🚀 Amazon E-Commerce Sales Analytics Dashboard (Power BI)

A complete end-to-end Amazon E-commerce Sales Analysis Dashboard built using Power BI, Excel, and DAX, covering sales performance, orders, courier tracking, category insights, and state-level business trends.

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📝 Project Summary

A real-world style Power BI project analyzing 19,720 Amazon orders using a sample dataset to uncover key business insights around sales, fulfillment, courier performance, top sizes, categories, and state-wise trends.

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📌 Overview

E-commerce businesses deal with thousands of daily transactions. Tracking performance manually becomes extremely time-consuming.

This dashboard provides a 360° view of business performance, helping sellers understand:

• Sales growth
• Top-selling categories
• Order distribution
• Courier & fulfillment performance
• Customer demand patterns
• State-wise business expansion opportunities

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🎯 Problem Statement

Amazon sellers face multiple challenges such as:

1. Lack of real-time visibility into sales across categories, states, and time periods.
2. Courier performance issues like delays, cancellations, and tracking gaps.
3. Difficulty identifying top-performing product sizes, categories & regions.
4. No centralized view for daily MTD (Month-to-Date) performance tracking.
5. Manual reporting causing inefficiency and slow decision-making.

This project solves these problems by building an automated, interactive dashboard.

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📂 Dataset

• Source: Sample e-commerce order dataset
• Total Rows: 19,720
• Format:
• Contains: Order-wise transactional details

✔ Columns Used in Dashboard

The dashboard uses key columns such as:
Order ID, Date, Status, Fulfilment, ship-service-level, Category,
Size, Courier Status, Qty, Amount, Ship City, Ship State,
Postal Code, Currency, B2B, Fulfilled-by, and more.

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🧰 Tools & Technologies

• Power BI – Data modelling & dashboard creation
• Excel – Raw data cleaning
• DAX – Measures and calculated fields
• Power Query – ETL, transformation & preprocessing

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🔧 Methods & Approach

The following steps were performed:

Data Cleaning (Excel + Power Query)

• Removed blank & null values
• Converted datatypes
• Managed text blanks → NA
• Removed duplicates
• Standardized date formats
• Normalized category & size values
• Cleansed courier and fulfillment fields

Data Modelling (Power BI)

• Star schema (single-table model as dataset supported)
• Relationship validation
• Measure tables

DAX Measures

• Total Sales
• Total Orders
• AOV (Average Order Value)
• MTD Sales
• Category-wise sales
• State-wise performance
• Courier tracking distribution

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📊 Dashboard Features

1️⃣ Sales KPIs

• Total Sales: 11.53M
• Total Transactions: 19.72K
• Average Order Value: ₹584.74

2️⃣ Delivery Partner Performance

• Ekart → 12,497 orders
• Easy Ship → 7,226 orders

3️⃣ Fulfillment Overview

• Amazon → 12,497
• Merchant → 7,226

4️⃣ Top 5 Product Sizes

• M → 3554 orders
• XL → 3308
• L → 3270
• XXL → 2768
• S → 2681

5️⃣ Top 10 States by Sales

• Maharashtra → 1.93M
• Karnataka → 1.45M
• Uttar Pradesh → 1.05M
• Telangana → 0.98M
• Tamil Nadu → 0.86M

6️⃣ Category Performance

• T-Shirts → 6.2M
• Shirts → 3.2M
• Blazers → 1.2M
• Others → Lower contribution

7️⃣ Courier Tracking Distribution

• Shipped → 16,897
• On the Way → 1,226
• Cancelled → 829
• Unshipped → 771

8️⃣ MTD Growth Trend

Day-by-day running total (18 April – 30 April) showing consistent growth.

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🖥 How to Run This Project

Option 1 — Direct View

Download the .pbix file and open it directly in Power BI Desktop.

Option 2 — Build It Yourself

1. Download the dataset (.xlsx).
2. Import into Power BI.
3. Apply data cleaning steps mentioned above.
4. Add DAX measures from PBIX file.
5. Recreate visuals as per dashboard screenshot.

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## 📁 Repository Structure
sales-analysis-amazon-dashboard-pbi-excel/
│
├── dashboard/
│ └── amazon_sales_dashboard
│
├── data/
│ └── amazon_Sales_Data.xlsx #raw file
│
├── images/
│ ├── page_1
│ ├── page_2
│
└── README.md

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📌 Results & Conclusion

This dashboard enables Amazon sellers to:

• Monitor daily performance
• Identify high-demand states
• Track courier delays
• Optimize product sizing and categories
• Improve fulfillment efficiency
• Make confident, data-driven business decisions

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🚀 Future Enhancements

• Add forecasting using Power BI
• Integrate with SQL database
• Add customer segmentation using RFM
• Automate refresh via Power BI Service
• Build mobile-optimized dashboard version

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👤 Author & Contact

Sharik Ansari

📧 Email: sharikkha8900@gmail.com
🔗 LinkedIn: https://www.linkedin.com/in/sharik-ansari-312021389?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app 🐙 GitHub: https://github.com/sharikansari0

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⭐ Support

If you found this project helpful, please give it a ⭐ on GitHub!