This project is an end-to-end Business Analytics case study built using the Olist Brazilian E-Commerce dataset. It demonstrates the complete analytics lifecycle—from data exploration in Microsoft Excel to database management in MySQL, exploratory data analysis in Python, and interactive dashboard development in Power BI.
The project focuses on identifying customer purchasing patterns, product performance, payment behavior, geographical sales distribution, and business opportunities through data-driven analysis and visualization.
The objective of this project is to transform raw e-commerce data into meaningful business insights that support strategic decision-making.
Key objectives include:
- Analyze sales performance across different regions.
- Identify top-performing product categories.
- Understand customer purchasing behavior.
- Evaluate payment preferences.
- Measure customer satisfaction through review analysis.
- Generate actionable business recommendations.
✅ End-to-End Business Analytics Project
✅ Microsoft Excel KPI Dashboard & Pivot Analysis
✅ SQL Data Cleaning & Business Queries
✅ Python Exploratory Data Analysis
✅ Interactive Power BI Dashboard
✅ Business Insights & Strategic Recommendations
This project is based on the Olist Brazilian E-Commerce Dataset, a publicly available dataset containing real-world Brazilian e-commerce transactions.
- Customers
- Orders
- Order Items
- Products
- Sellers
- Payments
- Customer Reviews
- Geolocation
- Product Categories
Dataset Source: Olist Brazilian E-Commerce Dataset (Kaggle)
Note: The raw dataset is not included in this repository due to its large size. The complete dataset can be downloaded from the official Kaggle source using the link above. All analyses, dashboards, and insights presented in this project were developed using this publicly available dataset.
| Tool | Purpose |
|---|---|
| Microsoft Excel | Initial Data Analysis, KPI Dashboard & Pivot Analysis |
| MySQL | Data Cleaning, Joins & Business Queries |
| Python | Data Validation & Exploratory Data Analysis |
| Power BI | Interactive Dashboard & Data Visualization |
| Git & GitHub | Version Control & Project Documentation |
Raw Olist Dataset
│
▼
Microsoft Excel
(KPI Dashboard • Pivot Analysis • Business Insights)
│
▼
MySQL
(Data Cleaning • Joins • KPI Queries • Business Analysis)
│
▼
Python
(Data Validation • EDA • Data Export)
│
▼
Power BI
(Interactive Dashboard & Visualization)
│
▼
Business Insights & Recommendations
Microsoft Excel was used as the first stage of the project for exploratory business analysis.
- Data Exploration
- KPI Dashboard Creation
- Pivot Table Analysis
- Revenue Analysis
- Product Category Analysis
- Customer Analysis
- Payment Method Analysis
- Geographical Analysis
- Business Insights
- Business Recommendations
- KPI Dashboard
- 8 Pivot Analysis Sheets
- Business Insights
- Business Recommendations
The SQL phase focused on transforming the raw data into a structured master dataset suitable for analysis.
- Database Creation
- Data Import
- Data Cleaning
- Missing Value Analysis
- Duplicate Checking
- Table Relationships
- Data Joins
- Master Table Creation
- KPI Queries
- Advanced Business Queries
- Business Insights
- Total Revenue
- Total Orders
- Total Customers
- Average Order Value
- Average Review Score
Python was used to validate and further analyze the SQL master dataset.
- Pandas
- NumPy
- Matplotlib
- Importing SQL Dataset
- Data Validation
- Exploratory Data Analysis (EDA)
- Data Cleaning Verification
- Data Visualization
- Exporting Final Dataset for Power BI
An interactive dashboard was developed in Power BI to visualize business performance.
Includes:
- Total Revenue
- Total Orders
- Total Customers
- Average Order Value
- Average Review Score
- Revenue by Product Category
- Revenue by State
- Revenue by City
Includes:
- Payment Method Analysis
- Review Score Analysis
- Seller Performance
- Customer Distribution
- Product Performance
- Key Business Insights
The following KPIs were calculated using SQL and validated through Python before being visualized in Power BI.
| KPI | Value |
|---|---|
| Total Revenue | 20,579,664.01 |
| Total Orders | 99,441 |
| Total Customers | 99,441 |
| Average Order Value | 206.95 |
| Average Review Score | 4.02 / 5 |
Based on the analysis performed across Excel, SQL, Python, and Power BI, the following business insights were identified:
- São Paulo generated the highest overall revenue among all states.
- São Paulo city contributed the largest share of total sales.
- Revenue was highly concentrated in major metropolitan areas.
- Bed, Bath & Table was the highest-selling product category.
- Beauty & Health and Computers & Accessories were among the top revenue-generating categories.
- A small number of product categories contributed a significant portion of total revenue.
- Credit Card was the most preferred payment method.
- Boleto was the second most frequently used payment option.
- Debit Card and Voucher transactions represented a relatively small share of total payments.
- Most customers gave products a review score of 5.
- Customer satisfaction was generally high across the platform.
- Low review scores accounted for only a small percentage of total orders.
- A limited number of sellers generated a large proportion of total revenue.
- Seller performance varied significantly across the marketplace.
Based on the findings, the following recommendations are proposed:
- Increase inventory for high-performing product categories.
- Focus marketing campaigns on high-revenue states and cities.
- Improve visibility of underperforming product categories.
- Strengthen seller performance monitoring programs.
- Continue promoting secure digital payment methods.
- Improve customer experience to maintain high review ratings.
- Expand operations into regions with lower market penetration.
- Use customer purchasing patterns for targeted marketing campaigns.
Quick-Commerce-Analytics
│
├── Excel
│ ├── KPI_Dashboard.png
│ ├── Geographic_Analysis.png
│ ├── Product_Analysis.png
│ ├── Customer_Payment_Analysis.png
│ ├── Business_Insights.png
│ └── Business_Recommendations.png
│
├── Images
│ ├── Dashboard.png
│ └── Business_Insights.png
│
├── Power BI
│ └── Quick Commerce BI File.pbix
│
├── Python
│ └── Quick_Commerce_Python_Analysis.ipynb
│
├── SQL
│ └── Quick_Commerce_SQL_Project.sql
│
└── README.md
| Folder | Description |
|---|---|
| Excel | KPI Dashboard, Pivot Analysis, Business Insights & Recommendations |
| SQL | Complete SQL Project Script |
| Python | Jupyter Notebook for EDA & Data Validation |
| Power BI | Interactive Dashboard (.pbix) |
| Images | Dashboard Screenshots |
| README.md | Project Documentation |
This end-to-end business analytics project successfully transformed raw e-commerce data into actionable business intelligence using Microsoft Excel, MySQL, Python, and Power BI.
- Successfully integrated multiple raw datasets into a unified master dataset.
- Developed KPI dashboards to monitor overall business performance.
- Identified top-performing states, cities, and product categories based on revenue.
- Analyzed customer purchasing behavior and payment preferences.
- Evaluated customer satisfaction using review score analysis.
- Designed an interactive Power BI dashboard for executive decision-making.
- Generated data-driven business insights and strategic recommendations to improve sales performance and customer experience.
- Business Analytics
- Data Cleaning
- Data Modeling
- SQL Query Writing
- KPI Development
- Exploratory Data Analysis (EDA)
- Data Visualization
- Dashboard Design
- Business Intelligence
- Problem Solving
Through this project, I gained practical experience in:
- Business Analytics
- Data Cleaning
- SQL Query Writing
- Exploratory Data Analysis (EDA)
- KPI Development
- Dashboard Design
- Data Visualization
- Business Intelligence
- Git & GitHub
- End-to-End Analytics Workflow
Potential future improvements include:
- Sales forecasting using Machine Learning
- Customer segmentation (RFM Analysis)
- Interactive Excel Dashboard
- Time-Series Analysis
- Customer Lifetime Value (CLV) Analysis
- Predictive Analytics using Python
MBA | Aspiring Business Analyst | Data Analyst
- Microsoft Excel
- MySQL
- Python
- Power BI
- SQL
- Business Analytics
- Data Visualization
- Dashboard Development
- Git & GitHub
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