What is the marketing value of an A/B test?

A/B testing or "AB Testing", "bucket test", "split testing" is more precisely a method to compare 2 versions of a web page, a mobile application. It allows you to find out which application works best.

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In practice, A/B Testing consists of testing which of two landing pages works best. It allows you to improve the conversion rates of SEO/SEA campaigns. Among other things, it optimises the buying process of an e-commerce site. It simply improves the UX or user experience of a website. The execution of an A/B test makes it possible to compare, for example, the existing variation in response to an experience. Targeted questions can be asked about changes made to the website or an application. Finally, it allows precise data to be collected concerning the impact of these modifications. It also avoids subjective debates within the marketing, design or engineering teams as there will be data to support it, allowing for more informed internal decision making. Assumptions could be made, for example, that the Call To Action would be better placed in green and on the homepage of the website. Visit Kameleoon.com for more information.

What can you test with A/B testing ?

You can test almost anything in a marketing document. In particular, titles, call to actions, body fonts, images... So, everything that can be modified. However, it is advisable to concentrate on the parts most likely to have a significant impact to avoid wasting too much time with tests. Similarly, on websites, you can test : headlines, calls to action, sales sheets, graphics or product descriptions. The subject line of an email is often one of the most tested elements as it has an impact on the open rate. You can test elements in relation to others. A newsletter A versus a landing page B for example, or newsletter B and landing page A...

A/B Testing: the benefits

A/B testing is a highly recommended method in digital marketing. It is a proven system. A/B testing is a proven method in digital marketing. It consists of establishing hypotheses, to better understand how certain elements influence the behaviour of a user; experiences are continuously improved, a given objective also improves over time; A/B testing identifies changes that have an effect on the users' behaviour.