Automated Root Cause Analysis System for Marketing Metrics

Project Overview

Developed an end-to-end analytics pipeline integrating Python, SQL, and Tableau to identify key performance drivers across marketing campaigns through statistical testing, variance decomposition, and time-series analysis. Engineered model evaluation and hypothesis-testing modules to quantify causal effects of creative, audience, and timing variables on engagement and conversion outcomes. Automated data extraction, transformation, and cleaning workflows, increasing diagnostic throughput and reducing manual analysis effort by 75%.

Technologies Used

Impact

Reduced manual diagnostic effort by 75% and improved accuracy of campaign performance insights.