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.

A food delivery service is facing challenges in achieving profitability across its operations. With a dataset of 1,000 food orders, the service seeks to understand the dynamics of its cost structure and profitability to identify strategic opportunities for improvement.

The dataset contains comprehensive details on food orders, including Order ID, Customer ID, Restaurant ID, Order and Delivery Date and Time, Order Value, Delivery Fee, Payment Method, Discounts and Offers, Commission Fee, Payment Processing Fee, and Refunds/Chargebacks. This data provides a foundation for analyzing the cost structure and profitability of the food delivery service.

Your task is to conduct:

1. Detailed Cost Analysis: Identifying the major cost components associated with delivering food orders, including direct costs like delivery fees and indirect costs like discounts and payment processing fees.
2. Profitability Evaluation: Calculating the profitability of individual orders and aggregating this data to assess overall profitability. This involves examining how revenue generated from commission fees measures against the total costs.
3. Strategic Recommendations for Improvement: Based on the cost and profitability analysis, identifying actionable strategies to reduce costs, adjust pricing, commission fees, and discount strategies to improve profitability. This includes finding a “sweet spot” for commission and discount percentages that ensures profitability across orders.
4. Impact Simulation of Proposed Strategies: Simulating the financial impact of the recommended strategies on profitability, using the dataset to forecast how adjustments in commission rates and discount strategies could potentially transform current losses into profits.

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India Food Delivery Market Size and Trends

The India food delivery market size is forecast to increase by USD 1.01 billion at a CAGR of 34% between 2024 and 2028. The food delivery market is experiencing significant growth, driven by the increasing cravings for diverse and popular dishes among consumers. Restaurants are recognizing the importance of delivering high-quality food and quick delivery times to meet customer queries. AI search features and AI-generated content are becoming increasingly popular, allowing customers to easily find their favorite dishes and receive personalized recipe suggestions through platforms like Recipe Rover. However, the market is also facing challenges, including the growing threat from direct delivery services and the need for efficient food preparation and customer support to maintain customer satisfaction. These trends and challenges highlight the importance of collaboration and partnerships between food delivery services and restaurants to meet the evolving demands of consumers.

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The food delivery market is witnessing a significant shift as technology continues to revolutionize the way we order and receive our meals. Quick commerce platforms, powered by AI-integrations, are leading this transformation by offering personalized product recommendations, streamlined user experiences, and efficient food delivery services. Generative AI plays a pivotal role in enhancing the functionality of these platforms. This advanced technology analyzes user data, including past orders, cravings, and preferences, to generate tailored recipe suggestions and meal planning ideas. AI-generated content, such as personalized food assistant widgets, engage users and offer real-time assistance in their food choices.

Moreover, the integration of AI-powered tools in food delivery platforms not only improves user experience but also addresses the challenges faced by startups in the industry. High costs and intense competition make it essential for businesses to optimize their operations and offer value-added services to customers. AI integrations help streamline food preparation, manage delivery time efficiently, and provide excellent customer support through AI search features and automated responses to common queries. However, the adoption of AI in the food delivery market is not without its challenges. Data privacy concerns are a significant hurdle, as the collection and analysis of user data are essential for AI-driven recommendations and services. Ensuring transparency and security in data handling is crucial for maintaining user trust and adhering to regulations. Moreover, the success of AI food assistants relies on the accuracy and relevance of the data they process. The density of restaurants and the availability of popular dishes in a given area are essential factors in generating accurate recommendations.

Additionally, AI-generated recipes must consider macro-nutrients and ingredient availability to ensure meal planning is feasible and healthy. Midjourney, a leading quick commerce platform, is addressing these challenges by focusing on the development of an AI-driven food assistant that caters to users' unique needs and preferences. By continuously learning from user interactions and feedback, this AI food assistant offers personalized recipe suggestions, meal planning ideas, and ingredient recommendations. It also integrates with popular food delivery services to ensure a seamless ordering and delivery experience. In conclusion, the integration of generative AI and quick commerce platforms is transforming the food delivery market by offering personalized recommendations, streamlined user experiences, and efficient services. While challenges such as data privacy concerns and the need for accurate data remain, the potential benefits of this technology make it an exciting development in the food industry.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Service Type

  Online
  Offline


Type

  Digital payment
  Cash on delivery


Geography

  India

By Service Type Insights

The online segment is estimated to witness significant growth during the forecast period. The food delivery market in India has witnessed significant growth due to the increasing preference for ordering in over dining out. Consumers can now easily order their favorite cuisines through a restaurant's website or mobile app or via multi-restaurant aggregators.

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The online segment was valued at USD 114.00 million in 2018 and showed a gradual increase during the forecast period. This convenience has particularly resonated with Gen-Z, who make up a large p

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.

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.

The millions-worth revenue derived from large-scale food delivery characterises the service as a relatively established phenomenon with potential growth. The current cross-sectional research examined online food delivery service quality on consumer satisfaction and reuse intention. Service quality was divided into seven categories (i.e., reliability, assurance, security, maintaining food quality, system operation, traceability, and perceived service value). Perceived service value offer the unique understanding of the online food delivery consumer satisfaction. Empirical data were elicited from 1352 valid respondents and subsequently assessed through the partial least square structural equation modelling. Findings revealed that reliability, assurance, maintaining food quality, system operation, traceability, and perceived service value could elevate customer satisfaction and optimize the intention to reuse food delivery services. Specific measures to improve service quality, including staff training, improved after-sales service, and system optimisation, were proposed to increase users’ satisfaction and intention to reuse optimally.

Success.ai’s Ecommerce Market Data for South-east Asia E-commerce Contacts provides a robust and accurate dataset tailored for businesses and organizations looking to connect with professionals in the fast-growing e-commerce industry across South-east Asia. Covering roles such as e-commerce managers, digital strategists, logistics experts, and online marketplace leaders, this dataset offers verified contact details, professional insights, and actionable market data.

With access to over 170 million verified profiles globally, Success.ai ensures your outreach, marketing, and research strategies are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution empowers you to excel in one of the world’s most dynamic e-commerce regions.

Why Choose Success.ai’s Ecommerce Market Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of e-commerce professionals across South-east Asia.
   • AI-driven validation ensures 99% accuracy, reducing communication inefficiencies and enhancing engagement rates.

2. Comprehensive Coverage of South-east Asia’s E-commerce Market

   • Includes professionals from key e-commerce hubs such as Singapore, Indonesia, Thailand, Vietnam, Malaysia, and the Philippines.
   • Gain insights into regional consumer trends, logistics challenges, and online marketplace dynamics.

3. Continuously Updated Datasets

   • Real-time updates capture changes in professional roles, company expansions, and market conditions.
   • Stay aligned with industry trends and emerging opportunities in South-east Asia’s e-commerce sector.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 170M+ Verified Global Profiles: Engage with e-commerce professionals and decision-makers across South-east Asia.
• Verified Contact Details: Gain work emails, phone numbers, and LinkedIn profiles for precision targeting.
• Regional Insights: Understand key trends in e-commerce, logistics, and consumer preferences in South-east Asia.
• Leadership Insights: Connect with online marketplace leaders, logistics managers, and digital marketing professionals driving innovation in the sector.

Key Features of the Dataset:

1. Comprehensive Professional Profiles in E-commerce

   • Identify and connect with professionals managing e-commerce platforms, online marketplaces, and logistics operations.
   • Target individuals responsible for digital marketing, supply chain management, and e-commerce strategies.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (apparel, electronics, food delivery), geographic location, or job function.
   • Tailor campaigns to align with specific business goals, such as logistics optimization, consumer engagement, or market entry.

3. Regional and Market-specific Insights

   • Leverage data on e-commerce trends, regional consumer behaviors, and logistics challenges unique to South-east Asia.
   • Refine marketing strategies and business plans based on actionable insights from the region.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Digital Outreach

   • Promote e-commerce solutions, logistics services, or online marketing tools to professionals in South-east Asia’s e-commerce industry.
   • Use verified contact data for multi-channel outreach, including email, phone, and digital campaigns.

2. Market Research and Competitive Analysis

   • Analyze e-commerce trends and consumer preferences across South-east Asia to refine product offerings and marketing strategies.
   • Benchmark against competitors to identify growth opportunities and high-demand solutions.

3. Partnership Development and Vendor Collaboration

   • Build relationships with e-commerce platforms, logistics providers, and digital marketing agencies exploring strategic partnerships.
   • Foster collaborations that enhance consumer experiences, improve delivery efficiency, or expand market reach.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers in the e-commerce industry seeking candidates for logistics, digital marketing, and platform management roles.
   • Provide workforce optimization platforms or training solutions tailored to the sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality e-commerce market data at competitive prices, ensuring strong ROI for your marketing, sales, and business development initiatives.

2. Seamless Integration

   • Integrate verified e-commerce data into CRM systems, analytics ...

Online conversion rates of e-commerce sites were the highest in the food & beverage sector, at 3.1 percent in the fourth quarter of 2024. Beauty & skincare followed, with a three percent conversion rate. For comparison, the average conversion rate of e-commerce sites across all selected sectors stood at just over two percent. How does conversion vary by region and device? The conversion rate, which indicates the proportion of visits to e-commerce websites that result in purchases, varies by country and region. For instance, since at least 2023, e-commerce sites have consistently recorded higher conversion rates among shoppers in Great Britain compared to those in the United States and other global regions. Furthermore, despite the increasing prevalence of mobile shopping worldwide, conversions remain more pronounced on larger screens such as tablets and desktops. Online shopping cart abandonment on the rise Recently, the rate at which consumers abandon their online shopping carts has been gradually rising to more than 70 percent in 2024, showing a higher difficulty for e-commerce sites to convert website traffic into purchases. By the end of that year, food and beverage was one of the product categories with the lowest online cart abandonment rate, confirming the sector’s relatively high conversion rate. In the United States, the primary reason why customers abandoned their shopping carts is that extra costs such as shipping, tax, and service fees were too high at checkout.

This dataset is from a study conducted on taxation and service delivery in Lusaka’s informal markets. Over 800 informal workers in 11 of Lusaka’s markets were interviewed in order to address two main questions: 1) What drives tax compliance among informal workers? 2) Does paying taxes affect demands for political representation among informal workers in the same way that political economy scholarship has found for the broader citizenry? To answer these questions and explore the potential reasons for low compliance, the survey is composed of 9 modules: Sampling (SA) – preliminary characteristics of the informal trader General Information (ID) – basic demographics, educational and household background information Tax Attitudes (TX) – range of fees and taxes paid and the benefits received from those payments Service Delivery and Accountability (SD) – services offered in the market and who could best deliver them Public Participation and Associational Membership (PP) – involvement in different associations and participation in public and community affairs Value Chains (VC) – source, frequency, and method of payment for merchandise sold Social Protection (SP) – plans made for retirement or difficult times Household Welfare (HW) – details on household assets and services Final (FI) – enumerator observations

Mobile phones dominate global digital commerce website visits and contribute to the largest share of online orders. As of the fourth quarter of 2024, smartphones constituted around 78 percent of retail site traffic globally, responsible for generating 68 percent of online shopping orders. Marketplace momentum Retail e-commerce has significantly increased globally over the past few years. Currently, the leading countries in retail e-commerce growth, such as the Philippines, have seen an increase of up to 24 percent. In 2024, the majority of online purchases worldwide were made on online marketplaces, incurring around a 30 percent share of consumer purchases. The top four retail websites for consumers to visit globally were all marketplaces, with the leading website being Amazon.com. Converting clicks When shopping online, website visits often do not end in purchases. This can be due to having second thoughts when online shopping, or simply due to consumers using the platforms to search for products. In 2024, the conversion rate of online shoppers globally was just over two percent, with food and beverages incurring the highest conversion rate from online purchases. Across the globe, almost 20 percent of all retail sales were conducted online. This figure is forecast to increase to at least 21 percent by 2027.