Using A / B tests with the parameterization of relevant / interest variables such as sociodemographic variables is a key aspect if products or services are being designed with a gender perspective. This is because it allows to quickly know the level of response to the design or the way of communicating a product or service based on the sex and age of the clients.

A / B tests are particularly useful if the company designs products that are sold online or provides services on web platforms, since they are quick to implement and the benefits of carrying out this test are visible in the short term.

In terms of the gender approach, it is key to be able to design products or services that are tailored to the people we want to reach. For example, if we are designing a new product or service aimed at women and we want to verify which communicational message works best in terms of sales conversion, then performing an A / B test will allow us to test different messages and select the one that worked best with the profile of desired women. Another ideal situation to use these tests is when it is known that some products or services are not reaching the profile of women who are wanted to be considered. In this case, a test can be designed that compares the performance of different messages or characteristics in both men and women, and then compares which ones work better in each group. 

As postulated by the gender approach, people are not a homogeneous group. Women and men have different needs, perceptions and realities. For this reason, A / B tests allow the design of products or services that adapt to the various characteristics of both groups. 

It is known as A / B testing or A / B testing, the creation of two (or more) versions of a product or service to determine which of them best meets the objectives that have been set. It is widely used for the development of web pages and applications, where communication messages, page layout, the inclusion or not of certain elements can improve the achievement of objectives.

In an A / B test, content “A” is randomly shown to half of the users and content “B” to the other half, to then measure and analyze the results of each one of them based on certain variables, such as conversion, engagement and / or rebound.

In the web application development environment, A / B testing is commonly used when the objective is to:

The following explains how the calculations are performed in order to verify the results of an A / B test. The video explains step by step how to do the A / B experiment calculation using Power BI. For this, it is recommended that those who carry out the technical exercise have advanced knowledge of Power BI and knowledge of inferential statistics. 

To simplify the explanation, the case of an advertising campaign via Facebook is used. You are going to promote a product from the catalog and, before launching the campaign, you want to pilot which content is the one that will generate the most conversions (clicks on the buy button). To do this, they design an A / B test, where they will show an advertisement for their product to a group of users and another advertisement to another group. The selection of which advertisement to show to each user is random, that is, each one has a 50% probability of seeing either of the two advertisements to be piloted.