{"id":72967,"date":"2025-02-07T05:12:36","date_gmt":"2025-02-07T05:12:36","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/is-your-analytics-really-reliable\/"},"modified":"2025-02-07T05:12:36","modified_gmt":"2025-02-07T05:12:36","slug":"is-your-analytics-really-reliable","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=72967","title":{"rendered":"is your analytics really reliable?"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"wtr-content\" data-bg=\"#0cacf8\" data-fg=\"#0cacf8\" data-width=\"5\" data-mute=\"\" data-fgopacity=\"1.00\" data-mutedopacity=\"1.00\" data-placement=\"top\" data-placement-offset=\"48\" data-content-offset=\"0\" data-placement-touch=\"top\" data-placement-offset-touch=\"0\" data-transparent=\"1\" data-shadow=\"1\" data-touch=\"\" data-non-touch=\"1\" data-comments=\"0\" data-commentsbg=\"#0cacf8\" data-location=\"page\" data-mutedfg=\"#0cacf8\" data-endfg=\"#f44813\" data-rtl=\"\">\n<p>All sectors and sizes<br \/>\nof companies are potentially affected by the unreliability of their data. Small<br \/>\nand medium-sized organisations generally do not have the time or resources to<br \/>\nassess quality and\/or resolve reliability issues. <\/p>\n<p>Large companies tend<br \/>\nto generate a lot of campaigns and new web pages. They cannot keep up with the<br \/>\npace of publication while respecting data quality standards.\u00a0 <\/p>\n<p>The poor quality of<br \/>\nanalytical data can take different forms, with sometimes serious consequences<br \/>\nfor your business:\u00a0 <\/p>\n<ul>\n<li>loss of income<\/li>\n<li>reduction in the ROI of marketing actions<\/li>\n<li>loss of quality in decision-making<\/li>\n<li>contamination of other data projects (CRM, Data Lake, CDP, etc.)<\/li>\n<li>decreased internal trust and credibility <\/li>\n<\/ul>\n<h2><strong>The intrinsic risk in web analytics <\/strong><\/h2>\n<p>Without preventive action, the quality of the data is altered by nature. Sources of error are diverse and inherent to some web technologies: unmeasured data, robot traffic, browser inaccuracies, traffic jams, etc.  We have identified, quantified and represented here the main risk factors that threaten the quality of your analytical data. <\/p>\n<h2><strong>The<br \/>\ncritical phase of collection <\/strong><\/h2>\n<p>The <a href=\"https:\/\/blog.atinternet.com\/en\/category\/topics\/implementation-and-tagging\/\">data collection<\/a> phase is critical<br \/>\nbecause it is permanent. Each optimisation, functionality, new campaign or new piece<br \/>\nof content poses a risk to the quality of data collection. An effective<br \/>\ncollection strategy brings together all the company\u2019s decision-making players<br \/>\nand adapts to each development on an ongoing basis. This reflection on data<br \/>\ncollection must be taken into consideration when defining a <a href=\"https:\/\/blog.atinternet.com\/en\/category\/topics\/data-governance-and-strategy\/\">data governance<\/a> policy.<\/p>\n<p>The more you update and enrich your mobile sites and<br \/>\napplications, the more likely you are to inadvertently alter your analytics<br \/>\ntags. This seems to be an elementary mistake, but it is very common to find<br \/>\nmissing, defective or duplicated tags, especially on large sites with a lot of<br \/>\ncontent. While these tagging problems, sometimes minimal, can be difficult to<br \/>\ndetect, they have a significant impact on performance. It is of vital<br \/>\nimportance to be vigilant over the integrity of tags! Checking the source code<br \/>\nof all pages is therefore essential, but who has the time to do this tedious<br \/>\nmanual task? <a href=\"https:\/\/blog.atinternet.com\/en\/3-essential-tools-analytics-data-quality-control\/\">Crawling tools<\/a> allow you to<br \/>\nautomatically browse the site, all pages and sections combined, to check the<br \/>\npresence of digital analytics tags. Others allow you to <a href=\"https:\/\/chrome.google.com\/webstore\/detail\/at-internet-tag-inspector\/epdfbeoiphkaeapcohmilhmpdeilgnok\">check your tags<\/a> live once they are<br \/>\nimplemented on a site. They can also issue a report indicating the problems to<br \/>\nbe solved.<\/p>\n<h2><strong>Robot traffic<\/strong><\/h2>\n<p>According to some estimates, robots (or<br \/>\n\u201cbots\u201d) account for more than half of web traffic. To know exactly<br \/>\nthe actual volume of flows, it is essential to have the means to identify and<br \/>\nexclude the part generated by the robots that visit your sites. However, some \u201cbad<br \/>\nbots\u201d can be very difficult to detect; hence the importance of working<br \/>\nwith a <a href=\"https:\/\/www.atinternet.com\/en\/\">digital analytics provider<\/a> that has the<br \/>\nexperience and means necessary to recognise and eliminate this traffic. The<br \/>\nability to discard flows caused by robots has direct consequences on data<br \/>\nquality.<\/p>\n<p>In addition to the qualitative aspect, the manual<br \/>\nsorting of this polluted traffic is enormous, if not impossible for the person<br \/>\nanalysing the data. As a first step, your Web Analytics provider should be able<br \/>\nto identify these robots using the official exclusion list published and<br \/>\nupdated regularly by the <a href=\"https:\/\/www.iab.com\/\">IAB<\/a>. It must then offer you the possibility to regenerate<br \/>\nyour data, over the desired period, excluding this robot traffic. <\/p>\n<h2><strong>Source allocation biases<\/strong><\/h2>\n<p>Some events, such as Facebook\u2019s overestimation of<br \/>\nvideo viewing time and the temporary suspension of two Google indicators by the<br \/>\nMedia Rating Council for \u201cnon-compliance\u201d with measurement<br \/>\nguidelines, have given companies reasons to question the accuracy and validity<br \/>\nof the data they receive.<\/p>\n<p>At a time when transparency seems to be lacking, one<br \/>\ncan begin to question the accuracy (and impartiality) of indicator calculation<br \/>\nin these restricted access systems. Here is an example with a very simple<br \/>\nquestion: can we really rely on the figures provided by an analytical tool for<br \/>\nthe source of a \u201csearch engine\u201d, when it is at the same time<br \/>\ngenerating the revenues of this tool? One of the last and most striking<br \/>\nexamples is the bias in the measurement of source attribution by Google\u2019s<br \/>\nanalysis tool. In other words, the conversion is automatically assigned to the<br \/>\nGoogle source (engine or sponsored link) if the visitor has clicked, even once,<br \/>\non a Google link in the last 6 months. The measurement tool thus completely<br \/>\nignores the sources of direct traffic (link in favourites, automatic entry in<br \/>\nan engine for example) to assign the conversion to itself. In other words, if<br \/>\nthe source is not determined, Google takes it over. The result: conversions<br \/>\nthat add up and numbers inflated in the counters of advertising channels like<br \/>\nGoogle Ads. Nearly <a href=\"https:\/\/moz.com\/blog\/guide-to-direct-traffic-google-analytics\">20% of conversions<\/a> are overestimated<br \/>\ndue to misallocation of sources.<\/p>\n<p>Fortunately, it is possible and simple to act to<br \/>\nreduce risks with the appropriate tools and procedures. The most difficult<br \/>\nthing is to be aware of the potential sources of errors.<\/p>\n<p>AT Internet offers <a href=\"https:\/\/www.atinternet.com\/en\/product\/actionable-data\/\">a wide range of tools for quality control<\/a> of analytical data. Fewer errors are therefore likely to alter your<br \/>\ndata and influence your decisions.<\/p>\n<p>If you are keen to find out more about data quality,<br \/>\ndownload our latest guide: <\/p>\n<figure class=\"wp-block-image\"><a href=\"https:\/\/content.atinternet.com\/data-quality-in-digital-analytics\/?xtatc=PUB-[DQ_guide_2019]-[inpost]-[EN]\" target=\"_blank\" rel=\"noreferrer noopener\"><img decoding=\"async\" loading=\"lazy\" width=\"790\" height=\"250\" src=\"https:\/\/blog.atinternet.com\/wp-content\/uploads\/2019\/09\/In-post-banner-Data-Quality-EN.jpg\" alt=\"Data Quality in Digital Analytics updated in 2019\" class=\"wp-image-15173\" srcset=\"https:\/\/blog.atinternet.com\/wp-content\/uploads\/2019\/09\/In-post-banner-Data-Quality-EN.jpg 790w, https:\/\/blog.atinternet.com\/wp-content\/uploads\/2019\/09\/In-post-banner-Data-Quality-EN-300x95.jpg 300w, https:\/\/blog.atinternet.com\/wp-content\/uploads\/2019\/09\/In-post-banner-Data-Quality-EN-768x243.jpg 768w\" sizes=\"auto, (max-width: 790px) 100vw, 790px\"\/><\/a><\/figure>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>All sectors and sizes of companies are potentially affected by the unreliability of their data. Small and medium-sized organisations generally do not have the time or resources to assess quality and\/or resolve reliability issues. Large companies tend to generate a lot of campaigns and new web pages. They cannot keep up with the pace of [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":72969,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[12765,10436],"dealstore":[],"offerexpiration":[],"class_list":["post-72967","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-analytics","tag-reliable"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>is your analytics really reliable? - 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=72967\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"is your analytics really reliable? - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"All sectors and sizes of companies are potentially affected by the unreliability of their data. 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