Are you looking to improve your site’s conversion rate? Thanks to the Google Optimize tool, A/B tests are now far simpler to perform. These tests enable you to make strategic modifications to your website based on the best reported conversion rates derived from your consumers’ behaviour.
What is A/B testing?
An A/B split test is a procedure that allows you to test two versions of the same web page and to measure their impact on consumers. The aim is to optimise your tactical choices to achieve a given conversion goal (generating prospects, validating a form, call back, etc.). A/B testing is a powerful tool which, in most cases, results in a considerable increase in conversion rates.
In the past, this method for optimising a website proved to be a rather costly solution that was relatively difficult to implement. Consequently, its use tended to be limited to major groups. Today, tools such as Google Optimize have rendered A/B testing far more accessible.
What is Google Optimize?
Google Optimize is a free Google A/B testing tool. It enables A/B tests to be performed rapidly and simply. The free version is, however, limited for use on predefined goals. The use of a fee-charging version is sometimes recommended, depending on your needs and your aims.
Our A/B testing methodology
It is our aim that each test offers you directly implementable results. This requires excellent preparation and documentation of the testing experience. Our procedure:
- Planning and defining strategy
We approach A/B tests firstly by defining your goals, identified target, tools for measuring conversion and the overall experience of your customers or site users.
We then establish theories in an aim to identify reasons for – or obstacles to conversion.
This is an essential step for it enables you to formalise each stage in your advancement, from strategy elaboration to theory rationalisation, via an exploration of consumer/user flow.
Following the established theories, we set up experiments over which site traffic is randomly and equitably redirected towards one or the other version.
As soon as the tests have reached a significant audience, we are capable of studying the rates of micro and macro conversion between the proposed versions. Theories are then confirmed and strategic implementation choices for permanent site modification become possible.