How to perform A/B testing on your website with Google Optimize

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When a website reaches a certain period in its life cycle, in which it already has a significant volume of visits, other opportunities for improvement begin to arise beyond attracting more traffic .

There is often a need to obtain more performance from users who already visit our website. We can act in several areas, for example, by improving the usability of the portal or increasing the conversion rate.

Whatever our objective, we will introduce changes to the website in order to improve it . But these modifications will not always bring the desired results. And if we act without a previously defined methodology, we can end up with negative consequences, such as a decrease in the number of transactions in an e-commerce.

For this reason, performing an A/B test on our website to determine whether a change will actually bring positive results will save us a lot of headaches. And how to do it? As in many other cases, Google has a free tool that italy telegram data allows us to perform A/B testing on a website: Google Optimize .

We are going to learn how to perform an A/B test with Google Optimize , but first let's define what type of test it is and what the purpose of performing an A/B test on a website is.

What is an A/B test on a website?
Also known as Split Test, this is an experiment that aims to determine whether one or more changes introduced (for example, changing the color of a call to action) on a web page have the desired effect or not . The main keys to this type of test are:

In the test, the original version of the web page (A) and one or more variants of it (B) will always be used.
We will need to define a test objective that can be quantified through a web metric. For example, reducing the bounce rate, increasing the conversion rate, etc.
In the variations of the original website, we will introduce changes to certain elements to see what effect they have on the defined objective. If we act on a single element (for example, the position of the purchase button on a product page), we will be able to determine with more confidence whether or not said change has been responsible for an improvement on the website. On the other hand, modifying several elements will mean that we will not be able to define which of them has had the most weight when producing results.
We must use a sample (the number of users who will go through the experiment) that is sufficiently representative of the traffic on our website or the segment to be analyzed.
The A/B test should have a defined duration, always avoiding external factors that could alter the test whenever possible.