This project is a Power BI sales analytics dashboard built to analyze Amazon product sales, trends, and customer engagement using real-world business intelligence techniques.
To provide clear insights into:
- Sales performance over time
- Top-performing products and categories
- Customer engagement through reviews
- YTD Sales – Year-to-date revenue
- QTD Sales – Quarter-to-date revenue
- YTD Products Sold – Total units sold
- YTD Reviews – Customer feedback volume
- Sales by Month (Line Chart)
- Sales by Week (Column Chart)
- Sales by Product Category (Table / Heatmap)
- Top 5 Products by Sales (Bar Chart)
- Top 5 Products by Reviews (Bar Chart)
- Data cleaning and transformation
- Data modeling and relationships
- Date tables and time intelligence
- DAX calculations (YTD, QTD)
- Interactive slicers and filters
- Conditional formatting
- Amazon_Combined_Data.xlsx
- Contains product, sales, date, and review data
- Sales show strong seasonal patterns
- Few products generate majority of revenue
- Certain categories lead in both sales and reviews
AMAZON REAL PROJECT.pbix– Power BI dashboardAmazon_Combined_Data.xlsx– Source data- Amazon Sales Dashboard.png
⭐ This project demonstrates Power BI, DAX, and business analytics skills suitable for Data Analyst and BI Analyst roles.
This analysis examines Amazon product sales data to evaluate sales performance, product demand, customer engagement, and category contribution.
The objective is to support data-driven decision-making using interactive Power BI dashboards and key performance indicators.
The dataset includes:
- Order dates (monthly & weekly granularity)
- Product categories and product names
- Sales value and quantity sold
- Customer reviews
The data was cleaned, transformed, and modeled using Power Query and Power BI data modeling techniques.
- Measures total revenue generated from the beginning of the year to date
- Used to track overall business growth and performance trends
- Tracks sales performance within the current quarter
- Helps identify short-term growth or slowdown
- Represents total units sold during the year
- Indicates product demand and inventory movement
- Measures customer engagement and feedback volume
- Acts as a proxy for product popularity and customer satisfaction
- Sales increase gradually across the year with noticeable peaks in later months
- Indicates seasonal demand and promotional impact
- Weekly analysis reveals short-term fluctuations
- Sales spikes suggest campaigns, discounts, or festive demand
- A small number of categories contribute the majority of total sales
- High-performing categories dominate both revenue and product volume
- Lower-performing categories show limited contribution and potential optimization areas
- A few products generate a disproportionately high share of revenue
- Highlights opportunities to focus marketing and inventory on top sellers
- Products with high reviews indicate strong customer engagement
- Some products show high reviews but lower sales, suggesting pricing or conversion opportunities
- Sales performance shows clear seasonality
- Revenue is concentrated among a limited set of products
- Customer reviews correlate strongly with top-performing products
- Weekly trends help identify short-term performance drivers
- Focus promotions on high-margin, high-performing products
- Optimize inventory for seasonal demand peaks
- Improve conversion for highly reviewed but lower-selling products
- Monitor weekly trends to align campaigns and pricing strategies
This analysis demonstrates how Power BI dashboards and KPIs can transform raw sales data into meaningful business insights.
The project highlights strong capabilities in data modeling, time intelligence, visualization, and analytical storytelling, making it suitable for Data Analyst and BI Analyst roles.