In this section, we are going to discuss how to evaluate A/B testing results to decide which marketing strategy works the best. By the end of this section, we will have covered how to run statistical hypothesis testing and compute the statistical significance. We will be mainly using dplyr and ggplot2 to analyze and visualize the data and evaluate the A/B testing results.
For those readers who would like to use Python instead of R for this exercise, you can refer to the previous section.
For this exercise, we will be using one of the publicly available datasets from the IBM Watson Analytics community, which can be found at this link: https://www.ibm.com/communities/analytics/watson-analytics-blog/marketing-campaign-eff-usec_-fastf/. You can follow this link and download the data, which is available in XLSX format, named WA_Fn-UseC_-Marketing...
