Is it true that, as Wikipedia says, "the term A/B test is used in the field of Digital Marketing and Web Analytics to describe random experiments with two variants"? However, it is worth noting that this type of experimentation has been conducted throughout history and has allowed us to discover incredible things.
A paradigmatic example, given its relevance in recent years, is the testing process that the pharmaceutical industry must go through to approve a vaccine.
In the second phase, two groups of people with similar characteristics are formed, with one group receiving doses of the vaccine. Then, comparisons are made between the vaccinated group and the non-vaccinated group.
This is a part of the process used to measure the success of a vaccine, taking into account the variations that may arise between the vaccinated and non-vaccinated or control group.
In this way, we could find hundreds of examples in different disciplines where testing of two variants is carried out in order to compare and make decisions.
In our field, A/B testing is an ally that continuously helps us pave the way and find more effective solutions.
The simple activity carried out to find more effective answers is the test itself.
This technique involves comparing performance. Depending on our goal, we will focus on the aspect we are interested in comparing.
Let's imagine we are in the stage of optimizing a campaign. Keep in mind that a campaign has many elements, such as the headline, image composition, text, colors, call-to-action, among others.
When conducting an A/B test, we need to select which element of our campaign we want to compare. In this case, we will test which design generates higher engagement.
It is important to highlight that A/B testing aims to contribute to a larger goal, which is improving the performance of our website.
How to proceed?
In this case, the design (A) we have been using for our campaign will be shown to one group of users, while the new variant (B) will be shown to another group of users. It is important to note that both groups belong to the same audience.
We are part of a vegetarian food venture and want to conduct an A/B test to determine which design may achieve higher engagement from users.
Once each variation has received a relevant amount of traffic, it will be possible to make a decision based on the statistics we gather.
Which of the two designs will tend to generate more interest from our audience? The answer will be provided once we carry out this experiment.
Conducting A/B tests for newsletter campaigns yields an 82% higher return on investment compared to those who do not perform these tests.
Let's consider another example:
The average conversion rate for an e-commerce website is usually between 1% and 3%. There are many factors that influence these percentages, making it a complex process.
The two versions we will compare will result in the effectiveness each one had in terms of conversion.
These variations, called A and B, are randomly shown to different users of the website. Some of them will see version A, while others will see version B.
The conversion rate percentages are the statistics we mentioned. Thanks to these numbers, we can understand which of the two variants has achieved better results.
There are also other indicators that will allow us to assess the performance of our tests, such as the Cost Thru Click (CTR), which is the average cost we will pay for each click on our ad.
If you're still unsure about the usefulness of conducting A/B tests, questions are always good triggers.
- Which of my ads grabs more attention from my audience?
- Why wouldn't a user click on my ad?
- Which headline is easier to understand?
- Which colors generate more engagement?
- Is it beneficial for the logo to appear?
By conducting A/B tests, we can gain insights and answers to these questions, allowing us to make data-driven decisions and optimize our marketing efforts.