A/B testing for more accurate results in marketing
Often, our intuition is a good advisor. However, that’s not always the case. Especially not in marketing when it comes to optimizing the budget to attain the most conversions. Fortunately, there are some useful methods to help us make use of data rather than having to trust our ”instinct” only. One of them is the A/B test, which is included in our marketing automation solution for newsletters and e-mails and can even do multivariate testing.
What is A/B testing?
An A/B test helps compare two or more versions of one set of content – a newsletter, landing page, etc. – to determine which version performs better. Changing several parts of a content at the same time is also called multivariate testing. Conducting the test typically involves the creation of equally large recipient/visitor groups that receive one of the versions at random. For example, you can compare different subject lines, headlines, images, or calls to action (CTAs) in a newsletter to determine if the open rate, click-through rate, pageview duration, or other KPIs change as a result. Because every marketing professional’s goal is to make the content as irresistible as possible for their target groups.
And how does that work with BSI?
We have good news: With BSI, A/B testing is straightforward – thanks to the ability to create multiple content variants in BSI Studio. One set of content can contain any number of variants, which means that even more than two versions can be compared: You can insert different content at various points, such as the subject line, the header image, the text, the senders, etc.
With BSI, you can define how to deal with the different variants: Which variants should you even consider? Do you want to conduct an automatic or a manual A/B test? Should the senders vary as well?
For the automatic A/B test, all you need to do is define the number of recipients in the test group, one KPI, how to measure it, and the test duration. Please note: Conducting an A/B test without first developing a hypothesis to test with metrics makes little sense. You absolutely need a hypothesis to understand the results accurately and draw a conclusion from the test. Therefore, always think about which variant will most likely perform better and why.
Speaking of which ... We are currently working on ensuring that the best KPI does not apply to everyone in the future, but rather that our AI decides for each recipient individually, which variant has the best chance of success. It’s a step toward hyperpersonalization.
Are you still guessing? Are you testing yet? To learn more about A/B testing and how you, too, can benefit from it, please contact our experts. On this note: Let the testing begin!