User Experience Metrics for Success

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 wish someone taught me this in my first year as a PM. It would’ve saved years of chasing the wrong goals and wasting my team's time: "Choosing the right metric is more important than choosing the right feature." Here are 4 metrics mistakes even billion-dollar companies have made and what to do instead with Ron Kohavi: 1. Vanity Metrics They look good. Until they don’t. A social platform he worked with kept showing rising page views… While revenue quietly declined. The dashboard looked great. The business? Not so much. Always track active usage tied to user value, not surface-level vanity. 2. Insensitive Metrics They move too slowly to be useful. At Microsoft, Ronny Kohavi’s team tried using LTV in experiments. but saw zero significant movement for over 9 months. The problem is you can’t build momentum on data that’s stuck in the future. So, use proxy metrics that respond faster but still reflect long-term value. 3. Lagging Indicators They confirm success after it’s too late to act. At a subscription company, churn finally spiked… but by then, 30% of impacted users were already gone. Great for storytelling but let's be honest, it's useless for decision-making. You can solve it by pairing lagging indicators with predictive signals. (Things you can act on now.) 4. Misaligned Incentives They push teams in the wrong direction. One media outlet optimized for clicks and everything was looking good until it wasn't. They watched their trust drop as clickbait headlines took over. The metric had worked. They might had "more MRR". But the product suffered in the long run. It's cliche but use metrics that align user value with business success. Because Here's The Real Cost of Bad Metrics - 80% of team energy wasted optimizing what doesn’t matter - Companies with mature metrics see 3–4× stronger alignment between experiments and outcomes - High-performing teams run more tests but measure fewer, better things Before you trust any metric, ask: - Can it detect meaningful change in faster? - Does it map to real user or business value? - Is it sensitive enough for experimentation? - Can my team interpret and act on it? - Does it balance short-term momentum and long-term goals? If the answer is no, it’s not a metric worth using. — If you liked this, you’ll love the deep dive: https://lnkd.in/ea8sWSsS

If you're a UX researcher working with open-ended surveys, interviews, or usability session notes, you probably know the challenge: qualitative data is rich - but messy. Traditional coding is time-consuming, sentiment tools feel shallow, and it's easy to miss the deeper patterns hiding in user feedback. These days, we're seeing new ways to scale thematic analysis without losing nuance. These aren’t just tweaks to old methods - they offer genuinely better ways to understand what users are saying and feeling. Emotion-based sentiment analysis moves past generic “positive” or “negative” tags. It surfaces real emotional signals (like frustration, confusion, delight, or relief) that help explain user behaviors such as feature abandonment or repeated errors. Theme co-occurrence heatmaps go beyond listing top issues and show how problems cluster together, helping you trace root causes and map out entire UX pain chains. Topic modeling, especially using LDA, automatically identifies recurring themes without needing predefined categories - perfect for processing hundreds of open-ended survey responses fast. And MDS (multidimensional scaling) lets you visualize how similar or different users are in how they think or speak, making it easy to spot shared mindsets, outliers, or cohort patterns. These methods are a game-changer. They don’t replace deep research, they make it faster, clearer, and more actionable. I’ve been building these into my own workflow using R, and they’ve made a big difference in how I approach qualitative data. If you're working in UX research or service design and want to level up your analysis, these are worth trying.

Align your UX metrics to the business KPIs. We've been discussing what makes a KPI in our company. A Key Performance Indicator measures how well a person, team, or organization meets goals. It tracks performance so we can make smart decisions. But what’s a Design KPI? Let’s take an example of a design problem. Consider an initiative to launch a new user dashboard to improve user experience, increase product engagement, and drive business growth. Here might be a few Design KPIs with ways to test them: →  Achieve an average usability of 80% within the first three months post-launch. Measurement: Conduct user surveys and collect feedback through the dashboard's feedback feature using the User Satisfaction Score. →  Ensure 90% of users can complete key tasks (e.g., accessing reports, customizing the dashboard) without assistance. Measurement: Conduct usability testing sessions before and after the launch, analyzing task completion rates. →  Reduce the average time to complete key tasks by 20%. Measurement: Use analytics tools to track and compare time spent on tasks before and after implementing the new dashboard. We use Helio to get early signals into UX metrics before coding the dashboard. This helps us find good answers faster and reduces the risk of bad decisions. It's a mix of intuition and ongoing, data-informed processes. What’s a product and business KPI, then? Product KPI: →  Increase MAU (Monthly Active Users) by 15% within six months post-launch. Measurement: Track the number of unique users engaging with the new dashboard monthly through analytics platforms. →  Achieve a 50% feature adoption rate of new dashboard features (e.g., customizable widgets, real-time data updates) within the first quarter. Measurement: Monitor the usage of new features through in-app analytics. Business KPI: → Drive a 5% increase in revenue attributable to the new dashboard within six months. Measurement: Compare revenue figures before and after the dashboard launch, focusing on user subscription and upgrade changes. This isn't always straightforward! I'm curious how you think about these measurements. #uxresearch #productdiscovery #marketresearch #productdesign

I’ve found it largely to be the case that people who work on products (PMs, Designers, Researchers, Devs, Content, etc.) want to have more clarity on metrics but struggle to do so. What's helped me over time is to think about metrics like individual pieces to a puzzle. My goal is to figure out how these pieces fit together as a puzzle, but frequently, no one really knows what the puzzle looks like—especially the “the business” or executives. What's also helped me is to work backwards. Business metrics are typically pieces that already exist, so part of the puzzle is there. I work with my cross-functional partners to 1. create metrics we can directly influence as the makers of the products, and 2. figure out if/how they connect to those business metrics. It’s finding the “fits” of the puzzle pieces. At the start, it often looks like a bunch of random puzzle pieces. Some of the pieces are metrics: CSAT, NPV, ARU, Conversion Rate, Time on Task, Error Rates, Alt Tag %, Avg Time Spent In App, Trial User Rate, etc. Some of the pieces are goals and actions: "A new Design System component," “Improve Product Accessibility,” “Fix Bugs,” “Raise Quality,” etc. Members of the product team have puzzle pieces, but struggle to understand how they fit as a puzzle. It's a classic chicken and egg problem. When we start to map the relationships between metrics (thinking about cause and effect), post-mapping looks like something like this: Improve Accessibility (goal) -> Create New Design System Component (action) -> Alt Tag % (metric) -> Error Rate (metric) -> Time on Task (metric) -> Trial User Rate (metric) -> Time Spent in App (metric) -> Conversion Rate (metric) -> NPV (metric) -> ARPU (metric) -> Revenue (metric). After seeing how the pieces might fit together, that's when basic statistical analyses like Correlation or Linear Regression help them calculate if there are, in fact, relationships between metrics. IMO, the hard part is explaining this in a way that 1. makes sense to a wide range of individuals, and 2. compels them to do this. What's helped me do this hard part is having a partially filled-out map that has the metrics people care about and completing more of the map with product partners, so we're all on the same page. Once we complete that map and run some basic statistical analyses, we have more credible arguments for if/how the work that goes into making products translates to business goals. Truthfully, not every exec is convinced, but at least we know we're making more credible decisions as a team. If an exec loves NPS, no matter what the data says, it's not on us to adult for them. We can hold our heads high and know we're doing a good job.

Ever noticed how two UX teams can watch the same usability test and walk away with completely different conclusions? One team swears “users dropped off because of button placement,” while another insists it was “trust in payment security.” Both have quotes, both have observations, both sound convincing. The result? Endless debates in meetings, wasted cycles, and decisions that hinge more on who argues better than on what the evidence truly supports. The root issue isn’t bad research. It’s that most of us treat qualitative evidence as if it speaks for itself. We don’t always make our assumptions explicit, nor do we show how each piece of data supports one explanation over another. That’s where things break down. We need a way to compare hypotheses transparently, to accumulate evidence across studies, and to move away from yes/no thinking toward degrees of confidence. That’s exactly what Bayesian reasoning brings to the table. Instead of asking “is this true or false?” we ask: given what we already know, and what this new study shows, how much more likely is one explanation compared to another? This shift encourages us to make priors explicit, assess how strongly each observation supports one explanation over the alternatives, and update beliefs in a way that is transparent and cumulative. Today’s conclusions become the starting point for tomorrow’s research, rather than isolated findings that fade into the background. Here’s the big picture for your day-to-day work: when you synthesize a usability test or interview data, try framing findings in terms of competing explanations rather than isolated quotes. Ask what you think is happening and why, note what past evidence suggests, and then evaluate how strongly the new session confirms or challenges those beliefs. Even a simple scale such as “weakly,” “moderately,” or “strongly” supporting one explanation over another moves you toward Bayesian-style reasoning. This practice not only clarifies your team’s confidence but also builds a cumulative research memory, helping you avoid repeating the same arguments and letting your insights grow stronger over time.

User research is great, but what if you do not have the time or budget for it........ In an ideal world, you would test and validate every design decision. But, that is not always the reality. Sometimes you do not have the time, access, or budget to run full research studies. So how do you bridge the gap between guessing and making informed decisions? These are some of my favorites: 1️⃣ Analyze drop-off points: Where users abandon a flow tells you a lot. Are they getting stuck on an input field? Hesitating at the payment step? Running into bugs? These patterns reveal key problem areas. 2️⃣ Identify high-friction areas: Where users spend the most time can be good or bad. If a simple action is taking too long, that might signal confusion or inefficiency in the flow. 3️⃣ Watch real user behavior: Tools like Hotjar | by Contentsquare or PostHog let you record user sessions and see how people actually interact with your product. This exposes where users struggle in real time. 4️⃣ Talk to customer support: They hear customer frustrations daily. What are the most common complaints? What issues keep coming up? This feedback is gold for improving UX. 5️⃣ Leverage account managers: They are constantly talking to customers and solving their pain points, often without looping in the product team. Ask them what they are hearing. They will gladly share everything. 6️⃣ Use survey data: A simple Google Forms, Typeform, or Tally survey can collect direct feedback on user experience and pain points. 6️⃣ Reference industry leaders: Look at existing apps or products with similar features to what you are designing. Use them as inspiration to simplify your design decisions. Many foundational patterns have already been solved, there is no need to reinvent the wheel. I have used all of these methods throughout my career, but the trick is knowing when to use each one and when to push for proper user research. This comes with time. That said, not every feature or flow needs research. Some areas of a product are so well understood that testing does not add much value. What unconventional methods have you used to gather user feedback outside of traditional testing? _______ 👋🏻 I’m Wyatt—designer turned founder, building in public & sharing what I learn. Follow for more content like this!

Most UX folks are missing the one skill that could save their careers. For a long time, many UXers have been laser-focused on the craft. Understanding users. Testing ideas. Perfecting pixels. But here’s the reality. Companies are cutting those folks everywhere, because they don’t connect their work to hard, actual, tangible $$$$$. So it’s viewed as a luxury. A nice-to-have. My 2 cents.. If you can’t tie your decisions to how it helps the business make or save money, you’re at risk. Full stop. But I have good news. You can quantify your $$ impact using basic financial modeling. Here’s a quick example.. Imagine you’re working on a tool that employees use every day. Let’s say the current experience requires 8 hours a week for each employee to complete a task. By improving the usability of the tool, you cut that time by three hours. Let’s break it down. If the average employee makes $100K annually (roughly $50/hr), and 100 employees use the tool, that’s $15K saved each week. Over a year, that’s $780K in savings.. just by shaving 3 hours off a process. Now take it a step further. What if those employees use those extra 3 hours to create more value for customers? What’s the potential revenue upside? This is the kind of thinking that sets a designer apart. It’s time for UXers to stop treating customer sentiment or usability test results as the final metric. Instea learn how your company makes or saves money and model the financial impact of your UX changes. Align your work with tangible metrics like operational efficiency, customer retention, or lifetime value. The best part? This isn’t hard. Basic math and a simple framework can help you communicate your value in ways the business understands. Your prototype or design file doesn’t need to be perfect. But your ability to show how it drives business outcomes? That does. — If you enjoyed this post, join hundreds of others and subscribe to my weekly newsletter — Building Great Experiences https://lnkd.in/edqxnPAY

When reporting on your impact as a UX Researcher, here are the best → worst metrics to tie your work to: 𝟭. 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 Every company is chasing revenue growth. This is especially true in tech. Tying your work to new (or retained) revenue is the strongest way to show the value that you’re bringing to the organization and make the case for leaders to invest more in research. Examples: - Research insights → new pricing tier(s) → $X - Research insights → X changes to CSM playbook → Y% reduction in churn → $Z 𝟮. 𝗞𝗲𝘆 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 This might not be possible for many UXRs, but if you can, showing how your work contributed to key decisions (especially if those decisions affect dozens or hundreds of employees) is another way to stand out. Examples: - Research insights → new ideal customer profile → X changes across Sales / Marketing / Product affecting Y employees' work - Research insights → refined product vision → X changes to the roadmap affecting Y employees' work 𝟯. 𝗡𝗼𝗿𝘁𝗵 𝘀𝘁𝗮𝗿 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 If you can’t directly attribute your work to revenue, that’s ok! The majority of research is too far removed from revenue to measure the value in dollars. The next best thing is to tie your work to core user engagement metrics (e.g. “watch time” for Netflix, “time spent listening” for Spotify). These metrics are north star metrics because they’re strong predictors of future revenue. Examples: - Research insights → X changes to onboarding flow → Y% increase in successfully activated users - Research insights → X new product features → Y% increase in time spent in app 𝟰. 𝗖𝗼𝘀𝘁 𝘀𝗮𝘃𝗶𝗻𝗴𝘀 For tech companies, a dollar saved is usually less exciting than a dollar of new (or retained) revenue. This is because tech companies’ valuations are primarily driven by future revenue growth, not profitability. That being said, cost savings prove that your research is having a real / tangible impact. 𝟱. 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻’𝘁 𝗯𝗲 𝘁𝗿𝗮𝗰𝗲𝗱 𝘁𝗼 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗮𝗯𝗼𝘃𝗲 Hot take: The biggest trap for researchers (and product folks generally) is focusing on user experience improvements that do not clearly lead to more engagement or more revenue. At most companies, it is nearly impossible to justify investments (including research!) solely on the basis of improving the user experience. Reporting on user experience improvements without tying them to any of the metrics above will make your research look like an expendable cost center instead of a critical revenue driver. — TL;DR: Businesses are driven by their top line (revenue) and bottom line (profit). If you want executives to appreciate the impact of (your) research, start aligning your reporting to metrics 1-4 above.

Try this if you struggle with defining and writing design outcomes: Map your solutions to proven UX Metrics Let's start small. Learn the Google HEART framework H - Happiness: How do users feel about your product? 📈 Metrics: Net Promotor Score, App Rating E - Engagement : Are users engaging with your app? 📈 Metrics: # of Conversions, Session Length A - Adoption: Are you getting new users? 📈 Metrics: Download Rate, Sign Up Rate R - Retention Are users returning and staying loyal? 📈 Metrics: Churn Rate, Subscription Renewal T - Task Success Can users complete goals quickly? 📈 Metrics: Error Rates, Task Completion Rate These are all bridges between design and business goals. HEART can be used for the whole app or specific features. 👉 Let's tie it to an example case study problem: Students studying overseas need to know what recipes can be made with ingredients available at home, as eating out regularly is too expensive and unhealthy. ✅ Outcome Example: While the app didn't launch, to track success and impact, I would have monitored the following: - Elevated app ratings and positive feedback, indicating students found the app enjoyable and useful - Increased app usage, implying more students frequently cooking at home - Growth in new sign-ups, reflecting more students discovering the app - Lower attrition rates and more subscription renewals, showing the app's continued value - Decrease in incomplete recipe attempts, suggesting the app was successful in helping students achieve their cooking goals. The HEART framework is a perfect tracker of how well the design solved or could solve the stated business problem. 💡Remember: Without data, design is directionless. We are solving real business problems. ------------------------------------------- 🔔 Follow: Mollie Cox ♻ Repost to help others 💾 Save it for future use