Author: Muhammad Shah
Tools Used: SQL (Data Modeling & Views), Power BI (DAX, Power Query), Data Visualization
Dataset: E-Commerce Public Dataset
This project delivers a 3-page interactive Power BI dashboard designed to analyze e-commerce business performance across revenue drivers, shipping/logistics performance, and customer payment behaviors.
- Key KPIs: Total Revenue, Total Orders, Average Order Value (AOV), Average Delivery Time, and Late Delivery Rate.
- Sales Trends: Revenue & order volume progression across time periods.
- State-Level Performance: High-level revenue distribution by region.
- Delivery Bottlenecks: Top 10 customer states experiencing the longest shipping delays.
- Delivery Compliance: On-Time vs. Late delivery ratio breakdown.
- Freight Analysis: Relationship between freight charges and delivery lead times across product categories.
- Seller Bottlenecks: Matrix analysis identifying sellers contributing to fulfillment delays.
-
Payment Method Distribution: Revenue share split by Credit Card, Boleto, Voucher, and Debit Card.
-
Installment Dynamics: Analysis showing how cart size (AOV) increases as credit installment options scale from 1 to 12.
-
Geographic Concentration: Top 10 cities by customer volume.
-
State Spending Summary: Detailed breakdown of state-level metrics (Customers, Revenue, AOV, Avg Installments).
Because the native Power BI file exceeds GitHub's file size limits, you can download the full .pbix file directly:
👉 Download Power BI Dashboard (.pbix))
(You can also view the static 3-page export inside the docs/ folder).
- Shipping Lead Times: Delivery delays correlate heavily with specific geographic regions, highlighting areas for local fulfillment center placement.
- Installment Impact: Order values steadily rise alongside higher installment counts, confirming flexible payment plans drive larger purchases.
- Regional Demand: High customer concentration in top metropolitan regions suggests localized logistics optimization can drastically decrease overall delay rates.