{"id":17273,"date":"2022-11-10T10:41:56","date_gmt":"2022-11-10T09:41:56","guid":{"rendered":"https:\/\/adpepper.com\/?p=17273"},"modified":"2023-08-15T10:40:53","modified_gmt":"2023-08-15T08:40:53","slug":"digitallexicon-a-b-tests","status":"publish","type":"post","link":"https:\/\/adpepper.com\/en\/digitallexicon-a-b-tests\/","title":{"rendered":"DigitalLexicon: A\/B Tests"},"content":{"rendered":"

How do A\/B tests work? <\/b><\/h1><\/div>

In our “DigitalLexicon” series, we introduce you to the most important terms and concepts from the field of online marketing. Today on the program: A\/B testing. What are A\/B tests, what are they used for and how do we work with A\/B tests at ad pepper. You can find all that out here.<\/p>\n<\/div>

What are A\/B tests?<\/strong><\/h2><\/div>

An A\/B test is basically a comparison of the performance of two versions of, for example, a landing page, a banner or similar. To simplify matters, from here on we will assume a landing page to be optimized. A\/B tests are conducted to test which changes can improve the performance of a landing page.<\/p>\n

For this purpose, two versions of the landing page are played out simultaneously, which differ in a single feature. This feature can be many things: The text in a button, the size of a headline, different icons or much more. The traffic that reaches the landing page is divided equally between the two variants, and then a comparison is made to see which variant performs better. The assessment of performance can be measured, for example, by indicators such as clicks, downloads or sales.<\/p>\n

In order to obtain meaningful results, some criteria should be met. The two groups to which the different versions are played should be randomly selected to avoid bias in the results. In addition, only one feature should be changed at a time, as this is the only way to determine with certainty where the performance differences are coming from. Also, an A\/B test needs enough traffic and runtime to provide meaningful results.<\/p>\n

At the end of the test cycle, it is evaluated which version of the landing page has a better performance, and this is used in the further process. Once a test cycle has been run, a new feature can be changed to keep driving landing page optimization forward.<\/p>\n<\/div>

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Other test types<\/strong><\/h2><\/div>

In addition to the classic A\/B test, there are other test variants. The best known of these are the split test and the multivariate tests.<\/p>\n

Split testing differs from classic A\/B testing in that split testing redirects users to two different URLs, whereas classic A\/B testing takes place on a single URL. For this reason, the split test is also called split URL test.<\/p>\n

Multivariate tests, unlike A\/B tests, examine not just a single distinguishing characteristic, but several at once. However, in order to run multivariate tests, a website with an enormous amount of traffic is required, as each variant of the landing page needs enough traffic to provide meaningful results.<\/p>\n

Another tool in A\/B testing is dynamic traffic allocation. Here, a classic A\/B test is performed, but users are gradually redirected automatically to the better performing landing page. Here, the evaluation of the performance is therefore already carried out automatically during the running test.<\/p>\n<\/div>

A\/B testing at ad pepper<\/strong><\/h2><\/div>

At ad pepper, A\/B tests are used on a daily basis. We use them to further optimize campaigns step by step. Whether for performance display campaigns, email marketing, or search engine marketing, A\/B testing is a key tool for custom campaign optimization. Our A\/B tests are evaluated according to individually set goals to ensure that the respective campaign is optimized precisely to meet the set goals. In this way, ad pepper delivers the optimum performance for all campaigns, entirely in line with individually set targets.<\/p>\n<\/div><\/div><\/div>

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