Dataset Overview 📝

This dataset includes key tourism and economic indicators for over 200 countries, spanning the years from 1999 to 2023. It covers a wide range of data related to tourism arrivals, expenditures, receipts, GDP, unemployment, and inflation, helping to explore the relationship between tourism and economic growth globally.

Tourism & Economic Indicators:

• tourism_receipts 💰: The total income a country generates from international tourism, measured in current US dollars.
• tourism_arrivals 🌍: The total number of international tourists who arrive in a country, measured in count.
• tourism_expenditures 🛍️: The amount of money spent by international tourists within the country, measured in current US dollars.
• tourism_exports 📈: The percentage of a country’s total exports derived from international tourism receipts.
• tourism_departures ✈️: The number of citizens or residents of a country who travel abroad for tourism.
• tourism_expenditures 🛫: The percentage of a country’s total imports spent on international tourism.
• gdp 📊: The total value of all goods and services produced in a country, expressed in current US dollars.
• inflation 📉: The annual percentage change in the average price of goods and services in a country.
• unemployment 👷‍♂️: The percentage of people within the labor force who are unemployed but actively seeking work.

Data Source 🌐

• World Bank: The dataset is sourced from the World Bank’s economic and tourism databases, offering reliable and up-to-date statistics on global tourism and economic indicators.

Potential Use Cases 🔍

• Tourism & Economic Impact Analysis 🌏: Analyzing the contribution of tourism to a country’s GDP, as well as how tourism spending impacts national economies.
• Cross-Country Comparison 📊: Comparing tourism arrivals, expenditures, and receipts relative to GDP and unemployment across countries and regions.
• Predictive Modeling 🤖: Building models to predict the future impact of tourism on economic growth and identify emerging trends.
• Policy Evaluation 🏛️: Helping policymakers assess the role of tourism in economic planning, especially regarding inflation and unemployment.
• Economic Forecasting 📈: Using historical data to forecast how tourism will influence economic conditions, helping in the development of economic policies.

Key Questions You Can Explore 🤔

• How do tourism receipts correlate with GDP growth in different countries? 💵
• What is the relationship between tourism expenditure and inflation rates? 📉
• How do tourism arrivals and departures vary by region and what are the key drivers? 🌍
• What role does tourism play in shaping unemployment rates across countries? 👥
• Which countries have seen the largest increase in tourism receipts over the past two decades? 📊

Important Notes ⚠️

• Missing Data 🚨: Some values may be missing for certain years or countries, especially for specific tourism indicators. Techniques like forward filling, backward filling, or interpolation may help in handling missing values during time series analysis.
• Currency & Inflation Adjustments 💲: When comparing tourism receipts and expenditures across countries, consider adjusting for inflation and exchange rates to ensure accurate year-on-year comparisons.

Tourism Revenues in the United States decreased to 20626 USD Million in July from 20913 USD Million in June of 2025. This dataset provides - United States Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Justification:

Columns:

• Location: A ten- -character random string that represents the location's name.
• Nation: Selectively selected from a roster of nations.
• Type: Selectively selected from a range of travel-related categories.
• Visitor count: A random integer value that indicates how many people have visited.
• Rating: A random float number in the range of 1.0 and 5.0 is used to indicate the rating.
• Revenue: A float value chosen at random to indicate the revenue received.
• Accommodation_Available: Returns a value of "Yes" or "No" depending on the availability of accomodation.
• Target Size: During the loop, the file size is measured and rows are added until the file size reaches roughly 310.43 KB.

You will have a tourism_dataset.csv file, roughly 310.43 KB in size, after executing this code. Depending on the data distribution and file overhead, adjustments can be required.

🌄 Dehradun Tourism Flow & Seasonal Demand Analysis

Overview

Comprehensive tourism impact analysis for Dehradun's retail sector, covering seasonal patterns, religious tourism (Chardham Yatra), and tourist spending behavior. Essential dataset for understanding how 2.5+ million annual tourists drive retail demand fluctuations throughout the year.

🎯 Key Tourism Impact

• Annual Tourists: 2.5 million (2025 projection), 25% of Uttarakhand's total
• Economic Value: ₹1,375+ crores tourism revenue annually
• Chardham Boost: 2.8 million religious pilgrims, +60% retail surge (May-Oct)
• Seasonal Variation: 40% spending increase during peak months (Apr-May)

📊 Dataset Contents (5 Data Tables)

1. Monthly Seasonal Patterns

• 12-month analysis of tourism influx and retail impact
• Temperature correlation with tourist arrivals (3°C to 35°C range)
• Business impact percentages from base level to +40% during peaks
• Category-wise demand: Summer essentials, monsoon gear, winter clothing

2. Chardham Yatra Religious Tourism

• Peak Season: May-October with 2.8M pilgrims
• Economic Impact: ₹450 crores during peak season
• Retail Categories: Religious items, travel essentials, food & lodging
• Pre/Post Yatra: Additional 700K visitors for preparation/return shopping

3. Annual Tourism Growth Trends

• Historical Data: 2023-2025 with growth projections
• Tourist Numbers: From 1.75M (2023) to 2.5M (2025)
• Per-Person Spending: ₹4,500 to ₹5,500 (22% increase)
• Revenue Growth: 75% increase over 3 years

4. Seasonal Tourist Demographics

• Gender Split: 40-52% female tourists across seasons
• Age Profile: 55-65% in target demographic (18-55 years)
• Stay Duration: 2.1 to 4.1 days average
• Daily Spending: ₹1,200-2,200 per person

5. Tourist Spending by Category

• Shopping/Retail: 20-35% of tourist budget (₹1,125 average)
• Food/Dining: 30-45% (₹1,575 average)
• Seasonal Variations: Winter shopping peaks at 35% of budget
• Business Opportunity Ratings: Retail marked as "Very High"

🔍 Critical Business Insights

• Peak Revenue Months: April-May (+40% retail sales boost)
• Religious Tourism Window: May-October (60% sales increase)
• Female Tourist Percentage: 40-52% across seasons
• Tourist vs Local Ratio: 25% tourist influence on retail demand
• Winter Premium: Longest stay duration (4.1 days) with high spending

🎯 Use Cases

✅ Seasonal Inventory Planning - Stock management based on tourist cycles ✅ Revenue Forecasting - Predict monthly sales fluctuations ✅ Marketing Calendar - Time campaigns with tourist seasons ✅ Staffing Optimization - Hire temp staff during peak tourism ✅ Product Mix Strategy - Seasonal category focus (summer/winter/monsoon) ✅ Pricing Strategy - Premium pricing during peak tourist months

📈 Key Patterns Revealed

• Summer Peak: Apr-Jun (35-40% boost) - highest tourist spending
• Monsoon Dip: Jul-Aug (10% boost) - lowest retail impact
• Religious Surge: May-Oct (+60%) - Chardham pilgrimage effect
• Winter Recovery: Nov-Dec (15-20% boost) - holiday tourism

🌟 Unique Value Proposition

First comprehensive dataset linking tourism patterns with retail demand in a Tier-2 Indian city. Combines weather data, religious tourism cycles, and spending behavior for complete seasonal business planning.

📊 Data Sources & Reliability

• Government Tourism Data: IBEF Uttarakhand Tourism Reports
• Religious Tourism: Chardham Yatra Statistics 2024
• Weather Correlation: Meteorological data with tourism patterns
• Spending Analysis: Tourist expenditure surveys and retail impact studies
• Growth Projections: Official state tourism department forecasts

🏷️ Perfect For

• Retail business seasonal planning
• Tourism industry analysis
• Hospitality revenue management
• Seasonal demand forecasting
• Marketing campaign timing
• Inventory optimization strategies

📍 Geographic Context

Location: Dehradun, Uttarakhand (Gateway to Himalayas) Tourism Type: Religious (Chardham), Adventure, Hill Station Market Position: Key transit hub for Himalayan destinations Economic Role: 25% share of state's tourism revenue

Data Period: 2023-2025 with monthly granularity Update Frequency: Annual Methodology: Tourism-retail correlation analysis

The absolute economic contribution of tourism in Israel was forecast to continuously increase between 2024 and 2029 by in total 8.5 billion U.S. dollars (+37.06 percent). After the ninth consecutive increasing year, the economic contribution is estimated to reach 31.6 billion U.S. dollars and therefore a new peak in 2029. Depited is the economic contribution of the tourism sector in the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the absolute economic contribution of tourism in countries like Bahrain and Jordan.

Tourist Arrivals in the United States increased to 6893068 in August from 6275257 in July of 2025. This dataset provides - United States Tourist Arrivals- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Success.ai’s Tourism Data for the Global Hospitality Sector offers a robust and reliable dataset tailored for businesses aiming to connect with professionals and organizations in the global tourism and hospitality industry. Covering roles such as hotel managers, travel consultants, tour operators, and decision-makers, this dataset provides verified profiles, business insights, and actionable data.

With access to over 700 million verified profiles globally, Success.ai ensures your marketing, outreach, and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is ideal for thriving in the competitive and dynamic global hospitality market.

Why Choose Success.ai’s Tourism Data?

1. Verified Profiles for Precision Outreach

   • Access verified business locations, employee counts, work emails, and decision-maker profiles in the global tourism and hospitality sector.
   • AI-driven validation ensures 99% accuracy, enhancing engagement and reducing inefficiencies in communication.

2. Comprehensive Global Coverage

   • Includes hospitality professionals and businesses from top tourism hubs such as North America, Europe, Asia-Pacific, the Middle East, and Africa.
   • Gain insights into regional hospitality trends, customer preferences, and emerging tourism markets.

3. Continuously Updated Datasets

   • Real-time updates ensure the accuracy of professional profiles, business expansions, and organizational changes.
   • Stay ahead of industry trends to capitalize on new opportunities in the hospitality sector.

4. Ethical and Compliant

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

Data Highlights:

• 700M+ Verified Global Profiles: Access tourism and hospitality data for professionals and organizations worldwide.
• Verified Contact Details: Gain work emails, phone numbers, and decision-maker profiles for targeted engagement.
• Business Insights: Understand employee counts, geographic locations, and operational details of hospitality businesses.
• Leadership Profiles: Connect with hotel executives, travel agency leaders, and tour company managers driving the global tourism sector.

Key Features of the Dataset:

1. Comprehensive Hospitality Profiles

   • Identify and connect with key players in hotels, resorts, travel agencies, and tour operators worldwide.
   • Target professionals managing operations, customer experience, and business strategy in the tourism industry.

2. Advanced Filters for Precision Campaigns

   • Filter businesses and professionals by location, industry focus (hotels, travel agencies, tour operators), or job roles.
   • Tailor campaigns to align with specific needs, such as marketing strategies, software adoption, or customer engagement.

3. Regional and Industry-specific Insights

   • Leverage data on global tourism trends, customer preferences, and market growth in hospitality.
   • Refine strategies to align with specific geographic or sector-specific opportunities.

4. AI-Driven Enrichment

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

Strategic Use Cases:

1. Marketing Campaigns and Outreach

   • Promote travel solutions, hospitality software, or customer engagement tools to professionals in the tourism sector.
   • Use verified contact data for multi-channel outreach, including email, phone, and digital campaigns.

2. Partnership Development and Collaboration

   • Build relationships with travel agencies, hotel chains, and tour operators exploring strategic alliances.
   • Foster collaborations that enhance customer experiences, expand market reach, or improve operational efficiencies.

3. Market Research and Competitive Analysis

   • Analyze global tourism trends, hospitality demands, and emerging markets to refine strategies.
   • Benchmark against competitors to identify growth opportunities and high-demand solutions.

4. Recruitment and Workforce Optimization

   • Target HR professionals and hiring managers recruiting for hospitality roles, from hotel managers to travel consultants.
   • Provide workforce management platforms or training solutions tailored to the hospitality industry.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality tourism and hospitality data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration

   • Integrate verified data into CRM systems, analytics tools, or marketing platforms via APIs or downloadable formats, enhancing productivity and simplifying workflows.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to guide data-driven decisions, refine targeting, and imp...

📢**April 18, 2023 Update ( Please use Version 2):** - Renamed "profit" to "revenue" for better understanding. - Normalized the values in Thai Baht (previously MTHB).

This dataset contains statistics on domestic tourism in Thailand from Jan 2019 to Feb 2023, broken down by province. The dataset includes information on the number of tourists, the occupancy rate, and the profits generated by tourism in each province, as well as the nationality of the tourists (Thai vs. foreign).

Sourced from raw data provided by the Official Ministry of Tourism and Sports Statistics, which was manually entered into Excel files 🙃. So I pre-processed the data using Python with the intention of making it more accessible in the appropriate format which has the potential to provide valuable insights into the domestic tourism industry in Thailand, including trends and patterns across different provinces over time. Researchers, analysts, and policymakers with an interest in the domestic tourism sector in Thailand may find this dataset useful for their work.

Banner credit: Tourism Authority of Thailand.

Cleaned and ready-to-use 77 Provinces, 8 Variables, 3 Years and 2 Months with total 30,800 rows.

Metadata * C02163V02608: Code for method of booking. * Method of Booking: Information about the method used for booking. * C02276V02746: Code for Domestic or Outbound. * Domestic or Outbound: Indicates whether the booking is for domestic travel (within the same country) or outbound travel (outside of the country). * TLIST(Q1): Code for Quarter of the Year. * Quarter: Indicates the quarter of the year in which the booking was made. * STATISTIC: Code of label for statistical measures * Statistic Label: Contains labels or identifiers for different statistical measures or metrics associated with the bookings. * UNIT: Specifies the unit of measurement for the statistic values in the dataset. For example. * VALUE: Contains the actual values of the statistics corresponding to the respective labels in the "STATISTIC" column.

The international tourism receipts in Sweden were forecast to continuously increase between 2024 and 2029 by in total 40.7 billion U.S. dollars (+16.92 percent). After the eighth consecutive increasing year, the tourism receipts is estimated to reach 281.3 billion U.S. dollars and therefore a new peak in 2029. Receipts denote expenditures by inbound tourists from other countries. Domestic tourism expenditures are not included. The forecast has been adjusted for the expected impact of COVID-19. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the international tourism receipts in countries like Denmark and Norway.

Tourism Revenues in Greece decreased to 3421.30 EUR Million in September from 4523.70 EUR Million in August of 2025. This dataset provides - Greece Tourism Receipts- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The international tourism expenditure in Sweden was forecast to continuously increase between 2024 and 2029 by in total 50.6 billion U.S. dollars (+13.62 percent). After the eighth consecutive increasing year, the expenditure is estimated to reach 422.3 billion U.S. dollars and therefore a new peak in 2029. Covered are expenditures of international outbound visitors to other countries from the selected region, including payments to foreign carriers for international transport. Domestic tourism expenditures are not included. The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the international tourism expenditure in countries like Iceland and Finland.

China Value Added of Tourism and Related Industry: Tourism: Tourism and Travel data was reported at 1,263.400 RMB bn in 2023. This records an increase from the previous number of 1,134.200 RMB bn for 2022. China Value Added of Tourism and Related Industry: Tourism: Tourism and Travel data is updated yearly, averaging 1,117.300 RMB bn from Dec 2017 (Median) to 2023, with 7 observations. The data reached an all-time high of 1,263.400 RMB bn in 2023 and a record low of 1,029.300 RMB bn in 2017. China Value Added of Tourism and Related Industry: Tourism: Tourism and Travel data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AVA: Value Added of Tourism and Related Industry.

This challenge is open to users from English speaking African countries.

The Tanzanian tourism sector plays a significant role in the Tanzanian economy, contributing about 17% to the country’s GDP and 25% of all foreign exchange revenues. The sector, which provides direct employment for more than 600,000 people and up to 2 million people indirectly, generated approximately $2.4 billion in 2018 according to government statistics. Tanzania received a record 1.1 million international visitor arrivals in 2014, mostly from Europe, the US and Africa.

Tanzania is the only country in the world which has allocated more than 25% of its total area for wildlife, national parks, and protected areas.There are 16 national parks in Tanzania, 28 game reserves, 44 game-controlled areas, two marine parks and one conservation area.

Tanzania’s tourist attractions include the Serengeti plains, which hosts the largest terrestrial mammal migration in the world; the Ngorongoro Crater, the world’s largest intact volcanic caldera and home to the highest density of big game in Africa; Kilimanjaro, Africa’s highest mountain; and the Mafia Island marine park; among many others. The scenery, topography, rich culture and very friendly people provide for excellent cultural tourism, beach holidays, honeymooning, game hunting, historical and archaeological ventures – and certainly the best wildlife photography safaris in the world.

The objective of this hackathon is to develop a machine learning model that can classify the range of expenditures a tourist spends in Tanzania. The model can be used by different tour operators and the Tanzania Tourism Board to automatically help tourists across the world estimate their expenditure before visiting Tanzania.

The absolute economic contribution of tourism in Latvia was forecast to continuously increase between 2024 and 2029 by in total 1.2 billion U.S. dollars (+42.97 percent). After the ninth consecutive increasing year, the economic contribution is estimated to reach 3.8 billion U.S. dollars and therefore a new peak in 2029. Depited is the economic contribution of the tourism sector in the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the absolute economic contribution of tourism in countries like Lithuania and Estonia.

Context

This dataset deals with the visitors of foreigners to INDIA.

It includes foreigners (not Indian), overseas Indian, and crew members, except for some of the foreign arrivals who are not considered tourists (diplomats, soldiers, permanent residents, visiting cohabitation, and residence).

The Indian Government has compiled, analyzed, and provided statistics on foreign tourists visiting Indian and overseas tourists by type.

The data materials were prepared for the purpose of utilizing them as basic data for establishing tourism policies and marketing strategies.

I created this dataset by rebuilding the data provided by the Indian Government for easy analysis.

Column Name and Explanation:

noftaii: No. of Foreign Tourist Arrivals in India (in Million) noftaiiagr: No. of Foreign Tourist Arrivals in India, Annual growth rate(in %)(compare to the previous year) noindfi: No.of Indian Nationals departures from India (in Million) noindfiagr: No.of Indian Nationals departures from India, Annual growth rate(in %)(compare to the previous year) nodtvasu: No. of Domestic Tourist Visits to all States/UTs nodtvasuagr: No. of Domestic Tourist Visits to all States/UTs feeftit: Estimated Foreign Exchange Earnings from Tourism in INR terms in Crores feeftitagr: Estimated Foreign Exchange Earnings from Tourism in INR terms, Annual growth rate(in %)(compare to the previous year) feeftust: Estimated Foreign Exchange Earnings from Tourism in US$ terms in Billions feeftustagr : Estimated Foreign Exchange Earnings from Tourism in US$ terms, Annual growth rate(in %)(compare to the previous year) wnoita: world level No. of International Tourist Arrivals in Millions wnoitaagr: world level No. of International Tourist Arrivals, Annual growth rate(in %)(compare to the previous year) witr: world level International Tourism Receipts in US$ Billion witragr: world level International Tourism Receipts in US$ Billion, Annual growth rate(in %)(compare to the previous year) aprnoita: In Asia and The Pacific Region, No. of International Tourist Arrivals in Million aprnoitaagr: In Asia and The Pacific Region, No. of International Tourist Arrivals in Million, Annual growth rate(in %)(compare to the previous year) apfitr: In Asia and The Pacific Region, International Tourism Receipts in US$ Billion apritragr: In Asia and The Pacific Region, International Tourism Receiptsin US$ Billion, Annual growth rate(in %)(compare to the previous year) ipwiita: India’s Position in World, Share of India in International Tourist Arrivals(in %) ipwirwta: India’s Position in World, India’s rank in World Tourist Arrivals ipwsiitr: India’s Position in World, Share of India in International Tourism Receipts (US$ terms) (in %) ipwirwtr: Position in Asia & the Pacific Region, India’s rank in World Tourism Receipts ipaprita: Position in Asia & the Pacific Region, Share of India in International Tourist Arrivals(in %)

Acknowledgments

Thanks to the Indian Ministry of Tourismfor making the data available to the general public. For more details, you can refer: https://github.com/kachhadiyaraj15/india_tourism_2014_2020

Cover Photo

Photo from Adobe Stock https://stock.adobe.com/in/images/collage-of-india-historical-monuments-architectural-buildings-and-ruins-a-india-tour-and-travel-banner-of-notable-tourist-destinations/142044867

coastal tourism is not identified as a specific sector in the the french classification of economic activities (naf). it includes a number of tourism related services e.g. hotels and restaurants, transport, renting, travel agencies, sporting activities, or other local services, most of which being geographically limited to coastal or shoreline areas. annual tourism accounts break down tourism consumption into main services as identified in the insee business database. using the latter, value added and employment are estimated. the share of coastal tourism consumption as part of the overall tourism consumption is roughly estimated using both the breakdown of hotel nights by tourism areas and statistical estimates of local consumption. hotel nights and visitor arrivals are usually published in tourism accounts; international data on visitor stays are usually published by eurostat and the unwto; and the number of cruise passengers is sourced from cruise industry associations.

China Value Added of Tourism and Related Industry: YoY: Tourism: Tourism and Travel data was reported at 2.900 % in 2022. This records a decrease from the previous number of 4.000 % for 2021. China Value Added of Tourism and Related Industry: YoY: Tourism: Tourism and Travel data is updated yearly, averaging 4.000 % from Dec 2017 (Median) to 2022, with 5 observations. The data reached an all-time high of 15.300 % in 2017 and a record low of -12.100 % in 2020. China Value Added of Tourism and Related Industry: YoY: Tourism: Tourism and Travel data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AVA: Value Added of Tourism and Related Industry.

China Value Added of Tourism and Related Industry: Tourism: Tourism and Tour data was reported at 283.400 RMB bn in 2023. This records an increase from the previous number of 240.700 RMB bn for 2022. China Value Added of Tourism and Related Industry: Tourism: Tourism and Tour data is updated yearly, averaging 214.140 RMB bn from Dec 2017 (Median) to 2023, with 7 observations. The data reached an all-time high of 283.400 RMB bn in 2023 and a record low of 194.300 RMB bn in 2017. China Value Added of Tourism and Related Industry: Tourism: Tourism and Tour data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AVA: Value Added of Tourism and Related Industry.

Enterprise-Value-To-Sales-Ratio Time Series for Easy Trip Planners Limited. Easy Trip Planners Limited, together with its subsidiaries, operates as an online travel agency in India, the Philippines, Singapore, Thailand, the United Arab Emirates, the United Kingdom, New Zealand, Brazil, the Middle East, and the United States. It provides reservation and booking services related to travel and tourism through ease-my-trip portal and app or in-house call centre, which includes a range of travel-related products and services, such as airline tickets, hotels, and holiday and travel packages; and train tickets, bus tickets, air charter services, and taxi rentals. The company also offers travel guides and updates, and other reservation activities. Easy Trip Planners Limited was incorporated in 2008 and is based in New Delhi, India.