in the comments or message me if you’d like the blank versions).

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.

Drawing from years of my experience designing surveys for my academic projects, clients, along with teaching research methods and Human-Computer Interaction, I've consolidated these insights into this comprehensive guideline. Introducing the Layered Survey Framework, designed to unlock richer, more actionable insights by respecting the nuances of human cognition. This framework (https://lnkd.in/enQCXXnb) re-imagines survey design as a therapeutic session: you don't start with profound truths, but gently guide the respondent through layers of their experience. This isn't just an analogy; it's a functional design model where each phase maps to a known stage of emotional readiness, mirroring how people naturally recall and articulate complex experiences. The journey begins by establishing context, grounding users in their specific experience with simple, memory-activating questions, recognizing that asking "why were you frustrated?" prematurely, without cognitive preparation, yields only vague or speculative responses. Next, the framework moves to surfacing emotions, gently probing feelings tied to those activated memories, tapping into emotional salience. Following that, it focuses on uncovering mental models, guiding users to interpret "what happened and why" and revealing their underlying assumptions. Only after this structured progression does it proceed to capturing actionable insights, where satisfaction ratings and prioritization tasks, asked at the right cognitive moment, yield data that's far more specific, grounded, and truly valuable. This holistic approach ensures you ask the right questions at the right cognitive moment, fundamentally transforming your ability to understand customer minds. Remember, even the most advanced analytics tools can't compensate for fundamentally misaligned questions. Ready to transform your survey design and unlock deeper customer understanding? Read the full guide here: https://lnkd.in/enQCXXnb #UXResearch #SurveyDesign #CognitivePsychology #CustomerInsights #UserExperience #DataQuality

During a Usability test, noticed that sometimes users tend to put on their 'best performance’ when they're being watched? You're likely witnessing the Hawthorne effect in action! Happens with us as well. When working from home, during meetings, you're more attentive, nodding more, and sitting up straighter, not just because you're engaged, but because you're aware that your colleagues can see you. This subtle shift in your behaviour due to the awareness of being observed is a daily manifestation of the Observation bias or Hawthorne effect. In the context of UX studies, participants often alter their behaviour because they know they're being observed. They might persist through long loading times or navigate more patiently, not because that's their natural behaviour, but to meet what they perceive are the expectations of the researcher. This phenomenon can yield misleading data, painting a rosier picture of user satisfaction and interaction than is true. When it comes to UX research, this effect can skew results because participants may alter how they interact with a product under observation. Here are some strategies to mitigate this bias in UX research: 🤝 Building Rapport:  Setting a casual tone from the start can also help, engaging in small talk to ease participants into the testing environment and subtly guiding them without being overly friendly. 🎯 Design Realistic Scenarios: Create tasks that reflect typical use cases to ensure participants' actions are as natural as possible.    🗣 Ease Into Testing: Use casual conversation to make participants comfortable and clarify that the session is informal and observational. 💡Set Clear Expectations: Tell participants that their natural behavior is what's needed, and that there's no right or wrong way to navigate the tasks. ✅ Value Honesty Over Perfection: Reinforce that the study aims to find design flaws, not user flaws, and that honest feedback is crucial. 🛑 Remind Them It's Not a Test: If participants apologise for mistakes, remind them that they're helping identify areas for improvement, not being graded. So the next time you're observing a test session and the participant seems to channel their inner tech wizard, remember—it might just be the Hawthorne effect rather than a sudden surge in digital prowess. Unmasking this 'performance' is key to genuine insights, because in the end, we're designing for humans, not stage actors. #uxresearch #uxtips #uxcommunity #ux

Your competition is stealing your customers right now because they understand one thing you don't. Understanding your customers fully = building products people actually want to use. That's the goal. To get there, you can either: - Rely on your gut instinct and assumptions. - Actually learn what your customers need, think, and want. Just carry out these daily tasks: 1. Talk to your customers directly -  ↳ Give them easy ways to provide feedback through uninstall surveys, reviews, or customer support channels.  ↳ Reach out to power users and start conversations. Many customers actively want to help improve your product. 2. Make feedback frictionless -  ↳ Customers won't go out of their way to give feedback, so reduce friction with quick surveys after key interactions, in-app prompts for feature requests, open-ended responses in support tickets, and direct access to a real person. 3. Observe how customers actually use your product -  ↳ Data tells a different story than surveys.  ↳ Use analytics to see what features people use most, where they drop off during onboarding, and what actions lead to churn vs. retention. 4. Test and iterate based on customer input -  ↳ When feedback patterns emerge, act on them.  ↳ If feature requests keep coming up, prioritize them.  ↳ If customers are confused about a function, improve the UX. 5. Build relationships with your best customers -  ↳ Your most engaged users can become your best resource.  ↳ Keep in touch with them, get their input on new features, and make them feel heard. I had a user who loved our product so much that they actively shared feedback and even tested features before launch. They'll hop on a Zoom call with just 15 minutes notice. Now all you have to do is commit to customer research, and you'll build products people actually want to use. As you progress, incorporate: - Regular customer interviews - User testing sessions - Data analysis routines It's more effective than building in isolation based on assumptions. ♻️ Repost if you agree ➕ Follow me Blaine Vess for more

People often say what they think they should say. I had a great exchange with 👋 Brandon Spencer, who highlighted the challenges of using qualitative user research. He suggested that qual responses are helpful, but you have to read between the lines more than you do when watching what they do. People often say what they think they should be saying and do what they naturally would. I agree. Based on my digital experiences, there are several reasons for this behavior. People start with what they know or feel, filtered by their long-term memory. Social bias ↳ People often say what they think they should be saying because they want to present themselves positively, especially in social or evaluative situations. Jakob's Law ↳ Users spend most of their time on other sites, meaning they speak to your site/app like the sites they already know. Resolving these issues in UX research requires a multi-faceted approach that considers what users say (user wants) and what they do (user needs) while accounting for biases and user expectations. Here’s how we tackle these issues: 1. Combine qualitative and quantitative research We use Helio to pull qualitative insights to understand the "why" behind user behavior but validate these insights with quantitative data (e.g., structured behavioral questions). This helps to balance what users say with what they do. 2. Test baselines with your competitors Compare your design with common patterns with which users are familiar. Knowing this information reduces cognitive load and makes it easier for users to interact naturally with your site on common tasks. 3. Allow anonymity  Allow users to provide feedback anonymously to reduce the pressure to present themselves positively. Helio automatically does this while still creating targeted audiences. We also don’t do video. This can lead to more honest and authentic responses. 4. Neutral questioning We frame questions to reduce the likelihood of leading or socially desirable answers. For example, ask open-ended questions that don’t imply a “right” answer. 5. Natural settings Engage with users in their natural environment and devices to observe their real behavior and reduce the influence of social bias. Helio is a remote platform, so people can respond wherever they want. The last thing we have found is that by asking more in-depth questions and increasing participants, you can gain stronger insights by cross-referencing data. → Deeper: When users give expected or socially desirable answers, ask follow-up questions to explore their true thoughts and behaviors. → Wider: Expand your sample size (we test with 100 participants) and keep testing regularly. We gather 10,000 customer answers each month, which helps create a broader and more reliable data set. Achieving a more accurate and complete understanding of user behavior is possible, leading to better design decisions. #productdesign #productdiscovery #userresearch #uxresearch

When you’re sharing your research recommendations, skip the “How Might We’s.” When I started out as a UXR, I thought my role was to conduct the study and share the facts. Anything more was overstepping. I’ve since learned that my role as a researcher is to take the insights from the study and turn them into action. Stakeholders request research when they don’t know which way to go or the next step to take. It’s not valuable to conduct all this work and then leave the team with more questions like “how might we resolve this?” Wasn’t that the point of the research? I’m not suggesting that researchers just hand out solutions, but it’s important to have a point of view on your work and what happens next. If all participants struggled to discover a key element on the page, skip the recommendation of “how might we improve discoverability of feature X” and cut to the chase — “Introduce feature X where users are most likely to discover it, such as place A, B, or C.” A couple tricks I like to use when framing my recommendations: 💡If I was the PM or primary decision maker on this project, what decisions would I make based on the findings from the research? How confident would I be in those decisions? The most confident decisions become your recommendations. 💡(Counterintuitively) Start with 'How Might We' or 'Consider...' in recommendations, then later remove them and revise for clarity and strength. This turns “How Might We improve the comprehension of the value prop” into “Improve the comprehension of the value prop…” You can fine tune the language, but now your recommendation feels much stronger, something the team should action on and not just “consider.” Not every recommendation may have a high degree of confidence or clear next steps (sometimes the recommendation is to do more research, because we still don’t know what to do). But for those that you’re confident in, your recommendations should sound like it.

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!