Conversion rate optimization (CRO) is a key process for turning website visitors into customers and increasing the conversion/purchase rate of existing customers in the best way. In this article, we provide an introduction to the topic, explain the basics and show why A/B testing is an essential tool for digital marketing.
A/B testing - the key basics
What is A/B Testing?
A/B testing is an experimental method in which two variants of a website or element are compared with one another. Variant A is the original version, while variant B is a modified version. The aim is to find out which variant delivers better results - in terms of more purchases, registrations or other desired actions.
How does A/B Testing work?
The process begins with a clear hypothesis. For example: “If the call-to-action button is greener, more visitors will click on it.” Half of the users see the original version (A), while the other half see the modified version (B) with a dark green button. By analyzing the results, well-founded decisions can be made about which version performs better.
Why is A/B Testing so important?
Without A/B testing, companies often rely on gut decisions or best practices that do not always lead to the desired results. With data-based tests, specific user preferences can be recognized and taken into account. In the long term, this leads to more effective measures with higher conversion rates and, as a result, higher sales. A/B testing can also help to prevent decisions that negatively affect the conversion rate.
Typical Use Cases
A/B tests can be applied to a wide range of areas: Landing pages, email marketing, product pages or checkout processes. Within these areas, test elements such as headlines, images, color schemes, texts or layouts can be changed. Even small changes can make a big difference to the conversion rate.
Important Hints for successful Tests
Only change one variable: To receive valid results, only one element per test should be changed.
Sufficient data quantity: A/B tests require sufficient traffic to achieve statistically significant results.
Be patient: Tests should run long enough to provide reliable results. To define the optimum runtime, we recommend calculating it in advance using an appropriate tool.
Conclusion
A/B testing is the first step towards a data-based approach to conversion rate optimization. It enables companies to continuously learn and improve. In the next article in the series, we will take a closer look at how hypotheses for A/B tests can be created and prioritized.
Our diva-e experts will be happy to help you strategically strengthen your CRO.
Patrick Schneider
