UX Problem Solving Techniques

💡How to frame problems in product design (7-step guide & tools) Framing problems effectively is a critical skill that can influence the quality of design. 1️⃣ Define the context: Start by establishing the context in which the problem exists. Understanding the environment, user demographics, technological constraints, and business objectives will help shape a comprehensive view of the issue. For example, identifying that users struggle with a mobile app while in areas with low connectivity provides a specific context to focus on. 2️⃣ Identify user needs & pain points: Collect and analyze data from user research such as interviews, surveys, usability testing, and field observations. Highlighting user pain points & needs is essential to framing a problem that is relevant. For instance, noting that users feel frustrated when they cannot quickly navigate through a menu can define a clear problem area. 3️⃣ Frame the problem: Techniques like the "Five Whys," which involves asking "why" five times to get to the root cause of a problem, can help uncover deeper insights. Additionally, considering the problem from different stakeholders' perspectives can open up new avenues for solutions. 4️⃣ Articulate the problem as a "How might we" question: Once you've identified a specific user need or a pain point, articulate the problem as an open-ended question that invites creative thinking. For example, "How might we make the menu navigation more intuitive so that users can find what they need with fewer taps?" 5️⃣ Break down large problems: Complex problems can often be overwhelming and difficult to tackle all at once. Break them down into smaller, manageable components that can be addressed individually. For example, a large problem like "improve the mobile app experience" can be broken down into "improving load times," "simplifying user interactions," and "enhancing visual appearance." 6️⃣ Specify constraints and criteria: Define what constraints must be considered, such as technological limitations, budgetary constraints, and time frames. Also, consider what success looks like for solving the problem. Clear criteria help to keep the problem-solving process focused and measurable. 7️⃣ Validate the problem statement: Before moving forward with solving the problem, validate the problem statement with real users and stakeholders to ensure it truly reflects their needs and the business goals. This may involve revisiting user research or conducting additional interviews. 🔨 Tools ✔ Untools: Curated collection of thinking tools and frameworks to help you solve problems (by Adam Amran)   https://untools.co/ ✔ UX Challenges: practical exercises to train yourself in crucial UX skills (by Tommy Geoco) https://lnkd.in/dZvakiJd ✔ Design thinking toolkit (by IBM) https://lnkd.in/dhV95BTf 🖼 Untools by Adam Amran #design #designthinking #productdesign #UX #userexperience #problemsolving #designprocess

Find the shape of your design decision. Fix the constraint, then scale the problem space. Most teams argue about design because they don’t know what kind of UX problem they’re in. Once you can read the UX metric stack, the next decision becomes obvious. In many of my work sessions with customers, we’re trying to make a call using constraints and UX metrics to understand where users actually are. These meetings can be groups of five to ten people, and there’s rarely time for deep analysis. Teams often need orientation to ideate. Not a full answer, but clarity on what the design signals mean and how to move forward. Iteration can always come later. In the moment, people want to know: what does this tell us, and what should we do next? Over time, I started noticing a pattern in how I frame design decisions and recommendations. With a small set of UX metrics in a stack, you can orient a team in about 30 seconds. You can quickly see which problems matter most. This is where design leverage starts. You cannot earn trust if people are not clear on what you are presenting to them. I often sit at the front end of fast, million dollar decisions at ZURB that compound over the life of an initiative. These decisions tend to fall into the same few shapes. Think of UX metrics as four layers that sit on top of each other. 1. Clarity Do people understand what this is and what to do next? 2. Ability Can they do it quickly and without mistakes? 3. Confidence Do they feel safe, in control, and willing to continue? 4. Commitment Do they come back, adopt it, recommend it, and rely on it? Here are the four common shapes of design problems. These patterns show up again and again. Each one tells you what kind of problem you actually have. → Confusion Trap Clarity is low. Everything above it becomes noisy or misleading. Users are not oriented. They guess, hesitate, and click around. The job here is to fix comprehension before touching polish, new features, or conversion tactics. →  Friction Wall Clarity is solid, but ability is low. Time is high, errors increase, and drop-offs appear. Users get it, they just cannot do it. The move is to remove steps, simplify flows, reduce cognitive load, tighten IA, and improve affordances. → Trust Gap Clarity and ability look fine, but confidence is low. Doubt, anxiety, perceived risk. The experience works, but it does not feel safe. This is common in fintech, healthcare, and AI. Teams need to focus on building reassurance. Transparency, guardrails, explanations, error prevention, and clear feedback about what happens next. → Adoption Leak The top of the stack is healthy, but commitment is weak. The design works, but there is no habit. The move is to focus on the value loop. Activation timing, defaults, reminders, integrations, onboarding sequence, and ongoing use cases. Finding the shape of your decision helps orient everyone on a team. You fix the lowest broken layer…and everything above it gets easier!

How do you figure out what truly matters to users when you’ve got a long list of features, benefits, or design options - but only a limited sample size and even less time? A lot of UX researchers use Best-Worst Scaling (or MaxDiff) to tackle this. It’s a great method: simple for participants, easy to analyze, and far better than traditional rating scales. But when the research question goes beyond basic prioritization - like understanding user segments, handling optional features, factoring in pricing, or capturing uncertainty - MaxDiff starts to show its limits. That’s when more advanced methods come in, and they’re often more accessible than people think. For example, Anchored MaxDiff adds a must-have vs. nice-to-have dimension that turns relative rankings into more actionable insights. Adaptive Choice-Based Conjoint goes further by learning what matters most to each respondent and adapting the questions accordingly - ideal when you're juggling 10+ attributes. Menu-Based Conjoint works especially well for products with flexible options or bundles, like SaaS platforms or modular hardware, helping you see what users are likely to select together. If you suspect different mental models among your users, Latent Class Models can uncover hidden segments by clustering users based on their underlying choice patterns. TURF analysis is a lifesaver when you need to pick a few features that will have the widest reach across your audience, often used in roadmap planning. And if you're trying to account for how confident or honest people are in their responses, Bayesian Truth Serum adds a layer of statistical correction that can help de-bias sensitive data. Want to tie preferences to price? Gabor-Granger techniques and price-anchored conjoint models give you insight into willingness-to-pay without running a full pricing study. These methods all work well with small-to-medium sample sizes, especially when paired with Hierarchical Bayes or latent class estimation, making them a perfect fit for fast-paced UX environments where stakes are high and clarity matters.

One of the most common UX failures I see in today’s fast-paced studies isn’t just bad analysis, but questionnaires that look fine-ish and measure the wrong thing. One technique that consistently fixes this problem is cognitive interviewing. It shifts questionnaire design from guessing what users mean to actually observing how they think while answering your questions. The idea is simple. Instead of launching a survey and hoping respondents interpret each item the way you intended, you sit with a small number of users and walk through the questionnaire with them. After they answer each question, you ask things like: Can you rephrase this in your own words? How did you decide on that answer? What did this phrase mean to you? What shows up is often uncomfortable but extremely useful. - People frequently give the correct answer for the wrong reason. - Seemingly clear terms like at risk, affect, or often are interpreted in very different ways. - True or false formats can hide misunderstanding instead of revealing it. - Response options that look clean on paper do not always match how people think. In practice, this means your survey can look statistically solid while measuring the wrong thing. From a UX perspective, cognitive interviewing strengthens questionnaires at several critical levels. It improves clarity by exposing ambiguous wording immediately. It protects construct validity by showing whether people are answering the concept you care about or a substitute they created on the fly. It reveals how users map their thinking onto response options. It increases confidence in downstream decisions because you know the data reflects real understanding, not lucky guesses. One of the most underrated parts of this technique is that it does not require large samples. A small number of carefully conducted interviews is usually enough to surface the most serious problems before a survey ever goes live. If you design UX questionnaires, this is one of the highest-leverage steps you can add to your workflow. It turns surveys from polished guesswork into instruments that actually measure what you think they measure.

I used to wonder how to make my UX work more impactful. I saw designers getting astonishing results for their clients/stakeholders so I knew it was possible. I just didn’t know how to actually do it. I knew the standard processes and tools. So I thought I should hit my stakeholders over the head with how they’re doing it wrong and be the guy always fighting for users. That should do the trick, right? Wrong. Turns out I needed to: → Listen more → Follow my gut → Break the rules That’s when things started clicking. I pieced this together a long time ago in the tech world. Now I apply it to client projects. And it works... We’ve helped our clients: → 3.5x their conversion (eCommerce) → Oversubscribe their A round by 55% (health tech) → Rack up 8 awards for innovation (education) Here’s exactly how we did it: 1. Understand goals and constraints: - How is success measured? - What time pressure exists? - What have they already tried? - What are the biggest challenges? - Who are their customers or users? - What unique assets or data do they have? Literally everything depends on this. Asking the right questions upfront means better insights, better design recommendations, and better collaboration. 2. Audit the current product: - Review every screen, state, and flow - Gather screencaps and recordings - Identify opportunities, risks, problems Step 1 was the big picture. This is about details. Experience, intuition, and judgement matter here. 3. Make recommendations: - Prioritize by impact - Call attention to the top 3 issues - Present findings clearly. We use slides. Show what's happening with the current product—and how to transform it. 4. Agree on priorities, timelines, and process: - What’s the most important thing to do next? - How will we execute the work? Too many designers get caught up on "right" process. Right depends on context. There are lots of ways to succeed. 5. Execute the work: - Research, design, prototyping, testing - Every decision or finding gets tied to goals or risks AI is speeding this part up. It's a wild time. 6. Communicate & collaborate throughout: - Design is a team sport—we win together - The whole team knows what’s happening, and why - Nobody's left guessing Pro tip: Clarity is a gift designers are well positioned to give product teams. Capture roadmap, process, and status in a single visual to do this. Not sure how? DM me. 7. Ship product: “Everyone has a plan until they get punched in the face”—Mike Tyson. Things get real when they're put in front of users. Do that fast, but not so fast that you don’t get a good signal from the market. – I love helping clients succeed. Over time, I found these traits help: Teamwork Pragmatism Bias for action Lightheartedness Commitment to quality Find your own way. Break the rules when needed. Stay focused on impact. That’s what makes the work meaningful—and what makes for truly successful products (and design careers).

Ideas can spark excitement, but they can also lead us astray. We often rush to build without understanding the problem. This can waste time and money. Too many customers jump in with their solutions. They think they know what’s best. But they miss the bigger picture. Here’s how to avoid that trap: 1. Identify the Issue: ↳ Understand what problem you are trying to solve. 2. Gather Input: ↳ Invite your team to share their thoughts and ideas. 3. Explore Solutions: ↳ Look for options that may cost less or work better. 4. Know Your Platform: ↳ Learn about features that can help solve your problem. 5. Focus on the User: ↳ Consider how your choices affect the end user. 6. Be Open-Minded: ↳ Stay flexible and ready to adapt your ideas. 7. Collaborate: ↳ Work together with your team for the best results. 8. Test Ideas: ↳ Try out different solutions before committing. 9. Keep Learning: ↳ Stay updated on new tools and features. 10. Embrace Feedback: ↳ Listen to input from your team and customers. 11. Document Everything: ↳ Keep track of ideas and solutions explored. 12. Reflect: ↳ Review what worked and what didn’t for future projects. Building the right solution starts with understanding the problem. Stay focused on the issue. Involve your team. Explore all options. This approach leads to better outcomes.

How I use AI to design the Top 1% user experience: (AI won’t replace, it’ll assist → if you know how) I tested countless AI workflows. Here’s the best one: 🧠 For Brainstorming → Use ChatGPT Prompt: "Generate 5 innovative UX ideas for a [specific product]. Consider user engagement, accessibility, cognitive load, and seamless interaction. Provide real-world examples and potential challenges for each idea." 🔍 For UX Research → Use DeepSeek Prompt: "Analyze the top pain points users face in [your industry]. Break down the psychological, behavioral, and technical challenges. Provide case studies, competitor insights, and suggestions to enhance usability." 📊 For Competitive Analysis → Use Perplexity Prompt: "Research the top-performing UX strategies in [your niche]. Analyze trends, user expectations, and key differentiators. Compare at least three successful companies, highlighting their UX strengths, weaknesses, and opportunities for improvement." 📐 For Wireframing → Use Claude Prompt: "Create a landing page that enhances UX and solves [paste problem statement]. Incorporate clear hierarchy, intuitive navigation, and mobile responsiveness. My goal is to [put your goal] and [goal 2]. Ensure accessibility compliance and smooth user flow." And this isn’t just a random AI trick. It’s built on: ✓ Years of UX expertise ✓ 100s of tested design iterations ✓ AI-assisted, human-approved strategies How to Use It: 1️⃣ Generate ideas with ChatGPT 2️⃣ Research pain points with DeepSeek 3️⃣ Analyze competitors with Perplexity 4️⃣ Wireframe instantly with Claude 5️⃣ Customize & refine for max conversion ⚠️ I'll let you in on the real secret: AI can assist, but it’s your creativity and empathy that make the experience truly exceptional. Blend this assistance into your process, and you’ll stand out effortlessly. PS. Do you use any of these AI tools for UX design? I'd appreciate you reposting this if it was helpful! Follow me for more insights like this!

Ignoring UX research is one of the quickest ways to build the wrong product. UX Research follows 5 key phases: → Empathize → Define → Ideate → Prototype → Test (continuously improving 🔁) 𝗘𝗺𝗽𝗮𝘁𝗵𝗶𝘇𝗲 Start by understanding your users Methods: • Interviews • Surveys • Field research • Analytics insights • User feedback 👉 Focus: uncover real needs and behaviors 𝗗𝗲𝗳𝗶𝗻𝗲 Make sense of what you’ve learned Outputs: • Personas • User stories • Problem definition • Journey mapping • Jobs to be done 👉 Focus: align on the right problem 𝗜𝗱𝗲𝗮𝘁𝗲 Think broadly before narrowing down Methods: • Brainstorming • User flows • Mind maps • Storyboards • Concept exploration 👉 Focus: explore multiple solution paths 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 Turn ideas into something testable Approaches: • Wireframes • Paper sketches • High-fidelity designs • Walkthroughs • Heuristic reviews 👉 Focus: make concepts tangible 𝗧𝗲𝘀𝘁 Evaluate with real users Methods: • A/B testing • First-click tests • Session recordings • Usability testing • Benchmarks 👉 Focus: identify what works and what doesn’t Strong products aren’t built on assumptions, they’re shaped through continuous learning and iteration. #UXResearch #UXUI #UXDesign #ProductThinking #UserExperience #imenmlika