A/B testing

A/B testing is a widely used analysis method in marketing.

As its name suggests, this process is used to measure the impact of a change between a version A and a version B.

a b test

Although it’s not a recent process, A/B testing is now used much more than it used to be, especially thanks to the rise of digital marketing.

The web has skyrocketed its use, and today's tracking tools offer all sorts of measurable data.

As a result, A/B testing is now much easier to perform. Its implementation time is also reduced.

A/B testing can be used to analyze marketing data on many different types of media.

It can be used to test emails, forms, advertisements, landing pages or standard pages on a website.

For example, it can be used to compare the opening rate of the same email message, sent with two different subject lines. It can also be used to analyze conversion and compare the effectiveness of a call-to-action button designed in two different colors.

Relatively simple to set up, A/B tests can be implemented via many dedicated platforms.

Generally speaking, they must concern very specific data and must only focus on a precise element of change while all other elements presented to the user remain identical. In some cases, several variables can be tested simultaneously. This is called multivariate testing.

Depending on the case, there are several types of A/B testing:

  • classic A/B testing, which presents users with two variations of a page on the same URL;
  • split testing, or redirect testing, which redirects traffic from your page to one or more distinct URLs; and
  • multivariate testing, which measures the impact of several changes on the same web page.

Simply put, A/B testing is a tool to help and guide you in validating different hypotheses statistically and concretely.

On its own, it’s not a key to understanding the behavior of internet users. It simply provides statistics that will then need to be studied with web analytics while taking other parameters into account.

In this sense, A/B testing can be fed by different elements such as web analytics data, ergonomic audits, user tests, or customer feedback.

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