AB Testing

What is A/B testing?

A/B testing is a marketing test tool aimed at optimizing the performance of a website, and in particular its conversions. These tests consist in proposing a version A and a variant B of the same project to a sample of users in order to determine the most efficient version, to then distribute it to the entire target.

Why set up an A/B test?

The implementation of A/B tests, coupled with skills webanalytics, allows you to validate hypotheses based on data from your site in order to optimize your conversions .

The objectives of A/B testing

The implementation of A/B tests allows:

  • To understand how certain elements can influence the behavior of your visitors .
  • To determine with precision the most appropriate solution in order to improve the  conversion of your visitors .

When to A/B test?

A/ B testing allows you to set up a certain number of concrete tests in order to determine the appropriate solution in relation to the habits of your visitors. These tests, to be conclusive, must be based on a relevant choice of indicators and be based on a certain volume of traffic in order to obtain conclusive data. Here is a non-exhaustive list of possible tests:

  • Test an emailing, its sending time, its subject, in order to improve its opening rate.
  • Test a Call-To-Action (CAT) on your e-commerce site to improve your conversion rate.
  • Test a banner ad (Display) in order to optimize its click-through rate (CTR).

What types of tests should be used to improve web performance?

Les tests A/B (Split testing) Multivariate testing (MVT)  Les tests multipages (Funnel testing) 

Also called split testing , these tests allow you to compare the conversion rates of 2 versions of a page (version A and version B). Each of the versions receives an equivalent number of visitors, and by measuring their interactions: leads or purchases, we determine the most effective. This is a simple and effective test method . It allows for example to compare the impact of the design on 2 different pages or to measure very precisely the impact of the modifications of a single element on a page. It is possible to compare, for example, the variations of colors or labels of a call-to-action (CTA). The test tools calculate the statistical reliability and indicate when the sufficient number of visitors has been reached.

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