What is A/B Testing ?
In the world of the digital marketing and web analytics there is a term you hear a lot: A/B testing. But what is A/B testing?
Today, Axovia's team will help you discover what A/B testing can do for you and what are its secrets.
As its name suggests, A/B testing is a tool that allows us to compare two options and choose the one that generates the best results. All through statistical results.
For example, if you want to see which color works best in a CTA button on a web page, you can resort to A/B testing.
You can have in version A the buy button in red and in version B. in green, now the A/B testing will have the opportunity to work its magic. During the A/B testing process some visits will go to version A, and the other half will go to version B. Now, through a statistical test of a probabilistic count, you will identify with what probability they will buy more from you; there will come a time when the data will be significant, that is, it is data that statistically will tell you which version gets more sales.
How does A/B testing work?
To start A/B testing what we need first is to identify what we are trying to improve. So, in this sense, we will always put our focus on those parts that are most likely to impact my business: if I have an e-commerce what I want to get are more sales, if I have a website to get leads, if I have clear my goal I will work on those parts that will generate more responses.
Something very important to take into account is that an A/B testing has to be done in parallel; that is to say, at the same time, it is not useful to test one week with one version and the following week with the other version because the market, the type of visits, the prices and even what I offer or my competitors may have changed.
Another relevant point in an A/B Testing is the data sample, that little by little we are learning from the information obtained and that know-how makes us 1% better than our competitors, then 2% and so on. In order to be relevant within digital marketing, you have to be obsessed with executing A/B testing.
