{"id":1410,"date":"2013-03-12T16:22:20","date_gmt":"2013-03-12T15:22:20","guid":{"rendered":"http:\/\/www.digitalresponse.es\/blog\/?p=1410"},"modified":"2013-03-12T16:22:20","modified_gmt":"2013-03-12T15:22:20","slug":"aplicaciones-del-test-ab-en-email-marketing","status":"publish","type":"post","link":"https:\/\/www.digitalresponse.es\/en\/aplicaciones-del-test-ab-en-email-marketing\/","title":{"rendered":"Applications of A\/B testing in Email Marketing"},"content":{"rendered":"<p><strong><br \/>\n<\/strong><br \/>\nA\/B testing is the most common mode of testing among marketers because of its simplicity and effectiveness in validating hypotheses involving a single variable.<br \/>\nA\/B testing consists of confronting two or more versions of the same message with the particularity that the same variable will change in each of the different versions. Examples of A\/B testing are:<!--more--><br \/>\n<strong>\u00a0<\/strong><br \/>\n<strong>Subject.<\/strong><br \/>\nLong vs. short<br \/>\nPersonalised vs. unpersonalised<br \/>\nMentioning the offer vs. mentioning the brand<br \/>\nImperative vs declarative<br \/>\nEtc...<br \/>\n<strong>\u00a0<\/strong><br \/>\n<strong>Offers<\/strong><br \/>\nDiscount vs. in-kind incentive<br \/>\nDiscounted price vs. free shipping costs<br \/>\nIncentive A vs. Incentive B<br \/>\nEtc...<br \/>\n&nbsp;<br \/>\n<strong>CTA's<\/strong><br \/>\nCopy: imperative vs declarative<br \/>\nPersonal vs. impersonal<br \/>\nIncluding the benefit vs. not including the benefit<br \/>\nDifferent designs and colours<br \/>\nEtc.<br \/>\nImages and colours<br \/>\n&nbsp;<br \/>\n<strong>From Email<\/strong><br \/>\nAnother element to test is the from email in both the sender's name and the email. So, for example, we can test the From Name:<br \/>\nReal name of a person vs. brand name<br \/>\nBrand name vs. product name<br \/>\n&nbsp;<br \/>\n<strong>Time of dispatch<\/strong><br \/>\nOne hour in the morning vs. one hour in the afternoon<br \/>\nOne day of the week vs. another day of the week<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n<strong>For A\/B testing to be effective, the following points must be taken into account:<\/strong><br \/>\n- We only need to introduce a single variable in the test, otherwise the conclusions we reach may be wrong. Let's imagine that we want to test which of two issues gives us a higher open rate. If we launch version A at 09.00 and version B at 12.00, we will not know if the increase in opens of the winning version is due to the subject or the time at which the campaign was launched.<br \/>\n&nbsp;<\/p>\n<p style=\"text-align: center;\">\n<p>&nbsp;<br \/>\n- The A\/B testing of copies, calls to action, offers and images has to be checked with the Click Through Rate and\/or the Click To Open Rate (also called reactivity).<br \/>\n&nbsp;<br \/>\n- When a version is a winner after a test, it should be used as an alternative version for a new test. In this way, improved versions will always be competing, so that the improvements in results will be sustained.<br \/>\nOnce we have the results, we have to make sure that the differences between the winning version and the rest are statistically significant. To do this, we have to resort to statistical calculations as we see below:<br \/>\nLet's assume that version A of a creative has generated 163 sales and version B 210. To find out if the confidence interval is significant we apply the following calculation:<br \/>\n&nbsp;<\/p>","protected":false},"excerpt":{"rendered":"<p>El A\/B testing es el modo de testeo m\u00e1s habitual entre los profesionales del marketing dada su simplicidad y eficacia para validar hip\u00f3tesis en las que interviene una sola variable. El A\/B testing consiste en confrontar dos o m\u00e1s versiones de un mismo mensaje con la particularidad de que existe una misma variable que cambiar\u00e1 [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[53,37],"tags":[55,56],"post_folder":[],"class_list":["post-1410","post","type-post","status-publish","format-standard","hentry","category-analisis-metricas","category-blog","tag-test-ab-2","tag-test-email-marketing"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Aplicaciones del test A\/B en Email Marketing - Digital Response<\/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:\/\/www.digitalresponse.es\/en\/aplicaciones-del-test-ab-en-email-marketing\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Aplicaciones del test A\/B en Email Marketing - Digital Response\" \/>\n<meta property=\"og:description\" content=\"El A\/B testing es el modo de testeo m\u00e1s habitual entre los profesionales del marketing dada su simplicidad y eficacia para validar hip\u00f3tesis en las que interviene una sola variable. 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