{"id":5118,"date":"2023-05-31T13:24:57","date_gmt":"2023-05-31T13:24:57","guid":{"rendered":"https:\/\/media-beats.com\/?post_type=glossary&p=5118"},"modified":"2026-01-02T18:18:32","modified_gmt":"2026-01-02T17:18:32","slug":"ab-testing","status":"publish","type":"glossary","link":"https:\/\/media-beats.com\/en\/glossar\/ab-testing\/","title":{"rendered":"A\/B-Testing"},"content":{"rendered":"

A\/B testing<\/strong> describes a testing method<\/strong> in which two variants are compared to achieve better results. You specifically test content, designs<\/a>, or processes and measure their impact using clear metrics. You gain reliable insights because users<\/a> are randomly assigned to a variant. This reduces gut decisions and enables well-founded optimizations. The method supports you in gradually achieving better performance<\/a>.<\/p>\n\n\n\n

How does A\/B testing work in marketing?<\/h2>\n\n\n\n

Two versions of an element run in parallel while users<\/a> are randomly distributed. You then evaluate measurable results such as clicks<\/a> or conversions<\/a>. Based on this, you identify which variant performs better.<\/p>\n\n\n\n

What are the advantages of A\/B testing?<\/h2>\n\n\n\n

Through systematic testing<\/strong>, you improve decision-making and reduce risks. In addition, you continuously optimize content without making major changes. Data-driven insights increase efficiency and boost the success rate of measures.<\/p>\n\n\n\n

Fields of application for A\/B testing<\/h2>\n\n\n\n