Target Brazil E-Commerce SQL Analysis

📌 Project Overview

This project analyzes 100,000+ orders from Target's Brazil e-commerce operations between 2016 and 2018 using SQL and Google BigQuery. The goal is to explore customer behavior, order trends, and purchasing patterns to generate actionable business insights.

🎯 Objectives

• Explore and understand the dataset structure.
• Analyze customer and order information.
• Identify peak ordering times.
• Generate business insights and recommendations.

🛠️ Tools & Technologies

• SQL
• Google BigQuery

📊 Key Insights

• Afternoon is the peak period for customer orders.
• Morning has the lowest number of orders.
• Customer and order data can help identify purchasing patterns.

💡 Business Recommendations

• Run targeted promotions and advertisements during peak ordering hours.
• Maintain sufficient inventory during high-demand periods.
• Use customer and order insights to support business decisions.

📂 Dataset

The dataset consists of eight CSV files:

• customers.csv
• geolocation.csv
• order_items.csv
• payments.csv
• reviews.csv
• orders.csv
• products.csv
• sellers.csv

🔍 Analysis Performed

• Examined column names and data types using INFORMATION_SCHEMA.COLUMNS.
• Investigated the order date and time range.
• Categorized order times into dawn, morning, afternoon, and night.
• Developed SQL queries to extract business insights.

👩‍💻 Author

Data Analyst | SQL | Google BigQuery