> OVERVIEW()

This case study focuses on analyzing delivery performance, customer experience, and operational efficiency within DoorDash’s New Verticals business (Grocery, Convenience, and Retail). The goal was to uncover how key factors—such as travel time, peak delivery hours, order substitutions, and product popularity—impact customer satisfaction and operational accuracy.

Using Python-based data analytics, the project identified patterns in delivery efficiency, substitution handling, and customer engagement behavior. These insights help inform strategies to reduce delivery delays, improve product availability, and increase retention through better fulfillment experiences.

The analysis was conducted using real delivery data in CSV format, leveraging data manipulation, aggregation, and visualization to translate raw data into actionable business insights.

>TOOLS_AND_TECH_STACK()

• Python (Pandas, NumPy) — data cleaning, manipulation, aggregation, trend analysis

• Matplotlib & Seaborn — visualizing delivery trends, substitution impact, product preferences

• Jupyter Notebook — interactive exploration, narrative explanations, insight documentation

• Excel/CSV — raw delivery datasets provided as input