A data-driven project analyzing e-commerce sales and customer behavior. It includes detailed insights on customer demographics, product trends, and sales patterns. Data is processed using Python and SQL, visualized in Power BI, and interactive dashboards provide actionable insights for business optimization.
This project provides a comprehensive analysis of e-commerce product sales and customer behavior. It delivers actionable insights on sales trends, product performance, and customer preferences to help businesses optimize strategies. The analysis involves data cleaning, transformation, and visualization using Python, SQL, and Power BI dashboards.
- Removed missing or duplicate values
- Standardized column names and formats
- Handled date and numeric data inconsistencies
- Merged multiple datasets to create a consolidated dataset for analysis
- Explored customer demographics: age, gender, profession
- Studied brand preferences and purchase patterns
- Analyzed sales trends over time (daily, monthly, yearly)
- Examined product performance by quantity sold, revenue, and category
- Performed queries to summarize:
- Total revenue and transactions per product/category
- Top-selling products and brands
- Inventory usage and replenishment trends
- Calculated key KPIs like total sales, average purchase value, and stock turnover
- Developed interactive dashboards to visualize insights:
- Customer Analysis Dashboard – Gender distribution, age groups, brand preferences
- Sales Analysis Dashboard – Revenue trends, top products, category-wise sales, and monthly performance
- Added filters and slicers to allow dynamic exploration of data
- Identified most profitable products and categories
- Discovered customer segments with high purchase frequency
- Observed seasonal sales trends and inventory gaps
- Provided actionable recommendations for stock optimization and marketing strategies