Understanding A/B Testing in Ecommerce with Data Science

While developing an e-commerce website, there could be different opinions about the choices of various elements, such as the shape of the buttons, the text on the Call to Action buttons, the colour of various UI elements or the copy on the website, or numerous other such things.

Some of these can be extremely crucial decisions; e.g., you may have to choose the copy and call to action on the payment page that maximise the chances of a customer completing a payment.

Often the choice of these elements is very subjective and it is difficult to predict which would perform better. To resolve such conflicts, you can use A/B testing. A/B testing provides a way for you to test two different versions of the same element and see which one is performing better. Let’s see how exactly this works.

Let’s say that you’re working on a website. Suppose A is the existing design of your website. This design is called the control. B is the newly designed version, which is called the variation. To conduct the A/B test, you have to randomly divide the traffic between the control and the variation, that is, A and B, and measure the performance of each design using the metrics that are important to your business. At the end of the test, the data collected will help you select the better performing version for your website. Now let's dive deeper into the concept.

In the video above, you saw an example where A/B testing was used to validate the impact of the change in the text feature of a website. Similarly, there are many other web features that can be validated through A/B testing.

You can see a few more case studies and applications of A/B testing in the real world by clicking this link.