User Experience for Content Management Systems

🔎 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

📈 Improve your case studies with UX metrics. If you've been avoiding metrics in your UX portfolio, it's time to change it! In a competitive job market, setting yourself apart means proving your efforts make a real impact on UX projects. This is also something recruiters and managers truly value. They want to see numbers and evidence, not just beautiful designs. Here are some common UX metrics to showcase in your projects: ✅ Task success ⏩ Example: Task success rate was increased by X% percent. Measure this during usability testing or by reviewing analytics tracking tools. ✅ User satisfaction ⏩ Example: User satisfaction rate improved by X points. Gather data through user surveys, star ratings, or other user feedback forms. ✅ Time spent on task ⏩ Example: The average time spent on task was decreased by X% After design changes measure time spent on tasks and compare it with the old design. ✅ Conversion rate ⏩ Example: Sign up rate increased by X% This is a powerful metrics that impacts business goals and is often applied to app/website sign ups, lead collection forms, etc. ✅ Feature adoption ⏩ Example: X% of users started using this new feature within a month. Track this with analytics tools to see how many users adopt the new feature and analyze whether it brings value to them. ✅ Error rate ⏩ Example: For the given task the error rate was decreased by X% To calculate the error rate, count the number of errors users make during completing task and compare it with old error rate. Which UX metrics do you use in your projects? Share your experiences. -------- Hi, I’m Tetiana Gulei I help you break into the UX design industry and grow as a designer. 🔔 Follow me for more UX insights and UX career tips. ✉️ Want me to review your portfolio? Send me a DM. #uxportfolio #uxdesign #uxtips

💎 Overview of 70+ UX Metrics Struggling to choose the right metric for your UX task at hand? MeasuringU maps out 70+ UX metrics across task and study levels — from time-on-task and SUS to eye tracking and NPS (https://lnkd.in/dhw6Sh8u) 1️⃣ Task-Level Metrics Focus: Directly measure how users perform tasks (actions + perceptions during task execution). Use Case: Usability testing, feature validation, UX benchmarking. 🟢 Objective Task-Based Action Metrics These measure user performance outcomes. Effectiveness: Completion, Findability, Errors Efficiency: Time on Task, Clicks / Interactions 🟢 Behavioral & Physiological Metrics These reflect user attention, emotion, and mental load, often measured via sensors or tracking tools. Visual Attention: Eye Tracking Dwell Time, Fixation Count, Time to First Fixation Emotional Reaction: Facial Coding, HR (heart rate), EEG (brainwave activity) Mental Effort: Tapping (as proxy for cognitive load) 2️⃣ Task-Level Attitudinal Metrics Focus: How users feel during or after a task. Use Case: Post-task questionnaires, usability labs, perception analysis. 🟢 Ease / Perception: Single Ease Question (SEQ), After Scenario Questionnaire (ASQ), Ease scale 🟢 Confidence: Self-reported Confidence score 🟢 Workload / Mental Effort: NASA Task Load Index (TLX), Subjective Mental Effort Questionnaire (SMEQ) 3️⃣ Combined Task-Level Metrics Focus: Composite metrics that combine efficiency, effectiveness, and ease. Use Case: Comparative usability studies, dashboards, standardized testing. Efficiency × Effectiveness → Efficiency Ratio Efficiency × Effectiveness × Ease → Single Usability Metric (SUM) Confidence × Effectiveness → Disaster Metric 4️⃣ Study-Level Attitudinal Metrics Focus: User attitudes about a product after use or across time. Use Case: Surveys, product-market fit tests, satisfaction tracking. 🟢 Satisfaction Metrics: Overall Satisfaction, Customer Experience Index (CXi) 🟢 Loyalty Metrics: Net Promoter Score (NPS), Likelihood to Recommend, Product-Market Fit (PMF) 🟢 Awareness / Brand Perception: Brand Awareness, Favorability, Brand Trust 🟢 Usability / Usefulness: System Usability Scale (SUS) 5️⃣ Delight & Trust Metrics Focus: Measure positive emotions and confidence in the interface. Use Case: Branding, premium experiences, trust validation. Top-Two Box (e.g. “Very Satisfied” or “Very Likely to Recommend”) SUPR-Q Trust Modified System Trust Scale (MST) 6️⃣ Visual Branding Metrics Focus: How users perceive visual design and layout. Use Case: UI testing, branding studies. SUPR-Q Appearance Perceived Website Clutter 7️⃣ Special-Purpose Study-Level Metrics Focus: Custom metrics tailored to specific domains or platforms. Use Case: Gaming, mobile apps, customer support. 🟢 Customer Service: Customer Effort Score (CES), SERVQUAL (Service Quality) 🟢 Gaming: GUESS (Game User Experience Satisfaction Scale) #UX #design #productdesign #measure

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 🔥🔥🔥

UX metrics work best when aligned with the right questions. Below are ten common UX scenarios and the metrics that best fit each. 1. Completing a Transaction When the goal is to make processes like checkout, sign-up, or password reset more efficient, focus on task success rates, drop-off points, and error tracking. Self-reported metrics like expectations and likelihood to return can also reveal how users perceive the experience. 2. Comparing Products For benchmarking products or releases, task success and efficiency offer a baseline. Self-reported satisfaction and emotional reactions help capture perceived differences, while comparative metrics provide a broader view of strengths and weaknesses. 3. Frequent Use of the Same Product For tools people use regularly, like internal platforms or messaging apps, task time and learnability are essential. These metrics show how users improve over time and whether effort decreases with experience. Perceived usefulness is also valuable in highlighting which features matter most. 4. Navigation and Information Architecture When the focus is on helping users find what they need, use task success, lostness (extra steps taken), card sorting, and tree testing. These help evaluate whether your content structure is intuitive and discoverable. 5. Increasing Awareness Some studies aim to make features or content more noticeable. Metrics here include interaction rates, recall accuracy, self-reported awareness, and, if available, eye-tracking data. These provide clues about what’s seen, skipped, or remembered. 6. Problem Discovery For open-ended studies exploring usability issues, issue-based metrics are most useful. Cataloging the frequency and severity of problems allows you to identify pain points, even when tasks or contexts differ across participants. 7. Critical Product Usability Products used in high-stakes contexts (e.g., medical devices, emergency systems) require strict performance evaluation. Focus on binary task success, clear definitions of user error, and time-to-completion. Self-reported impressions are less relevant than observable performance. 8. Designing for Engagement For experiences intended to be emotionally resonant or enjoyable, subjective metrics matter. Expectation vs. outcome, satisfaction, likelihood to recommend, and even physiological data (e.g., skin conductance, facial expressions) can provide insight into how users truly feel. 9. Subtle Design Changes When assessing the impact of minor design tweaks (like layout, font, or copy changes), A/B testing and live-site metrics are often the most effective. With enough users, even small shifts in behavior can reveal meaningful trends. 10. Comparing Alternative Designs In early-stage prototype comparisons, issue severity and preference ratings tend to be more useful than performance metrics. When task-based testing isn’t feasible, forced-choice questions and perceived ease or appeal can guide design decisions.

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?