For the results of an A/B test to be conclusive, the sample of people must be sufficiently large and a variable should be tested more than once.

Introduction to A/B Testing: What, Why and How

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?

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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.

What is A/B Testing?

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.

How to conduct an A/B test?

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.

The difference between A/B Testing and Multivariate Analysis (MVA)

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:

  • Test A/B1
  • Test A/B2
  • Test A/B3

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.

What elements can be measured with an A/B test?

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.

  • Calls to action (CTA): Calls to action (CTA) are among the marketing tools that generate the most conversions. Therefore, it is important to apply A/B testing on our CTAs in order to improve them and increase our conversion rate.
  • Contact forms: Contact forms also have a great influence on conversions, as well as providing important information about our potential customers. A/B testing will help us to find out how the audience reacts to the questions we ask, which forms are better or, for example, how many questions it is better to ask.
  • Copy: The message is very important when it comes to marketing, especially in the era of content marketing. That's why copy plays an important role in any strategy. Therefore, it is advisable to carry out an A/B test with different types of copy: descriptive, narrative, memorable phrases, quotes from market leaders, short sentences or large paragraphs, until you find out which one works best.
  • Placement of elements within the page: Although it may seem irrelevant, the location within the page also influences consumer behaviour and the success of a content or communication campaign. In this sense, it is important to test the performance of a CTA or any other marketing element depending on its position.
  • Size: The size of elements is also important and likely to impact the results of our marketing campaigns.
  • Colour: Colour has a proven effect on people's behaviour and emotions. Therefore, it is important to check which colours have the most positive impact on our audience, especially in standout elements such as titles, calls to action, etc.
  • Graphics and images: The graphics and images that go with any communication product also have an effect on the audience. With A/B testing we can identify which ones work best and in combination with which elements.
  • Email format: The format of emails is among the elements that are most frequently A/B tested. Regarding the format of an email, we can test multiple variables: the subject of the email, the length of the message, the tone of the email, the visual elements, etc. All these aspects can make a difference in our email marketing campaigns.
  • Sender: Whether an email has a generic sender or is signed with a name significantly affects its open rate. Personalisation helps to improve the performance of an email marketing campaign, so subjecting the sender of an email to an A/B test is one of the easiest ways to improve your open rate.
  • Tone of the message: Tone is one of the fundamental parts of content marketing. Depending on the type of content and the type of audience, a tone will be more or less efficient. We must remember that the way we say things is as or more important than what we say.
  • Target audience: Finally, the target audience is a very significant variable when it comes to A/B testing. Customer segmentation can help us to obtain higher response rates in our emails or to increase the effectiveness of our advertisements, among others.

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.


5 tips on how to carry out an A/B test efficiently

Finally, we offer 5 best practices for preparing and carrying out A/B tests effectively.

1. Choose which elements you want to test

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.

2. Conduct individual tests for each variable

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.

3. From general to specific

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.

4. Apply A/B testing throughout the customer journey

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.

5. Be consistent

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.



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.