jacqlinecpe/olist-ecommerce-brazilian-project-sql-power-bi

This repository is a business intelligence project that analyzes the Olist Brazilian e-commerce dataset, a public collection of roughly 99,000 real orders placed between 2016 and 2018. The author used SQL Server to clean and prepare the raw transactional data, then built an interactive Power BI report on top of it to answer questions about revenue, profitability, customer satisfaction, and delivery performance. The dataset spans several linked tables covering orders, order items, customers, products, reviews, payments, and geolocation. During cleaning, the author found 610 products with missing category information affecting over 1,600 customer orders, and chose to label them as unknown rather than delete the records, which preserved revenue data that would otherwise have been lost. A separate date table was also built to fix inaccurate year over year calculations caused by raw timestamp values. The finished Power BI file contains two dashboards. The first is an executive overview showing revenue, shipping cost, contribution margin, and order counts, broken down by month, by state, and by product category, with a toggle that lets a viewer switch which metric drives the rankings. The second dashboard focuses on customers and delivery, showing review score distribution, on time versus late delivery trends, average delivery time, and which states have the highest rates of late shipments. Reported findings include revenue of about 13.59 million Brazilian reais with 205 percent year over year growth, though margin percentage slipped slightly even as revenue grew. Watches, gifts, and health and beauty products generated the most revenue, and Sao Paulo produced the largest contribution margin. Average review score held at about 4.09 out of 5, but the late delivery rate reached nearly 8 percent and increased year over year, and the analysis links longer delivery times to lower review scores. The author recommends investigating logistics partners in high delay states, prioritizing high margin categories over pure revenue growth, and tracking delivery and satisfaction metrics together going forward.