The time when the marketing budget was spent without qualms, when we worked in the digital world, improving the effectiveness and profitability of our marketing strategy became one of the top priorities. Currently, marketing teams are able to make well-informed decisions and take a scientific approach to optimizing the experience of their visitors. Their decisions are based not on subjective intuition but on reliable data obtained especially through an A/B testing.
What is A/B testing and what is it for?
A/B testing is a technique of comparing the effectiveness of different elements. A version is displayed to one part of the users and the other version to another user group. Subsequently, with numerical methods and reviewing the statistics of each one, it evaluates which is the most convenient and the one that has worked best.
Generally, before testing, the parameters that will be evaluated are established, how they will be evaluated and the minimum time that we will have it in place until we obtain the objectives and the goals set.
A/B tests are typically used in landing pages or email campaigns to test which type of content works best for higher click-through rate (CTR) or open rate.
The items that are typically tested are calls to action, texts, colors, the subject of an email, the length of a contact form, or the location of a signup form.
It is important to note that this type of test involves modifying only one variable, a single element, since this will not influence the results of other variables that may intervene. When testing several variables at once we are faced with another type of test, multivariate tests, much more complex.
The origins of A/B testing
A/B testing basically involves comparing two versions of something to see which of the two is most effective. Therefore, it is not a new concept, but one that appeared before even the birth of the Internet.
British biologist and statistician Ronald Fisher was the first to introduce this idea through mathematics in the 1920s, showing how to analyze the differences between two different experiences in a scientific way. His work turned out to be an achievement in the scientific world. A few years later, the principle of A/B testing would begin to be used in clinical trials.
We would have to wait until the 1960s for this concept to be applied in the field of marketing. A/B testing as we understand it today, has been with us since the 1990s. It is fast becoming the model chosen by many marketing specialists to show different versions of their marketing messages to a sample of their customers to find out what type of message they prefer. Although A/B testing not only comes down to checking for different messages.
The development of the digital world offers new situations and perspectives by multiplying the possibilities for A/B testing and measuring its results. If we apply it to a website, A/B testing allows us to test an unlimited number of versions of that website to reliably measure the performance of each version based on indicators such as actions performed by users or their behavior on the website. Technological advances have also made it possible for A/B testing solutions to be available to test most people without the need for extensive knowledge of statistics or programming, often through highly visual and intuitive applications.
How does an A/B test work?
Running an A/B test is very simple and you don’t need technical knowledge to carry it out.
Simply create two versions (version A, version B) of the same element, for example, a call to action with the text “Save 50%!” and another with “Take advantage of the promotion!” and launch it to two groups of users over a certain period of time.
Which CTA had the highest click-through rate? Which one did you get the highest conversion rate with? As soon as you analyze the metrics and determine which call-to-action button works best, it’s the one you should implement to optimize your results.
However, before launching these versions, it is essential to set the goals you want to achieve: increase your subscriber list? Increase sales? Increase the opening rate of your email?
Only then can you set the corresponding metrics and then determine which of the versions you need to implement in your marketing strategy.
The three main functions of A/B tests
- Throw valuable information about what the user prefers. The first goal of A/B testing is to find out what users prefer. That is, what kind of elements or messages make them more likely to click on an ad, become leads, make a purchase or stay on a website. This information not only serves to improve the content on which we did the test, it also serves as a guide to optimize all subsequent content.
- Increase the conversion rate. A/B testing is one of the main tools of CRO or Convertion Rate Optimization, which refers to a number of systematic improvements for content to generate more leads, subscriptions, conversions, clicks, etc.
- Improve the user experience. Finally, A/B testing allows us to refine the content we offer our visitors to make their user experience more personalized and tailored to their expectations and needs.
A/B Tests: Helpful Tips
- Any change in the variable to be evaluated will be useful. However, we recommend that at the beginning of the tests you try to make the difference between the variables as much as possible to be able to decide more precisely the path you should take.
- We give you an example: if you have a website with yellow and green colors and want to optimize your Adsense ads, do not start by doing an A/B test by switching between shades of yellow. Start by testing which color works best, yellow or green.
- Another advice that we consider quite important is that you do not put a limit on the number of tests. You can almost always improve a result even if you think otherwise. Don’t settle for the first win! Have you done a test and managed to improve the conversion of your website? Then take a second to keep improving it!
- Finally, stop analyzing the data and results well. They are the key to improving your platform. Try to put yourself in the shoes of users to better understand what would be optimal.