E-Commerce Funnel Analysis with Olist Dataset

Overview

This project analyzes the customer purchase funnel using the Olist Brazilian E-Commerce Public Dataset. The goal is to map the journey from order creation to delivery, identify drop-off points, and uncover insights to optimize conversions—skills relevant to e-commerce and SaaS marketing roles.

Dataset

• Source: Olist Brazilian E-Commerce Public Dataset on Kaggle
• Description: Over 100,000 orders (2016-2018) across multiple tables: orders, customers, payments, items, products, and reviews.
• Files: Stored in data/ as CSVs (e.g., olist_orders_dataset.csv).

Tools

• SQL: SQLite for querying, managed via VS Code with SQLTools extension.
• Future Plans: Python (pandas, matplotlib) for data visualization.

Setup

1. Install SQLite: Download from sqlite.org (Precompiled Binaries for your OS).
2. Install VS Code SQLTools: In VS Code, go to Extensions > Search "SQLTools" > Install, plus "SQLTools SQLite" driver.
3. Import data: Use SQLite CLI or a GUI (e.g., DB Browser for SQLite) to load CSVs into olist.db.
4. Run queries: Open .sql files in VS Code and execute via SQLTools.

Progress

• Data cleanup (handling nulls, canceled orders)
• Funnel analysis (conversion rates, drop-offs)
• Segmentation (by payment type, product category)
• Python visualizations (planned)

Queries

• See queries/ folder for SQL scripts (e.g., funnel_analysis.sql).

Contact

• GitHub: [Your GitHub Username]
• Feel free to explore or suggest improvements!