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.

The Measurable AI FoodPanda Food & Grocery Transaction 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 (Hong Kong, Taiwan, Singapore, Thailand, Malaysia, Philippines, Pakistan)

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 FoodPanda 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.

The Measurable AI Rappi alternative 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 alternative data to produce actionable consumer insights for use cases such as: - User overlap between players - Market share analysis - User behavioral traits (e.g. retention rates, spending patterns) - Average order values - Promotional strategies used by the key players - Items ordered (SKU level data) Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - LATAM (Brazil, Mexico, Argentina, Colombia, Chile)

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 - MAIDs

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 Rappi 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 michelle@measurable.ai for a data dictionary and to find out our volume in each country.

Here are a few use cases for this project:

1. Menu App: An app could be equipped with the "Thai Food" computer vision model. Users could point their camera at a dish and the app would identify whether or not it is a particular Thai food class. This can help users quickly understand what they are eating, especially if they are unfamiliar with Thai cuisine.

2. Supermarket Shoppers Assistance: Implement this model in a mobile app or AR glasses used by shoppers in supermarkets. It could help them quickly identify Thai ingredients or ready meals by their class, thus ensuring they're buying the right product.

3. Smart Kitchen Assistance: This model can be integrated into a smart fridge or cupboard system to track the types of Thai food items stored inside. When linked with recipe suggestions, it can recommend what meals can be prepared with the available items.

4. Diet App Integration: Health and diet apps can leverage this model to accurately identify and track the calories and nutritional value of various Thai food items that the user consumes, as long as they provide a photo of their meal.

5. Customizable Food Ordering: Food delivery apps could use this model, allowing users to order by simply taking a photo of a dish. They wouldn't need to worry about the dish's name or how to spell it, making the ordering process more straightforward.

The number of users in the online food delivery market in Singapore was forecast to continuously increase between 2024 and 2028 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the indicator is estimated to reach **** million users and therefore a new peak in 2028. Notably, the number of users of the online food delivery market was continuously increasing over the past years.Find other key market indicators concerning the revenue growth and revenue.The Statista Market Insights cover a broad range of additional markets.

Abstract Purpose: The purpose this paper is to analyze the factors that influence the usage behavior of delivery applications. Design/methodology/approach: A survey method was used and a questionnaire was applied. The simple size comprised 344 respondents. The Structural Equation Modeling (SEM) with estimation by Partial Least Squares (PLS) was used to analyze thirteen hypotheses proposed in the survey model. Findings: The results support ten hypotheses and indicate that the Habit (β = 0.580; p-value

The Measurable AI GrabFood and GrabExpress Restaurant & Food Delivery Transaction datasets are leading sources 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 - SE Asia (Singapore, Indonesia, Thailand, Malaysia, Philippines, Vietnam, Cambodia)

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 GrabFood and Grab Express food delivery apps 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.

Food delivery apps (FDAs) are becoming increasingly popular among consumers as a convenient and quick way to get food. Given the fierce competition in the industry, understanding what drives continuance intent is crucial. The dataset documents the consumption value dimensions affecting continuance intention of FDA among Malaysian working professionals. A total of 319 usable responses were finalized which were collected from April 2022 to May 2022. The research model was built on the theory of consumption values with four dimensions comprising 17 items. The instrument consisted demographic variables and consumption value dimensions which influence continuance intention of FDAs. The demographics and stated variables dataset can be used to investigate the relationships and descriptions of factors in greater depth. The dataset will be useful to FDA service providers and future research in analysing Malaysian consumer behaviour and, as a result, increasing FDA repeat purchases.

Here are a few use cases for this project:

1. Smart Grocery Inventory Management: The model can be utilized for automatic tracking and inventory management in grocery stores or supermarkets. By identifying different food items, it could help to keep an accurate record of stocks, ensuring the optimal availability of products.

2. Online Food Ordering Services: Online food delivery platforms can use this model to visually identify and categorize food items in a menu. This would streamline the food ordering process, and could also suggest alternatives if a certain item is unavailable.

3. Nutritional Analysis Applications: By identifying food items, the model could be integrated into dietary and nutritional applications. Users can take a picture of their meal and the app could provide an analysis of the meal's nutritional content based on the identified food items.

4. Waste Reduction in Food Industry: Companies can use this model to monitor surplus food items in real-time and plan their production/sales accordingly. This would not only maximize the use of resources but also aid in reducing food waste.

5. Assisting Individuals with Dietary Restrictions: The model could be used to create an application that assists people with dietary restrictions or allergies. Such an app would analyze and identify foods in real-time and alert the user if a restricted item is detected in their meal.

ObjectiveTo analyze digital marketing trends of Brazil’s leading meal delivery application (MDA) company on Facebook (FB) and Instagram (IG) from 2011 to 2022.MethodsThis exploratory, longitudinal, and mixed-methods study examined a 10% sample of all posts published by this company during the study period. Posts were analyzed in terms of food categories, media and connectivity features, and advertising themes.ResultsThe company predominantly promoted unhealthy foods, frequently employing persuasive digital marketing strategies. While this pattern was consistent across both platforms, IG posts were more visually engaging and interactive, making greater use of brand elements, hashtags, conversations, emoticons, user interaction, and company tagging (all p

In 2024, Deliveroo reported marketing and overhead costs amounting to over 210 million British pounds in the UK and Ireland. The previous year, Deliveroo's marketing and overhead costs amounted to approximately 205 million British pounds.

Here are a few use cases for this project:

1. Restaurant Service Assistance: The "sushi-4" model can automate the ordering and inventory system in sushi restaurants. The model can identify plates consumed by customers to update the order list and also assist in managing inventory. It could also be used for menu creation and ordering system applications, matching the customer's request to specific sushi classes.

2. Quality Control in Sushi Manufacturing: Factories engaged in the production and packaging of sushi can use the model to check the quality and correctness of the products. For example, making sure that the correct food items are placed on their respective plates and packaging.

3. Sushi Delivery Services: Delivery companies or apps can use the model to verify the contents of an order before it leaves the restaurant, ensuring that customers receive exactly what they ordered.

4. Nutrition-tracking Apps: The model can be implemented in dietary and health applications where users can identify the type of sushi they eat and track nutritional facts (like calories, proteins, fats) linked with each class of sushi.

5. Culinary Education and Training: The model can be used in digital learning applications focused on educating chefs or sushi enthusiasts about the various types of sushi, their preparations, ingredients, and presentation styles.

Crude rate per 100,000 population: the number of fast food outlets is divided by the population of the area and multiplied by 100,000.

Rationale

The environment in which we live and work has positive and negative effects on our health and wellbeing. One component of the built-up environment is food outlets and the choices they provide. Meals eaten outside of the home tend to be associated with higher calories, and portion sizes tend to be bigger, which can make it more challenging to eat healthily [1,2]. The neighbourhood food environment is one important modifiable determinant of dietary behaviour and obesity [3].

The availability of fast food in our environment is one issue, within a complex system [4], which is associated with a range of negative health outcomes and contributes to the obesogenic nature of some of our neighbourhoods. Fast food is more abundantly available in the most deprived areas of England where obesity in children and adults and the associated health conditions, such as type 2 diabetes, hypertension, and heart disease are most prevalent [5,6].

This indicator is designed to help users understand the number of fast food outlets in an area taking the size of the population into account. It is intended to support national policy making and influence planning activities in local authorities [7] with the aim of reducing the availability of fast food, where this is deemed desirable, in order to improve health outcomes.

References

Sugar reduction programme: industry progress 2015 to 2020 - GOV.UK
Calorie reduction programme: industry progress 2017 to 2021 - GOV.UK
Dietary inequalities: What is the evidence for the effect of the neighbourhood food environment?
A foresight whole systems obesity classification for the English UK biobank cohort
The Association between Fast Food Outlets and Overweight in Adolescents Is Confounded by Neighbourhood Deprivation
The association between the presence of fast-food outlets and BMI
No new fast-food outlets allowed! Evaluating the effect of planning policy on the local food environment in the North East of England

Definition of numerator The numerator is a count, at a specific point in time, of fast food outlets in each geographic area. The inclusion criteria for counting fast food outlets is described in the methodology section below.

Definition of denominator Count of the population in each geographic area from Office for National Statistics (ONS) mid-year population estimates 2023.

Caveats

The Impact of Food Delivery Services In recent years there has been a large growth of food delivery services and meal delivery apps (MDAs). These companies allow customers to order food via mobile apps or websites for delivery to a chosen address. The availability of fast food through MDAs expands the geographic coverage of fast food outlets, increasing the likelihood that customers will order from outlets in neighbouring local authority areas, especially in urban settings. These apps extend the reach of fast food outlets beyond the immediate resident or visiting population.

Cross Local Authority Movements Some individuals may travel to neighbouring local authorities to access fast food outlets. Therefore, data showing fast food outlets within a specific area may underestimate actual exposure for the resident population. Users should consider data from neighbouring areas to gain a more comprehensive understanding of fast food exposure.

The Impact of Non-Resident Populations Movements for work, shopping, entertainment, or tourism also affect exposure. Local authorities with high numbers of fast food outlets per 100,000 residents may have large non-resident populations who are not included in the population denominator but are still exposed to these outlets.

Data Source and Methods The data may not fully capture all fast food outlets. Many businesses are multi-functional—offering dine-in, takeaway, and delivery—and may be categorized as restaurants or cafés, thus excluded from fast food counts. Inclusion based on business names helps mitigate this, but some outlets may still be missed. Conversely, some outlets categorized as ‘Takeaway/sandwich shop’ may not be considered fast food.

Data from the FSA FHRS is presumed accurate, but errors in collection, collation, and entry are possible. Categorization may vary between local authorities. For example, an outlet selling sandwiches and tea might be recorded as a Restaurant/Café/Canteen in one area and as a Takeaway/sandwich shop in another.

Different data sources use varying definitions and categorizations, so numbers may differ across datasets. The data here counts businesses identified as fast food outlets, not direct access for individuals, which is influenced by factors like opening hours, pricing, parking, and delivery options. It reflects premises use rather than individual access.

Here are a few use cases for this project:

1. Recipe Assistant Application: A cooking app or food recipe website could use this model to identify specific Ivorian dishes in user-uploaded photos, thus assisting users in identifying and preparing Ivorian cuisines, or even suggesting suitable recipes based on identified dishes.

2. Cultural Education and Research: Cultural institutions or educational platforms could use this model to educate students or the general public about Ivorian culinary practices. It could be used to classify photos in an online course or multimedia presentation about Ivorian cuisine.

3. Quality Control in Restaurants: Ivorian-themed restaurants could integrate this model into their quality control process, tagging each dish based on the model's prediction and ensuring that the food appearance meets an expected standard for the designated cuisine.

4. Diet and Nutrition Tracker Applications: A diet and nutrition app could use this model to help Ivorians or people who eat Ivorian food track their caloric intake more accurately based on the identified dishes.

5. Food Delivery Services: Such platforms could use this model to analyze and verify if the requested Ivorian dish matches with what is prepared for delivery, ensuring the right order has been made. Users could also use it to discover new dishes based on images alone.

Here are a few use cases for this project:

1. Smart Grocery Shopping: This model can be integrated into a mobile application that identifies household grocery items that need to be replenished. By scanning the fridge, the app can create a shopping list based on the items identified.

2. Dietary Management: The model can be used to track food consumption in homes, helping individuals and families maintain their diet plans. It can identify when certain healthy food items (like fruits, vegetables, cereal) are getting low, prompting a reminder for the users.

3. Health and Fitness Apps: It can be incorporated into health apps to track calorie intake. Users can simply take pictures of their meals, and the app can identify the food items and calculate nutritional value.

4. Waste Management: The model can be used in a smart bin application to identify food waste items and sort them accordingly for proper recycling or composting.

5. eCommerce or Delivery Apps: This model can be integrated into online grocery shopping platforms. Using the app, users can scan items they want, the system identifies the food items, and adds them to the cart for a swift shopping experience.

Here are a few use cases for this project:

1. Smart Inventory Management: The SAM AI model can be used in retail or warehouse environments to keep track of inventory. By using computer vision to identify the unique food classes on shelves, it can provide real-time updates on stock levels, helping to plan re-stocking and manage inventory more efficiently.

2. Enhanced Shopping Experience: Online retailers or grocery delivery apps could leverage SAM AI model to augment product discovery and automate the process of adding items to an online basket. For example, customers could submit a photo of their fridge or pantry, and the model would identify the food classes and suggest purchases based on what's missing or running low.

3. Food Waste Reduction: The model could be implemented in households or restaurants to keep track of the food stored. By identifying what foods are present, it could help to plan meals more efficiently, potentially proposing recipes based on available food and thus contributing to reducing food waste.

4. Diet and Nutrition Apps: Diet and nutrition apps could use the SAM AI model to help users log what they're eating more accurately. Users could simply take a picture of their meal, and the model would identify the food classes present, helping the app to calculate nutritional information.

5. Cooking and Recipe Apps: Cooking apps could use the model to suggest recipes based on the ingredients a user already has. The user could take a picture of their pantry or fridge, and the app could identify the foods available and propose recipes accordingly.

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!

Here are a few use cases for this project:

1. Grocery Store Inventory Management: The model can be used in supermarkets or retail stores to automate the task of identifying and counting the different types of Maggi Noodles, supporting the automatic replenishment of stock levels.

2. Nutrition and Caloric Density Analysis: Dieticians and nutritionists can utilize it to quickly identify different Maggi Noodles packages to calculate the total caloric and nutritional content based on the package size and type.

3. Food Delivery and Retail Apps: The model can be employed in apps for scanning packages to quickly fetch product detail information for customers before purchase or for verifying orders by delivery riders.

4. Supply Chain and Logistics: The model can be deployed to perform automated checking of products in warehouses or during truck loading to ensure the correct product is being shipped, helping to reduce errors.

5. Promotional Tracking: Marketers and retailers can use this model to track the presence and popularity of promotional packages (Maggi Masala Noodles 71g-promo, Maggi Masala Noodles 280g_promo, Maggi Masala Noodles 420g-promo) in stores or in customer baskets to assess the effectiveness of their marketing campaigns.

Here are a few use cases for this project:

1. Agricultural Sorting Applications: The "DuaLuoi" model can be implemented in smart farming technology to automatically sort and classify different types of melons during harvesting. This could improve efficiency in farms and reduce human errors.

2. Grocery Store Automation: Supermarkets can use this technology to automate the process of sorting inventory or aid in creating an automated checkout process by identifying different types of vegetables.

3. Food Delivery/Supply Chain Management: "DuaLuoi" could be used within supply chain systems to identify and track different types of melons in storage and during shipment, improving overall supply chain efficiency.

4. Educational Tools: In educational apps or augmented reality experiences designed to teach kids and adults about different vegetables, this models could be utilized for interactive learning experiences.

5. Plant Identification Apps: Developers can use this model in applications that help gardeners or farmers identify the types of melons they are growing, aiding in planting and maintenance decisions.

SUMMARY:

Vumonic provides its clients email receipt datasets on weekly, monthly, or quarterly subscriptions, for any online consumer vertical. We gain consent-based access to our users' email inboxes through our own proprietary apps, from which we gather and extract all the email receipts and put them into a structured format for consumption of our clients. We currently have over 1M users in our India panel.

If you are not familiar with email receipt data, it provides item and user-level transaction information (all PII-wiped), which allows for deep granular analysis of things like marketshare, growth, competitive intelligence, and more.

VERTICALS:

• Ecommerce (Amazon, Flipkart, Myntra, Nykaa)
• Taxi (Uber, Ola)
• Food Delivery (Swiggy, Zomato)
• OTT (Netflix, Amazon Prime Video, Disney+)
• Appstore (Apple App Store and Google Playstore)
• OTA (Expedia, Booking.com, GoIbibo)
• E-wallets (PhonePe, PayTM)
• Education (Byju's, Unacademy)

PRICING/QUOTE:

Our email receipt data is priced market-rate based on the requirement. To give a quote, all we need to know is:

• what vertical you are interested in
• how often do you wish to receive the data, and
• do you want any backdata (e.g. from 2019 onwards)

Send us over this info and we can answer any questions you have, provide sample, and more.