About

This project analyzes user conversion behavior across a real-world e-commerce platform using 42 million behavioral events from October 2019 (REES46 dataset). The analysis tracks users through the full purchase funnel — from product views to cart additions to completed purchases — identifying where drop-offs occur and why.
What's covered:

Full funnel construction with unique user counts and drop-off rates at each stage
Conversion rate segmentation by product category
Time-based analysis by hour of day and day of week
Single-session vs multi-session user cohort comparison
4 actionable business recommendations with expected impact

Tools used: Python, Pandas, Matplotlib, Power BI
Key finding: The largest drop-off occurs between cart and purchase, representing the biggest opportunity for revenue recovery through targeted interventions like cart abandonment emails and retargeting campaigns.