This project analyzes Flipkart sales data to uncover key insights related to product performance, customer purchasing behavior, and revenue trends. The objective was to transform raw e-commerce data into meaningful insights and build an interactive Power BI dashboard to support data-driven business decisions.
The dataset includes:
- Product details (Product Name, Category)
- Pricing information (Original Price, Discounted Price)
- Customer interactions (Ratings, Reviews)
- Sales metrics (Units sold, Revenue)
- Additional attributes (Discount %, Product popularity indicators)
- Removed duplicates and handled missing values (Excel)
- Standardized product categories and text fields
- Converted pricing columns into consistent formats
- Created calculated columns (discount %, revenue metrics)
- A small percentage of products contribute ~40–50% of total revenue
- Higher discounts increase sales volume but reduce profit margins
- Certain categories contribute over 60% of total revenue
- Highly rated products (4★+) generate significantly higher sales
- Sales show periodic spikes indicating seasonal demand
- KPI cards (Total Sales, Revenue)
- Product and category performance analysis
- Discount vs Sales relationship
- Customer rating impact analysis
- Time-based sales trends
- Excel → Data cleaning and preprocessing
- Power BI → Dashboard development
- Analytical techniques → Trend analysis, segmentation, comparative analysis
- Identifies top-performing products and categories
- Highlights impact of discounts on profitability
- Supports data-driven product and pricing strategies
For a complete breakdown of the project, including business problem, recommendations, challenges, and full analysis check detailed documentation.