UI/UX Design Principles

⏱️ How To Measure UX (https://lnkd.in/e5ueDtZY), a practical guide on how to use UX benchmarking, SUS, SUPR-Q, UMUX-LITE, CES, UEQ to eliminate bias and gather statistically reliable results — with useful templates and resources. By Roman Videnov. Measuring UX is mostly about showing cause and effect. Of course, management wants to do more of what has already worked — and it typically wants to see ROI > 5%. But the return is more than just increased revenue. It’s also reduced costs, expenses and mitigated risk. And UX is an incredibly affordable yet impactful way to achieve it. Good design decisions are intentional. They aren’t guesses or personal preferences. They are deliberate and measurable. Over the last years, I’ve been setting ups design KPIs in teams to inform and guide design decisions. Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < 60s (for critical tasks) 3. Time to first success < 90s (for onboarding) 4. Time to candidates < 120s (nav + filtering in eCommerce) 5. Time to top candidate < 120s (for feature comparison) 6. Time to hit the limit of free tier < 7d (for upgrades) 7. Presets/templates usage > 80% per user (to boost efficiency) 8. Filters used per session > 5 per user (quality of filtering) 9. Feature adoption rate > 80% (usage of a new feature per user) 10. Time to pricing quote < 2 weeks (for B2B systems) 11. Application processing time < 2 weeks (online banking) 12. Default settings correction < 10% (quality of defaults) 13. Search results quality > 80% (for top 100 most popular queries) 14. Service desk inquiries < 35/week (poor design → more inquiries) 15. Form input accuracy ≈ 100% (user input in forms) 16. Time to final price < 45s (for eCommerce) 17. Password recovery frequency < 5% per user (for auth) 18. Fake email frequency < 2% (for email newsletters) 19. First contact resolution < 85% (quality of service desk replies) 20. “Turn-around” score < 1 week (frustrated users → happy users) 21. Environmental impact < 0.3g/page request (sustainability) 22. Frustration score < 5% (AUS + SUS/SUPR-Q + Lighthouse) 23. System Usability Scale > 75 (overall usability) 24. Accessible Usability Scale (AUS) > 75 (accessibility) 25. Core Web Vitals ≈ 100% (performance) Each team works with 3–4 local design KPIs that reflects the impact of their work, and 3–4 global design KPIs mapped against touchpoints in a customer journey. Search team works with search quality score, onboarding team works with time to success, authentication team works with password recovery rate. What gets measured, gets better. And it gives you the data you need to monitor and visualize the impact of your design work. Once it becomes a second nature of your process, not only will you have an easier time for getting buy-in, but also build enough trust to boost UX in a company with low UX maturity. [more in the comments ↓] #ux #metrics

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

Let’s say you’re a marketer hoping to win traffic from anyone searching for the "Best Beatles Songs." In the past, your SEO strategy would be to target keywords, and create content with corresponding headlines. i.e. “Must-Listen Beatles Songs” But now you need a different game plan. As we see more and more AI-powered engines like Perplexity and ChatGPT enter the market, the way we find information is becoming drastically different. These companies are making rev-share deals with major publishers to ensure their models have current, fresh information that’s accurate, comprehensive and forward thinking. To win an AI-enhanced search, your content should address the question: why are people searching for Beatles’ songs in the first place? You need to consider broader context and user intent. For example, are users discovering The Beatles for the first time and looking for an introduction to their catalog, or are they superfans wanting deeper insights into the music’s impact on culture? Offer value that goes beyond listing songs—provide historical context, trivia, or playlists curated for different moods or occasions. Focus on interactive or multimedia content, such as videos, audio clips, or even AI-generated playlists to create a richer, more engaging user experience. Show the search engine that your content satisfies not just the initial question, but also the deeper exploration the user might engage in. By doing this, you position yourself to build a trusted relationship with users.

I walked into Miniso just to browse, but a tiny design detail caught my attention I reached for a perfume tester, expecting to spray it on my wrist. But there was no push-button. Just an open nozzle, forcing me to bring it close and take a sniff. Observations: 🛍️ Smart Product Placement: Perfumes were neatly arranged in visually appealing color blocks, making selection feel intuitive. 👃 Tester Trick: The tester bottles had no push-button sprays! Instead, customers had to directly sniff the nozzle—reducing impulse spraying by passersby and ensuring serious buyers engage more deeply. 👉 Behavioral Science in Action: 📌 Commitment Bias: If you take the effort to pick up and sniff, you're more likely to consider buying. 📌Scarcity Effect: No free-flowing spray means the product feels more 'exclusive.' 📌Decision Fatigue Reduction: Minimal distractions, clear choices, and a structured layout make buying easier. Retailers are getting smarter—it's not just about WHAT they sell but HOW they sell it. Have you noticed any clever behavioral tactics in stores lately? #BehavioralScience #RetailPsychology #ConsumerBehavior #MarketingStrategy #BrandExperience 

Stop relying on Naive RAG and check out Contextual RAG. Sharing my new hands-on article on A Comprehensive Guide to Building Contextual RAG Systems with Hybrid Search and Reranking! Check it out below where I have implemented this exact architecture as depicted in this diagram which I have custom made. This workflow covers: - Processing JSON and PDF Documents - Creating document chunks using standard methods like Recursive Character Text Splitting - Customizing Anthropic's Context Generation Prompt to generate context information for each chunk and prepend to the chunks - Storing chunks and their embeddings into a Vector DB and TF-IDF vectors into a BM25 Index - Implementing Hybrid Search using Reciprocal Rank Fusion - Adding a Reranker to improve retrieval quality - Standard LLM-based RAG response generation Inspiration for this is Anthropic's contextual retrieval research which I have also talked about a few weeks back. I have used standard LangChain constructs to implement this along with custom built functions for context generation for contextual retrieval. The article has detailed explanation of the architecture along with step-by-step hands-on code. Do check this out and share with others if useful!

2 — Solving Goal & Priority Misalignment with Is/Is Not + Perspective Circle.  SOLVING THINGS with SYSTEMS THINKING (STwST) — a series of mini, real-world applications of DSRP. When a team says, “We’re working hard but not pulling in the same direction,” it’s usually not a motivation problem. And it’s rarely a communication problem. It’s a distinction + perspective problem. Different people are carrying different mental pictures of what the goal is and is not, and different perspectives on what actually counts as a priority. So even when everyone uses the same words, they’re not aiming at the same thing. They might be reading the same page but interpreting it differently. Two simple thinking moves fix this. The first is an Is / Is Not list. Take the goal and the priorities and make them explicit: what this goal is, what it is not; what matters now, and what does not. This forces clarity where assumptions usually hide. The second is a Perspective Circle. You don’t need everyone to think the same way—but you do need everyone looking at the same picture. Different roles, levels, and functions can keep their own viewpoints, as long as they’re all anchored to the same shared view. Then keep that shared model on the table. Revisit it at the start of meetings. Use it when tradeoffs show up. Let people argue with it, stress-test it, and refine it. Don’t laminate it. Put it to work. Alignment doesn’t come from hearing the right words once. It comes from people rebuilding their own internal picture until it matches the shared one. When that happens, language cleans up, decisions get faster, resources line up, and the friction fades—because action always follows the mental model. If you listen carefully, misalignment announces itself in sentences that shouldn’t exist if the goal were truly shared. Those sentences are the signal. #STwST #SystemsThinking #CabreraLabPodcast #SystemsThinkingStandardsInstitute

This is a brilliant paper - hot off the press - which makes the point that for too long, behavioural insights have been seen as a tool for tweaking individual behaviour—nudges, default settings, and small interventions. But this report argues that behavioural insights has the power to shape entire systems, not just individuals. "Systemic change is fundamentally rooted in human behaviour: while structural, political, economic, or technological challenges may set the stage, it is the decisions and actions of individuals that ultimately drive change. Behind every challenge lies the potential for human behaviour to alter the course, provided the right behavioural pathways are identified and leveraged." Created by Marion Dupoux and colleagues at the European Commission Joint Research Centre, it outlines how to harness the full potential of behavioural insights, by: 1️⃣ Moving beyond Nudges: By doing more than influencing individual choices—they can inform policy mixes that integrate regulations, incentives, and behavioural interventions 2️⃣ Creating policy coherence: By helping to identify where different policies complement or contradict each other, leading to more effective, aligned strategies 3️⃣ Leading to systemic Impact: By embedding behavioural insights early in the policymaking process, we can design policies that work with human behaviour rather than against it The authors call for us to go further with behavioural insights: "We argue that a more proactive and systematic approach is needed for BI to contribute to systemic change. This involves ensuring behavioural interventions are crafted with scalability in mind, informing the design of traditional policy instruments from the outset and, last but not least, understanding and working with complex systems." One of my favourite parts of this resource is the section focussed on achieving a systemic impact with behavioural insights. In this section, the authors highlight several key principles: 1️⃣ Embracing interdisciplinary collaborations and research  2️⃣ Embedding behavioural insights across the policy cycle, and crucially, starting early  3️⃣ Fostering knowledge of behavioural insights  4️⃣ Making tools from behavioural insights more readily available "BI should be integrated into all phases of the policy process, with particular emphasis on the earliest stages, to ground policy design in human behaviour, enhance policy coherence, and ensure a better functioning system." Source: Dupoux, M. (2025). Unlocking the full potential of behavioural insights for policy. From influencing the individual to shaping the system. European Commission Joint Research Centre.

My agency, The Search Initiative, helped a client grow organic users by 250% in 6 months. From 266,409 to 932,409 monthly users. Zero ad spend. Here's the exact 4-part strategy we used 👇 1. Category Expansion and Opportunity Mapping We audited keyword gaps for volume, intent match, and ranking potential. Prioritised by commercial value first, not just search volume. Then built a universal template every category page had to follow. Intro copy, FAQs, internal links, schema, and media, plus a minimum content depth to compete in search results and show up in AI summaries. New categories launched in a structured order, not reactively. Every one got internally linked from relevant hubs on day one. 2. Technical SEO and Internal Linking Set hub-and-sibling linking rules so related categories and blog posts reinforced each other. Anchor text stayed consistent with slight variants to keep relevance signals tight. New categories connected to parent pages the moment they went live, not weeks later. Structured data got validated across every category template, increasing eligibility for rich results and better AI search visibility. Navigation updated so both users and Google could find new sections immediately. 3. Informational Content and Authority Building A content calendar went up around priority themes and commercial categories. Every piece got a brief first, defining target intent, depth, internal linking targets, and SEO goals. Content answered high-intent research queries using data and original insights to make each piece worth linking to. A consistent link-building campaign ran alongside, focused on relevance and authority over volume. 4. User Engagement and Discovery We found where users were dropping off, especially where too many options caused decision fatigue. Those flows got redesigned to get users to what they needed faster. Trending tags and popular searches got surfaced throughout the site for urgency and social proof. Recently viewed items and auto-saved searches improved return visits without forcing account creation. Email capture tied to real user value, price alerts, availability updates, and new releases, turned browsing intent into qualified leads. The results after 6 months: - Total users: 266,409 → 932,409 (+250%) - New users: 262,773 → 904,939 (+244%) - Returning users: 13,571 → 54,371 (+301%) Want results like this? Get a free audit from The Search Initiative 👇

🔹 Day 20 – Product Manager Interview Prep Series 🔹 📊 Analytics: Metrics Deep Dive 🎯 Define Success Metrics for Zoom (This was asked in a Google PM interview) 📌 Question: What are the top success metrics the Product Lead of Zoom should track? 🚀 Mission: Build the most seamless, reliable, and scalable virtual communication platform to empower global users — from individuals to enterprises — to connect, collaborate, and communicate effortlessly. 🎯 Goal: → Maximize user engagement and session reliability → Improve collaboration experience and feature adoption → Drive enterprise stickiness and seat expansion → Enable monetization via subscriptions and add-ons 👥 Users Involved: -Individuals – personal users using Zoom for one-off meetings -Teams/Businesses – internal collaboration and client meetings -Admins – manage Zoom access, security, and reporting -Educators – use Zoom for live virtual classes -Event Hosts – use Zoom Webinars or Events for large audiences 📈 Metrics by User Journey: 1️⃣ Viewers/Participants (Engagement & Experience) → Avg. Meeting Duration per User – Indicates session value → Meeting Join Success Rate – Frictionless entry = better UX → Time Spent per Day on Zoom – Measures stickiness → Session Quality Score – Drop rate, latency, AV issues 2️⃣ Hosts/Organizers (Activation & Retention) → % of Users Hosting Meetings Weekly – Measures creator activity → Invite Acceptance Rate – Tracks meeting relevance & trust → Recurring Meetings Scheduled – Indicates habitual use → Tool Usage Rate (whiteboard, polls, breakout rooms) – Signals collaboration quality 3️⃣ Enterprise Admins (Monetization & Scalability) → Seat Utilization Rate – Active vs. purchased seats → Expansion Revenue % – Upsells, added features → Renewal Rate – Signals enterprise satisfaction → IT Support Tickets per 1k Users – Tracks admin friction 🌟 North Star Metric: % of Weekly Active Users Hosting or Joining >1 Meeting with Quality Score >90% →Ties together adoption, frequency, and reliability ⚠ Counter Metrics: → Zoom Fatigue – Too much time per session may reduce productivity → Churn of Free Users – Could indicate unmet expectations → High Drop Rates – Suggest technical or UX issues → Server Costs per Meeting Hour – Monitors scalability efficiency 💬 If you were PM of Zoom, what would YOU measure first? Drop your thoughts below and let’s learn together ⬇ #ProductManagement #PMInterviewPrep #Zoom #BuildInPublic #MetricsMatter #Google #Analytics #DailyPrep #LinkedInNewsIndia #PMLife

Mind the Gap with AI We are in a race with AI, constantly comparing who is superior. AI is already far ahead in many areas. But what we often miss is this. There is a significant gap between how AI thinks and what humans actually understand or want. An AI can generate a plan that looks perfect. Often much better than what a human would design. But if a human does not have the mental model behind that plan, execution will fail. This gap is not about efficiency. It is about alignment. The real challenge is aligning human mental models with AI mental models. Perfect synchronization matters more than raw intelligence. Whenever you work with AI, pause and reflect. Mind the gap between how AI thinks and how humans think. That gap decides success or failure.