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https://github.com/naninsv/zomato-data-analysis

Comprehensive SQL-based analysis of Zomato's food delivery data. Covers customer behavior, restaurant performance, delivery efficiency, seasonal trends, and revenue optimization using advanced SQL queries.
https://github.com/naninsv/zomato-data-analysis

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Comprehensive SQL-based analysis of Zomato's food delivery data. Covers customer behavior, restaurant performance, delivery efficiency, seasonal trends, and revenue optimization using advanced SQL queries.

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README 

        
# 🍽️ Zomato Food Delivery Data Analysis Using SQL

![Zomato GIF](Photo/zomato_gif.gif)



## 📌 Introduction

The **food delivery industry**  is rapidly growing, with companies like **Zomato** leading the market. This project leverages SQL-based data analysis to extract insights on customer behavior, restaurant performance, delivery trends, and revenue patterns.



- 📊 **Customer behavior**

- 🍔 **Restaurant performance**

- 🚴 **Delivery trends**

- 💰 **Revenue patterns**



By analyzing structured datasets—including **orders, customers, restaurants, deliveries, and riders**—this project uncovers key business insights. Advanced **SQL techniques** such as **joins, aggregations, ranking functions, and window functions** help in optimizing operations, marketing strategies, and customer experience.



## 🎯 Objective

The primary goal of this project is to **analyze Zomato's operational and customer data** using **SQL** to extract actionable insights and enhance efficiency & profitability.



### 🔍 **Key Goals:**

✅ **Understanding customer behavior** – Identifying repeat customers, high-value customers, and spending patterns.  

✅ **Evaluating restaurant performance** – Ranking restaurants based on **revenue, order volume, and demand**.  

✅ **Analyzing delivery efficiency** – Measuring rider performance through **delivery times, delays, and efficiency comparisons**.  

✅ **Tracking seasonal demand** – Identifying trends based on **seasons, weekdays vs. weekends, and special events**.  

✅ **Optimizing marketing and promotions** – Determining the best time for **discounts & promotions** to maximize revenue.  



This **SQL-driven analysis** empowers **Zomato** to **optimize customer engagement, streamline logistics, and boost revenue generation** by improving **order fulfillment, delivery management, and targeted marketing strategies**.  



## 📂 Dataset Description

The project is based on multiple datasets:

- **📦 Orders:** Order details, customer ID, total amount, order date.

- **👤 Customers:** Customer demographics, customer ID, location.

- **🏬 Restaurants:** Restaurant details, revenue, and locations.

- **🚴 Deliveries:** Delivery time, delivery status, assigned rider.

- **🛵 Riders:** Rider details, delivery efficiency, performance.



## Entity-Relationship (ER) Diagram 🏗️



Below is the ER Diagram representing the database schema for this project:



![ER Diagram](ER.png)



## 🛠️ Methodologies Used



1. **Joins**  

   🔗 Combine multiple tables (e.g., INNER, LEFT JOIN) to integrate data across orders, customers, restaurants, deliveries, and riders.



2. **Aggregations**  

   🔢 Use functions like SUM, COUNT, and AVG to compute metrics such as revenue and order volumes.



3. **Ranking Functions**  

   📊 Employ RANK, DENSE_RANK, and ROW_NUMBER to order restaurants, riders, and other entities based on performance.



4. **Window Functions**  

   🔄 Utilize LAG and LEAD to compare current rows with previous or subsequent rows for trend analysis.



5. **Date Functions**  

   📅 Extract and manipulate date information using MONTH, YEAR, DATEADD, and DATEDIFF to analyze seasonal trends.



6. **Common Table Expressions (CTEs)**  

   📋 Break complex queries into manageable, reusable parts for clarity and maintenance.



7. **Filtering & Conditional Logic**  

   🔍 Apply WHERE, HAVING, and CASE statements to segment data and implement conditional analysis.



8. **Set Operations**  

   🔀 Merge or compare datasets using UNION, INTERSECT, and EXCEPT for comprehensive result sets.



9. **Data Type Conversion**  

   🔄 Use CAST and CONVERT to ensure data is in the correct format for analysis.



10. **Query Optimization**  

    ⚡ Enhance performance through indexing and efficient query design.



## 📌 Project Breakdown (Questions & Categories)



### 🟢 Basic Level:

1️⃣ Total orders per customer.  

2️⃣ Total revenue generated by each customer (CLV).  

3️⃣ Number of orders per restaurant.  

4️⃣ Revenue generated by each restaurant.  

5️⃣ Average order value.  



### 🔵 Intermediate Level:

6️⃣ Monthly sales trends analysis.  

7️⃣ Identifying high-value customers.  

8️⃣ Rider efficiency based on delivery times.  

9️⃣ Seasonal demand analysis.  

🔟 City-wise revenue ranking.  



### 🔴 Advanced Level:

🔢 Year-over-year revenue trends.  

🔢 Identifying peak ordering hours.  

🔢 Evaluating discount impact on sales.  

🔢 Identifying top-performing restaurants.  

🔢 Customer segmentation based on order behavior.  



## 📊 Key Findings

🔹 **High-Value Customers:** A small percentage of customers contribute to a large portion of total revenue.  

🔹 **Peak Order Times:** Most orders occur during lunch (12 PM - 2 PM) and dinner (7 PM - 9 PM).  

🔹 **Delivery Efficiency:** Some riders consistently deliver faster than others, impacting overall service ratings.  

🔹 **Seasonal Demand:** Orders increase during festival seasons and weekends.  

🔹 **Marketing Optimization:** Discounts during off-peak hours lead to increased orders without hurting profit margins.  



## 💡 Recommendations

🚀 **Improve delivery efficiency** – Optimize rider allocation and reduce delays.  

🎯 **Target high-value customers** – Personalized promotions for repeat customers.  

📢 **Enhance restaurant partnerships** – Identify and promote high-performing restaurants.  

📈 **Seasonal strategies** – Offer seasonal discounts to capitalize on peak demand.  

💰 **Dynamic pricing models** – Adjust pricing based on demand trends.



## 🔚 Conclusion

This **SQL-driven data analysis** provides actionable insights to help **Zomato optimize operations, improve customer satisfaction, and boost revenue**. With data-driven strategies, Zomato can enhance its food delivery services and strengthen its market position. 🚀