Understanding User Challenges

When entering unfamiliar territory, it's useful to keep returning to the first principles of User Research: 1/ Start with the assumption that you don't know what users need or how they think. (Epistemic Humility) 2/ What people say often differs from what they do. 3/ Many usage behaviors rely on unspoken, embodied knowledge that users cannot articulate. Research must surface this tacit dimension. 4/ User behaviors are shaped by their environments, tools, social contexts, and constraints - not just by preferences or rational choices. 5/ Research findings are only meaningful to the extent that they represent users' actual contexts, environments, and behaviors. The lab is never the same as the wild. 6/ Users can reliably identify problems they experience but are less reliable at envisioning effective solutions. Their struggles are data; their suggestions are hypotheses. 7/ The models we create of users and their behaviors are always simplifications. The map is not the territory. 8/ Focus research on the zone just beyond what you already know, where uncertainty is highest and learning potential is greatest. 9/ Research can never be comprehensive. Focus on reducing uncertainty in decision-critical areas rather than attempting exhaustive understanding. 10/ Package insights differently for different audiences (designers, engineers, executives) without compromising integrity or nuance.

People often have little or no introspective access to the processes that generate their judgments, choices, or behaviors (Nisbett & Wilson, 1977). One of my hobbies is to read classic studies and think about their applications in our today’s world. This paper is one of those studies that I genuinely believe every UX researcher should read and deeply understand. It delivers an uncomfortable but essential lesson: what users say about their decisions is often not a direct window into how those decisions were actually made. One of the most natural instincts in UX research is to ask users exactly what they think and why they made a particular choice. We ask what they liked, what confused them, what mattered most, and what influenced their decision. This feels intuitive and respectful. After all, who knows the user better than the user themselves? Yet decades of cognitive and behavioral research show that much of perception, evaluation, and decision making happens outside conscious awareness. When users explain their choices, they are often constructing a story rather than reporting the mechanisms that produced the behavior. This does not mean users are dishonest. It means the human mind is not designed for transparent self inspection. Classic work by Nisbett and Wilson demonstrated that people can be highly confident in their explanations while being wrong about the true drivers of their behavior. When pressed for reasons, the mind does not “look inside” and retrieve a causal record. Instead, it relies on plausible, culturally grounded explanations that make sense after the fact. These explanations feel true to the person giving them, but from a scientific perspective they are often post hoc rationalizations. This distinction matters deeply for UX research. Self reports are excellent for understanding narratives, beliefs, expectations, and meaning. They are far less reliable for uncovering how decisions actually unfold. Pushing users harder for “why” can make things worse by encouraging polished, socially acceptable stories that drift further away from the real drivers of behavior. Clear, confident quotes can be seductive, but behavior, timing, errors, and trade offs often tell a more accurate story. The takeaway is not to ignore users, but to listen differently. What users say helps us understand how they make sense of their experience. What they do helps us understand how they actually interact, decide, and struggle. This is why strong UX research cannot rely on a single method. We need qualitative approaches to capture meaning and lived experience, and quantitative and behavioral methods to reveal patterns and constraints users cannot articulate. Only by combining both can we get as close as possible to the user’s mind, even if it never becomes fully transparent. To learn more: https://lnkd.in/gKfybGiz

Intelligent people, whether or not highly educated, may not have great computer knowledge. Those of us who work in Tech Support sometimes forget that non-technical users don’t understand some of the concepts that we take for granted. Here are some real-life examples. Not understanding the difference between an operating system and an application. Not understanding the difference between the device and the operating system. Not understanding the difference between memory and storage. Not knowing that RAM and memory are the same thing. Not knowing that disk space and storage are the same thing. Not understanding the difference between the browser and other applications. Not knowing if an application is being run locally or on an Internet connected server. Not knowing whether their files are stored locally, in the cloud, or both. Not knowing how to find a file without opening the application first. How to save an attachment so they don’t need to open the email every time they want the attachment. Not knowing what accessibility features are available on their device, or how to enable them. As a Help Desk professional, you walk a fine line. You don’t want to explain something that will make a sophisticated user feel like you’re talking down to them, but you also don’t want to make the less knowledgeable user feel stupid. Here are some ways to achieve this balance: Ask relevant knowledge- and skill-based questions. Example 1: “I see that this file is only available from your cloud storage. Do you want to be able to open it when you’re not connected to the Internet?” Example 2: “Since you complained that the words are so small on the screen, it makes me wonder: are you aware that Microsoft has a built-in magnifier that you can turn on and off?” Offer assistance cheerfully without waiting to be asked, because they may not know what to ask. Example 1: “Would you like me to help you create a folder structure so you can separate the spreadsheets from the proposal documents?” Example 2: “You had to scroll a long way to find that document. Would you like me to show you how to sort them alphabetically or by date?” Be on the lookout for knowledge gaps, without assuming them. Example: A customer says, “I’m out of disk space.” You look and see 78% free space. They may mean they’re seeing “out of memory” errors, but don’t have the terminology to describe it. Ask, “Can you show me the message that you saw on the screen, and when that message appears?” #callmeifyouneedme #fifonetworks #helpdesk #techsupport #remotesupport

What users say isn't always what they think. This gap can mess up your design decisions. Here's why it happens: → Social desirability bias. → Fear of judgment. → Cognitive dissonance. → Lack of self-awareness. → Simple politeness. These factors lead to misinterpretation of user needs. Designers might miss critical usability issues. Products could fail to meet user expectations. Accurate feedback becomes hard to get. Biased data affects design choices. To overcome this, try these strategies: 1. Create a comfortable environment: Make users feel at ease. Comfort encourages honesty. 2. Encourage thinking aloud: Ask users to verbalize thoughts. This reveals their true feelings. 3. Use indirect questions: Avoid direct queries. Indirect questions uncover hidden truths. 4. Observe non-verbal cues: Watch body language. It often tells more than words. 5. Triangulate data: Use multiple data sources. This ensures a complete picture. 6. Foster honest feedback: Build trust with users. Trust leads to genuine responses. 7. Analyze discrepancies: Compare what users say and do. Identify and understand the gaps. 8. Iterate based on findings: Refine your design. Continuous improvement is key. 9. Stay aware of biases: Recognize potential biases. Work to minimize their impact. 10. Keep testing: Regular testing ensures alignment. Stay connected with user needs. By following these steps, designers can bridge the gap between user thoughts and statements. This leads to better products and happier users.

Great products are built when you don’t listen to users! But when you listen to PEOPLE. We call them "users," but those people have lives, goals, and frustrations outside of just interacting with our product. A user might say "I need a faster checkout process”. But a person might say "I want to spend less time online shopping and more time with my family." Understanding the deeper motivation can help us design solutions that address the core issue. This means going beyond traditional user research and actively listening to the people we are trying to help. What problems are they trying to solve? What are they hoping to ultimately accomplish?   What frustrations are we inadvertently creating for them? The key takeaway? Listen beyond user actions to understand the deeper human needs and aspirations. Create products that don't just get used, but that make a positive difference in people's lives.

🔹 Day 21 – Product Manager Interview Prep Series 🔹 🎯 RCA-Based Question: “Your team just launched a new onboarding flow. Instead of increasing activation, it's led to a spike in churn. How would you analyze and resolve this issue?” 📌 Step-by-Step Breakdown – Root Cause Analysis (RCA) As a PM, your goal is to understand user behavior, pinpoint the friction, and fix the flow without compromising long-term retention. 1️⃣ Clarify the Problem 🔍 Define “churn”: Is it users dropping mid-onboarding? Or completing onboarding but not returning? Ask: -What’s the exact drop-off point in the new flow? -Is the churn immediate (same day) or delayed (after 1–2 days)? -What does churn look like compared to the previous flow? 2️⃣ Quantify & Segment the Impact 📊 Dive deep into analytics: 📈 Timeframe: When was the new flow launched? Sudden spike or gradual rise in churn? 👥 User Segments: Are new users from a particular platform (iOS/Android/Web) churning more? 🌐 Geo/Cohort Analysis: Are certain regions, age groups, or acquisition channels seeing higher churn? 🧪 AB Testing: Compare churn between users on old vs. new flows (if test is live). 3️⃣ Identify Potential Root Causes 🧠 UX/UI Issues: -Too many steps or confusing layout? -New permission asks too early (e.g., location, notifications)? -Value not shown quickly enough? 🔧 Technical Issues: -App crashes, lags, or slow load times? -Broken API, failed calls, or validation errors? 🧭 Psychological Friction: Users feeling overwhelmed or not understanding the benefits? High cognitive load in first interaction? 4️⃣ Talk to Stakeholders & Users 👂 User Feedback: - Session recordings (Hotjar/FullStory) - User interviews or feedback surveys - App store reviews post-launch 🤝 Internal Teams: - Engineering: Check for bugs, crashes, error logs. - Design: Walk through usability testing insights. - Data Science: Get funnel drop-off visualization. 5️⃣ Suggest Short-Term & Long-Term Improvements 🛠 Short-Term Fixes: - Roll back the most friction-heavy step. - Add in-line help or tooltips at high drop-off points. - Highlight core product value earlier. 🚀 Long-Term Initiatives: - Redesign onboarding based on user mental models. - Introduce progressive disclosure – don’t show everything at once. - Run usability tests before full rollout. 6️⃣ Measure Success Track: ✅ Increase in activation rate 📉 Drop in onboarding churn 🧠 User comprehension (measured via surveys or task success rate) 🎯 Retention metrics over Day 1, Day 7, Day 30 🔁 PM Mindset Tip: Onboarding is your first impression. Make it intuitive, not intimidating. Test thoroughly, talk to real users, and iterate until value is delivered with clarity and ease. 💬 How would YOU debug a broken onboarding flow? Let’s brainstorm in the comments 👇 #ProductManagement #PMInterview #RootCauseAnalysis #Onboarding #UserChurn #UserExperience #LinkedInDaily #ActivationStrategy #ProductDesign #LinkedInNewsIndia

I’ve been designing + building products for 20 years. One AI project changed everything I thought I knew. It was 5 years ago. The brief: an AI assistant for financial advisors. "Easy" I thought. I brought the playbook - understand users, map needs, prototype, iterate. Within weeks, every method had failed. User-centred design has given us incredible tools: journeys, personas, usability testing. It created a shared language for innovation and put users at the centre of product development. But it also gave us something dangerous: the illusion that good process guarantees good outcomes. Where design methods break: 🔴 They treat all problems as design problems. Not every challenge needs a workshop. Some need engineering breakthroughs. Some need business model innovation. Some need regulatory change. When your only tool is empathy, everything looks like a user experience problem. 🔴 They assume user needs reveal future possibilities. Advisors thought they wanted better dashboards. Not "AI that predicts my clients needs and anxiety levels". Revolutionary products create needs people didn't know they had. 🔴 Confuses good process with good results. Following the method perfectly doesn't guarantee you're solving the right problem. Great design comes from insight, not adherence to frameworks. What building AI systems has taught me: 🤔 The old tools need rethinking. User research couldn't predict interactions with something that evolves. Journey maps couldn't map AI that creates new paths. Prototypes couldn't capture systems that learn and change. 🤔 The real design challenge isn't the interface - it's the intelligence architecture. Should the system interrupt or wait? Learn from the user or protect their privacy? Optimise for efficiency or explainability? These aren't UX decisions. They're ethical and technical decisions that determine trust, dependency, and agency. 🤔 And critically: AI systems create feedback loops that change user behaviour over time. Traditional design assumes static user needs. AI design requires predicting how your solution will reshape the problem space. We're designing systems that could shape human behaviour for generations. User research and workshops aren't enough anymore. We need a new playbook. What I've learnt: 🟢 Ask "should we?" before "how might we". Consider consequences, not just possibilities. What data does this use? How does it learn? What could break? 🟢 Develop systems thinking. Your decisions ripple through complex networks of technology, behaviour, and culture. 🟢 Design for responsibility, not just iteration. Every design choice becomes a values statement when scaled through AI. 🟢 Question the AI narrative. Not every problem needs an AI solution. Some need better human processes. 🟢 Partner deeply with engineers and data scientists. The best AI experiences emerge from true collaboration, not handoffs. The craft evolves. The responsibility remains the same. Let’s write new rules. Who’s in?

I just analyzed HeyGen (one of the leading AI avatar apps) product experience through a behavioral lens. There are two found powerful UX lessons that apply to nearly any digital product and one big miss. ✅ Unpacking abstract value: Instead of vague promises about being an "AI platform," HeyGen shows specific use cases: create avatars, generate videos, make UGC ads, and translate content. Research shows unpacking complex ideas into concrete examples helps users understand your value proposition and envision specific outcomes. ✅ Templates reduce friction: Their 53+ ready-to-use templates make creation logistically easier while providing psychological scaffolding—showing what's possible and reducing decision paralysis. ❌ The vanity barrier: To create your avatar, you need to record yourself. But what if you're not camera-ready when signing up? I wasn’t :) This mirrors the challenge Airbnb faced when asking hosts to photograph their homes (solved by sending professional photographers). It mirrors what Google found when asking small businesses to share pictures of their store on Google Maps (solved by lots of nagging). Users will procrastinate rather than create something they're not proud of. The behavioral insight: Your users want to look good. This is a barrier. It’s your job to make them shine. Self-image concerns do impact user adoption of features. What other products have you seen that thoughtfully address psychological barriers to engagement? If you're building AI features and struggling with adoption, this teardown reveals principles you can apply immediately—whether your users are having a good hair day or not. Link to the full teardown in comments 👇 #AI #ProductDesign #BehavioralScience

We've all been there - that amazing product idea that seems like a can't-miss hit. But far too often, those game-changing inventions end up failing spectacularly because of one critical oversight: not actually understanding user needs. Let's learn from some cautionary tales of failed products: 1. Google Glass: Google Glass failed to resonate with consumers due to privacy concerns and a lack of clear use cases. The product's intrusive nature and potential for surreptitious recording made people uncomfortable, while the high price point and limited functionality failed to address any specific consumer problem, leading to its downfall. Now we’ll be able to see if Apple can get it right with their headset. 2. Juicero: Juicero's expensive Wi-Fi-connected juicing machine was ridiculed for solving a non-existent problem. The device required proprietary, pre-packaged fruit pouches, but consumers quickly realized they could squeeze the pouches by hand, rendering the over-engineered and costly machine unnecessary. 3. Microsoft Zune: Microsoft's Zune struggled to compete with Apple's iPod, largely because it didn't offer a distinct advantage or address any particular customer issue. It entered a market dominated by an established competitor without a clear understanding of consumer desires, leading to its eventual discontinuation. These products missed the mark because the teams failed to deeply understand the human problems they were trying to solve. It's a trap that's easily avoided by embracing user research. User research builds empathy, mitigates risks, prevents costly misses, and ensures you're designing solutions to real problems your audience actually has. It's the critical step that separates products that flop from ones that flourish. What has been your experience with user research? I'd love to hear about other success stories, challenges faced, or lessons learned! #UserResearch #ProductDevelopment #ProductManagement #ProductInstitute

One of the biggest challenges in UX research is understanding what users truly value. People often say one thing but behave differently when faced with actual choices. Conjoint analysis helps bridge this gap by analyzing how users make trade-offs between different features, enabling UX teams to prioritize effectively. Unlike direct surveys, conjoint analysis presents users with realistic product combinations, capturing their genuine decision-making patterns. When paired with advanced statistical and machine learning methods, this approach becomes even more powerful and predictive. Choice-based models like Hierarchical Bayes estimation reveal individual-level preferences, allowing tailored UX improvements for diverse user groups. Latent Class Analysis further segments users into distinct preference categories, helping design experiences that resonate with each segment. Advanced regression methods enhance accuracy in predicting user behavior. Mixed Logit Models recognize that different users value features uniquely, while Nested Logit Models address hierarchical decision-making, such as choosing a subscription tier before specific features. Machine learning techniques offer additional insights. Random Forests uncover hidden relationships between features - like those that matter only in combination - while Support Vector Machines classify users precisely, enabling targeted UX personalization. Bayesian approaches manage the inherent uncertainty in user choices. Bayesian Networks visually represent interconnected preferences, and Markov Chain Monte Carlo methods handle complexity, delivering more reliable forecasts. Finally, simulation techniques like Monte Carlo analysis allow UX teams to anticipate user responses to product changes or pricing strategies, reducing risk. Bootstrapping further strengthens findings by testing the stability of insights across multiple simulations. By leveraging these advanced conjoint analysis techniques, UX researchers can deeply understand user preferences and create experiences that align precisely with how users think and behave.