{"id":25333950,"url":"https://github.com/naninsv/zomato-data-analysis","last_synced_at":"2025-04-08T08:21:06.717Z","repository":{"id":277309505,"uuid":"932010432","full_name":"naninsv/Zomato-Data-Analysis","owner":"naninsv","description":"Comprehensive SQL-based analysis of Zomato's food delivery data. 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This project leverages SQL-based data analysis to extract insights on customer behavior, restaurant performance, delivery trends, and revenue patterns.\n\n- 📊 **Customer behavior**\n- 🍔 **Restaurant performance**\n- 🚴 **Delivery trends**\n- 💰 **Revenue patterns**\n\nBy 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.\n\n## 🎯 Objective\nThe primary goal of this project is to **analyze Zomato's operational and customer data** using **SQL** to extract actionable insights and enhance efficiency \u0026 profitability.\n\n### 🔍 **Key Goals:**\n✅ **Understanding customer behavior** – Identifying repeat customers, high-value customers, and spending patterns.  \n✅ **Evaluating restaurant performance** – Ranking restaurants based on **revenue, order volume, and demand**.  \n✅ **Analyzing delivery efficiency** – Measuring rider performance through **delivery times, delays, and efficiency comparisons**.  \n✅ **Tracking seasonal demand** – Identifying trends based on **seasons, weekdays vs. weekends, and special events**.  \n✅ **Optimizing marketing and promotions** – Determining the best time for **discounts \u0026 promotions** to maximize revenue.  \n\nThis **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**.  \n\n## 📂 Dataset Description\nThe project is based on multiple datasets:\n- **📦 Orders:** Order details, customer ID, total amount, order date.\n- **👤 Customers:** Customer demographics, customer ID, location.\n- **🏬 Restaurants:** Restaurant details, revenue, and locations.\n- **🚴 Deliveries:** Delivery time, delivery status, assigned rider.\n- **🛵 Riders:** Rider details, delivery efficiency, performance.\n\n## Entity-Relationship (ER) Diagram 🏗️\n\nBelow is the ER Diagram representing the database schema for this project:\n\n![ER Diagram](ER.png)\n\n\n\n## 🛠️ Methodologies Used\n\n1. **Joins**  \n   🔗 Combine multiple tables (e.g., INNER, LEFT JOIN) to integrate data across orders, customers, restaurants, deliveries, and riders.\n\n2. **Aggregations**  \n   🔢 Use functions like SUM, COUNT, and AVG to compute metrics such as revenue and order volumes.\n\n3. **Ranking Functions**  \n   📊 Employ RANK, DENSE_RANK, and ROW_NUMBER to order restaurants, riders, and other entities based on performance.\n\n4. **Window Functions**  \n   🔄 Utilize LAG and LEAD to compare current rows with previous or subsequent rows for trend analysis.\n\n5. **Date Functions**  \n   📅 Extract and manipulate date information using MONTH, YEAR, DATEADD, and DATEDIFF to analyze seasonal trends.\n\n6. **Common Table Expressions (CTEs)**  \n   📋 Break complex queries into manageable, reusable parts for clarity and maintenance.\n\n7. **Filtering \u0026 Conditional Logic**  \n   🔍 Apply WHERE, HAVING, and CASE statements to segment data and implement conditional analysis.\n\n8. **Set Operations**  \n   🔀 Merge or compare datasets using UNION, INTERSECT, and EXCEPT for comprehensive result sets.\n\n9. **Data Type Conversion**  \n   🔄 Use CAST and CONVERT to ensure data is in the correct format for analysis.\n\n10. **Query Optimization**  \n    ⚡ Enhance performance through indexing and efficient query design.\n\n\n## 📌 Project Breakdown (Questions \u0026 Categories)\n\n### 🟢 Basic Level:\n1️⃣ Total orders per customer.  \n2️⃣ Total revenue generated by each customer (CLV).  \n3️⃣ Number of orders per restaurant.  \n4️⃣ Revenue generated by each restaurant.  \n5️⃣ Average order value.  \n\n### 🔵 Intermediate Level:\n6️⃣ Monthly sales trends analysis.  \n7️⃣ Identifying high-value customers.  \n8️⃣ Rider efficiency based on delivery times.  \n9️⃣ Seasonal demand analysis.  \n🔟 City-wise revenue ranking.  \n\n### 🔴 Advanced Level:\n🔢 Year-over-year revenue trends.  \n🔢 Identifying peak ordering hours.  \n🔢 Evaluating discount impact on sales.  \n🔢 Identifying top-performing restaurants.  \n🔢 Customer segmentation based on order behavior.  \n\n## 📊 Key Findings\n🔹 **High-Value Customers:** A small percentage of customers contribute to a large portion of total revenue.  \n🔹 **Peak Order Times:** Most orders occur during lunch (12 PM - 2 PM) and dinner (7 PM - 9 PM).  \n🔹 **Delivery Efficiency:** Some riders consistently deliver faster than others, impacting overall service ratings.  \n🔹 **Seasonal Demand:** Orders increase during festival seasons and weekends.  \n🔹 **Marketing Optimization:** Discounts during off-peak hours lead to increased orders without hurting profit margins.  \n\n## 💡 Recommendations\n🚀 **Improve delivery efficiency** – Optimize rider allocation and reduce delays.  \n🎯 **Target high-value customers** – Personalized promotions for repeat customers.  \n📢 **Enhance restaurant partnerships** – Identify and promote high-performing restaurants.  \n📈 **Seasonal strategies** – Offer seasonal discounts to capitalize on peak demand.  \n💰 **Dynamic pricing models** – Adjust pricing based on demand trends.\n\n## 🔚 Conclusion\nThis **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. 🚀\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnaninsv%2Fzomato-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnaninsv%2Fzomato-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnaninsv%2Fzomato-data-analysis/lists"}