A/B testing is widely used today to analyze the effectiveness of advertising and marketing strategies. Thanks to A/B tests, marketers and analysts can understand which website or app design can convert more traffic into sales.
The goal of A/B testing can be not only to increase sales, but also to increase subscribers or traffic, smartphone app installs or other targeted actions.
Obviously, in order to do A/B testing with Python, we should have at least two datasets that should be compared against each other in terms of achieving our goals. Typically, these datasets are divided into 2 types: control campaign and test campaign.
In this post, I want to share with you the results of A/B testing with Python of two such campaigns from one online store.
Python, Pandas, Plotly.
- Google Collaboratory: colab.google.com