ashishchamel/zomato-data-analysis
End-to-end Zomato analytics project using Python & Tableau
Zomato Platform Performance, Customer Intelligence & Growth Strategy
An end-to-end analytics case study focused on uncovering revenue drivers, customer behavior patterns, and restaurant performance insights within a food delivery platform ecosystem.
This project demonstrates data cleaning, feature engineering, segmentation analysis, and executive-level dashboard storytelling using Python and Tableau.
Live Dashboards
Interactive dashboards available on Tableau Public:
https://public.tableau.com/app/profile/ashish.chamel
Project Objective
The primary goal of this project was to simulate a real-world product analytics scenario by answering key business questions such as:
- What drives overall platform revenue?
- Which customer segments generate the most value?
- How do ratings impact order volume and revenue?
- Which cuisines and restaurants dominate performance?
- Is pricing aligned with perceived customer value?
Tools & Technologies
- Python (Pandas) — Data cleaning, transformation, and feature engineering
- Tableau — Dashboard development and visual analytics
- Excel — Raw data storage
Data Processing & Engineering (Python)
The full data pipeline is implemented in:
src/zomato_analysis.py
Steps Performed:
- Loaded multiple datasets (Orders, Users, Restaurants, Food)
- Removed duplicates and invalid transactions
- Standardized column names
- Merged datasets into a unified analytical table
- Engineered business metrics:
- Cost per item
- Rating buckets (Poor / Average / Good / Excellent / Unknown)
- Price segmentation (Budget / Mid / Premium)
- Year & Month extraction for trend analysis
- Exported final dataset for Tableau visualization
Raw datasets and Tableau workbook are intentionally excluded.
Dashboard Overview
1️ Executive Overview
Purpose: High-level performance snapshot for decision-makers.
Key Metrics:
- Total Revenue
- Total Orders
- Unique Customers
- Average Order Value
- Monthly Revenue Trends
- Revenue by City
- Revenue by Price Segment
- Revenue by Rating Bucket
Insights:
- Premium price segment contributes majority of revenue
- Revenue fluctuates seasonally
- Highly rated restaurants drive disproportionate revenue
2️ Customer Intelligence Dashboard
Purpose: Understand customer demographics and behavior.
Analysis Includes:
- Age vs Spend patterns
- Gender-based order distribution
- Orders by occupation
- Marital status segmentation
- Rating bucket vs repeat order trends
Insights:
- Young adults dominate order volume
- Students are the most active segment
- Higher ratings correlate with stronger repeat behavior
3️ Restaurant & Product Strategy Dashboard
Purpose: Identify growth opportunities and pricing optimization.
Analysis Includes:
- Top restaurants by revenue
- Cuisine performance analysis
- Rating vs price correlation
- Revenue vs volume concentration
Insights:
- Revenue concentrated among top-performing restaurants
- Premium pricing does not always guarantee higher ratings
- North Indian and Chinese cuisines dominate overall revenue
Strategic Recommendations
- Expand high-performing cuisine categories into new markets.
- Strengthen loyalty programs targeting student segments.
- Promote high-rated restaurants for retention growth.
- Support mid-tier restaurants with pricing and visibility optimization.
- Optimize premium pricing based on rating elasticity insights.
Repository Structure
zomato-data-analysis/
│
├── README.md
├── LICENSE
│
├── src/
│ └── zomato_analysis.py
│
└── dashboards/
├── executive_overview.png
├── customer_intelligence.png
└── restaurant_strategy.png
Notes
- Dataset and Tableau workbook are excluded to protect original work.
- Dashboards are publicly accessible via Tableau Public.
- Screenshots include watermark for ownership.
Author
Ashish Chamel
Data Analytics Portfolio
Tableau Public:
https://public.tableau.com/app/profile/ashish.chamel
LinkedIn:
https://www.linkedin.com/in/ashish-chamel
© Ashish Chamel | Data Analytics Portfolio