E-commerce Funnel & Revenue Analysis

Summary

A Python and SQL analysis project that processed 12,500+ user sessions to understand funnel performance, retention, customer value, and revenue opportunities.

Feature List

• Designed SQL queries with JOINs, subqueries, GROUP BY, and window functions
• Built Browse to Cart to Purchase funnel logic and cohort analysis
• Identified 38% cart drop-off and key friction points
• Automated AOV, retention rate, and CLV calculations with reusable Python functions
• Proposed optimizations projected to improve conversion rate by 8-12%