E-Commerce Platform Performance Analysis

Author: Swetha Julakanti
Project Type: Business Intelligence & Data Visualization
Tools: Power BI Desktop (Feb 2024 Edition)

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📌 Project Overview

This Power BI dashboard provides an interactive analysis of operational performance, customer behavior, and delivery metrics across three major e-commerce platforms: Swiggy Instamart, Blinkit, and JioMart. The dashboard is structured with KPI cards, visual summaries, and slicers for platform-wise filtering, helping stakeholders understand key trends in sales, sentiment, and delivery efficiency.

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🧰 Tools and Technologies

• Power BI Desktop – Report development & interactive visualization
• Power Query – ETL pipeline for data cleaning and transformation
• DAX – For dynamic KPIs and calculated measures
• Git + GitHub – Version control and documentation

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📊 Dataset Description

The dataset includes records from three e-commerce platforms with fields such as:

• Product category and platform
• Order and delivery dates
• Delivery status and refund requests
• Customer feedback sentiment
• Revenue, units, and order quantity
• Service ratings and delivery time

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🧹 Data Cleaning & Enrichment (ETL)

Using Power Query:

• Removed null or blank records
• Renamed and reformatted columns
• Extracted delivery performance metrics
• Derived new fields like:
  • Order Delay Status
  • Sentiment Label
  • Delivery Time (hrs)

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⚙️ DAX Measures Developed

• Total Revenue
• Total Orders
• Average Delivery Time
• Refund Rate
• Positive/Negative Sentiment Count
• Delayed Orders %
• Average Service Rating
• Delivery Delay Rate
• Platform-Wise KPIs

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🖥️ Dashboard Structure

The report is organized into the following sections:

1. Executive Summary

• Platform-wise KPIs (Orders, Revenue, Ratings)

• Revenue by Category

• Order Delay and Refund Overview

  

2. Delivery & Refund Analysis

• Refund Requests Distribution
• Delivery Delay Rate
• Average Delivery Time and Ratings

3. Customer Sentiment Analysis

• Feedback Type Breakdown
• Positive vs Negative Feedback
• Category-wise Sentiment Distribution

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💡 Key Insights

• All three platforms contribute nearly equally to overall revenue (~19.6M–19.8M)
• Personal Care and Grocery are the highest-selling categories
• 13.67% of total orders were delayed
• 54% of orders had no refund requests
• Customer sentiment is overwhelmingly positive, with over 84.6K positive reviews

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🔍 Evaluation & Results

• Dashboard is responsive and filters apply instantly
• Design supports both high-level overviews and granular drill-down
• Data model supports future data refreshes and scalability

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🧠 Lessons Learned

• Maintaining a clean data model ensures fast loading
• DAX formulas for delay % and refund rates helped uncover key inefficiencies
• Integrating customer sentiment improves actionable insights for business decisions

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🧭 Future Enhancements

• Add real-time order tracking via APIs
• Integrate regional breakdown via maps
• Extend analysis to include marketing campaign ROI
• Publish via Power BI Service for live online access

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📂 Repository Structure

📦 E-Commerce_Platform_Performance_Analysis
├── README.md
├── Ecommerce_Dashboard.pbix
├── datasets/
│   └── ecommerce_sample.csv
├── screenshots/
│   └── ecommerce_dashboard_overview.png