We will create a customized hotel bookings dataset tailored to your specific requirements. Data points may include hotel names, location details, pricing information, amenity lists, guest ratings, occupancy rates, and other relevant metrics.

Utilize our hotels bookings datasets for a variety of applications to boost strategic planning and market analysis. Analyzing these datasets can help organizations understand guest preferences and market trends within the hospitality industry, allowing for more precise operational adjustments and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: optimizing booking strategies, enhancing guest experience, and competitive benchmarking.

Expedia Group, Inc. is an American online travel shopping company for consumer and small business travel.

👉🏻 Expedia is the world’s largest online travel agency (OTA) and powers search results for millions of travel shoppers every day. In this competitive market matching users to hotel inventory is very important since users easily jump from website to website. As such, having the best ranking of hotels (“sort”) for specific users with the best integration of price competitiveness gives an OTA the best chance of winning the sale.

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👉🏻 Expedia has provided a dataset that includes shopping and purchase data and information on price competitiveness. The data are organized around a set of “search result impressions”, or the ordered list of hotels that the user sees after they search for a hotel on the Expedia website. In addition to impressions from the existing algorithm, the data contain impressions where the hotels were randomly sorted, to avoid the position bias of the existing algorithm. The user response is provided as a click on a hotel or/and a purchase of a hotel room.

Appended to impressions are the following: 1) Hotel characteristics 2) Location attractiveness of hotels 3) User’s aggregate purchase history 4) Competitive OTA information

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The USA Hotels Dataset from Booking.com is a rich collection of data related to hotels across the United States, extracted from Booking.com. This dataset includes essential information about hotel listings, such as hotel names, locations, prices, star ratings, customer reviews, and amenities offered. It's an ideal resource for researchers, data analysts, and businesses looking to explore the hospitality industry, analyze customer preferences, and understand pricing patterns in the U.S. hotel market.

Access 3 million+ US hotel reviews — submit your request today.

Key Features:

• Hotel Information: Includes hotel names, addresses, star ratings, and descriptions.
• Pricing Data: Nightly rates, discounts, and price variations by room type and season.
• Customer Reviews: Aggregated ratings and detailed user feedback from verified guests.
• Amenities: Detailed list of amenities provided by each hotel (e.g., Wi-Fi, parking, spa, swimming pool).
• Geographical Information: Hotel locations including city, state, and proximity to major landmarks.

Use Cases:

• Sentiment Analysis: Analyze customer reviews to gauge hotel service quality and guest satisfaction.
• Price Analysis: Compare pricing across different hotels, locations, and time periods to identify trends.
• Recommendation Systems: Build recommendation engines based on customer ratings, reviews, and preferences.
• Tourism and Hospitality Research: Understand patterns in hotel demand and services across various U.S. cities.

[Metadata] Locations of the Visitor Plant Inventory (VPI) in the State of Hawaii (updated December, 2021). Source: Hawaii Tourism Authority, September 2022. This inventory includes apartment hotels, bed and breakfasts (B&Bs), condominium hotels, hostels, hotels, individual vacation units, timeshares, and other types of visitor accommodations.

*** This dataset contains detailed information about hotel bookings, including cancellations. It includes variables such as booking date, cancellation status, lead time, customer type, and hotel type (city or resort). The dataset is useful for analyzing trends in hotel booking cancellations, understanding customer behavior, and predicting cancellation likelihood. Ideal for data science projects involving classification, time series analysis, or building predictive models to minimize hotel cancellations and optimize booking strategies.***

📌 Title: Hotel Booking Analysis Dashboard – Power BI

📌 Description: This Power BI dashboard provides an interactive analysis of hotel booking trends using historical booking data. It offers insights into total bookings, customer preferences, cancellations, and room allocation across different hotels.

🔹 Key Insights: ✅ Total Bookings: 119,386, with insights into lead time and average night stays. ✅ Booking Trends: Breakdown by year, month, and hotel type (City Hotel vs. Resort Hotel). ✅ Cancellations: Comparison of canceled bookings across different hotels. ✅ Customer Segments: Analysis by customer type, meal preferences, and market segments. ✅ Distribution Channels: Impact of booking sources on revenue and deposits. ✅ Parking & Room Types: Distribution of required car parking spaces and reserved vs. assigned room types.

📊 Technologies Used: Power BI for data visualization Excel/SQL for data preprocessing DAX for calculated measures 🔗 How to Use This Dashboard: Explore yearly and monthly booking trends to understand seasonality. Identify cancellation patterns to improve booking policies. Analyze market segments and customer behavior for targeted marketing. 🚀 If you find this dashboard insightful, leave feedback and connect with me! 😊

The year-over-year monthly change in the number of hotel bookings worldwide dropped to -66 percent in April 2020. The sharp change in hotel bookings was due to the impact of the coronavirus (COVID-19) pandemic on international travel and the hotel industry. Three years later, in April 2023, the monthly change in the number of hotel bookings was 99 percent.

The Luxury Hotel Market Report Segments the Industry Into by Service Type (Business Hotels, Airport Hotels, and More), Room Type (Standard Luxury Room, Suites, and More), ]booking Channel (Direct Booking (Brand Website, Call Center), Online Travel Agencies (OTA), and More), and Geography (North America, South America, Europe, Asia-Pacific, and More). The Market Forecasts are Provided in Terms of Value (USD).

Explore our extensive Booking Hotel Reviews Large Dataset, featuring over 20.8 million records of detailed customer feedback from hotels worldwide. Whether you're conducting sentiment analysis, market research, or competitive benchmarking, this dataset provides invaluable insights into customer experiences and preferences.

The dataset includes crucial information such as reviews, ratings, comments, and more, all sourced from travellers who booked through Booking.com. It's an ideal resource for businesses aiming to understand guest sentiments, improve service quality, or refine marketing strategies within the hospitality sector.

With this hotel reviews dataset, you can dive deep into trends and patterns that reveal what customers truly value during their stays. Whether you're analyzing reviews for sentiment analysis or studying traveller feedback from specific regions, this dataset delivers the insights you need.

Ready to get started? Download the complete hotel review dataset or connect with the Crawl Feeds team to request records tailored to specific countries or regions. Unlock the power of data and take your hospitality analysis to the next level!

Access 3 million+ US hotel reviews — submit your request today.

Comprehensive dataset containing 99,198 verified Hotel businesses in United States with complete contact information, ratings, reviews, and location data.

The Online Hotel Booking industry comprises establishments primarily providing hotel booking services via online platforms. These websites are third-party platforms for customers to research and make hotel reservations. Consequently, this industry excludes hotels that offer direct bookings on their websites and brick-and-mortar travel agencies. Over the past five years, more individuals willing to make online reservations have benefited the industry. However, rising inflation and consumer uncertainty since 2022 have collectively reduced travel demand. Therefore, over the five years to 2025, industry revenue is expected to grow at an annualized rate of 19.5% to $55.8 billion, including a 4.7% growth in 2025 alone. The surge in growth rate is due to the low pandemic base year when industry revenue suffered from travel restrictions. Traditionally, travelers could book hotel hotels directly on websites or via travel agencies. However, the introduction of online hotel booking services enables customers to search and browse hotels according to their desired criteria, compare rooms at different hotels, and finally make a reservation from the comfort of their homes. Consequently, the industry has grown due to its added convenience compared with its direct substitutes. The industry has grown strongly due to the consistent rises in the number of trips made by US travelers and inbound trips by non-US residents. Industry revenue will continue to grow over the next five years as the economy improves from the record-high inflation. As consumer confidence recovers, individuals will feel more financially comfortable traveling. However, the industry contends with higher competition from direct hotel websites as some customers still make reservations directly with hotels. Nonetheless, industry revenue is projected to increase at an annualized rate of 2.5% to $63.1 billion over the five years to 2030.

The Hotel Area KPIs dataset provides comprehensive insights into hotel performance metrics across global markets.

Sourced directly from hotel reservation systems, this dataset offers a real-time view of key performance indicators such as occupancy rates, average daily rates (ADR), revenue per available room (RevPAR), and booking patterns.

With weekly updates and both historical and forward-looking data, it enables hoteliers, investors, and analysts to track market trends, benchmark performance, and make data-driven decisions.

This dataset is invaluable for understanding seasonal variations, forecasting demand, and optimizing pricing strategies in the dynamic hospitality industry.

The dataset contains locations and attributes of Hotels, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. A database provided by the DC Taxi Commission (DCTC) and research at various commercial websites identified Hotels and DC GIS staff geo-processed the data.

The year-over-year change in the monthly number of online hotel searches worldwide decreased across all regions during March and April 2020. The sharp decrease in hotel searches was due to the impact of the coronavirus (COVID-19) pandemic on international travel and the hotel industry.

This Hotel Dataset: Rates, Reviews & Amenities(6k+) dataset includes hotel rates, guest reviews, and available amenities from two popular travel websites, TripAdvisor and Booking.com. The dataset can be used to analyze trends and insights in the hospitality industry, and inform decisions related to pricing, marketing, and customer service. Booking.com: Founded in 1996 in Amsterdam, Booking.com has grown from a small Dutch start-up to one of the world’s leading digital travel companies. Part of Booking Holdings Inc. (NASDAQ: BKNG), Booking.com’s mission is to make it easier for everyone to experience the world.

By investing in technology that takes the friction out of travel, Booking.com seamlessly connects millions of travelers to memorable experiences, a variety of transportation options, and incredible places to stay – from homes to hotels, and much more. As one of the world’s largest travel marketplaces for both established brands and entrepreneurs of all sizes, Booking.com enables properties around the world to reach a global audience and grow their businesses.

Booking.com is available in 43 languages and offers more than 28 million reported accommodation listings, including over 6.6 million homes, apartments, and other unique places to stay. Wherever you want to go and whatever you want to do, Booking.com makes it easy and supports you with 24/7 customer support. Tripadvisor, the world's largest travel guidance platform*, helps hundreds of millions of people each month** become better travelers, from planning to booking to taking a trip. Travelers across the globe use the Tripadvisor site and app to discover where to stay, what to do and where to eat based on guidance from those who have been there before. With more than 1 billion reviews and opinions of nearly 8 million businesses, travelers turn to Tripadvisor to find deals on accommodations, book experiences, reserve tables at delicious restaurants and discover great places nearby. As a travel guidance company available in 43 markets and 22 languages, Tripadvisor makes planning easy no matter the trip type. The subsidiaries of Tripadvisor, Inc. (Nasdaq: TRIP), own and operate a portfolio of travel media brands and businesses, operating under various websites and apps.

In June 2025, the region with the highest hotel occupancy rate worldwide was Europe, at 76 percent. Meanwhile, the region with the lowest occupancy rate that month was the Middle East, with 60 percent.

Hotel Booking Engine Market Outlook

The global hotel booking engine market size was valued at approximately USD 3.5 billion in 2023 and is expected to grow to USD 7.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 9.2%. The market's growth is driven by the increasing adoption of digital platforms for travel bookings and the growing preference for online reservations among consumers. The ease of access and the convenience provided by hotel booking engines are key factors contributing to this rapid expansion.

One of the primary growth factors for the hotel booking engine market is the proliferation of internet usage and the widespread adoption of smartphones. As more people gain access to high-speed internet and increasingly rely on their mobile devices for various daily activities, the trend towards online booking has surged. This has prompted hotels and travel agencies to invest in advanced booking engines to streamline their operations and enhance customer experiences. Furthermore, the convenience offered by these platforms, such as instant booking confirmations and secure payment options, has significantly bolstered their popularity.

Another significant driver is the growing emphasis on customer experience and personalization in the hospitality industry. Modern consumers expect a seamless and customized booking experience, which has led to the integration of artificial intelligence (AI) and machine learning (ML) technologies into booking engines. These technologies analyze user behavior and preferences to provide personalized recommendations, thereby improving customer satisfaction and loyalty. Additionally, the incorporation of features like virtual tours and real-time room availability updates further enhances the user experience, driving market growth.

The increasing competition among hotels and the need for a competitive edge have also fueled the adoption of advanced hotel booking engines. Hotels are leveraging these platforms to offer exclusive deals and personalized packages to attract and retain customers. The ability to manage bookings efficiently, optimize pricing strategies, and access valuable customer data for targeted marketing campaigns has made booking engines an indispensable tool for hoteliers. Moreover, the rising trend of direct bookings, which eliminates the need for intermediaries and reduces commission costs, further propels the market's expansion.

From a regional perspective, North America dominates the hotel booking engine market due to its well-established hospitality sector and high internet penetration rates. The presence of major market players and the rapid adoption of advanced technologies in this region also contribute to its leading position. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The burgeoning middle class, increasing disposable incomes, and the rapid growth of the tourism industry in countries like China and India are key factors driving the market in this region.

Deployment Type Analysis

The hotel booking engine market can be segmented by deployment type into cloud-based and on-premises solutions. Cloud-based booking engines have gained significant traction in recent years due to their flexibility, scalability, and cost-effectiveness. These solutions allow hotels to access their booking systems from anywhere with an internet connection, making it easier to manage reservations and update availability in real-time. Additionally, cloud-based systems often come with lower upfront costs and require less maintenance, which is particularly beneficial for small to medium-sized hotels with limited IT resources.

On the other hand, on-premises booking engines are still preferred by some larger hotel chains and establishments with specific security and customization requirements. These systems are installed directly on the hotel's servers, providing greater control over data and system configurations. While on-premises solutions typically involve higher initial investments and ongoing maintenance costs, they offer enhanced data security and the ability to tailor the system to the hotel's unique needs. This segment continues to hold a significant share of the market, particularly among luxury and high-end hotels that prioritize data privacy and bespoke functionality.

The growing preference for cloud-based solutions is also driven by the increasing adoption of Software-as-a-Service (SaaS) models in the hospitality industry. SaaS-based booking engines offer a subscription-based pricing struct

During the coronavirus (COVID-19) pandemic, a worldwide survey was conducted from July to August 2020 to determine which contactless options hotel guests preferred to use for check-in and check-out, as opposed to the traditional reception desk procedure. The contactless options given were hotel apps, webpages, and public kiosks. The results of the survey indicated that most respondents, ** percent, preferred to check in and check-out using a hotel app. Meanwhile, ** percent preferred to use a website, leaving only * percent of respondents who preferred to use a public kiosk.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_a728577abbe4ff3f3409b9129be28a53/view