UX Design And Business Strategy

This isn’t just a design trend. It’s a data-driven shift in how homes are created. How practical is this design? Here’s what AI is changing in residential design — backed by numbers: • AI-assisted design tools can reduce concept iteration time by 60–80% • Early-stage AI simulations cut construction change orders by up to 30% • Material optimization reduces waste by 10–20%, improving sustainability and cost control • Lighting and spatial simulations increase perceived space efficiency by up to 25% • Personalized design increases homeowner satisfaction and resale appeal — premium homes with unique architectural features often command 5–15% higher value These pebble stone stairs are a great example. AI helped: – Optimize stone size and layout for anti-slip safety – Simulate light reflection across textures at different times of day – Balance luxury aesthetics with long-term durability – Integrate the stairs seamlessly into the overall spatial flow The key insight: AI doesn’t replace architects or designers. It augments creativity with computation. Humans define taste, emotion, and vision. AI accelerates testing, optimization, and decision-making. The result.... • Better design decisions • Fewer costly mistakes • More sustainable builds • Truly personalized luxury AI is no longer just transforming software and semiconductors. It’s transforming how we design, build, and live. #AI #Architecture via @diycraftstvofficial #DesignInnovation #LuxuryDesign #SmartHomes #PropTech #FutureOfLiving #SustainableDesign

Data is everything in product design. Without data, we open ourselves up to: - Biases - Opinions - Confusion - Misalignment When we are data-informed and that data is accurate, we can truly make educated product decisions. I like to think of data in two layers: a) What’s happening and b) Why it’s happening. Let’s break it down. What’s happening: - Business data tells us how the business is doing - Marketing/sales data tells us where our customers come from - Retention data tells us when and why customers are leaving us - Engagement data tells us how customers are using our product Why it’s happening: - User research gives us rich insight into why something is happening - Voice of the customer data shows us how customers talk about our product - Usability scores show us how people perceive our product or feature experience in a measurable way - Product market fit & satisfaction scores give us a simple and actionable metric to track and improve over time In terms of accessing that data, methodologies vary, but generally speaking, I always advise the following: 1. Get access to growth and retention data through business dashboards. 2. Get access to product data through your product analytics tool. 3. Set up a cadence to gather customer reviews & comments, either manually or via automated tools. 4. Set up a cadence to speak to your users continuously to answer the why. 5. Set up a recurring survey to track satisfaction and usability. If you don’t have the data structure for any of the above, speak to your product and data team to see if you can change that. If not, rely on the data that you can actually get. PS: The list of metrics is indicative: Actual metrics will differ greatly from one company to another and largely depend on the industry, niche, as well as your data infrastructure and setup. — If you found this useful, consider reposting ♻️ How are you collecting and using data in your design process? What else are you tracking?

🔬 How To Run UX Research In B2B and Enterprise. Practical techniques of what you can do in strict environments, often without access to users. 🚫 Things you typically can’t do 1. Stakeholder interviews ← unavailable 2. Competitor analysis ← not public 3. Data analysis ← no data collected yet 4. Usability sessions ← no users yet 5. Recruit users for testing ← expensive 6. Interview potential users ← IP concerns 7. Concept testing, prototypes ← NDA 8. Usability testing ← IP concerns 9. Sentiment analysis ← no media presence 10. Surveys ← no users to send to 11. Get support logs ← no security clearance 12. Study help desk tickets ← no clearance 13. Use research tools ← no procurement yet ✅ Things you typically can do 1. Focus on requirements + task analysis 2. Study existing workflows, processes 3. Study job postings to map roles/tasks 4. Scrap frequent pain points, challenges 5. Use Google Trends for related search queries 6. Scrap insights to build a service blueprint 7. Find and study people with similar tasks 8. Shadow people performing similar tasks 9. Interview colleagues closest to business 10. Test with customer success, domain experts 11. Build an internal UX testing lab 12. Build trust and confidence first In B2B, people buying a product are not always the same people who will use it. As B2B designers, we have to design at least 2 different types of experiences: the customer’s UX (of the supplier) and employee’s UX (of end users of the product). In customer’s UX, we typically work within a highly specialized domain, along with legacy-ridden systems and strict compliance and security regulations. You might not speak with the stakeholder, but rather company representatives — who regulate the flow of data they share to manage confidentiality, IP and risk. In employee’s UX, it doesn’t look much brighter. We can rarely speak with users, and if we do, often there is only a handful of them. Due to security clearance limitations, we don’t get access to help desk tickers or support logs — and there are rarely any similar public products we could study. As H Locke rightfully noted, if we shed the light strongly enough from many sources, we might end up getting a glimpse of the truth. Scout everything to see what you can find. Find people who are the closest to your customers and to your users. Map the domain and workflows in service blueprints and . Most importantly: start small and build a strong relationship first. In B2B and Enterprise, most actors are incredibly protective and cautious, often carefully manoeuvring compliance regulations and layers of internal politics. No stones will be moved unless there is a strong mutual trust from both sides. It can be frustrating, but also remarkably impactful. B2B relationships are often long-term relationships for years to come, allowing you to make huge impact for people who can’t choose what they use and desperately need your help to do their work better. [continues in comments ↓] #ux #b2b

As CX leaders, solving problems starts with people. Design thinking gives us a clear path. We start by listening to users, defining real needs, and brainstorming ideas. We then build quick prototypes and test them early. Machine learning shifts the focus to data. It breaks issues into smaller parts and finds hidden patterns. We tune models and check how well they predict results. This helps us make smarter decisions fast. Both methods bring value to CX. Design thinking ensures we meet human needs. Machine learning gives us insights we might miss. Using them together unlocks new ways to delight customers. When should you use each? Use design thinking when you need empathy and creative ideas. Use machine learning when you have large data sets and need fast answers. Merging both gives you a balanced, human-led and data-driven CX strategy.

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).

After mentoring over 800 UX researchers over the past decade, I’ve noticed one clear pattern: The best researchers don’t just gather data, they drive action. They have habits that help them uncover insights, inspire teams, and de-risk decisions. Here are 8 of the most effective habits I’ve seen (and how you can start practicing them): 1. Comfort in ambiguity ↳ Great researchers don’t rush to conclusions. ↳ They embrace the grey areas and let insights emerge. Next time you’re synthesizing data, resist the urge to clean it up. Explore contradictions, which often lead to breakthroughs. 2. Ask the unasked questions ↳ They challenge assumptions and dig deeper. ↳ When everyone’s aligned, they ask, “What if we’re missing something?” Start every project with this question: “What don’t we know that could derail us?” 3. Endlessly curious ↳ Great researchers don’t just ask why, they ask what if? ↳ Curiosity fuels their creativity and problem-solving. Pick one unexpected user behavior from your data this week and explore why it’s happening. 4. Know when to stop ↳ They understand that more data doesn’t always mean better decisions. ↳ They recognize when diminishing returns set in and shift from research to action. Before starting a new study, ask, “What decision are we trying to inform?” If you already have enough data, stop and act. 5. Playful with insights ↳ They treat insights like puzzles, not checkboxes. ↳ The best researchers experiment with how findings are framed and presented. In your next synthesis, frame one insight in three different ways to spark new perspectives. 6. Thrive in collaboration ↳ Effective researchers know insights gain power when shared. ↳ They work closely with designers, PMs, and engineers to co-create solutions. Bring stakeholders's needs into research studies directly, help them make tough decisions and mitigate risk, watch how buy-in skyrockets. 7. Bring discomfort ↳ They don’t settle for validating assumptions—they challenge them. ↳ Insights that spark discomfort often lead to the biggest breakthroughs. If your findings aren’t sparking hard conversations, dig deeper. Research that challenges assumptions often drives transformation. 8. Unafraid to be ignored ↳ Effective researchers understand that not every insight will lead to action—and that’s okay. ↳ They focus on building a culture of evidence-based decision-making over time. Track the outcomes of your research. Revisit findings at the right moment, like a project pivot, a problem resurfaces, or priorities shift. Timing can turn a no into a valuable yes. What habits have made you a better researcher? Share them in the comments // Sick of begging people to listen to your research only to be met with a thumbs up emoji? I share strategies to deliver UXR impact on my Substack: https://lnkd.in/eR5M2geZ

Always choose Insights Over Design in a Dashboard Here's Why- One of the most important lessons I’ve learned from years of consulting is this: always prioritise insights over design. Why Insights Matter More Than Design in a Dashboard Imagine you’re tasked with creating a sales report. If your first thoughts are about: 🔸Adding shadows or borders, 🔸Choosing the perfect font, or 🔸Picking a fancy colour... You’re focusing on the wrong thing. No matter how polished your report looks, a total sales number in isolation won’t help anyone. The CEO or any stakeholder will still just see one number—and that won’t drive decisions. Now, consider this instead: 🔸Pair the total sales with last year’s numbers to show growth. 🔸Add monthly trends to highlight the best and worst-performing periods. Suddenly, your report is more than just a number—it’s a story. A story that helps people take action. The Right Approach 1. Focus on insights first: Ask yourself—what does the end-user really need to know? 2. Then think about design: Use design to enhance and highlight those insights, not as the starting point. What’s your take—do you prioritize insights or design? Have you had experiences where one approach significantly outperformed the other? Share your stories below! 💬 #PowerBI #Excel #DataAnalyst #DataAnalysis

I went to an AI UX workshop last night expecting recycled LinkedIn advice about "building AI trust through transparency." Instead, Isabella Yamin tore down LinkedIn's job posting flow using her CarbonCopies AI framework in real-time, while founders shared raw implementation struggles. It completely changed how I'm rethinking Maibel's onboarding flow. Here's what I stole from B2B SaaS principles to redesign emotional AI for B2C: 1️⃣ Progressive disclosure with purpose LinkedIn's fatal flaw? Optimizing for completion ease > Outcome quality. Recruiters are drowning in irrelevant applications because AI never learns what "qualified" means. The personalization paradox: How do we give users enough control without overwhelming them? Users don't want "frictionless". They want INFORMED control. 📌 At Maibel: I was falling into the same trap, making emotional coaching setup so simple that the AI couldn't understand user context. Now? Progressive complexity with clear trade-offs. Show users how their choices impact outcomes. → Want deeper insights? Add more context. → Want faster setup? Here's what the AI can't personalize. 2️⃣ Closed-loop data intelligence: What Platfio gets right They've built a platform for software agencies where where every data point feeds back into the entire system. User preferences in marketing flows shape proposals. Campaign performance shapes future recommendations. Every interaction becomes intelligence for future recommendations. 📌 At Maibel: Most wellness apps store emotional check-ins like digital journals. I'm turning them into predictive feedback loops. Emotional intelligence isn’t static but COMPOUNDS. Today's reflections shift tomorrow's suggestions. Patterns fuel prevention. Users' inputs on Monday could predict AND prevent Friday's breakdown. 3️⃣  Multi-modal creativity: Wubble's transparency approach Translating images and files into music - who'd have thought? They've cracked multi-modal creativity where users become co-creators, not passive consumers. The breakthrough moment for me: What if users could see how their visual environment contributes to emotional context? 📌 At Maibel: Users upload images of their day and see how AI analyzes emotional cues: cluttered workspace = overwhelm, junk food = stress eating. Multi-modal understanding users can contribute to and influence. 💡 The bottom line? B2B Saas gets one thing right: Every interaction has to earn trust. In B2B, failed AI means churn. In emotional AI, failed trust breaks belief in tech entirely. 📌 Here's what we're doing differently at Maibel: → Progressive complexity → Context-aware feedback → Multi-modal participation → Intelligence that compounds with every input. It's not just about building WITH AI. I'm designing systems that learn understand YOU before you even need to explain yourself. Kudos to Isabella, Shivang Gupta The Generative Beings, Shaad Sufi Hayden Cassar and everyone who shared deep product insights.

Understanding UI/UX at the Core - Series #Day6 🚀 ------------------------------------------------------------------------------- User Persona: designing for a real human, not an imaginary user. 👩💻 ------------------------------------------------------------------------------- One of the most common mistakes in design is saying, “Our users are everyone.” They aren’t. When we design for everyone, we usually end up designing for no one. This is where user personas become essential not as documents to impress stakeholders, but as tools to keep design human. A user persona is a representation of a real user, created from research, not assumptions. It captures goals, pain points, behaviours, motivations, and context the things that actually influence how someone uses a product. 💡 Why does this matter? Because design decisions change when you stop thinking about “users” and start thinking about a person. - A person with limited time. - A person under stress. - A person with specific needs and constraints. In UX, personas act like a constant reality check. When you’re stuck between design choices, they help answer questions like: - Would this make sense for them? - Would this add effort or reduce it? - Does this solve their actual problem or just look good? Personas also play a big role in alignment. They give designers, product managers, and developers a shared understanding of who we are designing for. This reduces subjective opinions and keeps conversations user-focused. 🚀 How do you create a meaningful persona? It starts with research, interviews, surveys, usability tests, analytics, and real conversations. Patterns are identified, common behaviours are grouped, and insights are synthesised into a clear, realistic profile. A good persona is not fictional creativity. It’s structured empathy. 🌻 When done well, personas influence everything, flows, features, content, and priorities. They help designers stay grounded, especially when personal bias tries to creep in. For fellow designers, this is an important mindset shift: - Personas are not deliverables. - They are decision-making tools. Great UX doesn’t come from designing ideal experiences. It comes from designing realistic experiences for real people, in real situations. 😊 Figma LinkedIn UX Touch☀️ #uiux #userexperience #creator #linkedin #persona #concept #lessons #uxcourse #job #juniordesigner #uiuxdesign #userpersona #uxprinciples #designthinking #productdesign #uidesign #designercommunity #designeducation #usercentereddesign

We often say “people don’t buy products, they buy feelings.” But here’s the twist; people don’t just buy feelings. They experience them through design. Every swipe, scroll, haptic pulse, sound cue, and animation is a moment of emotional choreography. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • FEATURES DON’T CONVERT - FEELINGS DO A smooth interface isn’t enough anymore. What converts is the emotion the experience evokes - relief, delight, confidence, or even belonging. You don’t remember the app that loaded fastest. You remember the one that made you smile when it did. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • FEELINGS DRIVE DECISIONS (AND REVENUE) → Cognitive fluency: Interfaces that are simple and predictable “feel right,” which reads as trustworthy and high quality. → Loss aversion: Users work harder to avoid losing what they’ve earned (credits, streaks, carts) than to gain something new. → Peak–End rule: People remember the emotional high point and the ending. Design your peaks and endings like they’re your brand. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • MICRO-INTERACTIONS = MICRO-EMOTIONS → Apple’s haptics reduce uncertainty and signal precision (visceral satisfaction confidence). → Netflix previews create open loops (Zeigarnik effect) that pull you into a session before you choose. → Duolingo blends encouragement + accountability: streaks (goal-gradient), “streak freeze” (loss aversion), leaderboards (social proof), and the owl’s tone (gentle shame → commitment). • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • CLOSE THE “AFFECTIVE GAP” BETWEEN GOOD AND GREAT Good brands ship usable features. Great brands shape feelings across the whole journey: → Visceral layer (first glance): Reduce cognitive load; make the next action obvious. → Behavioral layer (in use): Show progress, provide reversible choices, celebrate milestones. → Reflective layer (memory): End on a high, summarize achievement, invite sharing. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • MAKE EMOTION MEASURABLE Feelings aren’t fluffy if you pick the right lenses: → Confidence Task success without help, drop in abandonment at critical steps. → Progress Time-to-first-value, streak retention, return after day 7/30. → Belonging/Recognition Organic shares, community replies, unsolicited reviews. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • When emotion becomes part of UX, you don’t just create usability. You create affinity. Because features are copied. But feelings? Those are proprietary. ------------------------------------------ 💬 Let me know what you think 🔗 Share if helpful! 👉 Follow Anand Sankara Narayanan for brand stories & strategies ------------------------------------------