{"id":460,"date":"2021-03-18T19:49:04","date_gmt":"2021-03-18T19:49:04","guid":{"rendered":"https:\/\/ux.mynmi.net\/?page_id=460"},"modified":"2021-03-18T19:49:04","modified_gmt":"2021-03-18T19:49:04","slug":"kpi-summary","status":"publish","type":"page","link":"https:\/\/nmi.cool\/ux\/kpi-summary\/","title":{"rendered":"KPIs"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/images.unsplash.com\/photo-1592495981488-073153776d9a?ixid=MXwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHw%3D&amp;ixlib=rb-1.2.1&amp;auto=format&amp;fit=crop&amp;w=2772&amp;q=80\" alt=\"\" \/><figcaption>By <a href=\"https:\/\/unsplash.com\/@morganhousel\">Morgan Housel<\/a><\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\u2753What is a KPI?<\/h2>\n\n\n\n<p>We all know data collection is important. UX designers track data to have benchmarks to prove growth. Data can support UX designer&#8217;s ideas and recommendations better than unsupported opinions. <\/p>\n\n\n\n<p>In today&#8217;s climate, gathering data is rarely the challenge. The challenge is knowing exactly which numbers to track, and what exactly those numbers mean to an organization. That&#8217;s where KPI&#8217;s come in.<\/p>\n\n\n\n<p>Key performance indicators are <strong>quantifiable<\/strong> measurements that help an organization define and track the progress toward its&nbsp;goals. They can vary from industry to industry, or business to business. Therefore, key performance indicators for a dog might be: <\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Miles walked<\/li><li>Pounds of food consumed <\/li><li>Number of tricks performed  <\/li><li>Pats on head<\/li><\/ul>\n\n\n\n<p>While KPI&#8217;s for UX tends to include measurements such as: <\/p>\n\n\n\n<ol class=\"wp-block-list\"><li>Task success rate<\/li><li>Time on task<\/li><li>User error rate <\/li><\/ol>\n\n\n\n<p>And a few <strong><em>qualitative<\/em><\/strong> KPI&#8217;s for UX may include:<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li>System Usability Scale <\/li><li>Product Reaction Cards <\/li><\/ol>\n\n\n\n<p>While many of these may already seem familiar to you based on our lectures, I wanted to reiterate a few and sum them up once more.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Task Success Rate <\/h3>\n\n\n\n<p>Using scenario format, the researcher has a participant walk through a series of specific, articulate tasks, that have clear, well-defined end goals. If 8\/10 users successfully complete a task, the task success rate is: <br><\/p>\n\n\n\n<p><code><strong>8\/10 = 0.8 x 100 =<\/strong>&nbsp;<strong>80%<\/strong><\/code><\/p>\n\n\n\n<p>The main disadvantage to this metric is that doesn&#8217;t tell the researcher <em>why<\/em> the participant failed. That&#8217;s why it&#8217;s worthwhile to pair this with the Think Aloud Protocol, and having a dedicated notetaker jot down errors.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Time on Task<\/h3>\n\n\n\n<p>Time on task is the amount of time it takes for a user to complete a task. Usually, time is converted into seconds. Since time is a proportion, calculate the <strong><a rel=\"noreferrer noopener\" href=\"https:\/\/measuringu.com\/time_intervals\/\" data-type=\"URL\" data-id=\"https:\/\/measuringu.com\/time_intervals\/\" target=\"_blank\">geometric mean<\/a><\/strong> per task rather than use the standard old mean or average you&#8217;re used to from match class.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">User Error Rate<\/h3>\n\n\n\n<p>User error rate is a little more tricky to measure, and there&#8217;s a few ways to go about calculating this rate. <\/p>\n\n\n\n<p>For the purposes of this class, we&#8217;ll calculate the error rate as follows: Number of errors \/ total numbers of error opportunities. <\/p>\n\n\n\n<p>For example, if five out of 10 users enter their security question incorrectly in form field. The error occurrence rate is calculated like this: <\/p>\n\n\n\n<p><code>5\/10 = 0.05 x 10 = 50%<\/code><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">System Usability Scale<\/h2>\n\n\n\n<p>Originally created by John Brooke in 1986, the System Usability scale is a speedy and efficient way to measure usability. This questionnaire features 10 questions with Likert scale responses; from strongly agree to strongly disagree. <\/p>\n\n\n\n<ol class=\"wp-block-list\"><li><code>I think that I would like to use this system frequently.<\/code><\/li><li><code>I found the system unnecessarily complex.<\/code><\/li><li><code>I thought the system was easy to use.<\/code><\/li><li><code>I think that I would need the support of a technical person to be able to use this system.<\/code><\/li><li><code>I found the various functions in this system were well integrated.<\/code><\/li><li><code>I thought there was too much inconsistency in this system.<\/code><\/li><li><code>I would imagine that most people would learn to use this system very quickly.<\/code><\/li><li><code>I found the system very cumbersome to use.<\/code><\/li><li><code>I felt very confident using the system.<\/code><\/li><li><code>I needed to learn a lot of things before I could get going with this system.<\/code><\/li><\/ol>\n\n\n\n<p>Follow instructions in the slides to calculate and understand results. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Product Reaction Cards<\/h2>\n\n\n\n<p>Created by Microsoft, Product Reaction Cards are designed to gauge the intangible aspect of user experience: people&#8217;s perception of a product. There are <a rel=\"noreferrer noopener\" href=\"https:\/\/www.sciencedirect.com\/topics\/computer-science\/product-reaction-card\" data-type=\"URL\" data-id=\"https:\/\/www.sciencedirect.com\/topics\/computer-science\/product-reaction-card\" target=\"_blank\">118 cards<\/a>, with 60% of the cards being positive words and 40% being negative or neutral words. Product reaction cards are more than just asking people to pick the top five cards they feel describe their experience with a product: It&#8217;s a way to ask <em>why <\/em>people think that. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u2753What is a KPI? We all know data collection is important. UX designers track data to have benchmarks to prove growth. Data can support UX designer&#8217;s ideas and recommendations better than unsupported opinions. In today&#8217;s climate, gathering data is rarely the challenge. The challenge is knowing exactly which numbers to track, and what exactly those &hellip; <a href=\"https:\/\/nmi.cool\/ux\/kpi-summary\/\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">KPIs<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-460","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/nmi.cool\/ux\/wp-json\/wp\/v2\/pages\/460","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nmi.cool\/ux\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/nmi.cool\/ux\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/nmi.cool\/ux\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/nmi.cool\/ux\/wp-json\/wp\/v2\/comments?post=460"}],"version-history":[{"count":0,"href":"https:\/\/nmi.cool\/ux\/wp-json\/wp\/v2\/pages\/460\/revisions"}],"wp:attachment":[{"href":"https:\/\/nmi.cool\/ux\/wp-json\/wp\/v2\/media?parent=460"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}