Key Food Delivery StatisticsTop Food Delivery AppsFood Delivery Revenue by CountryProjected Food Delivery Market SizeFood Delivery Users by AppUS Food Delivery Market ShareFood Delivery Downloads by...

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.

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.

Due to a variety of new apps and delivery providers such as DoorDash, Grubhub and Uber Eats, it is easier than ever for U.S. consumers to order delivery meals. The size of the quick service restaurant delivery market is predicted to see growth between 2018 and 2022. According to the source, the size of the market in 2018 was **** billion U.S. dollars. This figure is forecast to rise to **** billion U.S. dollars in 2022.

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.

The coronavirus pandemic brought major changes to dining behaviors among restaurant-goers in the United States. Restaurant closures and social distancing measures resulted in an increasing demand for online food delivery, both directly through a restaurant's website or using a third-party delivery service. In 2020, the online restaurant delivery sector's share of the restaurant market was predicted to be ** percent, before the pandemic this figure was forecast at **** percent. The post-coronavirus market share was expected to rise as much as ** percent in 2025.

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.

Between 2019 and 2020, the coronavirus (COVID-19) pandemic led to a growth in the use of digital restaurant delivery services across ** countries worldwide. Digital delivery services are defined as meals or snacks ordered via mobile app, internet, or text message. In total, digital restaurant delivery increased ** percent globally, with the United States increasing the most at *** percent. Online restaurant delivery market worldwide In 2019, the market size of the global online food delivery sector reached ****** billion U.S. dollars. This figure was forecast to rise to as much as ****** billion by 2023. The sector became especially relevant during the coronavirus (COVID-19) pandemic, where social distancing and hygiene measures caused many restaurants to have to close their doors to the public. At the start of 2020, there was a dramatic decline in sit-down dining worldwide. However, this did not continue throughout the year, with the number of seated diners fluctuating depending on regulations and COVID-19 case numbers. Impact of COVID-19 on U.S. online food delivery The United States saw an increase in digital restaurant food orders between March 2020 and March 2021. Delivery orders increased by *** percent, while carry-out orders increased by *** percent. During that same year, the distribution of digital restaurant food orders was such that carry-out represented ** percent of all digital orders and delivery represented ** percent of all digital orders.

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.

The booming consumer food delivery market is projected to reach $250 billion in 2025, growing at a CAGR of 10% through 2033. Discover key market trends, drivers, restraints, and leading companies in this dynamic sector. Analyze regional market share data and understand the future of online food delivery.

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.

The global food delivery market is booming, projected to reach $133.95 billion by 2033, driven by smartphone adoption, changing lifestyles, and tech innovation. Learn about key players, market trends, and future growth in this comprehensive analysis.

Discover the booming North American online food delivery market! Our in-depth analysis reveals a $35.19B market in 2025, growing at a 7.72% CAGR. Explore key drivers, trends, and top players like Uber Eats & DoorDash. Get the data-driven insights you need! Recent developments include: November 2021 - DoorDash Inc., DoorDash Inc said it's buying Finnish food-delivery startup Wolt Enterprises Oy for about USD 8 billion. The biggest meal-delivery service in the U.S. said it's buying Finnish food-delivery startup Wolt Enterprises Oy for about $8 billion as it seeks to stay ahead of rivals in the race to satisfy soaring demand for the fast delivery of everything from food to prescriptions and pet supplies., June 2021 - Uber has been pushing itself beyond ride-hailing and has seen strength in its Uber Eats business due to the Covid-19 pandemic.. Notable trends are: Rise of Mobile Penetration in North America.

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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

The booming food delivery mobile app market is projected to reach $32.12 billion by 2033, driven by smartphone adoption and convenience. Learn about market trends, key players (Apple, Google, etc.), and challenges impacting this rapidly expanding sector. Explore regional market share and growth projections in our in-depth analysis.

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.

Dataset

This dataset was created by Ahmad10Raza

Contents

CONTEXT

The number of restaurants in New York is increasing day by day. Lots of students and busy professionals rely on those restaurants due to their hectic lifestyles. Online food delivery service is a great option for them. It provides them with good food from their favorite restaurants. A food aggregator company FoodHub offers access to multiple restaurants through a single smartphone app.

The app allows the restaurants to receive a direct online order from a customer. The app assigns a delivery person from the company to pick up the order after it is confirmed by the restaurant. The delivery person then uses the map to reach the restaurant and waits for the food package. Once the food package is handed over to the delivery person, he/she confirms the pick-up in the app and travels to the customer's location to deliver the food. The delivery person confirms the drop-off in the app after delivering the food package to the customer. The customer can rate the order in the app. The food aggregator earns money by collecting a fixed margin of the delivery order from the restaurants.

OBJECTIVE

The food aggregator company has stored the data of the different orders made by the registered customers in their online portal. They want to analyze the data to get a fair idea about the demand of different restaurants which will help them in enhancing their customer experience. Suppose you are hired as a Data Scientist in this company and the Data Science team has shared some of the key questions that need to be answered. Perform the data analysis to find answers to these questions that will help the company to improve the business.

Online Food Delivery Services Market Outlook

According to our latest research, the global online food delivery services market size reached USD 221.5 billion in 2024, driven by the rapid proliferation of internet connectivity, smartphone penetration, and evolving consumer lifestyles. The market is expected to expand at a robust CAGR of 10.8% from 2025 to 2033, projecting a value of USD 561.4 billion by 2033. The market’s impressive growth is primarily fueled by the convenience of digital ordering, increasing urbanization, and the integration of advanced technologies into food delivery platforms.

One of the most significant growth factors for the online food delivery services market is the increasing consumer demand for convenience and time-saving solutions. Modern urban lifestyles, characterized by hectic work schedules and limited time for meal preparation, have led to a surge in the adoption of online food delivery platforms. These platforms offer a wide variety of cuisines and restaurants at the fingertips of consumers, catering to diverse preferences and dietary needs. The rise of dual-income households and the growing millennial and Gen Z population, who are more tech-savvy and inclined toward digital solutions, further bolster the demand for online food delivery services. Additionally, the integration of real-time tracking, personalized recommendations, and loyalty programs enhances the overall user experience, making online ordering more appealing and habitual.

Another pivotal driver for the market’s expansion is the technological advancements within the food delivery ecosystem. The adoption of artificial intelligence, machine learning, and data analytics enables platforms to optimize delivery routes, predict consumer behavior, and streamline operations for both restaurants and logistics providers. Contactless delivery options, digital wallets, and seamless payment gateways have become industry standards, ensuring safety and convenience for users. Furthermore, the proliferation of cloud kitchens—kitchens focused solely on fulfilling online orders—has enabled restaurants to scale operations with lower overhead costs, thereby expanding the variety and reach of food offerings. These innovations are not only improving operational efficiency but are also fostering greater customer loyalty and higher order frequencies.

The COVID-19 pandemic has also played a transformative role in accelerating the adoption of online food delivery services. Lockdown measures and social distancing norms prompted a dramatic shift from dine-in to online ordering, with many first-time users becoming regular customers. Restaurants, both large chains and small independents, have increasingly partnered with delivery platforms to sustain their businesses and reach new customer segments. This paradigm shift has led to the emergence of hybrid business models, including virtual restaurants and subscription-based meal services. The post-pandemic era continues to witness sustained demand for online food delivery, as consumers have grown accustomed to the convenience and safety of digital ordering, further cementing the market’s long-term growth trajectory.

From a regional perspective, Asia Pacific stands out as the dominant force in the online food delivery services market, owing to its massive population base, rapid urbanization, and high smartphone penetration. China and India, in particular, are experiencing exponential growth, driven by a young, digitally connected demographic and a burgeoning middle class. North America follows closely, with the United States leading the charge due to its mature e-commerce infrastructure and a culture that embraces digital convenience. Europe is also witnessing significant growth, propelled by increasing internet usage and changing dining habits. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, supported by improving digital infrastructure and rising disposable incomes. Each region presents unique opportunities and challenges, shaping the global competitive landscape of online food delivery services.