📊 E-Commerce Sales & Customer Analytics (Olist Dataset)

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📌 Business Problem

E-commerce businesses generate large volumes of transactional and customer data, but often lack clear visibility into:

• Revenue trends and growth drivers
• Customer distribution and regional performance
• Operational efficiency (e.g., delivery performance)

Without proper analysis, this limits the ability to optimise growth, logistics, and market expansion.

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🎯 Objective

Analyse e-commerce data to:

• Identify revenue growth trends
• Understand customer and regional distribution
• Evaluate logistics performance
• Support data-driven business and operational decisions

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🧠 Solution Overview

This project combines SQL, Python, and Power BI to deliver a complete analytics workflow from raw data to business insights.

Key components:

• Data extraction and transformation using SQL
• Data analysis and visualisation in Python
• Interactive Power BI dashboard for business monitoring

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🛠️ Tech Stack

• SQL (MySQL) – data extraction and joins
• Python (Pandas, Matplotlib, Seaborn) – analysis and visualisation
• Power BI – dashboard and KPI tracking
• Git & GitHub – version control

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🔑 Database Configuration (Required)

For security reasons, database credentials are not stored in the code. To run this project locally, you must:

1. Create a file named .env in the root folder.
2. Add your MySQL credentials to the file:

DB_USER=your_username
DB_PASS=your_password
DB_HOST=localhost
DB_NAME=ecommerce_project

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📂 Dataset

Olist Brazilian E-Commerce Public Dataset ~100k orders (2016–2018) Download the Dataset on Kaggle

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📈 Dashboard Overview

The dashboard provides an executive view of e-commerce performance.

Key KPIs:

• Total Revenue
• Orders Volume
• Delivery Performance
• Regional Sales Distribution

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💡 Key Business Insights

• Revenue Growth:
  Revenue increased by 137% in 2017, indicating rapid business expansion

• Regional Dependency:
  São Paulo accounts for ~42% of revenue, highlighting strong regional concentration and potential expansion opportunities

• Logistics Improvement:
  Delivery times improved by 15% in Q3 2018, suggesting operational efficiency gains

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🎯 Business Impact

This analysis enables:

• Identification of growth opportunities across regions
• Better logistics and delivery optimisation
• Improved strategic planning based on sales trends
• Support for expansion and market diversification decisions

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📸 Visualizations

Executive Dashboard

Revenue Trend (Python)

Top States by Revenue

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🚀 How to Run

1. Clone repository
2. Run SQL queries
3. Execute Python scripts
4. Open Power BI dashboard

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