An interactive Power BI dashboard and data analysis project built using a dataset of 2,000+ furniture products scraped from AliExpress.
The project explores key sales patterns, product pricing behavior, and market trends in the online furniture retail domain — providing actionable insights for price optimization and demand forecasting.
This project analyzes e-commerce sales data from the furniture category on AliExpress.
By visualizing and interpreting metrics like price, sales volume, and discount impact, it helps uncover consumer behavior and market trends.
The dataset serves as a foundation for exploratory data analysis, business insights generation, and potential predictive modeling.
This dataset consists of 2,000 entries scraped from AliExpress, capturing essential product and sales information.
It provides an in-depth look into consumer purchasing behavior, price strategies, and market competitiveness in online retail.
✅ Sales Insights: View top-selling furniture products and sales trends
✅ Price Analysis: Compare original vs discounted prices
✅ Discount Impact: Explore how discounts affect sales volume
✅ Tag-Based Trends: Analyze performance by product tags and attributes
✅ Interactive Filters: Filter by price range, tag, and popularity levels
| Tool | Purpose |
|---|---|
| Power BI Desktop | For interactive dashboard visualization |
| Power Query | For data transformation and modeling |
| Excel / CSV | For data storage and preprocessing |
- Discounted products show a positive correlation with sales volume.
- Free shipping tags attract higher conversion rates.
- Premium furniture items maintain consistent sales despite higher pricing.
- Opportunities exist for price optimization and bundle offers.
- Download the
.pbixfile from this repository. - Open it in Power BI Desktop.
- Explore the visuals:
- Filter by product tags or price ranges
- Compare sold units vs discount levels
- Identify high-performing and underperforming product categories
Varshith Chilagani
🔗 LinkedIn