Artem Semenov

I have reviewed hundreds of UX case studies in my career. Most of them suffer from: - Lack of polish - Poor scannability - No engaging narrative - Failing to show the impact - Focusing too much on the process The solution is quite simple: 1. Create a story 2. Lead with the impact 3. Share your process at a high level 4. Keep it short and polished; optimize it for scanning 5. Share it in a format that is easily accessible and shareable The following cheatsheet will help you structure your case studies in a concise, engaging, and impactful manner. If you found this useful, consider reposting to your network ♻️ -- Bonus tip: You can't always measure the impact of an initiative, and that's totally ok. Here is what you can do instead: - Show the metrics you expected to move, even if you don't have the numbers - Did it have a strategic impact for the team/business? - Did you test the new flow/feature? Show the results #productdesign #uxdesign #uiux

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40 Kommentare

Great design delivers value to users faster. In my Glaringly Obvious conversation with Thomas Sutton, he made the case that product design teams must clearly define what design quality means, own it, and connect it directly to both user and business value. Without this clarity, design risks being misunderstood, undervalued, and ineffective. Love the conversation, check it out: https://lnkd.in/gVTU5jS5 Thomas’s product design quality framework maps a causal chain: → If design, technology, and support are good, people use the product. → If people use it and it works, you get product outcomes like user benefit, business value, and systemic impact. But here’s where it got me thinking: the speed of that chain matters. → Time to value for users How quickly someone feels the product is usable, useful, and enjoyable. → Time to create value for users   How fast the team can release improvements that increase that value. These two clocks run in parallel. When teams reduce the lag between user benefit and product iteration, design signals become clearer, trust builds faster, and business value compounds sooner. When they drift apart, users wait too long to see progress, and teams stall in endless refinement. In my conversations, here are some of the reasons decisions slow down: 1. Teams wait too long to test ideas, losing momentum. 2. They don’t have fast, clear ways to see what’s working. 3. Design work gets stuck in narrow solutions or tech limitations. 4. Research is incomplete, feedback overlooked, or siloed. 5. Too much refinement, not enough real signals. 6. Confirmation bias clouds decisions. 7.  Leaders struggle to align on user needs vs. business goals. 8. Limited resources stall iteration. That’s where collecting the right signals comes in. In our open framework Glare, we use UX metrics like usability, usefulness, and sentiment to quickly test whether design quality is landing. Helio helps us gather the metrics. They shorten both timelines: showing where users already get value, and where teams must act to deliver more. Building on Thomas’s framework: design quality goes beyond end outcomes. Quality also ensures the path to those outcomes is fast, clear, and measurable. #productdesign #uxmetrics #productdiscovery #uxresearch

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Many times in UX research, we do not have access to large datasets. We work with 5 participants. Or 8 diary entries. Or 12 responses from a hard-to-reach user group. And that is completely fine - until we start treating small datasets like they behave the same way larger ones do. Here are six mistakes that can quietly derail small-sample studies if we are not careful: Mistake 1: Underestimating model complexity Simple models can still be unstable with low N. Be selective. Use theory to guide your variables, and stick to the leanest model that answers the question. Mistake 2: Ignoring identifiability A model is not identifiable if it cannot produce clear, unique estimates. That means you might be modeling noise and not even know it. Simulating known data is a powerful way to catch this early. Mistake 3: Skipping assumption checks Small samples still require you to check assumptions like linearity and residual behavior. Ignoring these can quietly distort your conclusions. Mistake 4: Avoiding simulation Simulating data is not just for theory papers. It helps you stress-test your model before it meets messy real-world inputs. Mistake 5: Misusing Bayesian methods Bayesian tools can be helpful with small data, but only if you are thoughtful about your priors. In small-N settings, your priors can drive the entire result. Mistake 6: Not reporting decisions Every choice - your model structure, your priors, your checks - should be reported. In small-sample research, transparency is your credibility. If this resonates, I recommend checking out this upcoming workshop led by Mohsen Rafiei, Ph.D., which focuses exactly on these kinds of challenges in UX and behavioral research. You can find all the details and sign-up info here: https://lnkd.in/e4gt88cz

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Last month we ran usability testing remotely, step by step, here’s how. We didn’t have a fancy lab or dozens of users. Just a simple process that gave us clear insights: → Kickoff meeting to align on goals and roles → Recruited 5–7 users (a mix of new, regular, different backgrounds & devices) → Wrote short, task-based questions instead of leading ones → Did a dummy run inside the team to catch issues early → Ran 30–40 min structured sessions with facilitator + observer → Debriefed right after each call, then grouped findings into patterns Usability testing doesn’t have to be complicated. A clear process + the right users = insights that matter. What’s your go-to strategy for remote testing? #UX #UsabilityTesting

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The #Value of #UX in less than 5 minutes In the interview, Markus Pirker highlights the core value of user experience as "certainty." He explains that UX provides clarity in what to build, what to fix, and crucially what not to build. By involving real users in the development process, UX helps test assumptions and validate decisions, reducing the risks associated with product development. Markus Pirker, also CEO of Userbrain contrasts UX with guesswork, warning that building without user insight leads to wasted resources and ineffective products. He describes UX as a de-risking process that replaces speculation with informed choices. At Userbrain, this approach is operationalized through a platform that allows companies to observe real users interacting with their products, revealing both pain points and opportunities. In essence, UX transforms product development from guessing into a process driven by real user insights, increasing the likelihood of success. Thanks to all my speakers Ranjeet Tayi, Klaus Hofer, Clemens Lutsch, Verena Seibert-Giller, Stefan Barac, Benedikt Salzbrunn

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