UX Metrics And KPIs

⏱️ How To Measure UX (https://lnkd.in/e5ueDtZY), a practical guide on how to use UX benchmarking, SUS, SUPR-Q, UMUX-LITE, CES 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 (fully explained in videos → https://measure-ux.com). Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < Xs (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 a 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 > 30% (usage of a new feature per user) 10. Feature retention rate > 40% (after 90 days) 11. Time to pricing quote < 2 weeks (for B2B systems) 12. Application processing time < 2 weeks (online banking) 13. Default settings correction < 10% (quality of defaults) 14. Relevance of top 100 search requests > 80% (for top 5 results) 15. Service desk inquiries < 35/week (poor design → more inquiries) 16. Form input accuracy ≈ 100% (user input in forms) 17. Frequency of errors < 3/visit (mistaps, double-clicks) 18. Password recovery frequency < 5% per user (for auth) 19. Fake email addresses < 5% (newsletters) 20. Helpdesk follow-up rate < 4% (quality of service desk replies) 21. “Turn-around” score < 1 week (frustrated users -> happy users) 22. Environmental impact < 0.3g/page request (sustainability) 23. Frustration score < 10% (AUS + SUS/SUPR-Q) 24. System Usability Scale > 75 (usability) 25. Accessible Usability Scale (AUS) > 75 (accessibility) 26. Core Web Vitals ≈ 100% (performance) Each team works with 3–4 design KPIs that reflect the impact of their work. 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. [Useful tools in comments ↓]

GA4’s Average Session Duration is Lying to You Let me explain. If I told you the average salary in a room was $1 million… Would you believe everyone in that room is rich? Probably. BUT... Maybe 9 people make $20K, and one multi-millionaire skews the average. That’s exactly what’s happening with Average Session Duration **in GA4**. The Problem? GA4 Uses the Mean, Not the Median. 👉 Mean = "average" (adds all times, divides by sessions). 👉 Median = the middle value (what most users actually do). The difference? One long session can distort the entire number. Why This Screws Up Your Decisions Imagine 9 users spend 10 seconds on your site. And 1 person stays for 10 minutes. 🚨 GA4 reports an “Average Session Duration” of ~1 minute.** 🚨 But 90% of users actually left in 10 seconds. If you trust that number, you might think: ✅ “Our content is great!” ✅ “People love our site!” But in reality? Most visitors bounced. How to Fix This 🔹 Calculate the **Median** in BigQuery. 🔹 Segment by traffic source (email vs. paid ads vs. organic). 🔹 Track session drop-offs to see where people actually leave. Most brands trust GA4 blindly. But bad data leads to bad decisions - and lost revenue. Know of another metric that doesn’t make sense? DM me!

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 👇

A/B Testing in Google Ads: Best Practices for Better Performance Introduction to A/B Testing A/B testing in Google Ads is a crucial strategy for optimizing ad performance through data-driven insights. It involves comparing two versions of an ad to determine which one delivers better results.  Set Clear Goals Before conducting A/B tests, define clear objectives such as increasing click-through rates or conversions. Having specific goals will guide your testing process and help you measure success accurately.  Test Variables To effectively A/B test ads, focus on testing one variable at a time, such as the ad copy, images, or call-to-action. This approach will provide clear insights into what elements are driving performance. Create Variations Develop distinct ad variations with subtle differences to compare their impact. Ensure that each version is unique enough to produce measurable results but relevant to your target audience.  Implement Proper Tracking Set up conversion tracking and monitor key metrics closely to evaluate the performance of each ad variation accurately. Use tools like Google Analytics to gather meaningful data. Monitor Performance Metrics Regularly review performance metrics like click-through rates, conversion rates, and cost per acquisition to identify trends and patterns. Analyzing these metrics will help you make informed decisions. Scale Successful Tests Once you identify a winning ad variation, scale it by allocating more budget and resources to drive maximum results. Replicate successful strategies in future campaigns. Continuous Optimization Optimization is an ongoing process, so continue to test, refine, and adapt ad elements to enhance performance continuously. Stay updated with industry trends and consumer preferences. Analyze Results After conducting A/B tests, analyze the results comprehensively to understand the impact of your optimizations. Use the insights gained to inform future ad strategies. Summary  Following best practices for A/B testing in Google Ads can significantly improve the performance of your campaigns. By testing, analyzing, and optimizing ad variations, you can enhance engagement, conversions, and overall ROI. #MetaAds, #VideoMarketing, #DigitalAdvertising, #SocialMediaStrategy, #ContentCreation, #BrandAwareness, #VideoBestPractices, #MarketingTips, #MobileOptimization, #AdPerformance

I'm delighted to announce our latest research with Microsoft Advertising, which uncovers where customer journeys most often break down, and what that disconnect means for trust, conversion, and loyalty. Today’s shoppers move seamlessly across devices and channels, but the customer journey isn’t always as joined-up as it should be. Shoppers' expectations are higher than ever before, and they are intolerant of poor experiences. Our research found that device preference (e.g mobile, tablet, laptop, gaming etc) differs considerably across different age groups. And even within the same age group, preferences switch as purchase intentions rise. Consumers often want to start the discovery stage on mobile, but as purchase intent grows, they switch to laptops to have a more immersive experience. Brands that deliver a consistent experience across channels, device, platforms and more, inspire confidence, trust and boost conversion rates. The report focuses on four key pressure points: 🔍 Discovery – the complexity of how consumers search across screens and devices 🤝 Trust – the importance of consistency across every channel, device and ads. 🛒 Conversion – when shoppers click, but get disjointed experiences that drive them away 🧠 Personalisation – what ‘relevance’ really means in the AI era Built on insight from 2,000 UK consumers, Microsoft Advertising data and real brand examples, the report helps businesses turn unified commerce from concept into a practical strategy. 🔗 Get the full picture – download the report now https://lnkd.in/e9abZQQW

Dear CEOs and Founders, Seeing Google Search Console impressions up but clicks down? It's a common SEO puzzle! This often means your content is visible, but not compelling enough to click, or Search Engine Results Page (SERP) changes are at play. Key Reasons for this trend: 1. SERP Feature Changes: Google frequently updates the SERP layout with features like video carousels, image packs, featured snippets, People Also Ask boxes, and ads. These can push your organic listing down, reducing visibility and clicks. 2. Featured Snippets and AI Overviews: A featured snippet (position zero) or AI Overview can answer a user's query directly on the SERP, eliminating the need to click through to your site. This leads to higher impressions but fewer clicks. 3. Google Ads: More paid ads above organic results decrease visibility and lower your Click-Through Rate (CTR). 4. Irrelevant Keywords and Content Mismatch: Ranking for irrelevant keywords or having a search snippet that doesn't accurately reflect user intent can deter clicks. 5. Low Ranking Position: While impressions may increase from ranking for more keywords, appearing in lower positions (e.g., on the second page) significantly reduces clicks. 6. Unappealing Titles and Meta Descriptions: Poorly crafted or truncated titles and meta descriptions fail to attract users. 7. Competition: Stronger or more compelling search results from competitors can draw clicks away. 8. Structured Data Issues: Errors can remove rich snippets, reducing visual appeal and CTR. What you can do to improve clicks on your website? 1. Analyze your data: Use Google Search Console's Performance report to identify specific queries and pages with high impressions but low clicks. 2. Optimize titles and descriptions: Craft engaging, keyword-rich meta titles and descriptions that accurately reflect your content and encourage clicks. Consider using numbers or emotional triggers. 3. Improve ranking position: Focus on SEO strategies to achieve higher rankings for relevant keywords, as higher positions generally yield higher CTRs. 4. Use schema markup: Implement schema markup to enable rich snippets, making your search results more visually appealing and informative. 5. Match search intent: Ensure your content aligns with the intent behind your target keywords. Provide comprehensive answers for informational queries or strong product pages for commercial ones. 6. Monitor and adapt: Continuously observe your CTR and other key metrics in Search Console. A/B test different titles, descriptions, and content formats to see what resonates best with your audience. By carefully analyzing your data and implementing strategic changes, you can improve your CTR and drive more qualified traffic to your website! Drop a comment below if you're doing something different to improve clicks on your website from search engines. Thank you!

How User Calls and a Simple Text Update Led to a 3.25% Increase in CTR [A/B Experiment] 📈 I was recently working on a project looking for low-performing categories where CTR is low with high search volumes and significant revenue potential.💲 One such category was "Hospitals." Given the variety of reasons someone might search for a Hospital—whether for inquiries, appointment scheduling, or specialty information—it became clear that understanding user intent was key 🤔 To gain deeper insights, we conducted user calls to better understand why users were landing on our platform. Through these calls, we discovered that many users were attempting to book appointments, even though Justdial is an aggregator platform that does not offer direct booking for all healthcare providers. Booking functionality is available only for certain paid businesses 🏥 To address this, we needed to better guide users on how to proceed. While users could either call or submit an enquiry through our platform, engagement was still low. 📉 To improve this, we made a simple yet impactful change to the text on our CTAs. We updated the primary CTA from "Call Now" to "Call to Book" and the secondary CTA from "Send Enquiry" to "Check Availability" 📞 This small change resulted in a 3.25% increase in click-through rates. Knowing the context and nudging users at the right time can lead to better conversions. Solves problems for both the user & the business. 💡✅ #prodcutmanagement #experiment

How to #𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞𝐀𝐈𝐀𝐠𝐞𝐧𝐭s true effectiveness? Most people get excited about building agents, 𝐁𝐮𝐭 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐢𝐟 𝐚𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐢𝐬 𝐠𝐨𝐨𝐝 𝐞𝐧𝐨𝐮𝐠𝐡 𝐭𝐨 𝐭𝐫𝐮𝐬𝐭, how to measure their true effectiveness. Without the right evaluation, agents can become unreliable, costly, and even risky to deploy. 𝐂𝐨𝐫𝐞 𝐅𝐚𝐜𝐭𝐨𝐫𝐬 𝐭𝐨 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐚𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭: 𝟏. 𝐋𝐚𝐭𝐞𝐧𝐜𝐲 𝐚𝐧𝐝 𝐒𝐩𝐞𝐞𝐝 How fast does the agent finish tasks? A 2-second reply feels great, a 10-second lag frustrates users. 𝟐. 𝐀𝐏𝐈 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 Does the agent optimize API calls or combine requests smartly to reduce cost and delay? 𝟑. 𝐂𝐨𝐬𝐭 𝐚𝐧𝐝 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 Same result, different costs. One model might cost $0.25 per query, another $0.01. Efficiency matters. 𝟒. 𝐄𝐫𝐫𝐨𝐫 𝐑𝐚𝐭𝐞 How often does the agent fail or crash? If 20 out of 100 attempts fail, that’s a 20 percent error rate. 𝟓. 𝐓𝐚𝐬𝐤 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 Does the agent actually complete the job? If it resolves 45 out of 50 tickets, that’s a 90 percent success rate. 𝟔. 𝐇𝐮𝐦𝐚𝐧 𝐈𝐧𝐩𝐮𝐭 How much correction does the AI need? If humans edit every step, efficiency drops. 𝟕. 𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 𝐌𝐚𝐭𝐜𝐡 Does the AI follow instructions correctly? If asked for 3 bullet points but writes a paragraph, it is failing accuracy. 𝟖. 𝐎𝐮𝐭𝐩𝐮𝐭 𝐅𝐨𝐫𝐦𝐚𝐭 Is the answer in the right format? If JSON is expected but plain text comes back, that breaks workflows. 𝟗. 𝐓𝐨𝐨𝐥 𝐔𝐬𝐞 Does the agent use the right tools? For example, using a calculator API instead of “guessing” math answers. #AIAgents are not just about being flashy. They need to prove they are reliable, cost-effective, and scalable. Evaluating them across these nine factors ensures they’re truly ready for real-world use. 𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐟𝐚𝐜𝐭𝐨𝐫𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐢𝐠𝐧𝐨𝐫𝐞 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐰𝐡𝐞𝐧 𝐝𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬? #BuildAgents

What's your approach to designing user flows? ✏️ -Understand the User and Goals: Start by gaining a deep understanding of the target users, their needs, and their goals. Conduct user research, interviews, and surveys to gather insights into their behaviors, pain points, and motivations. Define User Personas: Create user personas to represent different segments of your target audience. Personas help humanize the users and guide the design process to meet their specific needs. -Map the User Journey: Outline the entire user journey from the initial touchpoint to the final goal. This involves understanding the various stages users go through when interacting with your product and identifying potential entry and exit points. Identify Key User Tasks: Identify the primary tasks users want to accomplish within your product. Focus on the core functionality and prioritize these tasks in the user flow. Create a Flowchart: Visualize the user flow by creating a flowchart. Use arrows to show the sequence of steps users will take to complete their tasks. Consider different scenarios and decision points they might encounter. Keep it Simple and Intuitive: Aim for simplicity and clarity in the user flow. Minimize the number of steps required to achieve a task and avoid unnecessary complexity that could confuse users. Consistency across Platforms: If your product is available on multiple platforms (e.g., web, mobile), ensure a consistent user flow across all of them. Users should feel comfortable and familiar with the flow, regardless of the device they are using. Anticipate User Errors: Design the user flow with the anticipation of user errors or confusion. Provide clear error messages and guidance to help users recover quickly. User Testing and Iteration: Test the user flow with real users through usability testing sessions. Analyze the feedback and data to identify pain points and areas of improvement. Iterate and refine the user flow based on the insights gained. Collaborate with the Team: Involve stakeholders, designers, developers, and other team members in the user flow design process. Collaborative efforts lead to a more comprehensive and well-rounded user experience. Consider Edge Cases: Take into account edge cases and less common scenarios in your user flow design. This ensures that your product is accessible and usable for all users, regardless of their specific circumstances. Accessibility and Inclusivity: Design with accessibility and inclusivity in mind. Ensure that the user flow is usable by people with disabilities and diverse backgrounds.

A must-have for each design case study → impact. No business metrics? No problem! You don't need quantitative metrics to show it. Often, you won't have access to them at all. That doesn't mean you can't demonstrate impact. You need to shift your thinking. Think before/during/after. Compare before and after: • How did your design improve upon the previous one? • How did you streamline the workflow? (fewer steps) • How did you improve accessibility for users? • Do a heuristics evaluation. How have they improved? • How have engagement metrics changed? (time to value) During the design process: • How did you improve the handoff process? • How did you contribute to the design system? • What insights did testing reveal? • How did you enhance design docs? • How did you influence design culture? After the project is done: • What do stakeholders say? (testimonials) • What did users think? • How can your design accommodate future growth?    There are things only business metrics can demonstrate. Even without them, you can show your work's impact. P.S. Share your ways to show a design impact. ✌️