This project aimed to evaluate and compare the impact of a company's new ad-based marketing strategy to it's current PSA-based one. Employing EDA and A/B testing, I not only spotlighted differences in performance, but also assessed if they were both statistically and practically significant enough to warrant implementation. Towards this aim, I utilized Python and its libraries such as Pandas for data exploration and processing, Matplotlib/Seaborn for visualization, and SciPy for statistical analysis. The results of this would enable more targeted marketing, optimized ad spend, and improved return on investment to deliver higher conversion rates and more effective marketing campaigns. Find link here.