{"id":157154,"date":"2025-03-26T05:49:00","date_gmt":"2025-03-26T05:49:00","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/a-taxonomy-visual-overview-of-70-ux-metrics-measuringu\/"},"modified":"2025-03-26T05:49:00","modified_gmt":"2025-03-26T05:49:00","slug":"a-taxonomy-visual-overview-of-70-ux-metrics-measuringu","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=157154","title":{"rendered":"A Taxonomy (Visual Overview) of 70+ UX Metrics \u2013 MeasuringU"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div>\n<p><a href=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/032525-FeatureImage-3-scaled.jpg\"><img fetchpriority=\"high\" decoding=\"async\" class=\"alignleft wp-image-43955 size-medium\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/032525-FeatureImage-3-300x169.jpg\" alt=\"A feature image showing multiple, colorful, square icons representing various functions that are arranged in a grid pattern. Large white text &quot;70+&quot; is prominently featured in the upper right quadrant of the image.\" width=\"300\" height=\"169\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/032525-FeatureImage-3-300x169.jpg 300w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/032525-FeatureImage-3-1024x576.jpg 1024w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/032525-FeatureImage-3-768x432.jpg 768w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/032525-FeatureImage-3-1536x864.jpg 1536w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/032525-FeatureImage-3-2048x1152.jpg 2048w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/032525-FeatureImage-3-600x338.jpg 600w\" sizes=\"(max-width: 300px) 100vw, 300px\"\/><\/a>Measuring the user experience starts with UX metrics. But there is no single UX measure\u2014no universal gauge that provides a complete view of the user experience. Instead, we rely on multiple metrics, each offering an incomplete yet complementary perspective.<\/p>\n<p>We\u2019ve identified over 70 UX metrics, encompassing action metrics (what people do) and attitude metrics (what people think and how they feel).<\/p>\n<p>Managing and mastering 70+ different metrics can be overwhelming, so it helps to categorize them. <a href=\"https:\/\/measuringu.com\/courses\/ux-metrics\/\">UX metrics<\/a> can be broken down into <a href=\"https:\/\/measuringu.com\/task-based-metrics\/\">task-based<\/a> and <a href=\"https:\/\/measuringu.com\/guide-to-study-based-ux-metrics\/\">study-level<\/a> metrics:<\/p>\n<ul>\n<li><strong>Task-based<\/strong>: These metrics focus on a representative user attempting to achieve a realistic goal (e.g., finding a movie, purchasing a product, booking a hotel room). Tasks are the cornerstone of usability testing. While assessing a task experience may not always be feasible due to budget, timeline, or product-access constraints, observing participants as they complete tasks can reveal usability issues and inform your design improvement efforts.<\/li>\n<li><strong>Study-level<\/strong>: These are collected once per study (unlike task metrics, which are gathered per task). These provide a broader reflection on a product experience, capturing factors such as overall satisfaction, perceived usability, usefulness, and behavioral intentions (e.g., likelihood to purchase or recommend).<\/li>\n<\/ul>\n<p>While it\u2019s useful to classify UX metrics based on these attributes, it\u2019s also helpful to view them in a high-level framework. Figure 1 organizes these metrics into a coherent structure\u2014thank you, Phil Siebler, for designing the infographic!<\/p>\n<p><a href=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/OverviewUXMetrics_1-2.png\" rel=\"attachment wp-att-43746\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43971 size-full\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/OverviewUXMetrics_1-2.png\" alt=\"Infographic table showing an overview of 70+ UX Metrics\" width=\"4167\" height=\"2284\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/OverviewUXMetrics_1-2.png 4167w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/OverviewUXMetrics_1-2-300x164.png 300w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/OverviewUXMetrics_1-2-1024x561.png 1024w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/OverviewUXMetrics_1-2-768x421.png 768w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/OverviewUXMetrics_1-2-1536x842.png 1536w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/OverviewUXMetrics_1-2-2048x1123.png 2048w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/OverviewUXMetrics_1-2-600x329.png 600w\" sizes=\"auto, (max-width: 4167px) 100vw, 4167px\"\/><\/a><\/p>\n<p class=\"wp-caption-text\" style=\"text-align: left;\"><strong>Figure 1:<\/strong> Overview of 70+ UX metrics (as of the publication date of this article).<\/p>\n<p>Beyond organizing UX metrics, we\u2019ve introduced three additional dimensions to consider when selecting metrics: Popularity, Ease of Collection, and Reference Benchmarks. Each dimension is represented in Figure 1 with a shape (triangle, circle, square) and a color (green, yellow, red).<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43747\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/3-key-dimensions.png\" alt=\"image of a green triangle, yellow circle, and red square\" width=\"120\" height=\"42\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/3-key-dimensions.png 104w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/3-key-dimensions-100x36.png 100w\" sizes=\"auto, (max-width: 120px) 100vw, 120px\"\/><\/p>\n<h2>1. Popular Usage (Triangles)<\/h2>\n<p>Selecting UX metrics isn\u2019t a popularity contest, but familiarity does play a role. When a metric is widely known, it\u2019s easier to communicate, interpret, and benchmark.<\/p>\n<p>We categorized metrics into three popularity levels based on our experience reviewing reports, published papers, and historical references (including our <a href=\"https:\/\/measuringu.com\/article\/correlations-among-prototypical-usability-metrics-evidence-for-the-construct-of-usability\/\">2009 paper on correlations among prototypical UX metrics<\/a>). Popularity evolves over time and isn\u2019t based on rigid thresholds:<\/p>\n<ul>\n<li><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43752\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Triangle-2.png\" alt=\"image of a green triangle\" width=\"30\" height=\"30\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Triangle-2.png 160w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Triangle-2-150x150.png 150w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Triangle-2-70x70.png 70w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Triangle-2-100x100.png 100w\" sizes=\"auto, (max-width: 30px) 100vw, 30px\"\/>Widely used<\/strong>: Metrics such as the <a href=\"https:\/\/measuringu.com\/10-things-sus\/\">SUS<\/a>, the <a href=\"https:\/\/measuringu.com\/seq10\/\">SEQ<\/a><sup>\u00ae<\/sup>, and <a href=\"https:\/\/measuringu.com\/task-completion\/\">completion rates<\/a> are industry standards.<\/li>\n<li><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43753\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-triangle-1.png\" alt=\"image of a yellow triangle\" width=\"30\" height=\"30\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-triangle-1.png 160w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-triangle-1-150x150.png 150w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-triangle-1-70x70.png 70w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-triangle-1-100x100.png 100w\" sizes=\"auto, (max-width: 30px) 100vw, 30px\"\/>Moderately used<\/strong>: Metrics such as Customer Effort Score (<a href=\"https:\/\/measuringu.com\/customer-effort-score\/\">CES<\/a>), <a href=\"https:\/\/measuringu.com\/pragmatic-hedonic\/\">AttrakDiff<\/a>, and <a href=\"https:\/\/measuringu.com\/voice-interaction\/\">SUISQ<\/a> have valid applications but are less common.<\/li>\n<li><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43754\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Triangle.png\" alt=\"image of a red triangle\" width=\"30\" height=\"30\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Triangle.png 160w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Triangle-150x150.png 150w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Triangle-70x70.png 70w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Triangle-100x100.png 100w\" sizes=\"auto, (max-width: 30px) 100vw, 30px\"\/>Rarely used<\/strong>: Metrics such as Microsoft Net Satisfaction (<a href=\"https:\/\/measuringu.com\/microsoft-nsat\/\">NSAT<\/a>) and the Technology Acceptance Model (<a href=\"https:\/\/measuringu.com\/tam\/\">TAM<\/a>) are used infrequently in applied UX research. However, infrequent use doesn\u2019t imply low quality\u2014metrics like eye-tracking are niche but valuable in the right context.<\/li>\n<\/ul>\n<h2>2. Ease of Collection (Circles)<\/h2>\n<p>The easier a metric is to collect, the more likely it will be used. However, some harder-to-collect metrics offer significant value, justifying the effort to collect them.<\/p>\n<ul>\n<li><strong> <img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43755\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Circle.png\" alt=\"green circle\" width=\"30\" height=\"30\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Circle.png 160w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Circle-150x150.png 150w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Circle-70x70.png 70w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Circle-100x100.png 100w\" sizes=\"auto, (max-width: 30px) 100vw, 30px\"\/>\u00a0Easy to collect<\/strong>: Single-item scales such as SEQ require minimal effort. Task time and completion rates are also relatively easy, especially with automated tools (e.g., <a href=\"https:\/\/measuringu.com\/muiq\/\">MUiQ<\/a><sup>\u00ae<\/sup>).<\/li>\n<li><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43756\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-Circle.png\" alt=\"yellow circle\" width=\"30\" height=\"30\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-Circle.png 160w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-Circle-150x150.png 150w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-Circle-70x70.png 70w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-Circle-100x100.png 100w\" sizes=\"auto, (max-width: 30px) 100vw, 30px\"\/>Moderate effort<\/strong>: <a href=\"https:\/\/measuringu.com\/errors-ux\/\">Error rates<\/a> provide rich diagnostic insights but require careful observation and precise definitions.<\/li>\n<li><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43757\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Circle.png\" alt=\"red circle\" width=\"30\" height=\"30\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Circle.png 160w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Circle-150x150.png 150w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Circle-70x70.png 70w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Circle-100x100.png 100w\" sizes=\"auto, (max-width: 30px) 100vw, 30px\"\/>Difficult to collect<\/strong>: Advanced metrics such as <a href=\"https:\/\/measuringu.com\/eye-tracking\/\">eye-tracking time to first fixation<\/a> and <a href=\"https:\/\/measuringu.com\/services\/facial-coding\/\">facial expression analysis<\/a> require specialized software and equipment, making them less accessible to UX researchers.<\/li>\n<\/ul>\n<h2>3. Reference Benchmarks (Squares)<\/h2>\n<p>Comparing a UX metric against an established dataset simplifies interpretation and improves stakeholder communication.<\/p>\n<ul>\n<li><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43759\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Square.png\" alt=\"green square\" width=\"30\" height=\"30\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Square.png 160w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Square-150x150.png 150w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Square-70x70.png 70w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Green-Square-100x100.png 100w\" sizes=\"auto, (max-width: 30px) 100vw, 30px\"\/>Well-benchmarked<\/strong>: Metrics such as the SUS and SEQ have extensive published norms. For example, a SUS score of 80 is considered very good\u2014better than 90% of a dataset of 500+ products (an A\u2212 on the <a href=\"https:\/\/www.researchgate.net\/publication\/324116412_The_System_Usability_Scale_Past_Present_and_Future\">Sauro\u2013Lewis curved grading scale for the SUS<\/a>). The SEQ also has <a href=\"https:\/\/measuringu.com\/adjective-interpretations-of-seq-scores\/\">established norms<\/a> indicating what constitutes an easy or difficult task.<\/li>\n<li><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43760\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-Square.png\" alt=\"yellow square\" width=\"30\" height=\"30\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-Square.png 160w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-Square-150x150.png 150w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-Square-70x70.png 70w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Yellow-Square-100x100.png 100w\" sizes=\"auto, (max-width: 30px) 100vw, 30px\"\/>Moderately benchmarked<\/strong>: The <a href=\"https:\/\/measuringu.com\/product\/suprq\/\">SUPR-Q<\/a><sup>\u00ae<\/sup> is well-structured but requires a license for scoring. Post-task confidence has limited benchmarks.<\/li>\n<li><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43761\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Square.png\" alt=\"red square\" width=\"30\" height=\"30\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Square.png 160w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Square-150x150.png 150w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Square-70x70.png 70w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Red-Square-100x100.png 100w\" sizes=\"auto, (max-width: 30px) 100vw, 30px\"\/>Context-dependent or limited<\/strong>: Metrics such as completion rates and task times depend heavily on the specific task and failure consequences, making external benchmarks challenging (although there are <a href=\"https:\/\/measuringu.com\/task-completion\/\">rough benchmarks for completion rates<\/a>). Metrics such as Customer Effort Score (CES) and clutter measurement show promise but lack sufficient data to establish reliable benchmarks.<\/li>\n<li><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43762\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Gray-Square.png\" alt=\"gray square\" width=\"30\" height=\"30\" srcset=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Gray-Square.png 160w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Gray-Square-150x150.png 150w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Gray-Square-70x70.png 70w, https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Gray-Square-100x100.png 100w\" sizes=\"auto, (max-width: 30px) 100vw, 30px\"\/><strong>No benchmarks<\/strong>: No benchmarks are available.<\/li>\n<\/ul>\n<p>Choosing the right UX metric starts with understanding what you\u2019re trying to measure. However, three practical considerations\u2014popularity, ease of collection, and reference benchmarks\u2014help guide decision-making.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-43768\" src=\"https:\/\/measuringu.com\/wp-content\/uploads\/2025\/03\/Picture1.png\" alt=\"green triangle circle and square\" width=\"120\" height=\"40\"\/><\/p>\n<p>Ideally, you want metrics that score well across all three dimensions. Instead of aiming for three red cherries on a slot machine, you want three green shapes (popular, easy to collect, and benchmarked). Among our 70+ UX metrics, only a few consistently meet all three criteria:<\/p>\n<ul>\n<li>SEQ (Single Ease Question)<\/li>\n<li>LTR\/NPS (Likelihood to Recommend \/ Net Promoter Score)<\/li>\n<li>SUS (System Usability Scale)<\/li>\n<\/ul>\n<p>While these three metrics offer a strong starting point, they shouldn\u2019t be the only ones you collect. In an upcoming article, we\u2019ll discuss which four metrics we recommend if you could pick only a few\u2014and why.<\/p>\n<p><em>Note: This taxonomy is a living document. Like anything else, the popularity of metrics can rise and fall, new methods make formerly difficult metrics easier to collect, and metrics that do not have benchmarks this year might have some next year. We plan to update this infographic over time, so in the future, you can <a href=\"https:\/\/measuringu.com\/an-overview-of-70-ux-metrics\/\">click here for the latest version<\/a>.<\/em><\/p>\n<\/p><\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Measuring the user experience starts with UX metrics. But there is no single UX measure\u2014no universal gauge that provides a complete view of the user experience. Instead, we rely on multiple metrics, each offering an incomplete yet complementary perspective. We\u2019ve identified over 70 UX metrics, encompassing action metrics (what people do) and attitude metrics (what [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":157155,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[14041,13896,11275,28882,9820],"dealstore":[],"offerexpiration":[],"class_list":["post-157154","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-measuringu","tag-metrics","tag-overview","tag-taxonomy","tag-visual"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A Taxonomy (Visual Overview) of 70+ UX Metrics \u2013 MeasuringU - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=157154\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A Taxonomy (Visual Overview) of 70+ UX Metrics \u2013 MeasuringU - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Measuring the user experience starts with UX metrics. 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