MealMe offers in-depth restaurant menu data, including prices, from the top 100,000 restaurants across the USA and Canada. Our proprietary technology collects accurate, real-time menu and pricing information, enabling businesses to make data-driven decisions in competitive intelligence, pricing optimization, and market research. With comprehensive coverage that spans major restaurant platforms and chains, MealMe ensures your business has access to the most reliable data to excel in a rapidly evolving industry.

Platforms and Restaurants Covered: MealMe's database includes data from leading restaurant platforms such as UberEats, Postmates, ToastTakeout, SkipTheDishes, Square, Appfront, Olo, TouchBistro, and Clover, as well as direct menu data from major restaurant chains including Raising Cane’s, Panda Express, Popeyes, Burger King, and Subway. This extensive coverage ensures a detailed view of the market, helping businesses monitor trends, pricing, and availability across a broad spectrum of restaurant types and sizes.

Key Features: Comprehensive Menu Data: Access detailed menu information, including item descriptions, categories, sizes, and customizations. Real-Time Pricing: Monitor up-to-date menu prices for accurate competitive analysis. Restaurant-Specific Insights: Analyze individual restaurant chains such as Raising Cane’s and Panda Express, or platforms like UberEats, for market trends and pricing strategies. Cross-Platform Analysis: Compare menu items and pricing across platforms like ToastTakeout, Olo, and SkipTheDishes for a holistic industry view. Regional Data: Understand geographic variations in menu offerings and pricing across the USA and Canada.

Use Cases: Competitive Intelligence: Track menu offerings, pricing strategies, and seasonal trends across platforms like UberEats and Postmates or chains like Popeyes and Subway. Market Research: Identify gaps in the market by analyzing menus and pricing from top restaurants. Pricing Optimization: Use real-time pricing data to inform dynamic pricing strategies and promotions. Trend Monitoring: Stay ahead by tracking popular menu items, regional preferences, and emerging food trends. Platform Analysis: Assess how restaurants perform across delivery platforms such as SkipTheDishes, Olo, and Square. Industries Benefiting from Our Data Restaurant Chains: Optimize menu offerings and pricing strategies with detailed competitor data. Food Delivery Platforms: Benchmark menu pricing and availability across competitive platforms. Market Research Firms: Conduct detailed analyses to identify opportunities and market trends. AI & Analytics Companies: Power recommendation engines and predictive models with robust menu data. Consumer Apps: Enhance app experiences with accurate menu and pricing data. Data Delivery and Integration

MealMe offers flexible integration options to ensure seamless access to our comprehensive menu data. Whether you need bulk exports for in-depth research or real-time updates via API, our solutions are designed to scale with your business needs.

Why Choose MealMe? Extensive Coverage: Menu data from 100,000+ restaurants, including major chains like Burger King and Raising Cane’s. Real-Time Accuracy: Up-to-date pricing and menu details for actionable insights. Customizable Solutions: Tailored datasets to meet your specific business objectives. Proven Expertise: Trusted by top companies for delivering reliable, actionable data. MealMe empowers businesses with the data needed to thrive in a competitive restaurant and food delivery market. For more information or to request a demo, contact us today!

MenuStat.org is an interactive online database of nutrition and menu information from top national restaurant chains. Each item is coded a mutually exclusive food category and descriptive information is coded into binary variables (e.g. on the kids menu). A new year of data is added annually and individual menu items are linked over time with a unique ID, allowing for trend analyses. On MenuStat.org, menu items that can be ordered together as part of a customizable or combination meal are linked together by a unique ID (Customizable_Build_ID) and all data can be exported for further analyses. NYC DOHMH developed and maintains MenuStat, but it is hosted externally

MealMe provides comprehensive restaurant menu data, including prices, from the top 100,000 restaurants across the USA and Canada. Our real-time, accurate data empowers businesses with actionable insights for market research, competitive analysis, pricing strategies, and trend forecasting.

Context

This dataset contains lists of Restaurants and their menus in the USA that are partnered with Uber Eats. Data was collected via web scraping using python libraries.

*This dataset is dedicated to the awesome delivery drivers of Uber Eats, hence the cover image

Download

kaggle API Command !kaggle datasets download -d ahmedshahriarsakib/uber-eats-usa-restaurants-menus

Content

The dataset has two CSV files -

1. restaurants.csv (40k+ entries, 11 columns)

   • id (Restaurant id)
   • position (Restaurant position in the search result)
   • name (Restaurant name)
   • score (Restaurant score)
   • ratings (Ratings count)
   • category (Restaurant category)
   • price_range (Restaurant price range - $ = Inexpensive, $$ = Moderately expensive, $$$ = Expensive, $$$$ = Very Expensive) - Source - stackoverflow
   • full_address (Restaurant full address)
   • zip_code (Zip code)
   • lat (Latitude)
   • long (Longitude)

2. restaurant-menus.csv (3.71M entries, 5 columns)

   • restaurant_id (Restaurant id)
   • category (Menu category)
   • name (Menu Name)
   • description (Menu description)
   • price (Menu price)

Acknowledgements

Data was scraped from - - https://www.ubereats.com - An online food ordering and delivery platform launched by Uber in 2014. Users can read menus, reviews, ratings, order, and pay for food from participating restaurants using an application on the iOS or Android platforms, or through a web browser. Users are also able to tip for delivery. Payment is charged to a card on file with Uber. Meals are delivered by couriers using cars, scooters, bikes, or foot. It is operational in over 6,000 cities across 45 countries.

Cover Image -

Photo by eggbank on Unsplash

Disclaimer

The data and information in the data set provided here are intended to use for educational purposes only. I do not own any of the data and all rights are reserved to the respective owners.

Inspiration

• How many Restaurants are around the USA?
• What are the Most Popular/Highly Rated Restaurants and menus?
• Is there any relationship between the price level and the popularity of a restaurant?
• Which menus are more expensive?
• Which menus are very common in the USA?

Update Frequency

1. The dataset will be updated weekly

A 2023 survey found that a majority of U.S. consumers, **** percent, reported trying new menu items at restaurants occasionally. Meanwhile, only *** percent of consumers stated that they never tried new menu items at restaurants.

This dataset provides restaurant inspections, violations, grades and adjudication information

iMenuPro is a well-established company that specializes in designing and printing menus for restaurants and other businesses. With a patented food list database, they enable users to create multiple menus quickly and easily. Their menu maker software features drag-and-drop functionality, allowing users to customize their menus with ease.

iMenuPro has a long history of working with over 10,000 restaurants and businesses worldwide, providing them with high-quality menu designs and QR code menus. Their menu app is accessible on any PC, Mac, Chromebook, or tablet, and offers features such as live-sync technology, auto-formatting, and daily menu updates. With their user-friendly interface and innovative tools, iMenuPro enables businesses to streamline their menu design and printing process, while also enhancing their overall customer experience.

The restaurant menu development service market is experiencing robust growth, driven by the increasing need for restaurants to optimize their menus for profitability and customer appeal. The market's evolution is shaped by several key factors. The rising popularity of online ordering and delivery platforms necessitates menus designed for ease of ordering and clear descriptions. Furthermore, the growing emphasis on dietary restrictions and health-conscious choices compels restaurants to incorporate diverse options catering to specific needs, such as vegan, vegetarian, gluten-free, and other specialized diets. This trend is amplified by the increasing awareness of food allergies and intolerances among consumers. Technological advancements, including menu engineering software and data analytics tools, are streamlining the menu development process, enabling restaurants to make data-driven decisions to maximize revenue and reduce food waste. Competition is intensifying, with a mix of established consulting firms and freelance experts vying for market share. This competitive landscape fosters innovation and drives the adoption of best practices in menu design and pricing strategies. The market is segmented by service type (e.g., full-service menu development, recipe development, costing and pricing analysis), restaurant type (e.g., fine dining, casual dining, fast-casual), and geographical region. While precise market sizing data is absent, a reasonable estimation, considering the involvement of numerous firms and the general growth in the restaurant industry, could place the 2025 market size at approximately $500 million, with a projected CAGR of 7% for the forecast period (2025-2033). This growth is expected to be fueled by continued technological advancements and evolving consumer preferences. The competitive landscape is diverse, encompassing large consulting firms like Aston and Chef Services Group, alongside smaller boutique agencies and individual consultants accessible via platforms like Upwork. This fragmentation of the market provides restaurants of all sizes with access to menu development services tailored to their specific needs and budgets. Larger firms typically offer comprehensive services, including market research, menu engineering, and implementation support, while smaller providers may focus on niche areas like recipe development or culinary consulting. Geographic variations exist, with developed markets in North America and Europe showing higher penetration of these services compared to emerging economies. However, the global reach of online platforms is bridging this gap, enabling smaller restaurants worldwide to access professional menu development expertise. Future growth will likely be influenced by factors such as macroeconomic conditions, technological disruptions, and evolving consumer eating habits, requiring restaurants to continuously adapt and innovate their menu offerings.

This study examined the effect of menu quality in the foodservice industry on customer satisfaction and purchase intentions, and clarified the role that menu reputation plays in the relationship between menu quality and purchase intentions. The findings revealed that high-quality menus with good taste, variety, and price satisfaction positively influence customer satisfaction, which in turn increases the customers' purchase intentions. Additionally, menu variety was found to have a significant moderating effect on the relationship between menu reputation and purchase intentions. This implies that restaurants offering diverse and tasty options can achieve high customer satisfaction, which enhances menu reputation and fosters customer loyalty. The study highlights the importance for restaurants to improve menu quality, especially variety, as it not only contributes to customer satisfaction but also positively affects purchase intentions and restaurant reputation. However, the results may be specific to certain areas or establishments, suggesting the need for further research with a broader dataset.

The New York Public Library is digitizing and transcribing its collection of historical menus. The collection includes about 45,000 menus from the 1840s to the present, and the goal of the digitization project is to transcribe each page of each menu, creating an enormous database of dishes, prices, locations, and so on. As of early November, 2016, the transcribed database contains 1,332,279 dishes from 17,545 menus.

The Data

This dataset is split into four files to minimize the amount of redundant information contained in each (and thus, the size of each file). The four data files are Menu, MenuPage, MenuItem, and Dish. These four files are described briefly here, and in detail in their individual file descriptions below.

Menu

The core element of the dataset. Each Menu has a unique identifier and associated data, including data on the venue and/or event that the menu was created for; the location that the menu was used; the currency in use on the menu; and various other fields.

Each menu is associated with some number of MenuPage values.

MenuPage

Each MenuPage refers to the Menu it comes from, via the menu_id variable (corresponding to Menu:**id**). Each MenuPage also has a unique identifier of its own. Associated MenuPage data includes the page number of this MenuPage, an identifier for the scanned image of the page, and the dimensions of the page.

Each MenuPage is associated with some number of MenuItem values.

MenuItem

Each MenuItem refers to both the MenuPage it is found on -- via the menu_page_id variable -- and the Dish that it represents -- via the dish_id variable. Each MenuItem also has a unique identifier of its own. Other associated data includes the price of the item and the dates when the item was created or modified in the database.

Dish

A Dish is a broad category that covers some number of MenuItems. Each dish has a unique id, to which it is referred by its affiliated MenuItems. Each dish also has a name, a description, a number of menus it appears on, and both date and price ranges.

Inspiration

What are some things we can look at with this dataset?

• How has the median price of restaurant dishes changed over time? Are there particular types of dishes (alcoholic beverages, seafood, breakfast food) whose price changes have been greater than or less than the average change over time?
• Can we predict anything about a dish's price based on its name or description?
  -- There's been some work on how the words used in advertisements for potato chips are reflective of their price; is that also true of the words used in the name of the food?
  -- Are, for example, French or Italian words more likely to predict a more expensive dish?

Acknowledgments

This dataset was downloaded from the New York Public Library's What's on the menu? page. The What's on the menu? data files are updated twice monthly, so expect this dataset to go through multiple versions.

AI-Driven Restaurant Menu Optimization Market Outlook

According to the latest research, the global AI-Driven Restaurant Menu Optimization market size reached USD 1.47 billion in 2024, demonstrating robust momentum across the foodservice technology landscape. The market is projected to expand at a CAGR of 19.8% from 2025 to 2033, culminating in a forecasted market size of USD 7.12 billion by 2033. This remarkable growth is primarily attributed to the increasing adoption of artificial intelligence (AI) technologies by restaurants seeking to enhance menu performance, drive profitability, and personalize customer experiences in a fiercely competitive environment.

One of the primary growth factors propelling the AI-Driven Restaurant Menu Optimization market is the escalating demand for data-driven insights that enable restaurants to optimize menu offerings and pricing strategies. With the proliferation of digital ordering platforms and the digitization of point-of-sale (POS) systems, restaurants now have access to vast amounts of customer data. AI-powered menu optimization tools leverage this data to analyze purchasing patterns, predict demand, and recommend menu adjustments in real time. This enables operators to identify high-performing dishes, remove underperforming items, and introduce new offerings tailored to evolving customer preferences. The growing emphasis on maximizing revenue per customer visit and minimizing food waste further underscores the critical role of AI-driven menu engineering in the modern restaurant industry.

Another significant driver for the expansion of the AI-Driven Restaurant Menu Optimization market is the rising consumer expectation for personalized dining experiences. AI-powered platforms empower restaurants to deliver hyper-personalized recommendations based on individual customer profiles, dietary restrictions, and historical ordering behavior. By integrating AI with loyalty programs and mobile apps, restaurants can dynamically highlight menu items, suggest upsell opportunities, and even adjust pricing in response to customer segments or real-time demand fluctuations. This level of personalization not only enhances customer satisfaction but also fosters brand loyalty and repeat business, positioning AI as a strategic differentiator for both independent operators and large chains.

The rapid technological advancements in AI algorithms, natural language processing, and cloud computing have also lowered the barriers to entry for restaurants of all sizes, fueling market growth. Cloud-based AI solutions offer scalability, ease of integration with existing restaurant management systems, and reduced upfront costs compared to traditional on-premises deployments. This democratization of AI technology enables small and medium-sized enterprises (SMEs) to access advanced menu optimization capabilities previously reserved for major industry players. As a result, a broader spectrum of restaurants is now investing in AI-driven solutions to stay competitive, streamline operations, and respond to shifting market dynamics.

Regionally, North America continues to dominate the AI-Driven Restaurant Menu Optimization market in 2024, accounting for over 41% of global revenue. The region’s leadership is bolstered by a mature restaurant industry, high digital adoption rates, and the presence of leading technology providers. Europe is following suit, driven by stringent regulations on food waste reduction and a growing focus on operational efficiency among restaurant operators. Meanwhile, the Asia Pacific region is witnessing the fastest CAGR, propelled by a burgeoning middle class, rapid urbanization, and the proliferation of online food delivery platforms. Latin America and the Middle East & Africa are gradually embracing AI-driven solutions, albeit at a slower pace, due to infrastructural and regulatory challenges. Overall, regional dynamics are expected to play a pivotal role in shaping the future trajectory of the market.

Component Analysis

The AI-Driven Restaurant Menu Optimization market is segmented by component into software and services, each playing a distinct role in the ecosystem. The software segment, which encompasses AI-powered menu engineering platforms, recommendation engines, and analytics dashboards, accounted for the largest share in 2024. These solutions are designed to automate the process of menu analysis, pricing optimization, and personalized recommendation generation. The

Data you can expect: - Metadata (country, region, city, coordinates, address, categories, description, operating hours, and more) - Contacts (phone contacts, email, website) - Social profiles (LinkedIn, Twitter, Instagram, Facebook) - Reviews (reviews and ratings on different sites) - Menus (categories, items, prices, descriptions and photos) - Other info (awards, Michelin stars, executive chef, popular dishes, average meal price, and more) - Photos (ambience, food, menu photos)

Let us know if you have a specific request, and we'll try to fulfil it.

How we deliver data: - We transform it to fit your system's data schema, (ease the pain and cost of having data engineers from your side) - We are completely flexible on the delivery format and method.

Analysis of ‘🍕 Pizza restaurants and Pizzas on their Menus’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/pizza-restaurants-and-pizzas-on-their-menuse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

About this Data

This is a list of over 3,500 pizzas from multiple restaurants provided by Datafiniti's Business Database. The dataset includes the category, name, address, city, state, menu information, price range, and more for each pizza restaurant.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do with this Data

You can use this data to discover how much you can expect to pay for pizza across the country. E.g.:

• What are the least and most expensive cities for pizza?
• What is the number of restaurants serving pizza per capita (100,000 residents) across the U.S.?
• What is the median price of a large plain pizza across the U.S.?
• Which cities have the most restaurants serving pizza per capita (100,000 residents)?

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

Get this data and more by creating a free Datafiniti account or requesting a demo.

This dataset was created by Datafiniti and contains around 10000 samples along with Longitude, Price Range Max, technical information and other features such as: - Date Updated - Categories - and more.

How to use this dataset

• Analyze Date Added in relation to Province
• Study the influence of Price Range Min on Address
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Datafiniti

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

National, regional, and local chain name restaurants that offer take-out-ready food prepared quickly with little to no table service and a consistent menu across locations.

Data compiled and categorized by CLF staff.

Data source: Johns Hopkins Center for a Livable Future online research

Date: 2013

The Restaurant Business Intelligence & Analytics Software market is experiencing robust growth, projected to reach $632 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 9.1% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of technology within the restaurant industry, driven by the need for enhanced operational efficiency and data-driven decision-making, is a primary driver. Restaurants are leveraging these solutions to optimize menu engineering, improve inventory management, personalize customer experiences through targeted marketing, and gain valuable insights into customer behavior and preferences. Furthermore, the rising popularity of online ordering and delivery services has increased the volume of data generated, further emphasizing the need for sophisticated analytics tools to effectively manage and interpret this information. Competition is intensifying with established players like NCR and Alteryx alongside specialized providers such as Avero Slingshot, Delaget, Mirus, Altametrics, and Actus Data. The market is segmented based on deployment type (cloud-based, on-premise), restaurant type (fast-casual, full-service, etc.), and functionalities offered (sales analysis, customer relationship management, etc.), although detailed segment data is currently unavailable. The projected growth trajectory suggests a substantial market expansion over the forecast period. While challenges such as the initial investment cost of implementing such software and the need for skilled personnel to interpret the data exist, the overall benefits in terms of improved profitability, reduced operational costs, and enhanced customer satisfaction significantly outweigh these limitations. Continued technological advancements, particularly in artificial intelligence (AI) and machine learning (ML) integration within these solutions, are expected to further fuel market growth. This integration will empower restaurants with predictive analytics, allowing for proactive decision-making and more effective resource allocation. The competitive landscape suggests opportunities for both established players and emerging companies to capitalize on the market's potential by focusing on niche functionalities and customized solutions tailored to specific restaurant segments.

AI-Driven Restaurant Menu Optimization Market Outlook

According to our latest research, the AI-Driven Restaurant Menu Optimization market size reached USD 1.18 billion globally in 2024, reflecting a robust growth trajectory spurred by rapid digital transformation in the food service industry. The sector is experiencing a compound annual growth rate (CAGR) of 19.7% from 2025 to 2033. By 2033, the market is forecasted to achieve a valuation of USD 5.89 billion, driven by increasing adoption of artificial intelligence in menu engineering, dynamic pricing, and personalized dining experiences. The primary growth factor continues to be the rising demand for operational efficiency and enhanced customer engagement within the restaurant sector.

One of the central growth drivers for the AI-Driven Restaurant Menu Optimization market is the escalating need for data-driven decision-making in the food service industry. Restaurants are leveraging AI-powered tools to analyze vast datasets, including customer preferences, purchasing patterns, and real-time inventory levels. This enables operators to optimize menu offerings, reduce food waste, and maximize profitability. As consumer expectations shift toward personalized experiences, AI solutions empower restaurants to tailor recommendations and pricing strategies, enhancing both customer satisfaction and revenue streams. This trend is particularly pronounced among quick service and full-service restaurants seeking to differentiate themselves in a competitive landscape.

Another significant factor boosting market expansion is the proliferation of cloud-based deployment models, which facilitate seamless integration of AI solutions into existing restaurant management systems. Cloud platforms offer scalability, flexibility, and lower upfront costs, making them accessible to small and medium-sized enterprises (SMEs) as well as large chains. This democratization of technology adoption is accelerating the penetration of AI-driven menu optimization tools across diverse end-user segments. Furthermore, advances in natural language processing and machine learning algorithms are enabling more sophisticated menu engineering capabilities, allowing restaurants to dynamically adjust offerings based on real-time demand signals and external variables such as weather and local events.

The growing emphasis on sustainability and cost-efficiency further fuels the adoption of AI-driven menu optimization solutions. By harnessing predictive analytics, restaurants can better manage inventory, minimize food spoilage, and align purchasing decisions with anticipated demand. This not only supports environmental goals but also enhances bottom-line performance. Additionally, regulatory pressures and evolving consumer preferences for healthier and more transparent menu options are prompting operators to leverage AI for continuous menu refinement. As the market matures, integration with customer analytics platforms and loyalty programs is expected to unlock new avenues for personalized marketing and cross-selling, thereby reinforcing the value proposition of AI-driven menu optimization.

Regionally, North America remains the dominant market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States leads in early technology adoption, with major restaurant chains and innovative startups driving widespread implementation of AI solutions. Europe exhibits strong growth potential, fueled by rising investments in digital transformation and a burgeoning food delivery ecosystem. Meanwhile, Asia Pacific is emerging as a high-growth region, supported by rapid urbanization, expanding middle-class populations, and increasing smartphone penetration. Latin America and the Middle East & Africa are also witnessing gradual uptake, albeit at a slower pace, as local operators begin to recognize the benefits of AI-driven menu optimization for operational agility and customer engagement.

Component Analysis

The Component</b&

The global Digital Menu Board Software market is experiencing robust growth, driven by the increasing adoption of digital signage in the hospitality and food service industries. The shift from static printed menus to dynamic, easily updatable digital displays offers significant advantages, including reduced printing costs, improved menu item presentation, enhanced customer engagement through targeted promotions and real-time updates (e.g., daily specials), and streamlined operational efficiency. This market is segmented by deployment type (on-premises vs. cloud-based) and application (hotels, restaurants, and other dining establishments). The cloud-based segment is witnessing faster growth due to its scalability, accessibility, and cost-effectiveness. Key players like NoviSign, Pickcel, and Signagelive are leveraging innovative features such as interactive menus and data analytics to gain a competitive edge. The market is geographically diverse, with North America and Europe currently holding significant market share, but Asia-Pacific is expected to witness substantial growth in the coming years due to rising disposable incomes and increasing urbanization in key markets like China and India. Challenges include the initial investment costs associated with hardware and software implementation, the need for reliable internet connectivity, and ongoing maintenance requirements. However, these challenges are being mitigated by the increasing affordability of digital signage solutions and the growing availability of robust cloud-based platforms. The long-term forecast indicates continued market expansion, fueled by technological advancements and evolving consumer preferences. The competitive landscape is characterized by a mix of established players and emerging startups. Established players are focusing on expanding their product portfolios and geographic reach, while startups are innovating with new features and business models. The market is witnessing consolidation through mergers and acquisitions, as larger companies seek to expand their market share. Factors such as the increasing use of mobile ordering and integration with loyalty programs are further driving the market's growth. Furthermore, the ability to personalize menus based on customer preferences and real-time data analytics are creating new opportunities for businesses to enhance customer experience and drive sales. The market's future hinges on continued technological innovation, particularly in areas such as artificial intelligence (AI) for personalized menu recommendations and integration with other point-of-sale systems.

Abstract Objectives: characterize the availability and assess the nutritional quality of children’s menus ofered in regular and fast food restaurants in Brazilian malls. Methods: this is an observational and cross-sectional study. Data were collected on the websites of each restaurant and in food sales applications, with a questionnaire consisting of two sections: characterization of the restaurant and characterization of the children’s menu. Data collection took place in ten capitals in the fve regions of Brazil. Results: 116 children’s menus were evaluated. The study identifed a higher number of regular (n=70, 60%) than fast-food (n=46,40%) restaurants. The cooking methods most used in the main dishes were grilled (n=236, 64%) and boiled (n=74, 20%), and in the side dishes were boiled (n=204, 53%) and fried (n=109, 28%). Only 40% (n=46) of the menus contained vegetables. Less than 10% (n=seven) ofered fruit as dessert, 31% (n = 36) had drinks included in the children’s menu and 22% (n=25) ofered gifts associated with the menu. Only 32 (28%) restaurants had the combination of beans and rice. Conclusion: most of the options ofered to children were of low nutritional quality, with low ofer of vegetables, fruits and the traditional beans and rice. The beverages included in the menus, most of them sugary, can contribute to a high-energy intake. There is a need to provide healthy options and encourage these choices.

We'll customize a Zomato dataset to align with your unique requirements, incorporating data on restaurant categories, customer reviews, pricing trends, popular dishes, demographic insights, sales figures, and other relevant metrics.

Leverage our Zomato datasets for various applications to strengthen strategic planning and market analysis. Examining these datasets enables organizations to understand consumer preferences and dining trends, facilitating refined menu offerings and marketing campaigns. Tailor your access to the complete dataset or specific subsets according to your business needs.

Popular use cases include optimizing menu assortment based on consumer insights, refining marketing strategies through targeted customer segmentation, and identifying and predicting trends to maintain a competitive edge in the restaurant and food service market.

We'll customize a DoorDash dataset to align with your unique requirements, incorporating data on restaurant types, menu items, pricing, delivery times, customer ratings, demographic insights, and other relevant metrics.

Leverage our DoorDash datasets for various applications to strengthen strategic planning and market analysis. Examining these datasets enables organizations to understand consumer preferences and delivery trends, facilitating refined menu offerings and optimized delivery strategies. Tailor your access to the complete dataset or specific subsets according to your business needs.

Popular use cases include optimizing menu offerings based on consumer insights, refining marketing strategies through targeted customer segmentation, and identifying and predicting trends to maintain a competitive edge in the food delivery market.