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A/B Testing: Know both sides of the same coin

Introduction of Appium in Mobile Automation Test

The A/B Test, also known as the most reliable way to learn user behaviors, is a comparative research method. That seeks to answer questions and experiment with solutions in the field of digital products.

The math is pretty simple. Two versions of the same product, that develop and expose A and B, to different groups of users. After the interaction between customer and product.

A series of data is collected, compared, and evaluated from pre-established metrics. Such as click-through rate, interaction time, exposure, traffic flow, lead generation, among others.

As we expect, the choice of metrics we use depends on the objectives of the action and acts as a fundamental basis for analysis.

After all, it is impossible to perform good A/B tests without having defined how the results will be evaluated and corroborated.

Visual differences may vary in size and relevance according to the solutions proposed for the initial questions. Moving from one version to another can be subtle, such as the color of a button, or very significant, as the repositioning of all components of the page.

The fact is that the size of the action that matters little since the objective of the A/B Test is not only to present hypotheses.

But also to solve doubts, to gather the reactions of users. And use the results to build strategies that solve real problems.

Side A: The benefits of this test

There is no perfect methodology that suits any situation. However, each approach is unique and brings certain advantages that others cannot.

The A/B Test, however, is not about the dialogue with the user, but about the observation. That is, people use a product without knowing that they are being part of an experiment, so they act naturally and without fear.

By evaluating this behavior closely and without interference from external factors, the team conducting the test can identify the user’s conduct model and gather important information for the evaluated product and/or service.

If employed intelligently, the A/B Test may even be a cheap method. Because it doesn’t require the application of expensive tools and the hiring of test users for the product. All you need to do is two versions of the experiment and software that randomly divides users into groups.

Besides, by defining relevant and enlightening metrics, data analysis can be quick and easy. That is if the project makes it clear that the number of conversions is the evaluation criterion. And this metric makes sense, as soon as the result displays, it is easy to understand it.

Thus, by evaluating the numbers it is possible to understand which version performs best and which content works best. Answering the initial question of the project.

The important thing is to keep in mind the relevance of the evaluation method and the uniformity of the data for a sensible and uncomplicated analysis.

Side B: Wait for data, not complex answers

However, the A/B method is not perfect and has its procedural limitations. The time and scenario in which it is applied should be evaluated sparingly so that time and money are not spent unnecessarily. Therefore, consider a few points before choosing to follow this line.

The A/B Test, for example, works only for properly terminated interfaces or something very close to it. You cannot simply test button colors in an interface without using content and reproduce the conditions necessary for the user experience.

If the search for the test is precisely to observe the user acting naturally. Everything must be done fluidly and completely for the best use of the experience.

Therefore, implementing A/B Testing early in a project may not be the best option.

Another point that should be taken into consideration when considering the A/B Test is the type of insight you seek. Because it exerts a comparative effect, this methodology does not generate many “whys”, because it is mainly based on data and metrics.

The ideal question to be elaborated in a project that applies this test is one that explores an analogy, not a deep knowledge of how things work and how they make the user feel.

No hurry, your test needs time and patience

Recently the Appsumo team developed a series of A/B Tests and came up with a surprising result. Only 1 out of 8 A/B tests produced relevant results for a good change in product/service. The motive? In general, it was a lack of patience.

During the test, the team testified that the rush to gather insights and execute the changes quickly hampered the collection of information that could improve the product. The anxiety in corroborating some hypothesis trampled efficiency, and so many tests were inconclusive. Therefore, when choosing to proceed with the A/B Test, have patience as a mantra.

After all,

Should We Use or not Use an A/B Test?

Basically, the A/B Test is a hypothesis test that may or may not be a good solution for your product, so it should be evaluated carefully.

In fact, what is known is that the best ideas are fruits of exploration and discovery, things that the A/B Test delivers easily?.

In particular, we believe in this methodology because we understand that data can be great allies. When it comes to better understanding the user experience.

We are often surprised by unexpected behaviors that could pass beaten if they were not properly tested and analyzed. After all, the basic idea of a hypothesis is that there is no predetermined outcome, right?

That is why we articulate in favor of the A/B Test whenever we have the opportunity. Precisely because we understand that the comparison analysis not only generates some important answers. But also points to the best solution without direct interference.


In the end, the result comes only via the user, which is who really matters here.

In doubt, open yourself to an experience with an A/B Test. Many companies use this methodology to collect data about their users and better understand the scenario in which they operate. While it takes a lot of work and patience, We’re sure the reward of achieving your product’s goals will be well worth it.

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