Quick Commerce Delivery & Customer Experience Analytics

Project Overview

This project is a complete end-to-end Business Intelligence and Operations Analytics solution built using SQL, Python, and Power BI.

The project analyzes operational performance, delivery efficiency, customer satisfaction, refund behavior, and revenue trends across major Indian quick-commerce platforms including:

• Blinkit
• Swiggy Instamart
• JioMart

The analysis was performed on 39,000+ delivery transactions to simulate real-world business reporting and operational analytics workflows used by modern startups and quick-commerce companies.

Business Problem

Quick-commerce companies operate in highly competitive environments where delivery efficiency, customer satisfaction, SLA performance, and operational optimization directly impact customer retention and profitability.

The business required a centralized analytics solution capable of:

• Monitoring operational KPIs
• Tracking delivery delays
• Identifying refund-related revenue leakage
• Evaluating customer satisfaction trends
• Analyzing peak operational demand periods
• Understanding revenue-driving customer segments

Project Objectives

• Analyze operational performance across platforms
• Identify delivery inefficiencies and SLA issues
• Perform customer satisfaction and refund analysis
• Build interactive Power BI dashboards
• Generate business insights and recommendations
• Simulate real-world Business Intelligence workflows

Tools & Technologies

| Tool | Purpose

| MySQL | SQL analysis & KPI extraction | | Python (Pandas) | Data cleaning & preprocessing | | Matplotlib & Seaborn | Exploratory Data Analysis | | Power BI | Dashboard development | | Jupyter Notebook | Python workflow | | GitHub | Portfolio hosting |

Dataset Information

• Dataset Source: Kaggle
• Domain: Quick Commerce / Delivery Operations
• Final Records: 39,911
• Analytical Columns: 15

Project Workflow

Raw Dataset ↓ Data Cleaning & Feature Engineering (Python) ↓ Exploratory Data Analysis (Python) ↓ Business KPI Analysis (SQL) ↓ Interactive Dashboard Development (Power BI) ↓ Business Insights & Recommendations

Key Business Insights

• Approximately 86% of deliveries were completed on time
• Delayed deliveries showed lower customer satisfaction ratings
• Swiggy Instamart generated the highest revenue
• Premium customer segments contributed highest revenue
• Morning and Evening Rush periods recorded highest operational demand
• Refund-related revenue leakage represented a major business risk

Power BI Dashboard Pages

1. Executive Overview

• Operational KPIs
• Revenue analysis
• Delivery performance

2. Delivery Operations Analysis

• SLA monitoring
• Delay analysis
• Peak-time operational performance

3. Customer Experience & Refund Analysis

• Customer satisfaction analysis
• Refund trends
• Service rating analysis

4. Revenue & Customer Segmentation

• Revenue contribution analysis
• Customer segmentation
• Platform revenue ranking

Business Recommendations

• Improve rider allocation during peak hours
• Strengthen SLA monitoring systems
• Reduce refund-related revenue leakage
• Enhance premium customer retention strategies
• Improve operational forecasting

Skills Demonstrated

• SQL Querying
• Data Cleaning
• Feature Engineering
• Exploratory Data Analysis
• KPI Reporting
• Dashboard Design
• Business Analysis
• Data Storytelling
• Power BI Development

Project Files

• Power BI Dashboard (.pbix)
• SQL Queries (.sql)
• Python Notebook (.ipynb)
• Project Report (.pdf)
• Dashboard Screenshots
• Dataset

Author

Aaryan Sharma Data Analyst Portfolio Project