Experience Design 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 👇

I’ve made games for 12+ years. My biggest mistakes? All ideas started with bad prototyping. Here are 5 hard-learned: 1. Prototypes don’t lie. ↳ Your prototype is brutally honest. 2. Don’t wait for perfection. ↳ Learn fast, move on - ugly is fine. 3. No one claps for your design docs. ↳ Let real people play, not your mom. 4. Prototypes boost morale. ↳ Long dev kills vibe, quick fun fuels it 5. Prototyping ≠ polishing. ↳ It’s a sketch, not a sculpture. 💡TIP: Build the smallest playable version of your core loop. → No art. → No polish. → No menus. → Just see if it’s fun. If it isn’t, nothing else matters. 🧱 Example: Want to make a horror roguelike? Just prototype: ↓ One room ↓ One enemy ↓ Basic tension mechanic If the loop isn’t scary now, it won’t be scarier with shaders. Prototype checklist: ✅ Core mechanic is in ✅ It feels something (tension, joy, etc.) ✅ Testers “get” what the game is about ✅ It breaks (but teaches you something) If YES: you’re on track. Prototyping isn't just for mechanics. Try these: → Visual style (Can I sell this mood?) → Control feel (Does jumping feel good?) → Onboarding (Can players figure this out?) All count. PROTOTYPING PITFALLS TO AVOID: ❌ Falling in love with your first idea ❌ Building full art assets too early ❌ Showing only to friends & family ❌ Refusing to cut features 🔥 Final tip: A prototype should answer this: "Should I keep building this?" If the answer is no, that’s not failure. That’s a massive win that saved you months (or years).

Download numbers are nothing but vanity metrics if your users are leaving through the back door as fast as they enter through the front. It is easy to get obsessed with the initial spike in user acquisition. We see it all the time here at Full Metal. Founders come to us beaming about hitting their first ten thousand downloads, but when we look at the active daily users, the picture has gone a bit pear-shaped. Here is the cold reality: nearly 71% of app users will have forgotten all about your app within three months. If you are paying £2 to £5 to acquire a single user in the UK—which is standard for many industries—and they leave immediately, you are essentially setting fire to your marketing budget. It is a massive drain on resources and a huge missed opportunity. We need to shift the conversation from acquisition to retention. We need to fix the leaky bucket. The data supports this shift. A study by Bain & Company found that increasing user retention by just 5% can boost profits by anywhere from 25% to a staggering 95%. That is where the real value lies. It is not about casting the widest net; it is about keeping the fish you catch. Consider the maths of churn. If you start with 10,000 users and have a 5% monthly churn, you are fighting a losing battle. But reduce that churn to 2%, and you will see thousands of additional active users within a single year. So, how do we stop the leak? Actionable Takeaways: ✅ Solve a genuine problem: This sounds obvious, but you would be surprised how many apps offer a solution looking for a problem. Ensure your app addresses a real-world headache for your users today, tomorrow, and next week. ✅ Check your "Sanity Metrics": Stop looking at total downloads. Focus on Active Users (DAU/MAU) and Retention Rate. These figures tell you if your business model actually works. ✅ Calculate Lifetime Value (CLTV): Connect engagement to your bottom line. If a user stays for twelve months, what are they worth? Now compare that to the cost of acquiring them. If the maths does not stack up, neither will the business. Building a loyal following means you get more value from each user and can finally stop pouring money into a strategy that isn't working. Read the full strategy in our latest blog: https://lnkd.in/emz2A--g Question: When you look at your current app metrics, are you tracking how many people stay, or just how many arrived? #AppRetention #SoftwareDevelopment #BusinessStrategy

🔎 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

A $12 prototype can make $50,000 of engineering analysis look ridiculous A team of engineers was stuck on a bearing failure analysis for six weeks. Vibration data, FFT analysis, metallurgy reports - they had everything except answers. The client kept asking for root cause and the engineers kept finding more variables to analyze. Temperature gradients, load distributions, contamination levels, manufacturing tolerances. Each analysis created more questions. Then the intern did something that made the engineers feel stupid. She 3D printed a transparent housing and filled it with clear oil so the engineers could actually see what was happening inside the bearing assembly. Took her four hours and $12 in materials. They watched the oil flow patterns and immediately saw the lubrication wasn't reaching the critical contact points. All their sophisticated analysis was based on assuming proper lubrication distribution. Wrong assumption. Six weeks of wasted effort. The visual prototype didn't just solve the problem - it changed how the engineers approach these types of investigations. Now they build crude mockups before diving into analysis rabbit holes. Cardboard, tape, clear plastic, whatever works. Physical models force you to confront your assumptions before you spend weeks analyzing the wrong thing. Sometimes the cheapest prototype teaches you more than the most expensive simulation. #engineering #prototyping #problemsolving

Prototypes aren't for testing your product. They're for testing your assumptions. Most teams get this backward, and it costs them weeks of wasted effort and a product nobody wants. A prototype isn't a tiny product; it's a medium for learning. It's a tool designed to ask a specific question and test a core assumption with the right audience. An unintentionally designed prototype is a flawed input, and even with advanced teams and tools, flawed inputs only amplify flaws. The true power of a prototype isn't in its polish, but in the intentional "message" it sends. To unlock this power and truly accelerate collective learning across your organization, you must design with intent: ✺ Low-Fidelity Prototypes: These are for asking foundational, "Does this even solve the right problem?" questions. They signal that everything is up for debate. The intentional message is: "Let's explore the idea, not the pixels." ✺ Medium-Fidelity Prototypes: Use these to test core user flows and information architecture. The intentional message is: "Is this journey intuitive?" By keeping them a little rough, you prevent stakeholders from getting fixated on visual design. ✺ High-Fidelity Prototypes: Reserve these for the final stages to test things like micro-interactions, brand consistency, or subtle emotional responses. The intentional message is: "We're almost there. What are we missing?" This is how you turn prototyping from a simple task into a strategic lever for change and Team Learning. It ensures your team isn't just building things, but is learning together and making better decisions about what to build and why. It's how you break down silos and create a "Holding Environment" for generative dialogue. What's a time you intentionally used a low-fidelity prototype to prevent a high-stakes meeting from spiraling? Let’s discuss in the comments below. #ProductDesign #SystemsThinking #StrategicDesign #UXStrategy #DesignLeadership #ComplexSystems #TeamLearning #Prototyping #OrganizationalDesign #Innovation

AI tools dramatically changed how I do product. Here’s a workflow I did recently that I recommend every PM to try. We’re working on a new product that needs a lot of product definition under a huge time crunch (sounds familiar, anyone?). Instead of weeks of back-and-forth with the team to get my vision and thinking across, I vibe coded a few prototypes to convey my concepts... and it was shockingly effective. Using Lovable or AI Studio, you can quickly bring your ideas to about 70% of where you want them to be (but like everything genAI… good luck closing the gap of the remaining 30% 🫠 ). For a prototype though, 70% is perfectly fine. These prototypes grasped our key concepts so well, that we decided to test them in user research to validate our hypotheses quickly. As we have pretty ambitious plans (plug.. watch this space), a figma prototype would have taken designers serious time to create, and static wireframes just wouldn’t have gotten us the learnings. While the quickly developed AI prototypes didn’t look or feel at all like the core interface, this actually played into our advantage as it removed bias, and let users give feedback on functionality rather than the UI. And a bonus tip: I fed the UXR Transcripts to Gemini, using it as a thought-partner to clarify my thinking when writing user journeys, requirements, definitions. Try this to get rid of the blank canvas problem of writing. I honestly couldn’t be more excited about the opportunities these tools bring. It allows me to spend my time focusing on deep thinking, direction, vision, and strategy (all that stuff PMs are uniqely positioned to do) instead of wasting time on tasks that require little or no creativity. (and yes for the sake of illustrating this post I built a prototype that builds prototypes 😵💫)

Your design system documentation has a 3-week lag problem 👇 Designer updates the button → Developer ships it → Someone hopefully remembers to update the docs. The result? 🤯 → "Is this the latest version?" 12 times per sprint → Hours wasted hunting for correct specs → 30% of components still using old tokens months later Most teams try to solve this with better processes. More meetings. Stricter update cadences. Automated reminders. That's optimizing the wrong thing. The only way to kill latency is to connect your tools so they document themselves. ✨ Here is the automated design system documentation workflow: Figma (API + MCP) → AI reads specs (I used Claude Code) → Mintlify auto-deploys What gets automated: → Screenshot exports from Figma frames → Spec extraction (spacing, colors, tokens) → Documentation updates → Pull requests with visual diffs ✨ You can even set up GitHub Actions to check tracked Figma frames weekly and create PRs automatically. The guide is available on today's newsletter. 🙌 What's your setup? #designsystem #documentation #productmanagement #productdesign

Classic Retention vs. 24-Hour vs. Rolling Retention Here is a breakdown of the three main types of retention: 1. Classic Retention Tracks how many users return on specific days after their first play. • Formula: (Users on Day X / Users on Day 0) * 100% • Example: Day 7 Retention = 30%, meaning 30 out of 100 users return exactly on the 7th day. • Use Case: Identifying when users drop off to improve retention strategies. 2. 24-Hour Retention Flexible for analyzing user interest after events or milestones within any 24-hour period. • Formula: (Users in last 24 hours / Users in previous 24 hours) * 100% • Example: 24-Hour Retention = 40%, meaning 40 out of 100 users active within the last 24 hours return. • Use Case: Assessing engagement post-event or update. 3. Rolling Retention Measures users who return on or after a specific day, reflecting long-term engagement. • Formula: (Users active on & after Day X / Users on Day 0) * 100% • Example: Day 7 Rolling Retention = 50%, meaning 50 out of 100 users returned at any point after Day 7. • Use Case: Tracking overall user engagement over time. ↳ Nuances to Remember: • Classic Retention is calendar-based and doesn't capture users who return after skipping days. • 24-Hour Retention offers a snapshot of activity, not just Day 1. • Rolling Retention is more forgiving, always higher than Classic Retention, and includes users who eventually return. Choosing the right metric depends on what you want to measure: immediate engagement, long-term loyalty, or response to events.

💡Design System Metrics Design system brings two main benefits: Consistency and Efficiency. It helps minimize usability issues and maintain design consistency. However, without metrics, it can be hard to tell how well the system performs. That’s why it’s recommended to define metrics up front when establishing a foundation for your design system. Here are some popular design system metrics: Product design process: ✔ Adoption rate. What % of products use the design system? The more the design system is used, the more time is saved. ✔ Average task completion time. The time designers spend on completing the task (for example, designing a new user flow). Compare before/after the design system. ✔ Design to development time. Design system should speed up the handoff process from designers to developers. ✔ Component usage. The number of components used across products vs the total number of components available in the design system. Compare the usage of components in design (Figma) and code (Github). This will help you identify unused components.  ✔ Effect on code. Measure code complexity and how much code developers change with each release. ✔ Number of component detachments (Figma). If some components are often detached, you won’t have the right picture of how effective the design system is. Design output quality: ✔ User interface design consistency. # of visual inconsistencies in a final design.  ✔ Error rates and usability issues. Whether the design system reduces error rates and usability issues. ✔ Design documentation state. % of outdated docs. Outdated docs increase the risk of releasing inconsistent design. ✔ Accessibility score. How the design system improves accessibility (e.g., WCAG score) Business: ✔ Return on Investment (ROI). ROI is a key metric that stakeholders analyze to understand if the investment in DS is paying off. ✔ Team satisfaction score. How do team members feel about the design system? Collect feedback to understand what problems team members face using a design system. ✔ Tech debt. After having the design system in place, there should be less tech debt. ✔ Average time to market. The time the product team spends on releasing a new feature/scenario. Compare before/after the DS. ✔ Company scalability. How does workload capacity change after having the design system? ✔ Brand consistency. There should be less work required to fix visual differences because the design system drives repeat usage. 📖 Guides and tools: ✔ Measuring DS success (by Nathan Curtis) https://lnkd.in/gA25QK73 ✔ Measuring the impact of a design system (by Cristiano Rastelli)  https://lnkd.in/dx5YMWta ✔ Design system metrics collection, checklist for Figma (by Romina Kavcic)  https://lnkd.in/gAeN_sfk 🖼 Design system adoption by Stylebit #designsystem #designsystems