Ecommerce Sales Dashboard

An interactive Power BI dashboard that turns raw ecommerce order data into decision-ready insights — covering sales, profitability, product performance, and regional trends across India.

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Table of Contents

• Overview
• Dashboard Preview
• Dataset
• Data Model
• Features
• Tools & Techniques
• Repository Structure
• How to Use
• Key Insights Enabled
• Future Improvements
• Author

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Overview

Ecommerce businesses generate large volumes of order-level data that's difficult to act on in raw spreadsheet form. This project consolidates order and product-level transaction data into a single Power BI data model and presents it through an interactive report — letting stakeholders explore sales, profit, and quantity trends by category, sub-category, state, and time period without touching the underlying data.

The dashboard answers questions such as:

• Which product categories and states drive the most revenue and profit?
• Where is volume high but margin low (risk areas)?
• How do sales trend over time, and where are the growth opportunities?

Dashboard Preview

Dataset

The dashboard is built on two linked datasets:

File	Grain	Key Fields
Orders.csv	One row per order	Order ID, Order Date, Customer, State/City
Details.csv	One row per line item	Order ID, Category, Sub-Category, Quantity, Amount, Profit, Payment Mode

Data Model

Orders.csv and Details.csv are joined on Order ID inside Power BI's data model, enabling analysis at both the order level (location, customer, timing) and the product-line level (category, quantity, profit). A calendar/date table supports time-intelligence measures (YTD, trends, period comparisons).

Features

• Sales Analysis — Total revenue trends and overall performance overview
• Profit Analysis — Profitability breakdown to flag high- and low-margin segments
• Quantity Analysis — Units sold across products and time
• State-wise Sales — Geographic performance comparison across Indian states
• Category-wise Sales — Performance by product category and sub-category
• Interactive Filters — Slicers for dynamic, self-service data exploration

Tools & Techniques

• Power BI Desktop — Report design and dashboard visualization
• Power Query — Data cleaning, transformation, and merging of Orders.csv and Details.csv
• DAX — Calculated measures for sales, profit, and quantity KPIs
• Data Modeling — Relationship building between fact and dimension tables

Repository Structure

Ecommerce-Sales-Dashboard/
├── ECOMMERCE SALES DASHBOARD.pbix   # Power BI report file
├── Orders.csv                       # Order-level dataset
├── Details.csv                      # Line-item level dataset
├── salesdashboard.png               # Dashboard preview image
└── README.md

How to Use

1. Clone or download this repository.
2. Open ECOMMERCE SALES DASHBOARD.pbix in Power BI Desktop (free to install).
3. If prompted, update the data source file paths to point to Orders.csv and Details.csv on your local machine, then click Refresh.
4. Use the slicers and filters on the report to explore sales, profit, and quantity by category, state, and time period.

Key Insights Enabled

• Identify top- and bottom-performing product categories by revenue and profit
• Spot high-growth vs. underperforming states for targeted regional strategy
• Track quantity sold alongside profit margin to flag high-volume, low-margin risk areas
• Filter dashboards dynamically for ad-hoc, self-service business analysis

Future Improvements

• Add year-over-year and month-over-month comparison visuals
• Incorporate customer segmentation (RFM analysis)
• Publish to Power BI Service for web-based interactive access
• Automate data refresh via a scheduled gateway

Author

Ashmit Srivastava LinkedIn · GitHub · Email