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Power BI · Business Intelligence

Madhav Ecommerce
Sales Dashboard

An interactive Power BI dashboard analyzing sales performance, customer behavior, and profit trends — transforming raw e-commerce data into actionable business intelligence.

🛠️ Power BI 📊 Microsoft Excel 📁 Data Analysis 📈 Business Intelligence
Madhav Ecommerce Sales Dashboard · Power BI

Project Overview &
Problem Statement

The Madhav Ecommerce Sales Dashboard is an interactive business intelligence project built using Power BI. It analyzes sales performance, customer behavior, and profit trends to support data-driven decision-making.

E-commerce businesses often face challenges in tracking sales and profit across regions, identifying top customers and products, analyzing monthly performance trends, and understanding payment preferences. This dashboard solves these problems by providing clear, interactive insights.

The objective was to build a unified view that monitors sales, profit, and quantity metrics, analyzes monthly profit trends, identifies top customers and states, understands category and payment distribution, and enables dynamic filtering across all dimensions.

Project Details
Category BI Dashboard
Tool Power BI
Data Prep Microsoft Excel
Domain E-Commerce
Filters Quarter · State
Source Files GitHub →

Key Performance Indicators

💰
438K
Total Sales Amount
📈
37K
Total Profit
📦
5,615
Total Quantity Sold
📊
121K
Average Order Value (AOV)

Key Insights

01
Clothing Dominates Category Sales
The clothing category holds the highest quantity share across all product categories, indicating strong consumer preference for apparel on this platform.
02
COD is the #1 Payment Mode
Cash on Delivery is the most preferred payment method, reflecting trust patterns in e-commerce and highlighting an opportunity to incentivize digital payments.
03
Seasonal Profit Dips Detected
Certain months show negative profit trends, suggesting seasonal demand fluctuations or high-discount promotional periods that erode margins.
04
Top Customers Drive Bulk Revenue
A small group of high-value customers contribute disproportionately to overall revenue — a classic Pareto distribution that warrants a targeted retention strategy.
05
Maharashtra & MP Lead in Sales
Maharashtra and Madhya Pradesh are the top-performing states by sales amount, making them priority markets for inventory planning and regional campaigns.
06
Sub-Category Profitability Varies
Profit by sub-category reveals uneven margins — some sub-categories are high revenue but low profit, flagging potential pricing or cost issues.

Tools & Technologies

📊 Power BI
📗 Microsoft Excel
📉 Column Charts
📊 Bar Charts
🍩 Donut Charts
🎛️ Slicers & Filters
🧹 Data Preprocessing
📌 KPI Cards

Key Features

🎛️
Interactive Dashboard
Fully interactive Power BI report with cross-filtering capabilities between all visuals for deep drill-down analysis.
🔀
Dynamic Filtering with Slicers
Quarter-wise (Q1–Q4) and state-wise slicers let users dynamically filter the entire dashboard to focus on specific segments.
📈
Multiple Visualization Types
Combines column charts, bar charts, and donut charts to present data in the most appropriate visual format.
🎯
Clear KPI Tracking
Prominent KPI cards display Total Sales, Profit, Quantity, and AOV at a glance for quick executive-level assessment.
💡
Business-Focused Insights
Every visual answers a specific business question around customers, products, regions, or timing.
🗺️
Regional & Customer Analysis
State-wise sales breakdown and top customer analysis help identify geographic focus areas and high-value segments.

Dataset Information

The dataset encompasses complete e-commerce transaction records preprocessed using Microsoft Excel before being loaded into Power BI for modeling and visualization.

Data fields included:

  • Customer details and identifiers
  • Sales metrics — Amount, Profit, Quantity
  • Product categories and sub-categories
  • Payment modes (COD, UPI, Credit Card, etc.)
  • State-wise geographic data
  • Date fields for monthly trend analysis
📁 Project Files — via GitHub
📊
Madhav_Dashboard.pbix
Power BI Report File
GitHub
📗
Orders.xlsx
Raw Sales Dataset
GitHub
📗
Details.xlsx
Product & Category Data
GitHub
📄
README.md
Project Documentation
GitHub

All files hosted on GitHub — click any row to download.

Takeaway

This dashboard converts raw e-commerce data into meaningful insights, helping businesses improve performance and make strategic decisions. By combining Power BI's visualization capabilities with Excel-based preprocessing, it demonstrates how business intelligence tools can bridge the gap between raw data and executive action — from regional planning to customer retention and payment strategy optimization.

Interested in
collaborating ?

Feel free to reach out for feedback, collaboration, or any data analytics opportunities.