Task Success Rate Evaluation

Everyone’s excited to launch AI agents. Almost no one knows how to measure if they’re actually working. Over the last year, we’ve seen brands launch everything from GenAI assistants to support bots to creative copilots but the post-launch metrics often look like this: • Number of chats • Average latency • Session duration • Daily active users Useful? Yes. But sufficient? Not even close. At ALTRD, we’ve worked on AI agents for enterprises and if there’s one lesson it’s this: Speed and usage mean nothing if the agent isn’t solving the actual problem. The real performance indicators are far more nuanced. Here’s what we’ve learned to track instead: 🔹 Task Completion Rate — Can the AI go beyond answering a question and actually complete a workflow? 🔹 User Trust — Do people come back? Do they feel confident relying on the agent again? 🔹 Conversation Depth — Is the agent handling complex, multi-turn exchanges with consistency? 🔹 Context Retention — Can it remember prior interactions and respond accordingly? 🔹 Cost per Successful Interaction — Not just cost per query, but cost per outcome. Massive difference. One of our clients initially celebrated their bot’s 1 million+ sessions - until we uncovered that less than 8% of users actually got what they came for. That 8% wasn’t a usage issue. It was a design and evaluation issue. They had optimized for traffic. Not trust. Not success. Not satisfaction. So we rebuilt the evaluation framework - adding feedback loops, success markers, and goal-completion metrics. The results? CSAT up by 34% Drop-off down by 40% Same infra cost, 3x more value delivered The takeaway: Don’t just measure what’s easy. Measure what matters. AI agents aren’t just tools - they’re touchpoints. They represent your brand, shape user experience, and influence business outcomes. P.S. What’s one underrated metric you’ve used to evaluate AI performance? Curious to learn what others are tracking.

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 👇

✅ How To Run Task Analysis In UX (https://lnkd.in/e_s_TG3a), a practical step-by-step guide on how to study user goals, map user’s workflows, understand top tasks and then use them to inform and shape design decisions. Neatly put together by Thomas Stokes. 🚫 Good UX isn’t just high completion rates for top tasks. 🤔 Better: high accuracy, low task on time, high completion rates. ✅ Task analysis breaks down user tasks to understand user goals. ✅ Tasks are goal-oriented user actions (start → end point → success). ✅ Usually presented as a tree (hierarchical task-analysis diagram, HTA). ✅ First, collect data: users, what they try to do and how they do it. ✅ Refine your task list with stakeholders, then get users to vote. ✅ Translate each top task into goals, starting point and end point. ✅ Break down: user’s goal → sub-goals; sub-goal → single steps. ✅ For non-linear/circular steps: mark alternate paths as branches. ✅ Scrutinize every single step for errors, efficiency, opportunities. ✅ Attach design improvements as sticky notes to each step. 🚫 Don’t lose track in small tasks: come back to the big picture. Personally, I've been relying on top task analysis for years now, kindly introduced by Gerry McGovern. Of all the techniques to capture the essence of user experience, it’s a reliable way to do so. Bring it together with task completion rates and task completion times, and you have a reliable metric to track your UX performance over time. Once you identify 10–12 representative tasks and get them approved by stakeholders, we can track how well a product is performing over time. Refine the task wording and recruit the right participants. Then give these tasks to 15–18 actual users and track success rates, time on task and accuracy of input. That gives you an objective measure of success for your design efforts. And you can repeat it every 4–8 months, depending on velocity of the team. It’s remarkably easy to establish and run, but also has high visibility and impact — especially if it tracks the heart of what the product is about. Useful resources: Task Analysis: Support Users in Achieving Their Goals (attached image), by Maria Rosala https://lnkd.in/ePmARap3 What Really Matters: Focusing on Top Tasks, by Gerry McGovern https://lnkd.in/eWBXpCQp How To Make Sense Of Any Mess (free book), by Abby Covert https://lnkd.in/enxMMhMe How We Did It: Task Analysis (Case Study), by Jacob Filipp https://lnkd.in/edKYU6xE How To Optimize UX and Improve Task Efficiency, by Ella Webber https://lnkd.in/eKdKNtsR How to Conduct a Top Task Analysis, by Jeff Sauro https://lnkd.in/eqWp_RNG [continues in the comments below ↓]

How far are we from having competent AI co-workers that can perform tasks as varied as software development, project management, administration, and data science? In our new paper, we introduce TheAgentCompany, a benchmark for AI agents on consequential real-world tasks. Why is this benchmark important? Right now it is unclear how effective AI is at accelerating or automating real-world work. We hear statements like: > AI is overhyped, doesn’t reason, and doesn’t generalize to new tasks > AGI will automate all human work in the next few years This question has implications for: - Companies: to understand where to incorporate AI in workflows - Workers: to get a grounded sense of what AI can and cannot do - Policymakers: to understand effects of AI on the labor market How can we begin on it? In TheAgentCompany, we created a simulated software company with tasks inspired by real-world work. We created baseline agents, and evaluated their ability to solve these tasks. This benchmark is first of its kind with respect to versatility, practicality, and realism of tasks. TheAgentCompany features four internal web sites: - GitLab: for storing source code (like GitHub) - Plane: for doing task management (like Jira) - OwnCloud: for storing company docs (like Google Drive) - RocketChat: for chatting with co-workers (like Slack) Based on these sites, we created 175 tasks in the domains of: - Administration - Data science - Software development - Human resources - Project management - Finance We implemented a baseline agent that can web browse and write/execute code to solve these tasks. This was implemented using the open-source OpenHands framework for full reproducibility (https://lnkd.in/g4VhSi9a). Based on this agent, we evaluated many LMs, Claude, Gemini, GPT-4o, Nova, Llama, and Qwen. We evaluated both success metrics and cost. Results are striking: the most successful agent w/ Claude was able to successfully solve 24% of the diverse real-world tasks that it was tasked with. Gemini-2.0-flash is strong at a competitive price point, and the open llama-3.3-70b model is remarkably competent. This paints a nuanced picture of the role of current AI agents in task automation. - Yes, they are powerful, and can perform 24% tasks similar to those in real-world work - No, they can not yet solve all tasks or replace any jobs entirely Further, there are many caveats to our evaluation: - This is all on simulated data - We focused on concrete, easily evaluable tasks - We focused only on tasks from one corner of the digital economy If TheAgentCompany interests you, please: - Read the paper: https://lnkd.in/gyQE-xZG - Visit the site to see the leaderboard or run your own eval: https://lnkd.in/gtBcmq87 And huge thanks to Fangzheng (Frank) Xu, Yufan S., and Boxuan Li for leading the project, and the many many co-authors for their tireless efforts over many months to make this happen.

𝗠𝗖𝗣-𝗘𝗡𝗔𝗕𝗟𝗘𝗗 𝗔𝗜 𝗔𝗚𝗘𝗡𝗧𝗦 𝗙𝗔𝗜𝗟 40-60% 𝗢𝗙 𝗧𝗛𝗘 𝗧𝗜𝗠𝗘 𝗢𝗡 𝗥𝗘𝗔𝗟-𝗪𝗢𝗥𝗟𝗗 𝗪𝗢𝗥𝗞𝗙𝗟𝗢𝗪𝗦: 𝗛𝗘𝗥𝗘'𝗦 𝗪𝗛𝗬 My daily work on LLM's workflow architectures (MCP-driven agent workflows) pushes me to the frontier of how 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 (𝗠𝗖𝗣𝘀) can be reliably exploited at scale. The 𝗟𝗶𝘃𝗲𝗠𝗖𝗣-101 study (arXiv:2508.15760) offers valuable insights into this challenge. 𝗕𝗘𝗡𝗖𝗛𝗠𝗔𝗥𝗞 - LiveMCP-101, a benchmark of 101 carefully curated real-world 𝗺𝘂𝗹𝘁𝗶-𝘀𝘁𝗲𝗽 queries 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗮𝘀𝗸𝘀 (average 5.4 steps, up to 15) stress-test MCP-enabled agents across web, file, math, and data analysis domains. - 18 𝗺𝗼𝗱𝗲𝗹𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲𝗱: OpenAI, Anthropic, Google, Qwen3, Llama. 𝗞𝗘𝗬 𝗙𝗜𝗡𝗗𝗜𝗡𝗚𝗦 - 𝗚𝗣𝗧-5 𝗹𝗲𝗮𝗱𝘀 with 58.42% Task Success Rate, dropping to 39.02% on "Hard" tasks - 𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗹𝗮𝗴𝘀 𝗯𝗲𝗵𝗶𝗻𝗱: Qwen3-235B at 22.77%, Llama-3.3-70B below 2% - 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗽𝗹𝗮𝘁𝗲𝗮𝘂: Closed models plateau after ~25 rounds; open models consume more tokens without proportional gains 𝗖𝗢𝗡𝗖𝗥𝗘𝗧𝗘 𝗧𝗔𝗦𝗞 𝗘𝗫𝗔𝗠𝗣𝗟𝗘𝗦 - 𝗘𝗮𝘀𝘆: Extract latest GitHub issues - 𝗠𝗲𝗱𝗶𝘂𝗺: Compute engagement rates on YouTube videos - 𝗛𝗮𝗿𝗱: Plan an NBA trip (team info, tickets, Airbnb constraints) with consolidated Markdown report 𝗙𝗔𝗜𝗟𝗨𝗥𝗘 𝗔𝗡𝗔𝗟𝗬𝗦𝗜𝗦 - 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗲𝗿𝗿𝗼𝗿𝘀: Skipped requirements, wrong tool choice, unproductive loops - 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗲𝗿𝗿𝗼𝗿𝘀: Semantic (16.83% for GPT-5, up to 27.72% for other models) and syntactic (up to 48.51% for Llama-3.3-70B) - 𝗢𝘂𝘁𝗽𝘂𝘁 𝗲𝗿𝗿𝗼𝗿𝘀: Correct tool results misinterpreted 𝗧𝗔𝗞𝗘𝗔𝗪𝗔𝗬𝗦 𝗙𝗢𝗥 𝗠𝗖𝗣 𝗪𝗢𝗥𝗞𝗙𝗟𝗢𝗪 𝗗𝗘𝗦𝗜𝗚𝗡 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, is the main bottleneck. Reliability requires: • External planning • Tool selection, ranking and routing (RAG-MCP, ...) • Variable passing between MCP & memory (Variables Chaining) • Schema validation • Trajectory monitoring • Efficiency policies, Budget-aware execution 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: The path forward isn't adding more tools, but engineering robust orchestration layers that make MCP chains dependable. What's your experience with AI agent workflows at scale? Have you experienced similar failure patterns? Many of these orchestration issues are ones I’ve needed to tackle in practice — always happy to compare notes with others working on advanced solutions. Link to the paper: https://lnkd.in/g8bbNK6E #AI #MachineLearning #Workflows #MCP #AIAgents #Productivity #Innovation   

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.

94% PM compliance. Only 22% of tasks were valid. That is not a typo. A processing site was completing almost every scheduled task on time. Leadership reported it as a win. The maintenance team was proud of it. Then we reviewed the actual tasks. The Uncomfortable Truth 78% of the PM program was either redundant, unexecutable as written, or targeting failure modes that did not exist on those assets. The site had been running 847 scheduled tasks. 340 of them should never have been there. This is not unusual. We see it on almost every site we assess. Why It Happens PM programs are built once and never validated. Tasks get added after every failure, every audit, every new OEM recommendation. Nothing gets removed. The program grows, compliance stays high, and nobody questions whether the tasks actually prevent failures. Maintenance planners measure completion. They do not measure effectiveness. What Changes It Review tasks at the task level, not the schedule level. For each task, ask five questions: 1. What failure mode does this prevent? 2. Is there evidence this failure mode occurs on this asset? 3. Can a technician execute this task as written? 4. Is the interval based on failure data or a guess? 5. Is this task duplicated elsewhere in the program? That site eliminated 340 tasks. Redirected 2,100 labour hours per year to tasks that actually matter. Unplanned downtime dropped 31% in the first 12 months. Reality Check Pull your PM completion report. Now pull your failure rate trend. If compliance is above 90% and failures are not declining, you are measuring the wrong thing. What does your compliance rate actually tell you? #ReliabilityEngineering #MaintenanceStrategy #AssetManagement

I am thrilled to share the most comprehensive and impactful research Project Management Institute has ever conducted on one of the profession’s most critical topics: #projectsuccess. This monumental study redefines how we understand and achieve success in the projects that shape our world. We began with an extensive review of 50 years of seminal literature, laying a foundation of knowledge and insights. Building on this, we conducted 90 in-depth interviews with a diverse range of voices: project professionals, sponsors, PMO leaders, executives, and intended beneficiaries. These conversations informed a robust global survey, engaging 9,500 project professionals, stakeholders, and beneficiaries across industries, who evaluated their recently completed projects. Our rigorous analysis and statistical modeling culminated in a groundbreaking new approach for understanding project success. This approach was further enriched through collaboration with a team of subject matter experts and 50+ interviews with #PMO leaders and community members, ensuring its relevance and applicability. This landmark report sets a new standard for what it means to deliver a successful project, offering transformative insights and actionable guidance for the profession. Here’s what you’ll discover: - > A Holistic Definition of Success: Establishes a shared perspective that aligns the priorities of diverse stakeholders, from practitioners to beneficiaries. - > A Universal Measurement Framework: Introduces a clear and consistent method for evaluating project success across industries and geographies. - > Key Success Drivers: Identifies and explains the factors that influence project outcomes, empowering practitioners and organizations to consistently deliver greater value. - > Global and Industry Insights: Provides a detailed measurement of project success rates worldwide, segmented by industry and project type, offering invaluable benchmarking data. - > Purpose-Driven Benefits: Highlights the profound impact of aligning projects with a higher purpose to achieve not just success, but significance. - > Practical Activation of Insights: Equips practitioners, executives, and the broader project management community with tools to activate success in real-world scenarios. - > A Vision for the Future: Guides the profession and its stakeholders toward outcomes that maximize success and elevate our world. Read the full report: https://lnkd.in/dv-387F7 Project Management Institute #thoughtleadership #projectsuccess #projectmanagementtoday

As UX researchers, we often encounter a common challenge: deciding whether one design truly outperforms another. Maybe one version of an interface feels faster or looks cleaner. But how do we know if those differences are meaningful - or just the result of chance? To answer that, we turn to statistical comparisons. When comparing numeric metrics like task time or SUS scores, one of the first decisions is whether you’re working with the same users across both designs or two separate groups. If it's the same users, a paired t-test helps isolate the design effect by removing between-subject variability. For independent groups, a two-sample t-test is appropriate, though it requires more participants to detect small effects due to added variability. Binary outcomes like task success or conversion are another common case. If different users are tested on each version, a two-proportion z-test is suitable. But when the same users attempt tasks under both designs, McNemar’s test allows you to evaluate whether the observed success rates differ in a meaningful way. Task time data in UX is often skewed, which violates assumptions of normality. A good workaround is to log-transform the data before calculating confidence intervals, and then back-transform the results to interpret them on the original scale. It gives you a more reliable estimate of the typical time range without being overly influenced by outliers. Statistical significance is only part of the story. Once you establish that a difference is real, the next question is: how big is the difference? For continuous metrics, Cohen’s d is the most common effect size measure, helping you interpret results beyond p-values. For binary data, metrics like risk difference, risk ratio, and odds ratio offer insight into how much more likely users are to succeed or convert with one design over another. Before interpreting any test results, it’s also important to check a few assumptions: are your groups independent, are the data roughly normal (or corrected for skew), and are variances reasonably equal across groups? Fortunately, most statistical tests are fairly robust, especially when sample sizes are balanced. If you're working in R, I’ve included code in the carousel. This walkthrough follows the frequentist approach to comparing designs. I’ll also be sharing a follow-up soon on how to tackle the same questions using Bayesian methods.

One of the key ways to demonstrate the value of UX research is by measuring success metrics. Without these, it can be hard to show the impact of your work on the product or the business. But how exactly can we measure success in a UX research project? Here are a few critical steps and metrics to consider: 1. Align with Business Goals: ↳ Start by identifying the KPIs tied to business goals. Whether it’s conversion, adoption, or drop-off rates, the research should connect to metrics that matter for the company’s success. By linking research insights directly to business outcomes, you show stakeholders how UX impacts their key priorities. 2. Behavioral Metrics: These are the data points tied to how users interact with your product, such as: ↳ Task Success Rate: How many users successfully complete the task? ↳ Time-on-Task: How long does it take users to complete a task? ↳ User Error Rate: How often do users make mistakes during the task? Tracking these helps identify friction points in the user journey and quantifies the effectiveness of your designs. 3. Attitudinal Metrics: These reflect how users feel about the product or experience: ↳ Net Promoter Score (NPS): How likely are users to recommend your product? Although this one is definitely not my favorite, most businesses care a lot about NPS. ↳ Customer Satisfaction (CSAT): How satisfied are users with the product? ↳ Perceived Ease of Use: How easy do users think the product is to use? Gathering these insights gives you a clear sense of user sentiment and overall satisfaction. 4. Usability Metrics: For more specific insights, you can track usability metrics like: ↳ System Usability Scale (SUS): A quick way to assess perceived usability. ↳ Completion Rates: How many users completed a given task without assistance? 5. Impact on KPIs: Finally, after research is complete and changes are implemented, re-measure these metrics to show improvements. Demonstrating a reduction in error rates or an increase in task success ties UX research directly to improved product performance. By clearly connecting UX metrics to business KPIs, you help stakeholders see the concrete value that research brings to the table. These success metrics aren’t just numbers — they’re proof of how UX research improves user experience and drives business impact. How do you measure success in your UX research projects?