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Introduction

Online Food Delivery Statistics: As the demand for convenience grows, online food delivery platforms are experiencing rapid expansion across multiple regions. The widespread adoption of smartphones, mobile apps, and a shift in consumer preferences towards contactless services fuel this growth.

These platforms cater to a wide array of options, from fast food to gourmet meals, reshaping the way people access food. By analyzing relevant statistics, businesses can gain a deeper understanding of market size, consumer demographics, popular cuisines, and regional preferences.

Furthermore, these insights reveal important details about delivery times, customer satisfaction, and spending habits, enabling companies to optimize their operations and improve customer experiences. This data-driven approach empowers businesses to make informed decisions and maintain a competitive edge in the dynamic market.

This dataset contains 21,321 food order records from various restaurants, capturing crucial details about customer preferences, order trends, pricing, and delivery performance. It includes 6 unique imaginary restaurants, such as Swaad, Aura Pizzas, Dilli Burger Adda, Tandoori Junction, The Chicken Junction, and Masala Junction. The dataset provides a comprehensive view of food delivery operations, making it highly valuable for data analysis, predictive modeling, and machine learning applications.

Key attributes in this dataset include restaurant details (restaurant name, subzone, city), order information (order ID, timestamps, order status, delivery time, distance, number of items), pricing breakdown (bill subtotal, packaging charges, total cost, discounts), and customer feedback (ratings, reviews, order cancellations). It also tracks key delivery insights such as rider wait time, preparation duration, and distance traveled, which can be useful for logistics optimization and demand forecasting.

This dataset can be leveraged for predicting delivery times, analyzing customer behavior, identifying top-performing restaurants, and optimizing pricing strategies. It is particularly useful for food delivery platforms, restaurant managers, and data scientists looking to improve delivery efficiency and customer satisfaction. With rich historical data, this dataset can also be used for building recommendation systems, identifying peak ordering times, and enhancing user experience in food delivery applications.

The market size of the global online food delivery sector was estimated at nearly *** trillion U.S. dollars in 2025, of which *** billion dollars were generated in the grocery delivery segment, and *** billion dollars in the meal delivery segment. By 2030, the online food delivery market is forecast to generate revenues reaching *** trillion U.S. dollars.

With a market share of ***percent, DoorDash dominated the online food delivery market in the United States as of June 2025. Meanwhile, Uber Eats held the second-highest share with ***percent.

In 2025, over ************* consumers globally used online food delivery services (grocery and meal delivery). Looking at the regional breakdown, Asia remains, by far, the biggest market for online food delivery, with approximately ****billion users in 2025. Europe comes in second place, with around *** million online food delivery users.

This dataset presents the share of total delivery occasions in the UK from 2022 to 2024, segmented by delivery aggregators including Uber Eats, Just Eat, Deliveroo, and others.

The dataset titled "Online Delivery Data" comprises 388 entries, each representing an individual's response to a survey concerning their preferences and experiences with online food delivery services in Australia. The dataset is structured into 53 columns, encompassing a wide range of information from demographic details to specific preferences and feedback on online food delivery services. Below is an in-depth description of its structure and the types of information it contains.

Dataset Overview Entries: 388 Attributes: 53 Core Attributes Description Demographic and Background Information

Age: The respondent's age. Gender: The gender of the respondent. Marital Status: Marital status of the respondent (e.g., Single, Married). Occupation: The respondent's occupation. Monthly Income: Monthly income category of the respondent. Educational Qualifications: Educational level achieved by the respondent. City: The city in Australia where the respondent resides. Family size: Number of members in the respondent's family. Service Utilization Preferences

Medium of ordering (P1 and P2): Primary and secondary preferences for ordering mediums, such as food delivery apps or direct calls. Meal preference (P1 and P2): Primary and secondary meal preferences. Preference reasons (P1 and P2): Primary and secondary reasons for their preferences. Perceptions and Attitudes

Various columns capture the respondent's attitudes towards ease and convenience, time-saving aspects, variety of choices, payment options, discounts and offers, food quality, tracking system, and several other factors related to online food delivery. Health and Hygiene Concerns

Specific concerns regarding health, delivery punctuality, hygiene, and past negative experiences with online food delivery services. Service Quality and Feedback

Attributes covering delivery time importance, packaging quality, customer service aspects (such as the number of calls to service and politeness), food freshness, temperature, taste, and quantity. Output: Likely a binary response (e.g., Yes or No) to a specific survey question, which could pertain to the respondent's overall satisfaction or willingness to recommend the service. Reviews: Open-ended feedback from respondents, providing qualitative insights into their experiences. Summary This dataset provides a comprehensive view of consumer preferences, behaviors, and satisfaction levels regarding online food delivery services in Australia. It encompasses a broad spectrum of variables from basic demographic information to detailed opinions on service quality, making it an invaluable resource for analyzing consumer trends, identifying areas for improvement in service delivery, and understanding the factors that influence customer satisfaction and loyalty in the online food delivery industry.

China has the largest online food delivery market in the world. As of June 2025, around *** million people in the country used online food delivery services, accounting for around *****percent of the nation's internet user base. The boom of online food delivery in China Chinese people love to eat out. The majority of Chinese consumers spend a decent amount of money while eating in restaurants. However, food delivery is gaining popularity, especially for lunch. The convenience of apps like Meituan Delivery and Ele.me, online payment methods, as well as the fast pace of urban life, were the main reasons behind the trend. Major consumer trends A 2023 survey found that about ** percent of Chinese respondents used food delivery services at least once a week. On average, app users spent between ** and *** yuan per order on online food delivery. Local cuisine, fast food, bubble tea, and other tea-based drinks were the most commonly ordered types of food for delivery.

In 2025, it was estimated that the online grocery delivery market had around *** billion users worldwide, while users in the meal delivery segment were estimated at *** billion that year. Both segments were forecast to experience growth in user base by 2030.

This dataset highlights the forecasted UK turnover market share for the top five delivery brands in 2025, alongside 2024 benchmarks. Brands include Domino's, McDonald's, KFC, Papa John's, and Burger King.

Saudi Arabia Food Delivery Data (2022-2025)

This synthetic dataset contains records of food delivery orders in Saudi Arabia from 2022 to 2025

Columns Overview:

Order Number – A unique identifier for each order. Order Date and Time – The timestamp indicating when the order was placed. Order_City – The city where the order was placed. Restaurant Type – The category of the restaurant (e.g., fast food). Total Bill (in Saudi Riyals) – The total amount paid for the order, providing financial insights. Delivery Duration (in minutes) – The time taken for the order to be delivered. Customer Rating (from 1 to 5 stars) – Customer feedback on the order, indicating satisfaction levels.

Dataset

This dataset was created by Ahmad10Raza

Contents

Online Food Order Dataset

Description: The dataset contains information collected from an online food ordering platform over a period of time. It encompasses various attributes related to Occupation, Family Size, Feedback etc..

Attributes:

Demographic Information:

Age: Age of the customer. Gender: Gender of the customer. Marital Status: Marital status of the customer. Occupation: Occupation of the customer. Monthly Income: Monthly income of the customer. Educational Qualifications: Educational qualifications of the customer. Family Size: Number of individuals in the customer's family. Location Information:

Latitude: Latitude of the customer's location. Longitude: Longitude of the customer's location. Pin Code: Pin code of the customer's location. Order Details:

Output: Current status of the order (e.g., pending, confirmed, delivered). Feedback: Feedback provided by the customer after receiving the order.

Purpose: This dataset can be utilized to explore the relationship between demographic/location factors and online food ordering behavior, analyze customer feedback to improve service quality, and potentially predict customer preferences or behavior based on demographic and location attributes.

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Online On-Demand Food Delivery Services Market Size 2025-2029

The online on-demand food delivery services market size is forecast to increase by USD 470.5 billion, at a CAGR of 26.9% between 2024 and 2029.

The market is experiencing significant growth, driven by the strategic partnerships between restaurants and online food aggregators. These collaborations enhance the reach and convenience of food delivery services, enabling restaurants to expand their customer base and aggregators to offer a wider selection of options. The market is further fueled by the increasing application of new technologies, such as artificial intelligence and machine learning, which streamline operations and improve the overall customer experience. However, the rising threat from direct delivery services poses a challenge. Companies must differentiate themselves through unique offerings, exceptional customer service, and innovative technologies to maintain a competitive edge in this dynamic market. Strategic partnerships and technological advancements present opportunities for growth, while the emergence of direct delivery services necessitates a focus on differentiation and customer satisfaction. Companies seeking to capitalize on market opportunities and navigate challenges effectively must stay agile and responsive to evolving consumer preferences and competitive landscapes.

What will be the Size of the Online On-Demand Food Delivery Services Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, with dynamic market dynamics shaping its applications across various sectors. Real-time tracking, user interface, and delivery vehicles are key components, ensuring seamless food delivery experiences for customers. Food safety regulations and restaurant partnerships are crucial in maintaining quality and trust. Meal kits and sustainability initiatives cater to diverse consumer preferences, while delivery networks optimize logistics and inventory management. Social responsibility is a growing concern, with companies implementing initiatives to reduce carbon footprint through cloud computing and route planning. Customer engagement is fostered through community engagement, customer service chatbots, and loyalty programs. Restaurant POS integration and order management systems streamline operations, enhancing order accuracy and customer retention. Fraud prevention and data security are essential in maintaining trust and transparency, while pricing models and data analytics inform strategic decision-making. Delivery scheduling and automation dispatch further improve efficiency, with API integration enabling seamless third-party partnerships. Commission structures and background checks ensure fair compensation for drivers, ensuring a reliable and efficient delivery network. The market's continuous unfolding is marked by ongoing innovations in food preparation, order confirmation, temperature control, and order tracking notifications. Delivery radius expansion and peak demand management cater to evolving consumer needs, with meal kits and dietary restrictions addressing diverse dietary preferences. Environmental impact is a growing concern, with companies investing in sustainable delivery vehicles and packaging solutions. User experience remains a top priority, with mobile applications and order history features enhancing the overall delivery experience. The market's evolving patterns reflect a commitment to meeting consumer demands while maintaining a responsible business model.

How is this Online On-Demand Food Delivery Services Industry segmented?

The online on-demand food delivery services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Business SegmentOFFDSLogistics-focused food delivery servicesTypeRestaurant-to-consumerPlatform-to-consumerEnd-userFamilyOffice buildingsPlatformMobileWebGeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Business Segment Insights

The offds segment is estimated to witness significant growth during the forecast period.In the on-demand food delivery services market, companies function as intermediaries between restaurants and customers. Customers can explore and compare restaurant menus, prices, reviews, and ratings through the company's website or mobile application. Once an order is placed and confirmed, the company forwards it to the respective restaurant for preparation and delivery. The restaurants manage the logistics of food delivery in this model, which primarily focuses on generating new orders for

Problem Statement:

Imagine you are working as a data scientist at Zomato. Your goal is to enhance operational efficiency and improve customer satisfaction by analyzing food delivery data. You need to build an interactive Streamlit tool that enables seamless data entry for managing orders, customers, restaurants, and deliveries. The tool should support robust database operations like adding columns or creating new tables dynamically while maintaining compatibility with existing code. ##Business_Use_Cases: Order Management: Identifying peak ordering times and locations. Tracking delayed and canceled deliveries. Customer Analytics: Analyzing customer preferences and order patterns. Identifying top customers based on order frequency and value. Delivery Optimization: Analyzing delivery times and delays to improve logistics. Tracking delivery personnel performance. Restaurant Insights: Evaluating the most popular restaurants and cuisines. Monitoring order values and frequency by restaurant.

#Approach: 1) Dataset Creation: Use Python (Faker) to generate synthetic datasets for customers, orders, restaurants, and deliveries. Populate the SQL database with these datasets. 2) Database Design: Create normalized SQL tables for Customers, Orders, Restaurants, and Deliveries. Ensure compatibility for dynamic schema changes (e.g., adding columns, creating new tables). 3) Data Entry Tool: Develop a Streamlit app for: Adding, updating, and deleting records in the SQL database. Dynamically creating new tables or modifying existing ones. 4) Data Insights: Use SQL queries and Python to extract insights like peak times, delayed deliveries, and customer trends. Visualize the insights in the Streamlit app.(Add on) 5) OOP Implementation: Encapsulate database operations in Python classes. Implement robust and reusable methods for CRUD (Create, Read, Update, Delete) operations. 6) Order Management: Identifying peak ordering times and locations. Tracking delayed and canceled deliveries. 7) Customer Analytics: Analyzing customer preferences and order patterns. Identifying top customers based on order frequency and value.

8) Delivery Optimization: Analyzing delivery times and delays to improve logistics. Tracking delivery personnel performance. 9) Restaurant Insights: Evaluating the most popular restaurants and cuisines. Monitoring order values and frequency by restaurant.

**##Results: ** By the end of this project, learners will achieve: A fully functional SQL database for managing food delivery data. An interactive Streamlit app for data entry and analysis. Should write 20 sql queries and do analysis. Dynamic compatibility with database schema changes. Comprehensive insights into order trends, delivery performance, and customer behavior.

##Project Evaluation metrics: Database Design: Proper normalization of tables and relationships between them. Code Quality: Use of OOP principles to ensure modularity and scalability. Robust error handling for database operations. Streamlit App Functionality: Usability of the interface for data entry and insights. Compatibility with schema changes. Data Insights: Use 20 sql queries for data analysis Documentation: Clear and comprehensive explanation of the code and approach.

Snapshot img { margin: 10px !important; } India Food Delivery Market Size 2025-2029

The India food delivery market size is forecast to increase by USD 1.5 billion at a CAGR of 28.8% between 2024 and 2029.

The food delivery market in India is shaped by the increasing prevalence of collaborations between restaurants and specialized delivery service providers. These partnerships are essential for expanding market reach, allowing restaurants to connect with a vast digital consumer base while enabling delivery platforms to diversify their offerings. This symbiotic relationship enhances the overall service proposition by leveraging shared data for customer insights and improved service personalization. The integration of online on-demand food delivery services has become a cornerstone of the modern food service landscape, driven by consumer demand for convenience. The efficiency of the underlying food logistics network is critical in ensuring timely and reliable service, which directly impacts customer satisfaction and retention. This collaborative framework is a key driver of the market's structure and growth trajectory.A significant trend influencing the market is the strategic use of social media as a primary channel for marketing and consumer engagement. Delivery platforms and their restaurant partners are actively creating content and promotional campaigns to attract and interact with customers, turning digital engagement into a direct driver of sales. This approach is transforming the way consumers discover and purchase food, blending entertainment with e-commerce. However, the market's expansion is met with the challenge of a growing preference among restaurants for establishing direct delivery services. This shift allows restaurants to maintain control over their brand, customer data, and pricing, thereby bypassing the commission-based models of third-party aggregators and creating direct competitive pressure on these platforms. Stricter regulations on foodservice disposables also add a layer of operational complexity.

What will be the size of the India Food Delivery Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
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The ongoing evolution of the food delivery market in India is marked by the continuous refinement of its core operational components. The interplay between the online ordering platform and its underlying last-mile delivery logistics network is constantly being optimized through new technologies. Innovations in real-time order tracking and digital payment integration are enhancing the transparency and convenience of online on-demand food delivery services. This dynamic environment necessitates that players in the food service sector continuously adapt their strategies. The development of advanced route optimization software is a critical area of focus, directly impacting the efficiency of delivery fleet management and overall service speed.Customer-centric strategies are also in a state of flux, with platforms leveraging sophisticated customer data analytics to personalize the user experience. The implementation of dynamic pricing algorithms and targeted promotional code engines reflects a more nuanced approach to demand management and customer retention. The restaurant aggregator model itself is being challenged and redefined by the rise of the direct-to-consumer model, pushing platforms to innovate their value proposition. Furthermore, the integration of cloud kitchen operations and dark store fulfillment models into the ecosystem indicates a strategic shift toward greater control over the supply chain and a more diversified service offering, reflecting the market's perpetual state of development.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. Service typeOnlineOfflineTypeDigital paymentCash on deliveryPlatform typeMobile applicationsWebsitesGeographyAPACIndia

By Service Type Insights

The online segment is estimated to witness significant growth during the forecast period.

The online food ordering segment operates through proprietary restaurant websites, mobile applications, and multi-restaurant aggregator platforms. This mode offers consumers extensive choices, enabling them to compare menus, prices, and delivery times conveniently. The segment's growth is heavily influenced by a large urban youth demographic that values speed and variety. The availability of numerous online food ordering applications, which account for approximately 73% of the total market, intensifies competition and drives innovation in user experience and service offerings.Digital engagement is central to

This statistic shows the takeout food delivery market share in Chicago, United States, as of April 2021. In that year, Uber Eats accounted for ** percent of the food delivery market in Chicago.

Online Food Delivery Market size was valued at USD 200.81 billion in 2021 and is poised to grow from USD 221.65 billion in 2022 to USD 442.50 billion by 2030, at a CAGR of 10.38% during the forecast period (2023-2030).

Smartphone food delivery apps such as Grubhub and Uber Eats allow users to order food online and have it delivered to their door. Due to the coronavirus (COVID-19) pandemic, many restaurants were unable to offer indoor dining in 2020. As a result, the number of smartphone food delivery app users was predicted to increase from **** million users in 2019 to **** million users in 2020. This growth was expected to continue, with an estimated **** million users by 2023.