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🌟 Project 1: Olist E-commerce Data Analysis

🧩 Context and Objectives

The goal of this project was to analyze the data from Olist, a Brazilian e-commerce platform, to address strategic issues such as customer behavior, product performance, logistics efficiency, and satisfaction.

📊 Data Used

• Relational database with 9 tables (orders, products, customers, sellers, deliveries, reviews…)
• Data imported into BigQuery
• Format: CSV
• Volume: ~100,000 rows

🛠️ Technical Skills Used

• Advanced SQL (joins, aggregations, window functions, subqueries, CASE, GROUP BY, HAVING)
• Data exploration and cleaning
• Descriptive analysis and KPI calculations: CLV, AOV, CAC, delivery delays, satisfaction rate
• Presentation of results and strategic recommendations

🧮 Analytical Approach

1. Understanding the business context and defining key questions
2. Exploration and cleaning of the data
3. Focused analysis on:
   • Customer behavior: purchase frequency, average basket
   • Product performance: bestsellers, satisfaction
   • Geographic analysis: most profitable regions
   • Monitoring of delivery times and customer satisfaction

📊 Key Insights

• Loyal customers generate on average 3 times more revenue than one-time buyers
• Long delivery times are strongly correlated with negative reviews
• Electronics generate good revenue but receive below-average ratings

💡 Recommendations

• Implement a loyalty program for high-value customers
• Target sellers with long delivery times to improve satisfaction
• Better frame product descriptions for electronics to avoid disappointment

🎓 What I Learned

• How to structure a SQL analysis around clear business objectives
• How to identify and present relevant insights concisely
• How to work in a team and deliver an oral data project presentation