The inspiration behind creating the OYO Review Dataset for sentiment analysis was to explore the sentiment and opinions expressed in hotel reviews on the OYO Hotels platform. Analyzing the sentiment of customer reviews can provide valuable insights into the overall satisfaction of guests, identify areas for improvement, and assist in making data-driven decisions to enhance the hotel experience. By collecting and curating this dataset, Deep Patel, Nikki Patel, and Nimil aimed to contribute to the field of sentiment analysis in the context of the hospitality industry. Sentiment analysis allows us to classify the sentiment expressed in textual data, such as reviews, into positive, negative, or neutral categories. This analysis can help hotel management and stakeholders understand customer sentiments, identify common patterns, and address concerns or issues that may affect the reputation and customer satisfaction of OYO Hotels. The dataset provides a valuable resource for training and evaluating sentiment analysis models specifically tailored to the hospitality domain. Researchers, data scientists, and practitioners can utilize this dataset to develop and test various machine learning and natural language processing techniques for sentiment analysis, such as classification algorithms, sentiment lexicons, or deep learning models. Overall, the goal of creating the OYO Review Dataset for sentiment analysis was to facilitate research and analysis in the area of customer sentiments and opinions in the hotel industry. By understanding the sentiment of hotel reviews, businesses can strive to improve their services, enhance customer satisfaction, and make data-driven decisions to elevate the overall guest experience.

Deep Patel: https://www.linkedin.com/in/deep-patel-55ab48199/ Nikki Patel: https://www.linkedin.com/in/nikipatel9/ Nimil lathiya: https://www.linkedin.com/in/nimil-lathiya-059a281b1/

The TripAdvisor Vietnam Hotel Reviews Dataset is a comprehensive collection of user-generated reviews from the popular online travel platform TripAdvisor. This dataset offers valuable insights into the experiences, opinions, and ratings provided by individuals who have stayed at various hotels across Vietnam.

The dataset encompasses many hotels in different cities and regions of Vietnam, including popular tourist destinations such as Hanoi, Ho Chi Minh City, Da Nang, Nha Trang, and more. The reviews cover a diverse spectrum of accommodation types, ranging from budget guesthouses to luxurious resorts, providing a comprehensive representation of the Vietnamese hospitality industry.

Each review entry in the dataset includes a rich set of information, offering researchers, developers, and data analysts an in-depth understanding of hotel performance and customer satisfaction. Key attributes of the dataset include:

Review Text: The actual text of the review left by the user, which contains detailed descriptions, opinions, and feedback about their hotel experience.

Rating: The overall rating provided by the reviewer, typically ranging from 1 to 5 stars, reflects their satisfaction level with the hotel.

Date: The review was posted, enabling temporal analysis and tracking changes over time.

Location: The hotel's geographic location allows researchers to analyze regional variations in hotel performance and customer preferences.

The TripAdvisor Vietnam Hotel Reviews Dataset is valuable for various applications, including sentiment analysis, opinion mining, natural language processing, customer behavior analysis, recommender systems, and more. Researchers can leverage this dataset to gain deep insights into customer experiences, identify patterns, trends, and sentiments, and develop data-driven strategies for the Vietnamese hotel industry.

PromptCloud offers unparalleled data extraction services, enabling businesses to access real-time, comprehensive data from the global travel industry. Our Global Travel Data Dataset is a fundamental resource for companies aiming to understand and capitalize on travel market trends. It provides insights into airfare fluctuations, hotel pricing strategies, traveler preferences, and destination popularity. This dataset is invaluable for tracking industry movements, understanding customer sentiment, and staying ahead in the competitive travel sector.

We are committed to putting data at the heart of your business. Reach out for a no-frills PromptCloud experience- professional, technologically ahead and reliable.

Beyond basic travel data, PromptCloud caters to a wide range of travel-related data needs, from airline databases to hotel aggregators. Our advanced web scraping services are fully customizable, allowing clients to choose their data sources, collection frequencies, and specific data points. This flexibility ensures that our data extraction solutions are perfectly tailored to each client's unique requirements. Our sophisticated data aggregation technology allows for efficient extraction from multiple travel sources, making it ideal for travel agencies, hoteliers, and market researchers.

Leveraging over a decade of experience in travel data extraction, PromptCloud stands as an authoritative source in the field. We ensure the highest data quality and reliability, with a rigorous verification process that underscores our commitment to accuracy and trustworthiness.

Concerning the three selected segments, the segment high income has the largest indicator with 41.41 percent. Contrastingly, low income is ranked last, with 23.52 percent. Their difference, compared to high income, lies at 17.89 percentage points. Find other insights concerning similar markets and segments, such as a ranking by country regarding number of users in the package holidays segment of the travel & tourism market and a ranking by country regarding revenue in the travel & tourism market. The Statista Market Insights cover a broad range of additional markets.

Hotel Online Reputation Management Software Market size was valued at USD 176 Million in 2023 and is projected to reach USD 586 Million by 2031, growing at a CAGR of 16.28% during the forecast period 2024-2031.

Global Hotel Online Reputation Management Software Market Drivers

The market drivers for the Hotel Online Reputation Management Software Market can be influenced by various factors. These may include:

Increasing Digital Transition: The hospitality industry is witnessing a significant digital transformation with hotels increasingly focusing on their online presence. This shift includes adopting online reputation management (ORM) software to monitor and enhance their digital footprint. As more people rely on online reviews for hotel bookings, having a robust reputation management strategy becomes critical for maintaining competitive advantage.
Rising Influence of Online Reviews: In recent years, the influence of online reviews on consumer decision-making has surged. Customers increasingly turn to platforms like TripAdvisor, Google Reviews, and Booking.com to read reviews before booking a hotel. Hotels are investing in ORM software to systematically monitor, analyze, and respond to reviews—thereby improving their online reputation and attracting more guests.
Enhancing Customer Experience: Hotels aim to provide excellent customer experiences which, in turn, lead to positive reviews and higher ratings. ORM software plays a crucial role in understanding customer sentiment, addressing complaints promptly, and implementing improvements based on feedback. Satisfied customers are more likely to return and recommend the hotel, which has a direct positive impact on the hotel’s revenue.
Competitive Market Dynamics: The hospitality industry is highly competitive, with hotels striving to differentiate themselves through superior service and positive online presence. ORM software helps hotels benchmark their reputation against competitors by comparing reviews, ratings, and overall customer sentiment. This competitive analysis helps hotels identify strengths and areas for improvement, enabling them to stay ahead of the competition.
Integration with Social Media: Social media platforms are integral to modern marketing and communication strategies. ORM software integrates with social media channels, allowing hotels to track mentions, reviews, and comments across various platforms in real-time. This integration enables hotels to engage promptly with their audience, address concerns, and leverage positive feedback to enhance their brand image.
Increasing Importance of SEO: Search engine optimization (SEO) is crucial for online visibility. Positive online reviews and a strong reputation significantly boost a hotel’s SEO ranking. ORM software helps hotels manage their online reviews effectively, which in turn positively impacts their SEO efforts. By maintaining a high rank in search engine results, hotels can attract more organic traffic and potential customers.
Advances in AI and Analytics: The incorporation of artificial intelligence (AI) and advanced analytics in ORM software provides hotels with deeper insights into customer behavior and sentiment. AI-powered tools can analyze large volumes of data to identify trends, predict potential issues, and recommend actionable strategies. These insights help hotels make informed decisions to enhance their reputation and customer satisfaction.
Mobile and Cloud Technology: The rising adoption of mobile and cloud-based solutions has made ORM software more accessible and user-friendly. Hotels can now manage their online reputation from anywhere, at any time, using mobile devices. Cloud-based ORM solutions offer scalability, flexibility, and real-time updates, making it easier for hotels to monitor and respond to online feedback promptly.

The Booking Hotel Listings Dataset provides a structured and in-depth view of accommodations worldwide, offering essential data for travel industry professionals, market analysts, and businesses. This dataset includes key details such as hotel names, locations, star ratings, pricing, availability, room configurations, amenities, guest reviews, sustainability features, and cancellation policies.

With this dataset, users can:

Analyze market trends to understand booking behaviors, pricing dynamics, and seasonal demand.
Enhance travel recommendations by identifying top-rated hotels based on reviews, location, and amenities.
Optimize pricing and revenue strategies by benchmarking property performance and availability patterns.
Assess guest satisfaction through sentiment analysis of ratings and reviews.
Evaluate sustainability efforts by examining eco-friendly features and certifications.

Designed for hospitality businesses, travel platforms, AI-powered recommendation engines, and pricing strategists, this dataset enables data-driven decision-making to improve customer experience and business performance.

Use Cases

Booking Hotel Listings in Greece
Gain insights into Greece’s diverse hospitality landscape, from luxury resorts in Santorini to boutique hotels in Athens. Analyze review scores, availability trends, and traveler preferences to refine booking strategies.

Booking Hotel Listings in Croatia
Explore hotel data across Croatia’s coastal and inland destinations, ideal for travel planners targeting visitors to Dubrovnik, Split, and Plitvice Lakes. This dataset includes review scores, pricing, and sustainability features.

Booking Hotel Listings with Review Scores Greater Than 9
A curated selection of high-rated hotels worldwide, ideal for luxury travel planners and market researchers focused on premium accommodations that consistently exceed guest expectations.

Booking Hotel Listings in France with More Than 1000 Reviews
Analyze well-established and highly reviewed hotels across France, ensuring reliable guest feedback for market insights and customer satisfaction benchmarking.

This dataset serves as an indispensable resource for travel analysts, hospitality businesses, and data-driven decision-makers, providing the intelligence needed to stay competitive in the ever-evolving travel industry.

This dataset contains TripAdvisor guest reviews for major hotels in Salalah, Oman, collected through web scraping. It provides insights into guest satisfaction, sentiment, and ratings, making it a valuable resource for marketing, hospitality and tourism research, sentiment analysis, and tourism marketing studies.

𝐇𝐨𝐭𝐞𝐥𝐬 𝐈𝐧𝐜𝐥𝐮𝐝𝐞𝐝 𝐢𝐧 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 The dataset features guest reviews from the following hotels in Salalah:

• Al Baleed Resort Salalah by Anantara • Belad Bont Resort • Crowne Plaza Resort Salalah • Fanar Hotel and Residences • Hilton Salalah Resort • Juweira Boutique Hotel • Millennium Resort Salalah • Salalah Gardens Hotel • Salalah Rotana Resort

𝐓𝐢𝐦𝐞 𝐂𝐨𝐯𝐞𝐫𝐚𝐠𝐞 The dataset captures all available guest reviews from the beginning of each hotel's presence on TripAdvisor up until February 2025.

𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞 𝐭𝐨 𝐊𝐡𝐚𝐫𝐞𝐞𝐟 𝐓𝐨𝐮𝐫𝐢𝐬𝐦 𝐎𝐦𝐚𝐧 𝐕𝐢𝐬𝐢𝐨𝐧 2040 This dataset is particularly beneficial for the following government agencies: • Ministry of Heritage and Tourism - Oman • Oman Chamber of Commerce & Industry (OCCI) • Dhofar Municipality and Dhofar Tourism Department • National Centre for Statistics and Information (NCSI) • Oman Vision 2040 Implementation Follow-up Unit • Ministry of Commerce, Industry, and Investment Promotion • Oman Tourism Development Company (OMRAN) • Ministry of Transport, Communications, and Information Technology (MTCIT) • Dhofar Governorate Office • Ministry of Environment and Climate Affairs

It also serves as a valuable resource for researchers, policymakers, and marketing, hospitality & tourism professionals to enhance Salalah’s tourism sector, improve guest satisfaction, and support Oman’s long-term vision for a thriving and sustainable tourism industry.

Salalah experiences a surge in visitors during the Khareef season (monsoon season), a critical period for the hospitality industry. This dataset can help analyze guest experiences, identify service gaps, and optimize offerings during this peak tourism period.

Oman Vision 2040 Goals The dataset aligns with Oman’s Vision 2040, which prioritizes tourism sector growth, economic diversification, and enhanced customer experiences. By leveraging sentiment analysis and guest insights, policymakers and hotel managers can develop data-driven strategies to improve hospitality services, attract more visitors, and enhance Salalah’s reputation as a premier travel destination.

Potential Use Cases Sentiment Analysis: Understanding guest satisfaction trends over time Tourism & Hospitality Research: Evaluating service quality and hotel performance across different years Marketing Insights: Identifying key drivers of positive and negative reviews for strategic decision-making Machine Learning & NLP: Training models for text classification, sentiment prediction, and recommendation systems

Concerning the two selected segments, the segment female has the largest population by gender with 50.99 percent. Contrastingly, male is ranked last, with 49.01 percent. Their difference, compared to female, lies at 1.98 percentage points. Find other insights concerning similar markets and segments, such as a ranking by country regarding revenue in the vacation rentals segment of the travel & tourism market and a ranking by country regarding number of users in the package holidays segment of the travel & tourism market. The Statista Market Insights cover a broad range of additional markets.

Concerning the five selected segments, the segment 25-34 years has the largest population by age with 23.69 percent. Contrastingly, 18-24 years is ranked last, with 15.68 percent. Their difference, compared to 25-34 years, lies at 8.01 percentage points. Find other insights concerning similar markets and segments, such as a ranking by country regarding number of users in the cruises segment of the travel & tourism market and a ranking by country regarding revenue in the vacation rentals segment of the travel & tourism market. The Statista Market Insights cover a broad range of additional markets.

The number of users ranking in the 'Hotels' segment of the travel & tourism market is led by China with 245.02 million users, while the United States is following with 153.66 million users. In contrast, Austria is at the bottom of the ranking with 4.77 million users, showing a difference of 240.25 million users to China. Find other insights concerning similar markets and segments, such as a ranking by country regarding number of users in the travel & tourism market and a ranking by country regarding number of users in the cruises segment of the travel & tourism market. The Statista Market Insights cover a broad range of additional markets.

The global casino hotel industry had a market worth US$ 191 billion in 2022, and it is anticipated that it will reach a market value of US$ 321.4 billion by 2033, growing at a CAGR of 4.8%. The market for tourism is expanding, which may be linked to rising traveler interest in these casino hotels.

Report Attributes	Details
Estimated Market Value (2022)	US$ 191 billion
Expected Market Value (2023)	US$ 214.5 billion
Projected Forecast Value (2033)	US$ 321.4 billion
Anticipated Growth Rate (2023 to 2033)	4.8% CAGR

Report Scope

Report Attributes	Details
Growth Rate	CAGR of 4.8% from 2022 to 2032
Market value in 2023	US$ 214.5 billion
Market value in 2033	US$ 321.4 billion
Base Year for Estimation	2022
Historical Data	2018 to 2022
Forecast Period	2023 to 2033
Quantitative Units	US$ billion for value
Report Coverage	Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends, and Pricing Analysis
Segments Covered	Type Consumer Orientation Age Group Region
Regions Covered	North America Latin America Western Europe Eastern Europe South Asia and Pacific East Asia Middle East and Africa
Key Countries Profiled	United States Canada Mexico Brazil Germany United Kingdom France Spain Italy Poland Russia Czech Republic Romania India Bangladesh Australia New Zealand Japan China South Korea GCC countries South Africa Israel
Key Companies Profiled	Palms Casino Resort Caesars Entertainment Corporation City of Dreams Manila Eldorado Resort Casino Foxwoods Resort Casino Galaxy Entertainment Group Ltd MGM Resorts International Palms Casino Resort DraftKings Inc. Wynn Resorts Limited
Customization & Pricing	Available on Request

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

Yelp Reviews dataset to explore ratings and reviews for local businesses, including restaurants, bars, cafes, and hotels. Popular use cases include analyzing customer sentiment, benchmarking business performance, and gaining insights into local market trends. Datapoints include: business ID, review author, rating, date, content, image, and more.

Success.ai’s B2B Contact Data for Global Hospitality Executives provides access to verified contact information for decision-makers shaping the hotel and hospitality industry worldwide. Sourced from over 170 million verified professional profiles, this dataset includes work emails, direct phone numbers, and LinkedIn profiles for key executives and leaders in hotels, resorts, and hospitality groups. Whether you’re targeting hotel owners, general managers, revenue directors, or operations executives, Success.ai delivers accurate, relevant, and timely data to enhance your outreach and drive business growth.

Why Choose Success.ai’s Hospitality Executives Data?

1. Comprehensive Contact Information
2. Access verified work emails, phone numbers, and LinkedIn profiles of hospitality executives worldwide.

3. AI-driven validation ensures 99% accuracy, enabling confident and efficient communication with the right individuals.

4. Global Reach Across Hospitality Segments

5. Includes profiles of hotel owners, general managers, sales directors, revenue managers, and operations leaders in hotels, resorts, and hospitality chains.

6. Covers North America, Europe, Asia-Pacific, South America, and the Middle East, ensuring a truly global perspective.

7. Continuously Updated Datasets

8. Real-time updates keep your data fresh and actionable, allowing you to engage with the most current decision-makers in the hospitality industry.

9. Ethical and Compliant

10. Adheres to GDPR, CCPA, and other global data privacy regulations, ensuring that all outreach efforts are ethical and legally compliant.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes hospitality executives and decision-makers across diverse markets.
• 50M Work Emails: AI-validated for precise, reliable outreach.
• 30M Company Profiles: Offering insights into hotel groups, hospitality brands, and independent properties.
• 700M Global Professional Profiles: Enriched data supporting a broad range of strategic initiatives.

Key Features of the Dataset:

1. Hospitality Decision-Maker Profiles
2. Identify and connect with CEOs, general managers, sales directors, and revenue managers responsible for key operational and strategic decisions in hotels and related hospitality businesses.

3. Engage with individuals who shape guest experiences, manage pricing strategies, oversee supply chains, and direct marketing efforts.

4. Advanced Filters for Precision Targeting

5. Filter contacts by region, hotel brand, property size, star rating, job title, and other criteria to tailor your outreach for maximum relevance and impact.

6. Refine campaigns to target decision-makers aligned with your product or service offerings.

7. AI-Driven Enrichment

8. Profiles are enriched with actionable data points, giving you the insights needed to personalize messaging and boost engagement rates.

Strategic Use Cases:

1. Sales and Vendor Relationships
2. Present technology solutions, guest amenities, or operational improvements to hotel owners, procurement managers, or operations leaders.

3. Build partnerships with hospitality executives seeking quality suppliers and innovative offerings.

4. Marketing and Brand Expansion

5. Target marketing and revenue directors to promote your services—such as branding, digital marketing tools, or loyalty programs—across hotel portfolios.

6. Engage with decision-makers who can influence brand positioning and campaign investments.

7. Investment and Development Opportunities

8. Connect with hospitality executives exploring renovations, expansions, or new property launches.

9. Identify strategic partners for joint ventures or acquisitions within the hospitality sector.

10. Recruitment and Talent Acquisition

11. Reach HR professionals or general managers looking to staff hotels with high-quality personnel.

12. Offer recruitment solutions, training programs, or staffing services directly to key decision-makers.

Why Choose Success.ai?

1. Best Price Guarantee

2. Access top-quality verified data at competitive prices, ensuring you maximize ROI on your outreach efforts.

3. Seamless Integration

4. Integrate verified contact data into your CRM or marketing automation platforms via APIs or downloadable formats for effortless data management.

5. Data Accuracy with AI Validation

6. Trust in 99% accuracy for confident targeting, optimized conversions, and enhanced relationship-building in the hospitality sector.

7. Customizable and Scalable Solutions

8. Tailor datasets to focus on specific regions, hospitality segments, or job functions, adapting as your business goals evolve.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enrich existing CRM records with verified hospitality contact data to sharpen targeting and personalization.

3. Lead Generation API

4. Automate lead generation, streamlining your outreach and enabling efficient scalin...

The Unlimited Service Hotel market has emerged as a revolutionary segment within the hospitality industry, redefining the guest experience by combining luxury with value. This model caters to discerning travelers who seek expansive amenities and comprehensive services, often including unlimited dining options, bever

A. Market Research and Analysis: Utilize the Tripadvisor dataset to conduct in-depth market research and analysis in the travel and hospitality industry. Identify emerging trends, popular destinations, and customer preferences. Gain a competitive edge by understanding your target audience's needs and expectations.

B. Competitor Analysis: Compare and contrast your hotel or travel services with competitors on Tripadvisor. Analyze their ratings, customer reviews, and performance metrics to identify strengths and weaknesses. Use these insights to enhance your offerings and stand out in the market.

C. Reputation Management: Monitor and manage your hotel's online reputation effectively. Track and analyze customer reviews and ratings on Tripadvisor to identify improvement areas and promptly address negative feedback. Positive reviews can be leveraged for marketing and branding purposes.

D. Pricing and Revenue Optimization: Leverage the Tripadvisor dataset to analyze pricing strategies and revenue trends in the hospitality sector. Understand seasonal demand fluctuations, pricing patterns, and revenue optimization opportunities to maximize your hotel's profitability.

E. Customer Sentiment Analysis: Conduct sentiment analysis on Tripadvisor reviews to gauge customer satisfaction and sentiment towards your hotel or travel service. Use this information to improve guest experiences, address pain points, and enhance overall customer satisfaction.

F. Content Marketing and SEO: Create compelling content for your hotel or travel website based on the popular keywords, topics, and interests identified in the Tripadvisor dataset. Optimize your content to improve search engine rankings and attract more potential guests.

G. Personalized Marketing Campaigns: Use the data to segment your target audience based on preferences, travel habits, and demographics. Develop personalized marketing campaigns that resonate with different customer segments, resulting in higher engagement and conversions.

H. Investment and Expansion Decisions: Access historical and real-time data on hotel performance and market dynamics from Tripadvisor. Utilize this information to make data-driven investment decisions, identify potential areas for expansion, and assess the feasibility of new ventures.

I. Predictive Analytics: Utilize the dataset to build predictive models that forecast future trends in the travel industry. Anticipate demand fluctuations, understand customer behavior, and make proactive decisions to stay ahead of the competition.

J. Business Intelligence Dashboards: Create interactive and insightful dashboards that visualize key performance metrics from the Tripadvisor dataset. These dashboards can help executives and stakeholders get a quick overview of the hotel's performance and make data-driven decisions.

Incorporating the Tripadvisor dataset into your business processes will enhance your understanding of the travel market, facilitate data-driven decision-making, and provide valuable insights to drive success in the competitive hospitality industry

The Home Boutique Hotel market has emerged as a vibrant segment within the broader hospitality industry, providing unique accommodations that blend the comfort of home with the charm of boutique-style offerings. Catering to travelers seeking authentic experiences, these establishments often feature personalized serv

We asked Chinese consumers about "Hotel / private accommodation online bookings by brand" and found that "Ctrip" takes the top spot, while "KAYAK" is at the other end of the ranking.These results are based on a representative online survey conducted in 2024 among 2,331 consumers in China. Seeking valuable insights about users of various accommodation platforms worldwide? Check out our

We asked Indian consumers about "Hotel / private accommodation online bookings by brand" and found that "MakeMyTrip" takes the top spot, while "KAYAK" is at the other end of the ranking.These results are based on a representative online survey conducted in 2024 among 2,129 consumers in India. Seeking valuable insights about users of various accommodation platforms worldwide? Check out our

The real per capita consumer spending on restaurants and hotels in the Netherlands was forecast to decrease between 2024 and 2029 by in total 70.7 U.S. dollars (-3.78 percent). This overall decrease does not happen continuously, notably not in 2028. While the real restaurants- and hotels-related per capita spending was increasing earlier, it deteriorated and the real restaurants- and hotels-related per capita spending was forecast to reach 1,799.82 U.S. dollars in 2029. Consumer spending, in this case per capita spending concerning restaurants and hotels, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group 11. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.Find more key insights for the real per capita consumer spending on restaurants and hotels in countries like Luxembourg and Belgium.