Return Analysis

Returns are expensive for ecommerce businesses because they reduce revenue, increase operational costs, and can reveal product or customer behavior issues. The goal of this project is to build an end-to-end data pipeline that answers questions such as: How do return rates change over time? Which product categories have the highest return rates? Which SKUs drive the most revenue loss from returns? Which customers return the most items? The output is a set of analytics tables in BigQuery and a dashboard in Looker Studio for reporting.