E-Commerce Analytics Dashboard

Interactive Power BI dashboard that transforms raw sales data into actionable business intelligence for online retailers

📖 Table of Contents

• 🎯 The Problem
• ✨ The Solution
• 📊 Key Features
• 🛠️ Technical Stack
• 📫 Contact

🎯 The Problem

Online retailers struggle with:

Hidden profit leaks - Products with high revenue but razor-thin margins Wasted marketing spend - Can't identify which channels drive profitable sales Dead inventory - Products sitting unsold for months, tying up capital Poor customer retention - Don't know which customers are at risk of churning Scattered data - Information spread across Shopify, Google Analytics, Facebook Ads, etc.

Result: 30-40% of potential profit left on the table.

✨ The Solution

A comprehensive 4-page Power BI dashboard that automatically: ✅ Identifies profit killers (high-revenue, low-margin products) ✅ Flags marketing waste (poor ROAS channels) ✅ Segments customers by value (RFM analysis) ✅ Tracks inventory health (stock alerts, dead inventory) ✅ Recommends budget optimization (+$8,600/month potential) ✅ Forecasts revenue trends (3-month projections)

📊 Key Features

1. Executive Overview

Real-time KPIs: Revenue, profit margin, AOV, order volume Revenue forecasting: Trend analysis with 3-month projections Automated alerts: "Winter Jacket: $47K revenue but only 7% margin" Channel breakdown: Website, mobile, Instagram, Facebook performance Geographic insights: Sales distribution by location

2. Product Performance Analysis

Performance matrix: 4-quadrant analysis (Stars, Cash Cows, Question Marks, Dogs) Profit margin tracking: Color-coded conditional formatting Inventory alerts: Low stock warnings, reorder point notifications Dead inventory detection: Products with 60+ days no sales Category benchmarking: Margin health by product category

3. Customer Analytics

RFM segmentation: Champions, Loyal, At-Risk, Hibernating customers Lifetime value analysis: Customer worth by segment Retention metrics: Repeat purchase rate, purchase frequency Behavioral patterns: One-time vs. repeat buyer analysis Geographic distribution: Customer location mapping

🛠️ Technical Stack

Core Technologies:

Power BI Desktop - Dashboard development and visualization DAX - 30+ custom measures for calculations and business logic Power Query (M) - Data transformation and ETL Python - Dataset generation and automation

Pandas - Data manipulation NumPy - Numerical operations

Data Architecture:

Data Model: Star schema with 5 dimension tables Relationships: One-to-many between fact and dimension tables Row Count: 13,000+ transactions, 3,000 customers, 25 products Time Period: 12 months (Jan 2024 - Dec 2024)

Key Technical Features:

Time intelligence calculations (YoY, MoM growth) Conditional formatting with dynamic thresholds Cross-visual filtering and drill-through Automated insight generation using DAX logic Interactive slicers with preset date ranges RFM customer segmentation algorithm

🚀 Installation & Setup: Prerequisites

Power BI Desktop (free) - Download here Python 3.8+ (for data generation) - Download here

Option 1: Quick Start (Use Pre-Generated Data)

Clone the repository

bash
git clone https://github.com/bilalrizvi21/ecommerce-analytics-dashboard.git cd ecommerce-analytics-dashboard

Open the dashboard

Double-click Ecommerce_Dashboard_Portfolio.pbix

Power BI Desktop will open automatically

Data is already loaded and ready to explore

Start exploring!

Click through the 4 pages

Use slicers to filter by date, category, channel

Hover over visuals for detailed tooltips

Option 2: Generate Fresh Data

Install Python dependencies

bash pip install pandas numpy

Run data generation script

bash cd scripts python generate_ecommerce_data.py

Load into Power BI

Open Ecommerce_Dashboard_Portfolio.pbix

Home → Transform Data → Data source settings

Point to newly generated CSV files in /data folder

Refresh

🎓 Key Learnings:

This project demonstrates:

Technical Skills

• Advanced DAX measure creation (time intelligence, customer segmentation)

• Data modeling with star schema design

• ETL pipeline development using Power Query

• Python for data generation and automation

• Conditional formatting and dynamic visualizations

Business Analytics:

• Customer segmentation using RFM methodology

• Marketing attribution and ROAS analysis

• Product profitability tracking

• Inventory health monitoring

• Revenue forecasting techniques

Data Storytelling:

• Translating complex data into actionable insights

-Designing executive-friendly dashboards

• Creating automated alert systems

• Building business recommendations from data

📝 Use Cases:

For E-commerce Managers

• Identify profit leaks in under 5 minutes

• Make data-driven inventory decisions

• Optimize product mix based on profitability

For Marketing Teams

• See true ROAS by channel and campaign

• Understand customer acquisition costs

• Reallocate budgets to highest-performing channels

For Executives

• Single source of truth for business health

• Revenue forecasting for financial planning

• Quick identification of growth opportunities

📫 Contact

Bilal Rizvi

LinkedIn: linkedin.com/in/rizvibilal

GitHub: github.com/bilalrizvi21

Email: whomebilal11@gmail.com

Need a custom analytics dashboard for your business? I specialize in transforming complex data into clear, actionable insights for e-commerce and retail businesses. Let's talk about your data challenges.

🙏 Acknowledgments

Dataset structure inspired by real e-commerce transaction patterns

Dashboard design principles from Microsoft Power BI best practices

Color schemes optimized for accessibility and business context

⭐ Star:

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