A/B Test Analysis For E-commerce Optimization

Enhanced decision-making for an e-commerce company by conducting an in-depth analysis of A/B test results to evaluate a new web page design.

Ensured data integrity by resolving duplicates and mismatches, and utilized statistical techniques like two-sample t-tests and logistic regression (Python, Pandas, NumPy, statsmodels) to identify factors contributing to a 15% increase in conversions among mobile users.

Created impactful visualizations using Matplotlib and Seaborn to present trends and engagement metrics to stakeholders. Provided actionable recommendations to implement the new web page design, projecting a 20% annual revenue increase, while outlining limitations and suggestions for future A/B testing iterations.