In this project, our goal is to perform an A/B test to determine if a new version of a website page outperforms an old version. To achieve this, we conduct an experiment where we collect data on visitor behavior from both pages. We then analyze the data using an A/B test to determine if there is a statistically significant improvement. By applying practical statistics, we can make an informed decision on whether the new version should be implemented or not.
An A/B test, also known as a split test, is a statistical experiment used to compare two different versions of a webpage, email, or any other marketing asset. The goal of an A/B test is to determine which version performs better in terms of a specific metric, such as click-through rate, conversion rate, or revenue.
This allows you to make data-driven decisions and optimize your marketing assets for improved performance.
A/B testing is widely used in various industries, including e-commerce, digital marketing, and web development, to continuously improve user experiences and drive better business outcomes.
This is a Python project so you need a Python notebook like Jupyter or any alternative and this is generally what will be needed:
- Jupyter notebook
- Pandas library
- Numpy library
- Matplotlib library
- Statsmodels library
- Scikit-learn library
- ab_data.csv : Data collected from visitors to perform An A/B test.
- countries.csv : Additional Data collected from visitors to be used in Regression.
- Analyze_ab_test_results_notebook.ipynb : The Notebook that has all the work from codes, Step-by-step explanations, visualizations, and conclusions.
- Analyze_ab_test_results_notebook .html : The Notebook in HTML format so it can be opened with a browser.