{"id":9749,"date":"2018-07-09T10:21:59","date_gmt":"2018-07-09T08:21:59","guid":{"rendered":"https:\/\/www.makingscience.com\/blog\/como-disenar-con-datos\/"},"modified":"2018-07-09T10:21:59","modified_gmt":"2018-07-09T08:21:59","slug":"como-disenar-con-datos","status":"publish","type":"post","link":"https:\/\/www.makingscience.com\/en\/blog\/como-disenar-con-datos\/","title":{"rendered":"How to do UX design driven by DATA"},"content":{"rendered":"<div>How to do UX design driven by DATA<\/div>\n<div>Today the consumption of digital products is growing exponentially, requiring companies to meet user demands is a priority task. This is one of the big reasons why <strong>design processes are changing<\/strong>; Design is no longer understood as a discipline subject to mere aesthetic criteria, since the <strong>functional and experience principles<\/strong> prevail over it.  The first steps when designing a digital product begin with deepening the knowledge and understanding of our users, to finally <strong>empathize with the user and anticipate their needs and motivations<\/strong>.<\/div>\n<div>Almost all the <strong>analysis tools (Google Analytics, Webtrends, Clicktale, etc.<\/strong>) provide <strong>quantitative data<\/strong>: where does your traffic come from? What did they visit? What information is relevant? In what process did they fall? ? Are they new or repeat users? What is the conversion rate?  <strong>Much more complicated<\/strong> is obtaining information from the <strong>qualitative data<\/strong> that will give us information on <strong>how and why something happens<\/strong>.  To obtain a <strong>qualified analysis<\/strong>, we will have to <strong>study both types of data and relate them<\/strong> in order to interpret them and thus obtain the broadest possible perspective.<\/div>\n<div><strong>How do we measure the behavior of users?<\/strong>  There are two kinds of data that we can use: <\/p>\n<ul>\n<li>The <strong>quantitative data<\/strong>, which are obtained by a <strong>deductive method<\/strong> and are characterized by being objective and numerical in nature. In practice, it is based on quantifying attitudes, opinions and behaviors.<\/li>\n<li>On the other hand, the <strong>qualitative data<\/strong> come from <strong>inductive techniques<\/strong>, and their nature is subjective and descriptive. It consists of understanding the reasons and motivations of the users from their particular interactions with the website.<\/li>\n<\/ul>\n<\/div>\n<div>A very specific example, for a specific need, is the use of <strong>Card sorting<\/strong>, which will help us <strong>to validate the understanding of the terminology used in the navigation of a product<\/strong>; these data are usually represented in <strong>&#8220;similarity matrices&#8221;<\/strong> or in <strong>&#8220;dendrograms&#8221;<\/strong> they show how the users tested relate content.<\/div>\n<div>Regarding <strong>the quantitative data part<\/strong>, we can look at the <strong>traffic of a page<\/strong>, <strong>the bounce rate<\/strong>, <strong>the route<\/strong> that a user follows to find the information or what type of information a user or another seeks to detect content problems of the page.  When structuring the content of a page, it is necessary to analyze which is the most relevant information for the users and which are the interaction data of the users with the elements. This <strong>interaction data<\/strong> provides\u00a0 help to understand the movement of these users by a page (scroll maps, hover maps and click maps)<\/div>\n<div><img decoding=\"async\" src=\"\/wp-content\/uploads_old\/2018\/07\/Imagen1.png\"\/><\/div>\n<div><img decoding=\"async\" src=\"\/wp-content\/uploads_old\/2018\/07\/mapas.png\"\/><\/div>\n<div>Another of the most used methods for analyzing the structure of a web are the so-called <strong>&#8220;eye-tracking&#8221;<\/strong> though this technique is currently <strong>being replaced by AI tools<\/strong>; these tools generate hypotheses of vision patterns that users follow when entering a web. Observing this data we can decide <strong>where and how to place each element<\/strong> according to the <strong>desired objective<\/strong>.  Designing from data strengthens innovative thinking, allows us to take the leap to the next level from solid pillars that backup our design decision making.  There is <strong>no single model or methodology<\/strong> since each starting point is different and that leads us to use different techniques and resources depending on the particular nature of the website.All this is a small part of the ways in which we use <strong>the data<\/strong> when designing to achieve <strong>an increase in empathy with the user<\/strong>.<\/div>\n","protected":false},"excerpt":{"rendered":"<p>How to do UX design driven by DATA Today the consumption of digital products is growing exponentially, requiring companies to meet user demands is a priority task. This is one of the big reasons why design processes are changing; Design is no longer understood as a discipline subject to mere aesthetic criteria, since the functional [&hellip;]<\/p>\n","protected":false},"author":21,"featured_media":9750,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[813,830,918],"tags":[63,930],"class_list":["post-9749","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cro-en","category-design-creativity-en","category-ux-ui-en","tag-creativity-design","tag-ux-cro-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/posts\/9749","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/users\/21"}],"replies":[{"embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/comments?post=9749"}],"version-history":[{"count":0,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/posts\/9749\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/media\/9750"}],"wp:attachment":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/media?parent=9749"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/categories?post=9749"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/tags?post=9749"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}