User Experience Research Techniques

🔬 UX Concept Testing. How to test your UX design without spending too much time and effort polishing mock-ups and prototypes ↓ ✅ Concept testing is an early real-world check of design ideas. ✅ It happens before a new product/feature is designed and built. ✅ It helps you find an idea that will meet user and business needs. ✅ Always low-fidelity, always pre-launch, always involves real users. 🚫 Testing, not validation: ideas are not confirmed, but evaluated. ✅ What people think, do, say and feel are often very different things. ✅ You’ll need 5 users per feature or a group of features. ✅ You will discover 85% of usability problems with 5 users. ✅ You will discover 100% of UX problems with 20–40 users. 🚫 Poor surveys are a dangerous, unreliable tool to assess design. 🚫 Never ask users if they prefer one design over the other. ✅ Ask what adjectives or qualities they connect with a design. ✅ Tree testing: ask users to find content in your navigation tree. ✅ Kano model survey: get user’s sentiment about new features. ✅ First impression test: ask to rate a concept against your keywords. ✅ Preference test: ask to pick a concept that better conveys keywords. ✅ Competitive testing: like preference test, but with competitor’s design. ✅ 5-sec test: show for 5 secs, then ask questions to answer from memory. ✅ Monadic testing: segment users, test concepts in-depth per segment. ✅ Concept testing isn’t one-off, but a continuous part of the UX process. In design process, we often speak about “validation” of the new design. Yet as Kara Pernice rightfully noted, the word is confusing and introduces bias. It suggests that we know it works, and are looking for data to prove that. Instead, test, study, watch how people use it, see where the design succeeds and fails. We don’t need polished mock-ups or advanced prototypes to test UX concepts. The earlier you bring your work to actual users, the less time you’ll spend on designing and building a solution that doesn’t meet user needs and doesn’t have a market fit. And that’s where concept testing can be extremely valuable. Useful resources: Concept Testing 101, by Jenny L. https://lnkd.in/egAiKreK A Guide To Concept Testing in UX, by Maze https://lnkd.in/eawUR-AM Concept Testing In Product Design, by Victor Yocco, PhD https://lnkd.in/egs-cyap How To Test A Design Concept For Effectiveness, by Paul Boag https://lnkd.in/e7wre6E4 The Perfect UX Research Midway Method, by Gabriella Campagna Lanning https://lnkd.in/e-iA3Wkn Don’t “Validate” Designs; Test Them, by Kara Pernice https://lnkd.in/eeHhG77j UX Research Methods Cheat Sheet, by Allison Grayce Marshall https://lnkd.in/eyKW8nSu #ux #testing

Somebody just stopped pursuing a new product idea because of results from a consumer panel. It was a good idea—a fresh take on an everyday product. The product concept was presented to the panel alongside a more conventional version of the product. Scores for the new product were just so-so. Good-bye, new product idea. It’s a classic example of the waste that goes into new product development. Months or even years are spent perfecting the idea, and then the concept is presented to consumers in an artificial setting and—BAM!—it passes or fails. Does it really have to be so binary? It’s hard to learn very much from testing on a binary basis. You can learn a lot more by testing several versions of the concept. Maybe each version emphasizes a different benefit. Or solves a different problem. Or maybe each version has radically different branding. The more you test, the more you learn. You might learn, for example, that a particular benefit of the new product concept wildly outperforms other benefits with an audience you didn’t expect. Maybe this leads you to redesign your product and open up a new, incremental niche market. Binary testing feels very satisfying: a new product concept either works or it doesn’t. But the emotional need for the closure that binary testing delivers can kill good ideas too early in the development process. Instead, testing variations in parallel opens up pathways for new products that weren’t initially obvious. It’s a much better way to assess the potential of a new product idea. #demandvalidation #testing #marketresearch

Most Excel users stop at formulas and PivotTables. But that’s just the surface. Would you like to stand out from the crowd? You need to start thinking like an analyst. Here are 4 data analysis techniques that will take your Excel skills to the next level. Just to be clear, PivotTables are great for summarizing data. But they're limited in helping you analyze it. Here's why. Data tables, including PivotTables, are good at two things: Looking up exact values. Comparing exact values. Quite frankly, this is more reporting than analysis. 1) Visual Analysis > Data Tables Tables summarize. Charts reveal. Visuals like: Histograms (for distributions) Scatter plots (for relationships) Line charts (for trends) ...make patterns jump out. Good luck seeing these patterns in a monster PivotTable. Instead, PivotTables feed your charts. 2) RFM Analysis: This is a simple but powerful analysis technique to evaluate customers: (R)ecency: How recently they purchased. (F)requency: How often they purchase. (M)onetary: How much they spend. RFM analysis is super simple to implement in Excel. **AND** It's not just for customers. At its core, RFM analysis is about analyzing data based on behaviors. You can define the analysis however you would like. Take healthcare as an example. Analyzing patients: (A)ge (B)lood pressure (W)eight (E)xercise minutes per week The possibilities are endless! 3) Cluster Analysis Sometimes, patterns aren’t apparent until you group the data. Two examples: Segment users by behavior Classify patients by characteristics Start with a scatter plot of two columns. Look for any clusters. Then, figure out what defines each cluster. Better yet... Use Python in Excel for cluster analysis. Python in Excel is included in Microsoft 365 subscriptions. It's your gateway to battle-tested analytics like k-means clustering. This will allow you to scale to using many columns to find hidden patterns. It's the future of Excel. 4) Logistic Regression This one’s for when you want to predict something like yes/no, true/false, approve/deny, etc. It helps answer questions like: Approve this application? Will the customer churn? Is this claim fraudulent? You can implement logistic regression using Solver. Better yet... Use Python in Excel. People have implemented logistic regression using Solver for years. But here's the problem. It's error-prone and doesn't scale. Python in Excel eliminates these problems and gives you way more insights. It's the future of Excel.

Over-structuring a user interview can kill organic discovery. While planning a user interview, keep this balance in mind: 70% structure, 30% exploration. 📐70% is a structured framework Build a discussion guide with clear objectives. Define your must-have insights. Map your key questions. If you’re researching a checkout flow, plan questions about payment methods, form fields, and error states. 📐30% is where the magic happens Leave a little room for the unexpected. A user mentions they switch between mobile and desktop mid-checkout? Follow that thread. That passing comment about why they never use feature X? Dig deeper. It could lead to a breakthrough insight! 💡Pro tip: Start with a 60-minute timer, but only plan for 45. Keep the extra 15 minutes for spontaneous tangents. Have a list of probing questions ready, but choose your own adventure as it happens. Here’s a useful discussion guide template to bookmark: https://lnkd.in/dXcZqJDY I send a newsletter out every fortnight filled with best-in-the-biz tips for researchers. Don’t miss out, sign up here: https://lnkd.in/daufT7SJ

Ever spent months building a product, only to realize no one’s willing to pay for it? I’ve seen this happen more times than I’d like to admit—especially with first-time tech founders. One big reason? They didn’t talk to enough customers/users before building their solution. In some cases, they didn’t talk to anyone at all! Trust me, skipping these interviews is like flying blind—it rarely ends well. Building something people actually want starts here. Here’s what I’ve learned about doing user interviews effectively:   𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 Focus on understanding, not pitching. Speak less. Listen more. Respect their time—15-20 minutes is enough. Ask open-ended questions to dig deeper. Find out if it’s a real pain point, not just a "nice-to-have." 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗔𝘀𝗸 What’s the hardest part of this problem? When did it last happen? What caused it? How did you try solving it? Did it work? Why was it so difficult to address? What don’t you love about existing solutions? 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗡𝗼𝘁 𝘁𝗼 𝗔𝘀𝗸 "Would you buy this if I built it?" (It’s hypothetical and leads to false positives.) "Do you think this is a good idea?" (People want to be polite and will often say yes.) "Would you pay X amount for this?" (Pricing feedback without context isn’t reliable.) The goal is to uncover the truth, not get the answers you want to hear! #startups #startupindia #incubator #management #founder #uservalidation

Rapid concept testing provides design metrics to make faster decisions. We recently wrapped up a 12-week design cycle with a customer, using Helio to collect 37,000 answers from 2000+ participants across 44 concept areas. Working with a stakeholder group of 10+ people, we created incredible velocity. The key to achieving this scale is understanding how we want the design to drive the business. We build confidence in the lift as a leading indicator by aligning concepts with a design metric. Multivariate testing provides us with a baseline for comparison. How does this work? First, we define the KPIs we want to measure on the live site. Then, we identify leading indicators that correlate with these KPIs to give us a sense of potential lift using design surveys. KPIs ↳ KPIs provide quantifiable measures of performance and impact, allowing for objective assessment and comparison over time. Leading Indicators ↳ Leading indicators offer early signals about the concept's potential success or issues, guiding necessary adjustments before full-scale implementation. A single survey test can't answer every design question, but using many questions—in this customer example, over 3,700—gives us strong signals. Here are a few design metrics we use to drive design decisions. Often, we combine these indicators to create a compelling signal. → Comprehension → Desirability → Viability → Usability → Sentiment → Response Time → Feeling → Reaction The future of design relies on fast research and easy access to audiences. #productdesign #productdiscovery #userresearch #uxresearch

Why do some qualitative studies generate groundbreaking insights while others barely scratch the surface? The secret is not in the data collected, but in matching your methodology to your research goals. The 5 qualitative research methods nobody talks about: 1. Phenomenology • Perfect for understanding perceptions • Uses deep interview analysis • Captures lived experiences 2. Ethnography • Based on extended fieldwork • Documents cultural patterns • Gives insider perspective 3. Narrative Inquiry • Uses conversations & artifacts • Finds patterns in experiences • Tells people's stories 4. Case Study • Answers specific questions • Uses multiple data sources • Creates rich context 5. Grounded Theory • Perfect for unexplored topics • Analyzes data continuously • Builds new theories Pick your method based on your goal: → Want experiences? Use phenomenology → Need cultural insights? Try ethnography → Looking for stories? Go narrative → Seeking answers? Case study works → Building theory? Grounded theory fits Most researchers fail because they pick the wrong method for their research question. The right method = better research. 🗞️ Join 7,278+ researchers on my weekly newsletter: https://lnkd.in/e4HfhmrH P.S. Do you check method-research-question fit?

Grappling with selecting the most appropriate statistical methods? Fear not, for I bring you a comprehensive guide to help you navigate this intricate landscape. Below are a set of 4 tables that serve as a beacon for those seeking to unlock the secrets hidden within their data. Table 1: Parametric and Nonparametric Methods The table contrasts the parametric and nonparametric methods for various statistical analyses. From descriptive statistics to regression models, it covers a wide range of techniques, ensuring you have the right tools for the job, whether your data follows a normal distribution or not. Table 2: Comparing Proportions Dealing with categorical data? This table has got you covered. It outlines the statistical methods to compare proportions, unveiling the secrets of association between variables, changes in proportions, and comparisons between groups. Table 3 & 4: Semi-parametric and Non-parametric Methods From logistic regression to survival analysis, diagnostic accuracy, and agreement between diagnostic methods, it equips you with the knowledge to tackle even the most complex data scenarios. With these tables at your disposal, you'll be armed with the knowledge to make informed decisions, ensuring your statistical analyses are robust, reliable, and tailored to your specific research needs. The key to successful data analysis lies in understanding the assumptions and conditions of each statistical method. Embrace these tables as your guide, and let the power of statistical analysis illuminate your path to insightful discoveries. PS: What statistical methods have you found particularly valuable in your field or projects? Share in the comments below!

This guide is an essential resource for anyone engaged in qualitative research, particularly in fields such as public health, international development, and social sciences. It offers a comprehensive approach to qualitative data collection, emphasizing practical application while maintaining methodological rigor. The guide highlights the importance of capturing rich, context-specific data to understand the complex social, cultural, and behavioral factors influencing various issues. Structured to support both novice and experienced researchers, this field guide provides step-by-step instructions for methods such as participant observation, in-depth interviews, and focus group discussions. It also includes modules on ethical guidelines, data documentation, and management, ensuring the integrity and quality of the research process. Through case studies, examples, and exercises, it fosters an interactive learning experience, equipping researchers to handle real-world challenges effectively. For professionals and organizations aiming to develop robust, ethically sound, and culturally sensitive research frameworks, this guide is an indispensable tool. It bridges the gap between theoretical knowledge and practical implementation, empowering users to produce meaningful and impactful insights that drive informed decision-making and transformative outcomes.

You held a focus group. People chatted. You took notes. But was it actually good data? This practical guide shows you how to design, run, and analyse focus groups that go beyond ‘talking’ and deliver real insights. What You’ll Learn from This Guide ✔️ When and why to use focus groups Especially helpful for understanding human–environment dynamics, with lower time and resource demands than surveys. ✔️ How to pair surveys and focus groups effectively Learn three ways to use them together for richer, more actionable data. ✔️ What can go wrong—and how to fix it Avoid common pitfalls like groupthink, oversharing, and power imbalance with smart facilitation techniques. ✔️ Participant selection done right Segmentation tips, ideal group sizes, and how to encourage honest, balanced contributions. ✔️ Writing better questions A 6-step process for crafting open-ended, non-leading prompts that surface meaningful responses. ✔️ Planning the session From picking a neutral venue to crafting a detailed process agenda. ✔️ Data analysis you can actually use A clear walkthrough on identifying themes, patterns, and meaning—with a bonus qualitative checklist. #FocusGroup 🔔 Follow me for similar content