This project analyzes e-commerce revenue performance using an Amazon-style dataset covering 2020–2024.
The objective is to transform raw sales data into executive-ready KPIs and operational insights using Power BI and DAX time intelligence.
The dashboard is designed to support recurring business reviews, trend analysis, and category-level performance evaluation.
Role: Data Analyst – Business Intelligence Reporting
Tools: Power BI, DAX, Python (EDA), Excel
Focus Areas: Revenue trends, YoY growth, category contribution, demand seasonality
Raw sales datasets are difficult for stakeholders to interpret and do not support fast decision-making.
This project addresses the need for a consolidated reporting view to:
- Track revenue performance over time (yearly and quarterly)
- Compare current performance against previous year (PY)
- Identify high-performing categories and products
- Detect seasonality and growth volatility
- Support inventory and marketing planning decisions
- Type: Simulated e-commerce dataset (Amazon-style)
- Time Period: 2020–2024
- Granularity: Quarterly, Category, Product
- Key Fields: Year, Quarter, Revenue, Category, Product, CustomerID
- Total Revenue (2020–2024): $91.83M
- Previous Year Revenue (PY): $73.66M
- YoY Growth: 25%
- Top Product Revenue Range: ~$0.30M – $0.34M per product
- Total Revenue KPI
- Previous Year Revenue (PY)
- YoY Growth %
- Quarterly revenue trend across multiple years
- Quarterly category contribution analysis
- Top-performing products by revenue
- YoY growth trend by category
- Interactive slicers for self-serve analysis
- Data cleaning and transformation using Power Query
- Data modeling with a Date Table to enable time-intelligence calculations
- Total Revenue
- Previous Year Revenue (PY)
- YoY Growth %
- Category and product-level aggregations
- Seasonality: Quarterly revenue trends reveal peak and non-peak demand periods, enabling better planning.
- Growth Visibility: YoY Growth provides clearer performance insight than revenue alone.
- Revenue Concentration Risk: A small number of categories contribute a disproportionate share of total revenue.
- Product Prioritization: Product-level analysis identifies high-impact products driving overall performance.
- Prioritize marketing and inventory investment in consistently high-performing categories during peak quarters.
- Focus promotions and supply optimization on top-demand products.
- Monitor categories with volatile YoY trends to improve pricing and promotion strategies.
- Extend analysis to include profitability metrics such as Cost, Profit, and Margin.
- Profitability analysis (Cost, Profit, Margin)
- Customer segmentation (new vs repeat customers)
- Revenue forecasting using time-series models
- Geographic performance analysis
Basavachetan Dadge
Business Analytics | Data Visualization | BI Reporting