Welcome to the E-Commerce Data Analysis repository! 🎉 This project combines the power of Power BI and SqlServer to analyze and visualize e-commerce data. Dive in to explore actionable insights, stunning dashboards, and how SQL and Power BI can transform raw data into business intelligence.
This project focuses on analyzing key aspects of an e-commerce business, including sales, customer behavior, product performance, and supplier analysis. Using SQL-SERVER for data extraction and transformation, and Power BI for creating interactive dashboards, we deliver insights that drive decision-making.
- Data is stored in a SqlServer database.
- SQL queries were used for:
- Extracting relevant information.
- Transforming raw data into a structured format.
- Performing calculations before importing into Power BI.
Transform data into visual stories with our interactive dashboards:
- Customer Insights Dashboard: Analyze customer behavior, churn risks, and segmentation.
- Sales Dashboard: Track sales performance, trends, and category-wise contributions.
- Product Performance Dashboard: Evaluate pricing strategies, discounts, and sales by category.
- Supplier Insights Dashboard: Assess supplier contributions and market share.
- Category Insights Dashboard: Measure the growth and performance of different categories.
Key data transformations and calculations performed:
- Calculating customer churn risk.
- Extracting revenue and profit by category and supplier.
- Joining and integrating data from multiple tables.
- Deriving insights like sales growth, price comparisons, and more.
- Churn Risk: Identify customers at risk based on purchase frequency.
- Segmentation: Group customers by behavior, demographics, and regions.
- Lifetime Value: Estimate lifetime spending based on order history.
- Revenue by Category: Calculate revenue contribution for each category.
- Profit Analysis: Compare sale prices and market prices to derive profits.
- Top Performers: Identify top-selling products and categories.
- Pricing Analysis: Evaluate discount percentages and pricing strategies.
- Supplier Contribution: Measure revenue generated by suppliers.
- Sales Growth: Analyze year-over-year sales performance.
- Revenue Contribution: Understand supplier-specific revenue contributions.
- Performance Analysis: Highlight high-performing suppliers.
- Growth Trends: Track category performance over time.
- Discount Analysis: Compare sale prices across categories for insights.
The dataset includes the following tables:
- Customers: Customer details (e.g., ID, name, address, and DOB).
- Orders: Order information (e.g., ID, date, delivery status, total).
- OrderDetails: Product details for each order.
- Products: Product-specific information (e.g., category, pricing, supplier).
- Suppliers: Supplier details (e.g., name, location, contact).
- Categories: Information on product categories.
- Power BI: For creating dashboards and visualizing insights.
- MySQL: For database management and SQL queries.
- DAX (Data Analysis Expressions): For advanced calculations in Power BI.
- Clone the Repository:
git clone https://github.com/your-username/e-commerce-data-analysis.git
Set Up MySQL Database: Import the provided schema and data. Open Power BI: Connect to the MySQL database. Import tables and define relationships. Build Dashboards: Use the provided DAX formulas to create measures and visualizations. 🎯 Conclusion This project showcases how Power BI and MySQL work together to analyze e-commerce data. From customer behavior to supplier performance, these insights can help businesses make informed decisions.
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