UX Design For Cloud-Based Solutions

⏱️ 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

🔹 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

🔎 UX Metrics: How to Measure and Optimize User Experience? When we talk about UX, we know that good decisions must be data-driven. But how can we measure something as subjective as user experience? 🤔 Here are some of the key UX metrics that help turn perceptions into actionable insights: 📌 Experience Metrics: Evaluate user satisfaction and perception. Examples: ✅ NPS (Net Promoter Score) – Measures user loyalty to the brand. ✅ CSAT (Customer Satisfaction Score) – Captures user satisfaction at key moments. ✅ CES (Customer Effort Score) – Assesses the effort needed to complete an action. 📌 Behavioral Metrics: Analyze how users interact with the product. Examples: 📊 Conversion Rate – How many users complete the desired action? 📊 Drop-off Rate – At what stage do users give up? 📊 Average Task Time – How long does it take to complete an action? 📌 Adoption and Retention Metrics: Show engagement over time. Examples: 📈 Active Users – How many people use the product regularly? 📈 Churn Rate – How many users stop using the service? 📈 Cohort Retention – What percentage of users remain engaged after a certain period? UX metrics are more than just numbers – they tell the story of how users experience a product. With them, we can identify problems, test hypotheses, and create better experiences! 💡🚀 📢 What UX metrics do you use in your daily work? Let’s exchange ideas in the comments! 👇 #UX #UserExperience #UXMetrics #Design #Research #Product

💡Measuring UX using Google HEART HEART is a framework developed by Google for evaluating the user experience of a product. It provides a holistic view of the UX by considering both qualitative & quantitative metrics. HEART stands for ✅ Happiness: How satisfied users are with using your product. It can be measured through surveys and ratings (quantitative) and reviews and user interviews (qualitative). Tracking happiness is right when you analyze the general performance of your product. ✅ Engagement: How actively users are interacting with the product. This includes metrics like the number of visits, time spent on the product, frequency of interactions, and the depth of interactions (e.g., the number of features used). Analyzing engagement will help you understand how compelling & valuable the product is to users. ✅ Adoption: How effectively the product attracts new users and converts them into active users. Key metrics include user sign-ups, onboarding completion rates, and activation rates (e.g., the percentage of users who perform a key action after signing up). Understanding adoption helps identify barriers during product onboarding. ✅ Retention: How well the product retains its users over time. It focuses on reducing churn and keeping users engaged over the long term. Metrics like retention rate and cohort analysis are used to measure retention. Improving retention involves addressing pain points, providing ongoing value, and fostering a sense of loyalty among users. ✅ Task success: How effectively users can accomplish their goals or tasks using the product. This includes metrics like task completion rate, error rate, and time to complete tasks. User journey mapping, user interviews, and usability testing can help identify usability issues and optimize the user flow to enhance task success. ❗ Top 3 mistakes when using HEART 1️⃣ Placing too much emphasis on quantitative metrics at the expense of qualitative insights. While quantitative data is valuable for analysis, it's essential to complement this with qualitative data, such as user feedback and observations, to gain a deeper understanding of user behavior and preferences. 2️⃣ Ignoring the context of interaction: Failing to consider the context in which users interact with the product can lead to misleading interpretations of the data.  3️⃣ Lack of user segmentation: Not segmenting users based on relevant factors such as demographics, behavior, or usage patterns can obscure important insights and lead to generic conclusions that may not apply to all user groups. 📺 Guide to using Google HEART: https://lnkd.in/dhkwy_jN 🚨 Live session "How to measure design success" 🚨 I will run a live session on measuring design success in February. Will talk about how to choose the right metrics for your product & how to measure product's success in meeting business goals   https://lnkd.in/dgm6t_jf #UX #design #productdesign #metrics #measure

Build it and they will come? 🤔 When product teams launch a highly-requested feature, they tend to expect users to engage with it. But things don’t always work out that way. 😬 This is what Lyft discovered when they launched Women+ Connect. Despite the clear benefits and the demand for the feature, not all drivers who were eligible were opting into it. 🔍 The challenge? Simply telling users about a feature isn’t always enough to drive action. When Irrational Labs partnered with Lyft, here’s what our brilliant behavioral scientist Isabel Macdonald, PhD and team learned, working closely with Robyn Bald and Kirsten M.: 🚀 A simple shift in messaging—based on behavioral science—can massively impact feature engagement. Irrational Labs tested several behaviorally-informed messages and all outperformed the control. The winning message? “Just checking. Looks like you are not opted into Women+ Connect. Is this correct? Tap to review.” What this does: The question creates a desire for resolution and nudges the driver to take action (versus do nothing). The result? Compared to the control group, this approach got 173% more opt-ins from new drivers. 📈 So, what’s the takeaway for product teams? 👉🏼 Product success doesn’t come just from building great features. You have to frame them in ways that resonate with your users, capture their attention, and motivate them to act. 💡 Curious to see how small changes can lead to massive impact? Check out the link in the comments to learn how we helped Lyft get great engagement with a great feature. 👇🏼 #BehavioralScience #ProductManagement #UserEngagement #WomenInTech #IrrationalLabs #Lyft

Most designers apply B2C UX to enterprise software. That’s a huge mistake. B2B UX Isn’t Just B2C with More Buttons. B2B UX needs efficiency, precision & integration—not just pretty screens. What makes B2B UX different? → Workflows are complex & multi-step → Data density is higher & more critical → Users are specialists, not casual consumers → Speed & automation matter more than aesthetics How I design for this: ✓ Talk to engineers, analysts, ops teams. ✓ Deep research into user workflows. ✓ I optimize for function, not fluff ✓ Usability > trends Also: I have technical expertise—I understand development, automation, and dev constraints. If your B2B UX isn’t working, it’s time for a rethink. Let’s talk.

The UX of Agentic Graph Systems: Beyond Chat Interfaces 🌓 Agentic graph systems represent a significant evolution in AI architecture, combining the structured knowledge representation of graphs with the generative capabilities of large language models to create systems capable of autonomous planning and execution. As AI systems evolve from simple pattern-matchers to agentic systems with continuous observe-think-act cycles, our user interfaces must evolve accordingly. Current chat-based interfaces, while familiar, prove fundamentally limiting for the complex workflows that true agentic systems enable. Traditional chat interfaces impose substantial constraints when working with AI agents on complex tasks. The sequential, text-based format creates a "linear conversation flow" that makes it difficult to represent non-linear workflows with dependencies, branches, or parallel processes. These interfaces struggle with visualization capabilities, making it challenging to display complex data structures or relationships. They also feature inefficient error correction mechanisms—when an AI makes a mistake, fixing it through chat creates verbose, confusing conversation histories that increase cognitive load for users. Vector-based RAG approaches, while prevalent, flatten complex relationships and lose crucial connections between entities. Graph-based systems address fundamental limitations by embedding structural knowledge representation within neural processing frameworks. This architecture enables the capture of complex relational networks that define enterprise knowledge—preserving not just what information exists, but how it connects across organizational boundaries. Visualizing these relationships becomes essential for human understanding and interaction with the system. Several compelling alternative UI paradigms that offer significant advantages over traditional chat: Agent Dashboards: Command centers showing AI reasoning and confidence levels, enabling "observable autonomy" where users can monitor and influence AI decision-making without constant micromanagement. Workflow Graphs: Visual, editable task maps that transform linear task lists into spatial, interactive mind maps where tasks, decisions, and actions are visually represented and modifiable. Editable AI Notebooks: Structured, persistent documents that both AI and humans can continuously update and reference, creating shared, actionable memory that preserves context. Multimodal UIs: Interaction beyond text, incorporating drag-and-drop interfaces, voice commands, and spatial representations that reduce friction in human-AI collaboration. Interactive visualization transforms abstract data relationships into intuitive visual mappings, enabling developers and users to clearly understand connections. Continuez en commentaire :

Most companies see business, customer, and UX metrics as separate stories. I had Bruno M. (JP Morgan Chase, HealthEquity), who led the journey-centric transformation to make these separate layers work together. I love the simplicity of the approach, when every job to be done or journey get structured with 3 layers of metrics. That way, every level of the journey framework is consistent: 1️⃣ Business Layer (Top Layer) This layer focuses on traditional KPIs that matter most to executives — the metrics that indicate how the journey contributes to overall business performance. Examples include: - Revenue - Conversion rates - Cost savings (e.g., shorter average handle time) - Retention / Churn rates These help executives and general managers see how customer experience links directly to financial and operational performance. 2️⃣ Customer Experience Layer (Middle Layer) Here, Bruno connects business KPIs to customer sentiment using metrics like: - NPS (Net Promoter Score) - CSAT (Customer Satisfaction) While he’s critical of NPS (“hard to know what’s really broken just from NPS”), he acknowledges it remains a key business-facing metric that helps secure buy-in from leadership. However, he stresses that NPS alone is meaningless — its value emerges only when overlaid with other measures like completion rates or drop-off data. 3️⃣ UX / Behavioral Layer (Bottom Layer) The third layer goes deeper into the user experience where the actual friction or success of the journey can be observed. Examples include: - Task completion rates - Time on task - Error rates - Drop-offs or conversion funnels These granular metrics help teams act quickly and connect customer behaviors directly to business outcomes. 🤝 How It All Connects Bruno envisions a single dashboard where you can: - Click into a “job to be done” or journey. - See the KPI layer, CX layer, and UX layer all linked together. This way: - Executives can see how journeys drive business. - CX teams can track satisfaction and loyalty. - Product and design teams can pinpoint usability and behavioral issues. He calls this layered approach the core of accountability in journey management. Making sure everyone from the CEO to the UX designer looks at the same truth through their own lens. Check out the Episode for a deep dive, this one is 🔥🔥🔥

During meetings with stakeholders, we often hear about 𝒓𝒆𝒅𝒖𝒄𝒊𝒏𝒈 𝒃𝒐𝒖𝒏𝒄𝒆 𝒓𝒂𝒕𝒆𝒔, 𝒊𝒏𝒄𝒓𝒆𝒂𝒔𝒊𝒏𝒈 𝒓𝒆𝒕𝒆𝒏𝒕𝒊𝒐𝒏, 𝒂𝒏𝒅 𝒐𝒑𝒕𝒊𝒎𝒊𝒛𝒊𝒏𝒈 𝒄𝒐𝒏𝒗𝒆𝒓𝒔𝒊𝒐𝒏 𝒇𝒖𝒏𝒏𝒆𝒍𝒔. If you're feeling confused and overwhelmed about how to do all of this, you're not alone. Here's something for those new to the world of metric-driven design. Trust me, your designs can make a real difference :) 𝗙𝗶𝗿𝘀𝘁 𝘁𝗵𝗶𝗻𝗴𝘀 𝗳𝗶𝗿𝘀𝘁, 𝗴𝗲𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲𝗿𝘀 𝗔𝗡𝗗 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 → Talk to real users. Understand their pain points. But also, grab coffee with the marketing team. Learn what those metrics mean. You'd be surprised how often a simple chat can clarify things. 𝗠𝗮𝗽 𝗼𝘂𝘁 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝗳𝗹𝗼𝘄 → Sketch it out, literally. Where are users dropping off? Where are they getting stuck? This visual approach can reveal problems you might miss otherwise and which screens you need to tackle. 𝗞𝗲𝗲𝗽 𝗶𝘁 𝘀𝗶𝗺𝗽𝗹𝗲, 𝘀𝘁𝘂𝗽𝗶𝗱 (𝗞𝗜𝗦𝗦)→ We've all heard this before, but it's true. A clean, intuitive interface can work wonders for conversion rates. If a user can't figure out what to do in 5 seconds, you might need to simplify. 𝗕𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗱𝗲𝘀𝗶𝗴𝗻 → Trust isn't built by security badges alone. It's about creating an overall feeling of reliability. Clear communication, consistent branding, and transparency go a long way. 𝗠𝗮𝗸𝗲 𝗶𝘁 𝗲𝗻𝗴𝗮𝗴𝗶𝗻𝗴 → Transform mundane tasks into engaging experiences. Progress bars, thoughtful micro-animations, or even well-placed humor can keep users moving forward instead of bouncing off. Remember, engaged users are more likely to convert and return, directly impacting your key metrics. 𝗧𝗲𝘀𝘁, 𝗹𝗲𝗮𝗿𝗻, 𝗿𝗲𝗽𝗲𝗮𝘁 → Set up usability tests to validate your design decisions. Start small - even minor changes in copy or button placement can yield significant results. The key is to keep iterating based on real data, not assumptions. This approach improves your metrics and also sharpens your design intuition over time. 𝗗𝗼𝗻'𝘁 𝗿𝗲𝗶𝗻𝘃𝗲𝗻𝘁 𝘁𝗵𝗲 𝘄𝗵𝗲𝗲𝗹 → While it's tempting to create something totally new, users often prefer familiar patterns. Research industry standards and find data around successful interaction models, then adapt them to address your specific challenges. This approach combines fresh ideas with proven conventions, enhancing user comfort and adoption. Metric-driven design isn't about sacrificing creativity for numbers. It's about using data to inform and elevate your design decisions. By bridging the gap between user needs and business goals.

Your best ideas die in dashboards. They fail because you waited too long for answers. Most teams don’t lack data. In fact, they’re buried in it. But it’s often stuck in dashboards or behind groups of people who aren’t designed or organized to help you decide what to do next. The real problem is clarity. Without it, decisions slow down. Direction gets fuzzy. Dashboards are built to reduce risk, not to help teams move forward with confidence. I see teams launch a new idea, only to wait and see if it works. They wait for analytics to catch up. Wait for users to churn (or not). Wait to find out if it worked. By then, momentum is gone. That’s why defining your UX metrics upfront changes everything. It gives you three fast ways to know what’s happening: → Attitude, why they feel the way they do (whether they trust it, get it, or feel lost) → Behavior, how users interact (where they click, what they skip, where they get stuck) → Performance, what happened (like completion rates, errors, or time on task) You stop relying on lagging indicators and start seeing live signals, while there’s still time to make the idea work. Here’s how to think about this: 👉 If you’re redesigning an onboarding flow to help new users activate faster. You don’t want to just know if it worked weeks later, you want to know what’s working and why right now. Here’s how defining UX metrics up front helps you uncover the story fast: 🟦 Attitudinal Metrics These early signs show emotional friction. This issue goes beyond usability problems to gaps in clarity, confidence, and credibility. → Trust: Only 36% of users said they trust the product with their data after onboarding → Expectations: 41% said the steps didn’t match what they expected → Helpfulness: Only 33% felt the tips and instructions were helpful → Satisfaction: 48% reported feeling satisfied after onboarding 🟩 Behavioral Metrics Reflects the attitudinal story that users aren’t just slow, they’re unsure and disengaged. → Completion: Only 62% finished onboarding → Comprehension: 27% answered a comprehension check incorrectly (about how to import data) → Effort: Users took an average of 12 clicks to complete a 5-step flow → Intent: 46% skipped optional setup steps, signaling disengagement → Usability: Heatmaps show users repeatedly hovered over unclear icons with no labels or tooltips 🟨 Performance Metrics These lagging indicators validate the issue, but UX metrics let you act before the damage spreads. → Activation rate down 18% → Retention after Day 1 down 12% → Click-back rate to onboarding emails spiked 2x Set your metrics early, and you don’t wait for clarity...you create it. #productdesign #uxmetrics #productdiscovery #uxresearch