{"id":12171,"date":"2019-06-11T10:47:13","date_gmt":"2019-06-11T05:17:13","guid":{"rendered":"https:\/\/www.www.adpushup.com\/blog\/?p=12171"},"modified":"2024-05-09T17:42:48","modified_gmt":"2024-05-09T12:12:48","slug":"types-of-ad-tests","status":"publish","type":"post","link":"https:\/\/www.adpushup.com\/blog\/types-of-ad-tests\/","title":{"rendered":"6 Types of Ad Tests for Publishers and How to Run Them","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"\n

There is no rulebook to answer which ad practices drive the maximum revenue. So, in order to test, publishers switch between ad types or sizes to see which ones get more impressions. Or, they simply go with their gut. The result? They never get empirical data on what actually<\/em> works and what doesn\u2019t.<\/p>\n\n\n\n

Ad testing is a form of A\/B testing<\/a>, i.e., the comparison of performance between two variants. But that\u2019s not limited to just the ad type (for the publisher), or, for example, the color of the CTA button (for the advertiser). In ad tech, the scope of testing has far expanded from plain testing. Testing conventions have changed and publishers now have granular data to make mindful decisions.<\/p>\n\n\n\n

Different types of testing, on different elements and parts of a website, empower publishers with the data they need to grow. Each test has a different aim, but a common goal\u4e00to increase ad revenue. For example, an A\/B test between two ad sizes might aim to achieve more ad impressions. While an A\/B test between static and dynamic ad types may target better CTR.<\/p>\n\n\n\n

There are many testing ideas which publishers can execute to find out the best results for themselves. But whatever the case, the thumb rule is that publishers have to test. Let\u2019s move along and discuss some ad testing ideas which may help publishers get started.<\/p>\n\n\n\n

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