The code was tested using Python version 3.9. For other necessary libraries please use requirements.txt
pip install -r requirements.txtThis project is dealing with Starbucks advertising promotion campaign data and concentrated on
- evaluation how successful the new promotion campaign was using A/B Testing. The optimization is based on maximization of two types of metrics
- Incremental Response Rate (IRR)
- Net Incremental Revenue (NIR)
- building a Machine Learning model in order to detect hidden patterns and so making the promotion more profitable.
starbucks/
├── Starbucks.ipynb # the notebook with AB Testing evaluation and Machine Learning optimization
├── README.md # readme file
├── requirements.txt # all necessary libraries
├── Test.csv # test data
├── training.csv # training data
├── test_results.py # python script to test and compare results
The main findings of the code can be found at the post
- "Why should your company do an AB testing? Starbucks A/B Testing Step by Step Part I" available here.
- "Random Forest: a Machine Learning Model in Marketing Strategy & Decision Making, Better than A/B testing ? Starbucks optimizing the results with Random Forest after A/B Testing Part II" available here
Must give credit to Starbucks for providing the data.