A/B testing is one of the main methods for optimizing conversion rates . With two or more variables, you can identify the best approach for your website or app in a given situation.
The goal of A/B testing is to find out which variant generates more conversions, whether it's the image of a home banner , a copy , or even the color of a button. Working with simultaneous tests, it's possible to solve problems and answer specific questions quickly and assertively .
In this article, we will show you how to quickly validate hypotheses through simultaneous A/B testing. This way, you can quickly produce information to further boost your marketing and growth strategies .
Data types and sources
There is a wide variety of data that can indicate the characteristics finland phone number resource and behaviors of your audience. Demographic, registration or behavioral data are some of the examples used to raise hypotheses for A/B testing.
Are users from a certain location more engaged when they encounter elements from their own culture? And do younger users tend to convert more when the site features a certain color palette?
To stay up to date with your users' information, you need to connect to different data sources . Each one, with its own characteristics, can provide more accurate insights on a given topic. Google Analytics and some CRM platforms are examples of audience-focused data sources.
Tip: It’s important to be careful when looking at data in these tools, as there are pitfalls that can arise. Making the most of your data goes beyond simply collecting it. You need to make inferences and analyze it to understand what it really means. Outdated or inadequate data can lead you to form a distorted picture of your audience.
Creating hypotheses for A/B testing
It all starts with a hypothesis. But how do you start developing this hypothesis? What questions do you ask? There are several ways to start this process.
It is important to keep in mind that the goal of A/B testing is to provide more accurate information about your users. Therefore, looking for gaps in the conversion funnels and trying to correct them is one way to come up with hypotheses for A/B testing.
Another way is to put yourself in your users' shoes. This allows you to have a different and broader view, aiming to improve your audience's experience . Browsing your own website, interviewing users, asking for feedback and analyzing event data are excellent exercises to create hypotheses for A/B testing.