Throughout my learning process, both from Datacamp and my individual research, I have found a lot of useful skills that a data scientist should know. One that I found most crucial and intriguing is called A/B Testing.
The idea of A/B testing is to present different content to different variants (user groups), gather their reactions and user behavior and use the results to build product or marketing strategies in the future.
A/B testing is a methodology of comparing multiple versions of a feature, a page, a button, headline, page structure, form, landing page, navigation and pricing etc. by showing the different versions to customers or prospective customers and assessing the quality of interaction by some metric (Click-through rate, purchase, following any call to action, etc.).
This method allows decision makers to choose the best design for a website by looking at the analytics results obtained with two possible alternatives A and B. A/B testing allow business decisions to be backed by facts and numbers which is becoming increasingly important factor in a data-driven world.