A/B testing plays a major role in the development of effective and productive marketing strategies. It is one of the most widely applied measurement and optimisation methodologies in digital marketing, but what exactly does it entail and how should it be applied to achieve efficient results?
A/B testing is one of the most effective methodologies for measuring the performance of our digital marketing efforts. The advantage of this method is that it does not stop at simple measurement, but rather promotes the correction and optimisation of the content analysed. It also analyses the content in detail and therefore provides concrete results that lead to very specific corrective actions.
The implementation of A/B testing is key for marketing teams to discover which of their strategies and content work best and which factors or elements need to be improved.
Below, we explore what A/B testing is and how it should be conducted to achieve optimal results.
A/B Testing —also called "Split Testing"— is a measurement and optimisation methodology used in digital marketing that consists in comparing the results of two variables —usually A and B—.
The objective of A/B testing is to identify which variable provides a better result in relation to the marketing objective we want to achieve. This is precisely why it is so important that the variables we compare are of the same type. It would make no sense to do an A/B test comparing the title of an article with the body of an email. If we compare two variables of different types, the results of the A/B test will not be conclusive.
One of the great advantages of A/B testing is that it can be applied to many elements and contents: websites, Calls to Action (CTAs), emails, ads, messages, copys, etc. In fact, almost any element can be A/B tested, even the title of an article or the image that goes with it.
Below, we explore in detail how this method is executed and how it can benefit your marketing strategies.
As we have already seen, A/B testing consists of comparing two variables (A and B) of the same type —for example, the subject of an email— to see which of them has a better performance. Between the two variables, one of them acts as the control variable and the other as the treatment variable.
The control variable is the main or current version that we want to test, while the treatment variable is the one to which we apply changes and use as a contrast to evaluate the performance of the control variable.
Thus, we can perform multiple A/B tests, alternating the treatment variables, until we find one that performs better than the control variable.
A/B testing is often confused with multivariate testing due to the similarity between the two methods. However, they are different methodologies that are applied differently.
A/B testing involves the comparison of a single treatment variable with a control variable, hence the generic name "A" and "B". Although we can compare a control variable with more than one treatment variable, such as B1, B2 and B3, we cannot do so by comparing them all at the same time in a single analysis.
That is, in an A/B test we always compare two variables. If we want to test multiple treatment variables for a single control variable, we will have to perform as many A/B tests as the number of treatment variables we want to test:
In contrast, multivariate analysis allows a larger number of variables to be tested simultaneously, generating more complex combinations than A/B testing, e.g. A/B/C/D. With this approach, one can perform two or more A/B tests at the same time.
Multivariate analysis is often applied to high traffic websites so that there is enough data to ensure meaningful and reliable results between more than two variables.
A/B testing can be applied to multiple elements: landing pages, emails, calls to action, etc. In fact, we can measure almost any part of our digital marketing campaigns.
However, if you are just getting started in the world of A/B testing, below is a list of some of the elements that are most commonly tested using this method.
Obviously, each business is different and the variables or elements to be measured must be aligned with the needs and the marketing objectives of each organisation.
Finally, we offer 5 best practices for preparing and carrying out A/B tests effectively.
Once we have tested the most obvious elements listed above, we should start experimenting by applying A/B testing to other less usual elements, but which could be decisive for our business model.
In fact, it is possible that the most surprising results will be those obtained by analysing variables that, at first sight, seemed either obvious or not very apparent. For example, the target audience of an email, the launch time of a content or the alignment between an email and a landing page, among many other possibilities.
It is important to perform specific tests for each variable you want to test so that you can understand how the variable actually affects the metric you want to optimise.
In other words, in an A/B test it is essential to test each hypothesis separately. For example, if we are launching a new email marketing campaign and we want to find out the impact of the subject, the body and the attached image of the email on the generation of leads, we should perform a test for each of these elements, with their respective control variable and treatment variable.
If we try to test all the elements at the same time, it will be impossible to identify which variable is generating the greatest impact on conversion. Therefore, it is essential to test each identified variable individually, no matter how insignificant it may seem.
If individual tests for smaller variables, such as the elements of a CTA or an email, do not provide consistent results that allow us to make a decision, we may need to try running an A/B test on an entire element, whether it is an email or a contact form.
Sometimes it is necessary to make radical changes to assess how the entire element performs compared to its original version. For example, if it is an email, we can perform an A/B test by considering the entire email as a single variable. The same applies for a landing page or a CTA.
Despite being made up of different parts, a first general test can help us to draw initial useful insights. Once we have performed a first A/B test on a macro level, we can carry out a series of A/B tests on more specific variables to further fine-tune the details of the element under analysis. The more tests we carry out, the better the results of our campaigns will be.
It is a common knowledge that A/B testing is ideal for measuring conversion rate, but we should not stop at this metric. In fact, it is recommended to apply A/B testing throughout the entire sales process and the customer journey to monitor several metrics, such as visits, click-through rate, traffic, demo requests, sales and much more.
The goal is to use A/B testing as a comprehensive tool to measure and improve the performance of each stage of the conversion funnel. This will give us a more complete and accurate picture of how the changes we make influence the overall success of our digital marketing campaigns.
A/B testing should be an ongoing process that is carried out from time to time. Just because we have analysed a specific element of our marketing campaign and achieved efficient results, that does not mean that we do not need to explore this element again in the future.
It is likely that results on a given element will vary over time, as customer needs and preferences are constantly evolving.
Conclusion
A/B testing is a crucial methodology for measuring and optimising digital marketing strategies. This method allows you to identify which variables generate the best results and provides specific corrective measures.
To carry out A/B tests effectively, it is important to choose the right variables to test, perform individual tests for each variable, test from the general to the particular, apply A/B testing throughout the customer journey and be consistent in the process.