The Measurable AI UberEats E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Taiwan, Japan, Australia) - Americas (United States, Mexico, Chile) - EMEA (United Kingdom, France, Italy, United Arab Emirates, AE, South Africa)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the UberEats food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

We'll customize a Uber Eats 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 Uber Eats 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.

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

The food delivery market has seen significant growth over the past decade. Led by platform-to-consumer services, such as DoorDash and Uber Eats, food delivery has expanded from takeaways to anything...

In 2024, Uber Eats generated approximately ***** billion U.S. dollars in global revenue, surpassing food delivery competitors Delivery Hero and DoorDash, whose worldwide revenue amounted to about ***** billion and ***** billion U.S. dollars, respectively. Regional dynamics and expansion While Uber Eats maintains its global leadership, regional players are making significant strides in their respective markets. In China, Meituan's delivery services are expected to generate nearly ** billion yuan in revenue for 2024, showcasing the massive scale of the Chinese market. Meanwhile, Delivery Hero has found particular success in Asia, with the region contributing approximately * billion euros to its revenue, more than double its European earnings. These regional disparities highlight the importance of tailored strategies for different markets in the food delivery industry. Market leaders and future prospects As the food delivery landscape continues to evolve, companies are exploring new avenues for growth and expansion. DoorDash, while dominating the U.S. market with a ** percent share, is setting its sights on Europe through strategic acquisitions like Wolt. Uber Eats, having successfully expanded beyond its ride-hailing roots, now controls ** percent of the U.S. online food delivery market and ranks as the second most downloaded food delivery app globally. Even in emerging markets, companies like Brazil's iFood are making waves, with a market value of *** billion U.S. dollars and operations in over ***** cities. These developments underscore the dynamic nature of the food delivery industry and its potential for further growth and innovation.

Online/app-based food delivery is a growth market in the face of pressure on traditional quick-service restaurants. This has inspired both new start-ups and bigger brands with logistics and online clout to explore such services, with competition springing up in major cities in markets such as the US, UK, and Europe. Read More

Analysis of ‘UBER Stock Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/varpit94/uber-stock-data on 21 November 2021.

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

What is UBER?

Uber Technologies, Inc., commonly known as Uber, is an American technology company. Its services include ride-hailing, food delivery (Uber Eats and Postmates), package delivery, couriers, freight transportation, and, through a partnership with Lime, electric bicycle and motorized scooter rental. The company is based in San Francisco and has operations in over 900 metropolitan areas worldwide. It is one of the largest firms in the gig economy. Uber is estimated to have over 93 million monthly active users worldwide. In the United States, Uber has a 71% market share for ride-sharing and a 22% market share for food delivery. Uber has been so prominent in the sharing economy that changes in various industries as a result of Uber have been referred to as uberisation, and many startups have described their offerings as "Uber for X".

Information about this dataset

This dataset provides historical data of Uber Technologies, Inc. (UBER). The data is available at a daily level. Currency is USD.

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

Just Eat may be the best example of perfect market fit in the wrong country. Launched in Denmark in 2001, the team slowly realised they had built a great service for local businesses, but in a...

We have the most extensive research available on the industry in the Food Delivery App report. Find out the market size of the online food delivery industry, revenue breakdowns by app, detailed...

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

The rising consumer demand for online food delivery has significantly increased the consumption of disposable cutlery, leading to much greater plastic pollution worldwide. This study investigates the impact of green nudges on single-use cutlery consumption in China. Collaborating with Alibaba’s food delivery platform Eleme (similar to Uber Eats and DoorDash), we analyzed detailed customer-level data and found that the green nudges — changing the default to “No Cutlery†and rewarding consumers with “green points†— increased the share of “No Cutlery†orders by 648%. The aggregate environmental benefits are significant: if the green nudges were applied to all of China, more than 21.75 billion sets of single-use cutlery could be saved every year, equivalent to a 20.4% of plastic waste reduction in the food delivery industry or a 6.12% reduction in China’s total municipal plastic waste.

The global online food ordering market is expanding rapidly, driven by a surge in smartphone penetration, the proliferation of food delivery apps, and a growing preference for convenience. The market size, valued at 123480 million in 2025, is projected to reach 374090 million by 2033, exhibiting a CAGR of 9.6% throughout the forecast period. Major market drivers include the rising number of internet users, the increasing popularity of meal delivery services, and advancements in delivery logistics. Regionally, North America dominates the market, followed by Europe and Asia Pacific. Key trends influencing the market include the integration of AI and machine learning for personalized food recommendations and order tracking, the emergence of food delivery-only restaurants, and the growing popularity of health-conscious food options. However, competition from traditional food delivery services, regulatory concerns regarding food safety and data privacy, and rising labor costs may restrain market growth. Major companies operating in the market include Just Eat Takeaway, Meituan, Alibaba, Zomato, Swiggy, Uber Eats, Deliveroo, DoorDash, Delivery Hero, Goldbelly, Foodhub, Domino's Pizza, HungryPanda, iFood, McDonalds, KFC, Pizza Hut, and Demaecan.

In 2025, the total market size of the online food delivery industry in the United States was estimated at *******billion U.S. dollars, with the grocery delivery segment generating an estimated ******billion U.S. dollars in revenue and the meal delivery segment around ******billion dollars. A leading market The United States is home to the second biggest online food delivery market in the world after China. While grocery delivery accounts for nearly two-thirds of the food delivery market in the U.S., the meal delivery segment is increasingly relevant, as homegrown players continue expanding their reach beyond borders. The race for market share between DoorDash and Uber Eats may have crowned the former leader of its home market, but the latter dominates on the global front. In 2023, Uber Eats was the leading online food delivery company in the world with nearly **** billion U.S. dollars in revenues. DoorDash’s global ambitions DoorDash achieved impressive success in recent years. As data on the total dollar value of orders made on DoorDash marketplaces demonstrates, the San Francisco-based startup’s GMV reached nearly ** billion U.S. dollars in 2023, up from a mere * billion dollars in 2019. That’s more than 700 percent growth in just 4 years. In addition to this, DoorDash revealed its ambitions of global expansion with its recent acquisition of Helsinki-based company Wolt, a major player in the European food delivery market. As of 2024, DoorDash operated in ** countries.

Denna datauppsättning uppfyller specifikationerna för systemet ”Delning av utsläppssnåla fordon vid förnyelse av fordonsparker för uthyrning, hyrköp, leasing av fordon, leveransplattformar och taxi- och VTC-bokningscentrum” som finns på webbplatsen schema.data.gouv.fr

Tento datový soubor splňuje specifikace režimu „Sdílení vozidel s nízkými emisemi při obnově vozového parku v oblasti pronájmu, koupě na splátky, leasingu vozidel, doručovacích platforem a taxislužby a rezervačních středisek VTC“, který je k dispozici na internetových stránkách schema.data.gouv.fr.