There is no perfect website design. Even if it is honed many times, there will always be something that can be improved.
As a rule, the quality of website design is evaluated not only in terms of visual beauty and usability, but also in terms of conversions, which it brings. These can be purchases of goods, applications with customer emails and phone numbers, clicks on certain buttons. All of this is measured and defined as conversion.
The calculations of conversions themselves can be made in different ways: with Excel, internal CRM systems, as well as with programming languages and special mathematical libraries.
Python is the go-to programming language for conducting A/B testing of different website design versions. A/B testing in Python can help to boost website conversion rates by experimenting with various designs and user experiences.
With Python, web developers can easily monitor and analyze customers’ behavior on their websites, which allows them to tweak their designs accordingly. They can test specific components such as button size and color, font styles, image placements or changing matching content optimization to enhance user experience.
By measuring results of both version A and B (control group) on a sample group randomly assigned to either version through experiments, developers can choose the best-performing combination from a range of iterations.
Using Python for A/B testing enables designers to achieve better UX for visitors that drive traffic growth by showing personalized content that suits potential clients’ needs ultimately translating into higher profits for businesses. At the link below you can see an example of A/B testing with Python.
