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

Snapshot img { margin: 10px !important; } India Food Delivery Market Size 2025-2029

The India food delivery market size is forecast to increase by USD 1.5 billion at a CAGR of 28.8% between 2024 and 2029.

The food delivery market in India is shaped by the increasing prevalence of collaborations between restaurants and specialized delivery service providers. These partnerships are essential for expanding market reach, allowing restaurants to connect with a vast digital consumer base while enabling delivery platforms to diversify their offerings. This symbiotic relationship enhances the overall service proposition by leveraging shared data for customer insights and improved service personalization. The integration of online on-demand food delivery services has become a cornerstone of the modern food service landscape, driven by consumer demand for convenience. The efficiency of the underlying food logistics network is critical in ensuring timely and reliable service, which directly impacts customer satisfaction and retention. This collaborative framework is a key driver of the market's structure and growth trajectory.A significant trend influencing the market is the strategic use of social media as a primary channel for marketing and consumer engagement. Delivery platforms and their restaurant partners are actively creating content and promotional campaigns to attract and interact with customers, turning digital engagement into a direct driver of sales. This approach is transforming the way consumers discover and purchase food, blending entertainment with e-commerce. However, the market's expansion is met with the challenge of a growing preference among restaurants for establishing direct delivery services. This shift allows restaurants to maintain control over their brand, customer data, and pricing, thereby bypassing the commission-based models of third-party aggregators and creating direct competitive pressure on these platforms. Stricter regulations on foodservice disposables also add a layer of operational complexity.

What will be the size of the India Food Delivery Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
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The ongoing evolution of the food delivery market in India is marked by the continuous refinement of its core operational components. The interplay between the online ordering platform and its underlying last-mile delivery logistics network is constantly being optimized through new technologies. Innovations in real-time order tracking and digital payment integration are enhancing the transparency and convenience of online on-demand food delivery services. This dynamic environment necessitates that players in the food service sector continuously adapt their strategies. The development of advanced route optimization software is a critical area of focus, directly impacting the efficiency of delivery fleet management and overall service speed.Customer-centric strategies are also in a state of flux, with platforms leveraging sophisticated customer data analytics to personalize the user experience. The implementation of dynamic pricing algorithms and targeted promotional code engines reflects a more nuanced approach to demand management and customer retention. The restaurant aggregator model itself is being challenged and redefined by the rise of the direct-to-consumer model, pushing platforms to innovate their value proposition. Furthermore, the integration of cloud kitchen operations and dark store fulfillment models into the ecosystem indicates a strategic shift toward greater control over the supply chain and a more diversified service offering, reflecting the market's perpetual state of development.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. Service typeOnlineOfflineTypeDigital paymentCash on deliveryPlatform typeMobile applicationsWebsitesGeographyAPACIndia

By Service Type Insights

The online segment is estimated to witness significant growth during the forecast period.

The online food ordering segment operates through proprietary restaurant websites, mobile applications, and multi-restaurant aggregator platforms. This mode offers consumers extensive choices, enabling them to compare menus, prices, and delivery times conveniently. The segment's growth is heavily influenced by a large urban youth demographic that values speed and variety. The availability of numerous online food ordering applications, which account for approximately 73% of the total market, intensifies competition and drives innovation in user experience and service offerings.Digital engagement is central to

This dataset presents the share of total delivery occasions in the UK from 2022 to 2024, segmented by delivery aggregators including Uber Eats, Just Eat, Deliveroo, and others.

This dataset highlights the forecasted UK turnover market share for the top five delivery brands in 2025, alongside 2024 benchmarks. Brands include Domino's, McDonald's, KFC, Papa John's, and Burger King.

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.

1.1 Industry Landscape of Fast Delivery Services in India

India’s fast delivery ecosystem is characterized by intense competition among multiple players offering expedited grocery and food delivery services with promised delivery windows as low as 10 to 30 minutes. Companies such as Blinkit, Zepto, Swiggy Instamart, and JioMart have emerged as frontrunners, leveraging vast logistic networks, technology-driven supply chains, and extensive consumer data analytics (Bain & Company, 2025; Expert Market Research, 2024). The sector’s growth trajectory is robust, with the online food delivery market alone valued at USD 48.07 billion in 2024 and projected to grow at a CAGR of over 27% through 2034 (Expert Market Research, 2024).

1.2 Importance of Customer Ratings and Reviews

Customer reviews and ratings provide granular feedback on delivery agents’ punctuality, professionalism, order accuracy, and communication. These metrics are crucial for operational refinements, agent training, capacity planning, and enhancing customer experience (Kaggle dataset: VivekAttri, 2025). Sentiment analysis applied to textual reviews further uncovers nuanced customer emotions and service pain points, enabling predictive insights and proactive service improvements.

1.3 Dataset Overview

The focal dataset includes structured customer reviews and numerical ratings collected for fast delivery agents across India’s leading quick-commerce platforms. Key variables encompass agent identity, delivery timestamps, rating scores (typically on a 1-5 scale), customer comments, and transactional metadata (VivekAttri, 2025). This dataset serves as the foundation for exploratory data analysis, machine learning modeling, and visualization aimed at performance benchmarking and predictive analytics.

2. Data Handling and Preprocessing Methodologies

2.1 Data Acquisition and Integration

The dataset is sourced from Kaggle repositories aggregating customer feedback across platforms, with metadata ensuring temporal, geographic, and service-specific contextualization. Effective data ingestion involves automated pipelines utilizing Python libraries such as Pandas for dataframes and requests for API interfacing (MinakshiDhhote, 2025).

2.2 Data Cleaning and Normalization

Critical preprocessing steps include:

• Removal of Redundant and Irrelevant Columns: Columns unrelated to delivery agent performance (e.g., user identifiers when anonymized) are discarded to streamline analysis.

• Handling Missing Values: Rows with null or missing ratings/reviews are either imputed using domain-specific heuristics or removed to maintain data integrity.

• Duplicate Records Elimination: To prevent bias, identical reviews or ratings are deduplicated.

• Text Cleaning for Reviews: Natural language preprocessing (NLP) techniques such as tokenization, stopword removal, lemmatization, and spell correction are applied to textual data to prepare for sentiment analysis.

• Standardization of Rating Scales: Ensuring uniformity when ratings come from different sources with varying scales.

2.3 Feature Engineering

Derived features enhance modeling capabilities:

• Sentiment Scores: Using models like VADER or BERT-based classifiers to convert textual reviews into quantifiable sentiment metrics.

• Delivery Time Buckets: Categorization of delivery durations into intervals (e.g., under 15 minutes, 15-30 minutes) to analyze performance impact.

• Agent Activity Levels: Number of deliveries per agent to assess workload-performance correlation.

• Temporal Features: Time of day, day of week, and seasonal effects considered for delivery performance trends.

3. Exploratory Data Analysis (EDA) and Visualization

3.1 Rating Distribution and Statistical Summary

A comprehensive statistical summary outlines mean ratings, variance, skewness, and kurtosis to understand central tendencies and rating dispersion among delivery agents.

Table 1: Rating Summary Statistics for Delivery Agents (2025 Dataset Sample)

|| Metric | Value | |----------------------|----------------| | Mean Rating | 3.8 ± 0.15 | | Median Rating | 4.0 | | Standard Deviation | 0.75 | | Skewness | -0.45 | | Kurtosis | 2.1 | | Number of Ratings | 250,000+ | | | | --- | --- | | | |

Data validated with 95% confidence interval from Kaggle 2025 dataset (VivekAttri, 2025).

3.2 Geographical and Platform-Based Ratings Comparison

Heatmaps and bar charts illustrate rating variations across cities and platforms. For instance, Blinkit shows higher average ratings in metropolitan regions compared to tier-2 cities, reflecting infrastructural disparities.

3.3 Service Attributes and Rating Correlations

Scatter plots and corr...

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.

🛒 E-Commerce Customer Behavior and Sales Dataset 📊 Dataset Overview This comprehensive dataset contains 5,000 e-commerce transactions from a Turkish online retail platform, spanning from January 2023 to March 2024. The dataset provides detailed insights into customer demographics, purchasing behavior, product preferences, and engagement metrics.

🎯 Use Cases This dataset is perfect for:

Customer Segmentation Analysis: Identify distinct customer groups based on behavior Sales Forecasting: Predict future sales trends and patterns Recommendation Systems: Build product recommendation engines Customer Lifetime Value (CLV) Prediction: Estimate customer value Churn Analysis: Identify customers at risk of leaving Marketing Campaign Optimization: Target customers effectively Price Optimization: Analyze price sensitivity across categories Delivery Performance Analysis: Optimize logistics and shipping 📁 Dataset Structure The dataset contains 18 columns with the following features:

Order Information Order_ID: Unique identifier for each order (ORD_XXXXXX format) Date: Transaction date (2023-01-01 to 2024-03-26) Customer Demographics Customer_ID: Unique customer identifier (CUST_XXXXX format) Age: Customer age (18-75 years) Gender: Customer gender (Male, Female, Other) City: Customer city (10 major Turkish cities) Product Information Product_Category: 8 categories (Electronics, Fashion, Home & Garden, Sports, Books, Beauty, Toys, Food) Unit_Price: Price per unit (in TRY/Turkish Lira) Quantity: Number of units purchased (1-5) Transaction Details Discount_Amount: Discount applied (if any) Total_Amount: Final transaction amount after discount Payment_Method: Payment method used (5 types) Customer Behavior Metrics Device_Type: Device used for purchase (Mobile, Desktop, Tablet) Session_Duration_Minutes: Time spent on website (1-120 minutes) Pages_Viewed: Number of pages viewed during session (1-50) Is_Returning_Customer: Whether customer has purchased before (True/False) Post-Purchase Metrics Delivery_Time_Days: Delivery duration (1-30 days) Customer_Rating: Customer satisfaction rating (1-5 stars) 📈 Key Statistics Total Records: 5,000 transactions Date Range: January 2023 - March 2024 (15 months) Average Transaction Value: ~450 TRY Customer Satisfaction: 3.9/5.0 average rating Returning Customer Rate: 60% Mobile Usage: 55% of transactions 🔍 Data Quality ✅ No missing values ✅ Consistent formatting across all fields ✅ Realistic data distributions ✅ Proper data types for all columns ✅ Logical relationships between features 💡 Sample Analysis Ideas Customer Segmentation with K-Means Clustering

Segment customers based on spending, frequency, and recency Sales Trend Analysis

Identify seasonal patterns and peak shopping periods Product Category Performance

Compare revenue, ratings, and return rates across categories Device-Based Behavior Analysis

Understand how device choice affects purchasing patterns Predictive Modeling

Build models to predict customer ratings or purchase amounts City-Level Market Analysis

Compare market performance across different cities 🛠️ Technical Details File Format: CSV (Comma-Separated Values) Encoding: UTF-8 File Size: ~500 KB Delimiter: Comma (,) 📚 Column Descriptions Column Name Data Type Description Example Order_ID String Unique order identifier ORD_001337 Customer_ID String Unique customer identifier CUST_01337 Date DateTime Transaction date 2023-06-15 Age Integer Customer age 35 Gender String Customer gender Female City String Customer city Istanbul Product_Category String Product category Electronics Unit_Price Float Price per unit 1299.99 Quantity Integer Units purchased 2 Discount_Amount Float Discount applied 129.99 Total_Amount Float Final amount paid 2469.99 Payment_Method String Payment method Credit Card Device_Type String Device used Mobile Session_Duration_Minutes Integer Session time 15 Pages_Viewed Integer Pages viewed 8 Is_Returning_Customer Boolean Returning customer True Delivery_Time_Days Integer Delivery duration 3 Customer_Rating Integer Satisfaction rating 5 🎓 Learning Outcomes By working with this dataset, you can learn:

Data cleaning and preprocessing techniques Exploratory Data Analysis (EDA) with Python/R Statistical analysis and hypothesis testing Machine learning model development Data visualization best practices Business intelligence and reporting 📝 Citation If you use this dataset in your research or project, please cite:

E-Commerce Customer Behavior and Sales Dataset (2024) Turkish Online Retail Platform Data (2023-2024) Available on Kaggle ⚖️ License This dataset is released under the CC0: Public Domain license. You are free to use it for any purpose.

🤝 Contribution Found any issues or have suggestions? Feel free to provide feedback!

📞 Contact For questions or collaborations, please reach out through Kaggle.

Happy Analyzing! 🚀

Keywords: e-c...

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 ...

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.

Customer Retention with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used for customer retention purposes, such as performing a shopper retention analysis over time for a specific company.

Inquire about a CE subscription to perform more complex, near real-time competitive analysis functions on public tickers and private brands like: • Choose a pair of merchants to determine spend overlap % between them by period (yearly, quarterly, monthly) • Explore cross-shop history within subindustry and market share (updated weekly)

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Competitive Analysis

Problem A grocery delivery brand needs to assess overall company performance, including customer acquisition and retention levels relative to key competitors.

Solution Consumer Edge transaction data can uncover performance over time and help companies understand key drivers of retention: • By geography and demographics • By channel • By shop date

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on customer retention for company-wide reporting • Reduce investment in underperforming channels, both online and offline • Determine demo and geo drivers of retention for refined targeting • Analyze customer acquisition campaigns driving retention and plan accordingly

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period • Churn • Cross-Shop • Average Ticket Buckets

The custom-by-year-month level data used for this research are obtained from Eleme, Alibaba’s food ordering and delivery platform which is similar to DoorDash and Uber Eats. The data included 197,062 randomly selected users’ monthly food ordering history, their green points history, their tree planting records, and their personal characteristics from 1st Jan 2019 to 31st Dec 2021 in ten major Chinese cities. All of the consumers in the sample placed at least one food delivery order during the research period. Among the ten cities, there were three “treated” cities, including Beijing, Shanghai, and Tianjin. Seven cities were the “control” cities, including Qingdao, Xi’an, Guangzhou, Nanjing, Hangzhou, Wuhan, and Chengdu. According to China’s provincial statistical yearbook, these cities had a total population of 157.36 million as of 2020. Eleme provided monthly data on each customer’s food ordering history, including the number of orders, number of “No Cutlery” orders, and total ex...

The Measurable AI Netflix and Other Streaming Services Email Receipt Datasets details data from subscription and cancellation email such as premium members, family plans, most popular shows, cancellation emails etc.

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 (Japan) - EMEA (Spain, United Arab Emirates)

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 Careem Now 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.

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

Agriculture faces major challenges in the decades to come due to increasing resource pressures, severe weather and climate change, population growth and shifting diets, and economic development. Rice is one of the most important crops globally considering its role in the Earth system, food security, and providing livelihoods with more than 1 billion people depending on rice. Tools and systems that can help monitor production and support risk management are needed for decision making by many end users and governments. Futures are a tool used to manage or hedge risk, reduce volatility, improve food security, and maximize efficiency and profit on the open market. Currently, the rice futures market has little high quality and timely information available to make strategic or application specific decisions to reduce risk and maximize profit. The global rice futures market is thinly traded causing extreme price fluctuation orders of magnitude. The innovation of Rice Decision Support System (RiceDSS) is the seamless fusion of operational satellite remote sensing monitoring metrics of rice agriculture, rice yield modeling, and weather forecasts to generate near real time information on rice extent, growth stages, production forecasts and statistical uncertainty. RiceDSS uses a state-of-the-art open source framework with advanced automation routines, web-GIS, and mobile technologies to support visualization and delivery of information to support global food security programs and commodity markets.

Techsalerator’s Import/Export Trade Data for Nigeria provides a comprehensive overview of international trade activities involving Nigerian companies. This dataset offers a detailed examination of trade transactions, documenting and categorizing imports and exports across various industries in Nigeria.

To access Techsalerator’s Import/Export Trade Data for Nigeria, please contact us at info@techsalerator.com or visit Techsalerator Contact with your specific requirements. We will provide a customized quote based on your needs, with delivery available within 24 hours. Ongoing access options can also be discussed.

Techsalerator's Import/Export Trade Data for Nigeria integrates information from customs reports, trade agreements, and shipping records, providing businesses, investors, and trade analysts with an in-depth understanding of Nigeria’s trade landscape.

Key Data Fields

• Company Name: Identifies the companies involved in trade transactions, aiding in locating potential partners or competitors and tracking industry-specific trade patterns.
• Trade Volume: Provides details on the quantity or value of goods traded, offering insights into the scale and economic impact of trade activities.
• Product Category: Specifies the types of goods traded, such as raw materials or finished products, helping to understand market demand and supply chain dynamics.
• Import/Export Country: Identifies the countries of origin or destination for traded goods, revealing regional trade relationships and market access.
• Transaction Date: Records the date of transactions, showcasing seasonal trends and shifts in trade dynamics over time.

Top Trade Trends in Nigeria

• Oil and Gas Sector: Nigeria's economy heavily relies on the export of crude oil and natural gas, which dominate its trade activities and significantly impact the country’s trade balance.
• Agricultural Exports: Nigeria is seeing growth in agricultural exports, such as cocoa, cashews, and sesame seeds, as part of its efforts to diversify away from oil dependency.
• Growing Manufacturing Sector: The government’s push to increase local manufacturing, especially in sectors like textiles, food processing, and cement, is slowly reshaping Nigeria’s export profile.
• Increased Imports of Consumer Goods: With a growing middle class, Nigeria is importing more consumer goods such as electronics, vehicles, and pharmaceuticals, which highlight increasing domestic demand.
• Digital Trade Initiatives: Nigeria's booming digital economy is influencing trade, particularly in sectors such as e-commerce and financial services, as it embraces tech-driven global markets.

Notable Companies in Nigerian Trade Data

• Nigerian National Petroleum Corporation (NNPC): As the key player in Nigeria’s oil and gas industry, NNPC’s exports of crude oil and gas drive the country’s trade activities.
• Dangote Group: One of the largest industrial conglomerates in Africa, Dangote’s cement and sugar exports, along with its expanding oil refinery, contribute significantly to Nigeria's trade economy.
• Flour Mills of Nigeria: A leading exporter of processed food products, contributing to Nigeria’s growing agro-processing sector.
• Indorama Eleme Petrochemicals: A major exporter of petrochemical products, including fertilizers, supporting Nigeria’s non-oil export diversification.
• MTN Nigeria: Though primarily a telecom company, MTN is involved in digital trade through its e-commerce ventures and mobile financial services.

Accessing Techsalerator’s Data

To access Techsalerator’s Import/Export Trade Data for Nigeria, please reach out to us at info@techsalerator.com with your specific requirements. We will provide a tailored quote based on the number of data fields and records required, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields:

• Company Name
• Trade Volume
• Product Category
• Import/Export Country
• Transaction Date
• Shipping Details
• Customs Codes
• Trade Value

For detailed insights into Nigeria’s import and export activities and trends, Techsalerator’s dataset is an essential resource for making informed and strategic decisions.