{"id":6561,"date":"2023-12-14T15:13:00","date_gmt":"2023-12-14T13:13:00","guid":{"rendered":"https:\/\/masterbase.com\/es\/es\/es\/es\/?p=6561"},"modified":"2024-01-19T19:19:21","modified_gmt":"2024-01-19T17:19:21","slug":"tests-a-b-three-challenges-you-must-overcome-to-get-great-benefits","status":"publish","type":"post","link":"https:\/\/masterbase.com\/en\/pruebas-a-b-tres-desafios-que-debes-vencer-para-obtener-grandes-beneficios\/masterbase\/","title":{"rendered":"Tests A \/ B: Three challenges you must overcome to obtain great benefits"},"content":{"rendered":"

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And the results can be measured and clearly communicated to customers. There are hundreds of success stories with A\/B testing, such as an eCommerce strategy that generated 36% more cart closures or a marketing test in a healthcare service that increased leads by 638%.<\/p>\n

But that level of results is not easy to achieve. In working with companies to optimize conversion using A\/B testing we have observed some commonalities in the challenges faced by companies, whether large or small, B2B or B2C, e-commerce or lead generation.<\/p>\n

Challenge 1: Knowing what to test<\/h3>\n

You can't put out two versions of the same communications piece and simply expect a lift in engagement metrics. Marketers quickly learn that some small changes that are attractive because they are easy to implement are not impactful enough to drive change. They also know that dramatic modifications can result in losses.<\/p>\n

To know what to test, you must first know where to test. This is where your customer data can be very decisive. Run an analysis of metrics within the lifecycle, sales funnel or type of communications you do, to move them from one state to another and identify where they are filtered. Where do potential customers fall?<\/p>\n

Challenge 2: Running valid tests<\/h3>\n

Here one could make the mistake of believing that one has discovered a formula to increase conversion, when in fact this is not the case. Another possible mistake is to overlook a conversion increase.<\/p>\n

A\/B testing is a successful tactic because of its predictive power. For the test results to play a truly predictive role, you must ensure that they reflect the customer's behavior and that it was the change made in the test that triggered the new results.<\/p>\n

To achieve this, the experiment must be set up and monitored in a scientific manner and avoid threats to validity such as:<\/p>\n