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๐ Project 1: Olist E-commerce Data Analysis
๐งฉ 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.
๐ Data Used
- Relational database with 9 tables (orders, products, customers, sellers, deliveries, reviewsโฆ)
- Data imported into BigQuery
- Format: CSV
- Volume: ~100,000 rows
๐ ๏ธ 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
๐งฎ Analytical Approach
- Understanding the business context and defining key questions
- Exploration and cleaning of the data
- 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
๐ 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
๐ก 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
๐ 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