The goal of this project is to analyze multi-year Amazon sales data using Power BI to uncover sales trends, customer behavior, and product performance.
The analysis focuses on transforming raw transactional data into actionable insights that help improve product strategy, reduce losses, and optimize operations across regions.
This project answers critical business questions such as:
- How are sales performing over the years and months?
- Which product categories and items generate the highest revenue and profit?
- Which regions contribute most to sales and profitability?
- What payment methods do customers prefer?
- How do cancellations and returns impact total revenue?
Amazon faced challenges in understanding its long-term sales performance due to:
- High return and cancellation rates affecting net revenue.
- Large product variety making it difficult to identify best- and worst-performing products.
- Inconsistent regional performance requiring strategic focus.
- Customer behavior variation (payment methods, purchase timing).
- Limited visibility into salesperson performance and order-level details.
This analysis solves these issues by cleaning the data, modeling it properly, and visualizing key metrics in Power BI.
- Revenue by Month → Seasonality and monthly demand trends.
- Revenue by Year → Long-term performance growth.
- Revenue by Product Category → Sports, Clothing, Toys, Electronics, etc.
- Revenue by Product Name → Top-selling products like Present, If, Strategy, Relate, Show.
- Revenue by Region → Quantity and profit distribution across South America, Asia, Europe, etc.
- Revenue by Payment Method → Debit Card, Amazon Pay, PayPal, Gift Card, Credit Card.
- Order Status Distribution → Completed, Cancelled, Returned, Pending.
- Sales by Day → Daily trend visualization.
- Top 10 Products Table → Unit Price, Total Sales, Profit Margin, Avg. Discount.
- Top Salespersons Table → Performance comparison.
- Recent Cancelled/Returned Orders → Detailed transactional view.
| Metric | Value |
|---|---|
| Total Sales | 5.94M |
| Net Profit | 2.07M |
| Quantity Sold | 28K |
| Orders | 5,000 |
| Return Rate | 26.50% |
| Cancellation Rate | 24.32% |
Dashboard Pages
- Power BI for:
- Data modeling & relationships
- Interactive dashboards
- DAX calculations
- Advanced visuals
- Removed duplicates and fixed inconsistent product names
- Handled missing and invalid values
- Standardized date formats
- Calculated key measures: Profit, Profit Margin, Return/Cancellation %, Avg. Order Value
- Created new fields: Month, Year, Region Groups, Status Flags
- Total Sales: 5.94M
- Return Rate: 26.50% (very high)
- Cancellation Rate: 24.32%
Sports, Clothing, Toys, and Electronics dominate the revenue.
Products like “Present” and “If” lead in sales and profit margin.
- South America & Asia → highest quantity sold
- North America & Asia → highest profit contribution
Customers mostly use Debit Card and Amazon Pay.
- May and June are peak sales months
- January also performs strongly
Adam Smith and Bradley Howe are the top performers.
- Investigate reasons for returns
- Improve product descriptions & quality control
- Offer exchange incentives instead of refunds
- Increase stock for Sports, Clothing, and Toys
- Promote best-selling items like "Present" and "If"
- Increase marketing in North America & Asia
- Review pricing strategy in South America
- Promote Amazon Pay and Debit Card
- Offer incentives to boost Credit Card usage
- Focus promotions in May, June, and January
- Reward top performers
- Use analytics to guide training and targets
Mohamed Nasser
Data Analyst | BI Developer | Aspiring Data Scientist