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A Guide to Data Analytics in the Travel Industry

By Talo Thomson

Published on March 21, 2023

Today, modern travel and tourism thrive on data. For example, airlines have historically applied analytics to revenue management, while successful hospitality leaders make data-driven decisions around property allocation and workforce management.

While this industry has used data and analytics for a long time, many large travel organizations still struggle with data silos , which prevent them from gaining the most value from their data. To fully realize data’s value, organizations in the travel industry need to dismantle data silos so that they can securely and efficiently leverage analytics across their organizations.

• What is big data in the travel and tourism industry?

Organizations in the travel and tourism vertical use big data and analytics to find patterns in structured and unstructured data that allow them to make informed business decisions. As an industry with tight margins, travel and tourism companies can use analytics to detect trends that help them reduce costs, decide future product and service offerings, and develop successful business strategies.

For example, companies in this vertical can use big data and analytics to:

Forecast customer demands

Personalize services

Market travel packages

Optimize pricing

Increasingly, companies like Expedia may combine these capabilities into a single package that, for example, bundles hotel and airfare packages at a reduced rate and markets these packages to a targeted group of people.

What types of data are collected?

Travel organizations collect different data using various sources. Some common data sources in the travel and tourism industry include:

User-generated content (UGC): Data obtained from questionnaires and social networks, including photo or survey data

Device data: Data obtained from third-party resources that includes GPS data, mobile roaming data, Bluetooth data, and mobile browser data

Transaction data: Data obtained from web services, such as Google Analytics, that includes web search, web page visits, or online booking information

Businesses that can integrate this data across silos (and build more comprehensive user profiles) boast a significant competitive advantage.

Why is data analytics important for travel organizations?

Travel can be stressful and emotionally fraught. With data analytics , travel organizations can gain real-time insights about customers to make strategic decisions and improve their travel experience. By aggregating and analyzing data from multiple sources, these companies can understand customer behavior and market trends so they can provide the types of customer experiences that create brand loyalty.

For example, if an airline needs to cancel a flight, it can leverage data analytics to notify customers of the change and help them adjust their travel plans. This can transform a would-be bad experience into a positive one, making people less likely to leave bad reviews (and more likely to recommend the airline to others).

• What are common data challenges for the travel industry?

Some companies struggle to optimize their data’s value and leverage analytics effectively. When companies lack a data governance strategy , they may struggle to identify all consumer data or flag personal data as subject to compliance audits. They may also suffer from data duplication, which undermines their analytics models.

Data security

The travel industry collects, transmits, processes, and stores a wide range of personally identifiable information (PII) from customers, which are of interest to bad actors, as cybercriminals target this valuable data. What’s more, many companies struggle with rigid legacy technologies that increase the risk of a data breach.

Regulatory compliance

Additionally, this PII is often highly regulated. All companies in the travel industry that collect PII need to comply with privacy laws like the European Union General Data Protection Regulation (GDPR) or the California Privacy Rights Act (CPRA). These regulations introduce two particular challenges that these companies must address to remain compliant:

Right to be forgotten : Companies need a mechanism to delete all of a consumer’s PII upon request

Right to access : Companies need to provide consumers copies of or access to the PII they use when a person requests it

Companies must also provide paths to consumers wishing to make these requests.

Data access management

Both data privacy and security require an organization to have appropriate data access controls in place. Companies need to ensure they grant access only to resources and data that people need to perform their job functions. Otherwise, they risk a data privacy violation.

Lack of data culture

While many companies want to be data-driven, many lack a data culture where everyone in the organization values, practices, and encourages responsible data use. Without a strong data culture, the organization is unable to align data and analytics to business outcomes because people don’t have access to the data that allows them to achieve these goals.

Data ethics

Without effective data governance , many organizations lack the ability to manage consumers’ data ethically. Ethical data management requires travel organizations to go beyond the minimum baseline requirements of data privacy and protection law and focus on building trusting relationships that ensure data trustworthiness.

How is data analytics used in the travel industry?

The travel and tourism industry can use predictive, descriptive, and prescriptive analytics to make data-driven decisions that ultimately enhance revenue, mitigate risk, and increase efficiencies. Below are a few examples:

Revenue management and optimization

Big data analysis enables companies to make data-driven decisions about pricing based on historical transactional data. Data analytics offers a comprehensive view of what is or isn’t working, and these insights can inform new business goals and drive revenue optimization.

Customer experience personalization

Aggregating and analyzing data across customer touchpoints enables companies to understand consumer preferences and expectations. When companies know this, they can offer experiences and upgrades tailored to unique consumer desires.

Data-driven marketing

Using historical customer data, companies can engage in  data-driven marketing  of travel deals or pricing based on people’s known interests and needs. For example, travel websites can leverage transaction data to encourage more clicks and conversions.

Audience segmentation

With big data and analytics, travel companies can create more detailed marketing segments that drive better customer journeys. For example, they can create micro segmentations that incorporate multiple factors such as:

Socioeconomic status

Reason for travel

Geographic region

These micro segmentations enable travel businesses to market more effectively to unique consumer types.

Seasonality and trend predictions

Many online travel companies use dynamic and flexible pricing strategies to respond to changes in demand and supply. Using predictive analytics, travel companies can forecast customer demand around things like holidays or weather to set optimum prices that maximize revenue.

Reputation oversight

While reputation is important across all industry verticals, it’s particularly critical in the travel industry. Consumers often look at online reviews or talk to friends before making a decision. With advanced analytics, travel organizations can engage in sentiment analysis to identify common sentiments and resolve problems, mitigating revenue and brand impact.

• Use cases for analytics in travel and tourism

How can travel and tourism companies use data analytics to improve business ROI? Below are a few examples demonstrating how these organizations wield data as a strategic asset for the business.

Airlines Reporting Corporation (ARC)

This data company acts as a vital intermediary between airlines and travel groups like Kayak or Expedia, settling transactions and offering data products about travel to third parties.

Having been in business for over 50 years, ARC had accumulated a massive amount of data that was stored in siloed, on-premises servers across its 7 business domains. When it embarked on a digital transformation and modernization initiative in 2018, the company migrated all its data to AWS S3 Data Lake and Snowflake Data Cloud to provide accessibility to data to all users.

Using Alation, ARC automated the data curation and cataloging process. “So much is automatic — the metadata extraction, curation, labeling, query log ingestion, and building out the lineage — it’s a big help,” says Leonard Kowk, senior data analyst. By automating these time-consuming processes, ARC was able to achieve a faster time-to-value for its digital transformation strategy.

Today, ARC data project teams use Alation’s self-service capabilities to conduct their own research, accelerating time-to-market for new products.

Virgin Australia

A competitor in the Australian aviation landscape for over 20 years, Virgin Australia ’s expanded IT infrastructure led to a heavily siloed architecture that created communication gaps across its business lines. With business areas interpreting data differently, executives received inconsistent information, which hindered informed decision-making.

To become a data-driven organization, the Data Platforms team chose Alation because it provided a business-oriented, easy-to-use solution that enabled collaboration across all business units. By building a governance framework to address data usage and quality issues, Virgin Australia was able to standardize definitions to facilitate data discovery and build trust.

Today, Virgin Australia uses Alation to implement a consistent data quality management strategy that provides executives with actionable business insights.

Finland’s national airline, Finnair , wanted to break down data silos to standardize metrics and support better communication across teams. Finnair chose Alation because it was easy to use for all users, from nontechnical to data experts and auditors. They found people could easily learn the intuitive platform (giving them a faster time-to-use) while also supporting advanced analytics and the unique needs of data auditors.

With Alation’s secure and scalable cloud-based platform, Finnair now has business intelligence dashboards and reports for a single source of truth across operations, customer experience, and financial teams. To support documentation, Finnair leverages Alation’s artificial intelligence (AI) and machine learning (ML) metadata recommendations to ensure data quality.

Today, Finnair uses Alation for data discovery , enabling people to share knowledge across the organization by searching for a term or phrase to find data sources and articles. “Alation enables us to make more informed decisions and create more personalized encounters with our customers,” says Minna Kärhä, Finnair’s head of data. “We have been able to combine datasets that we didn’t combine before.”

• How Alation supports data analytics in the travel industry

Alation Data Catalog automates time-consuming manual processes that create barriers for travel organizations who want to establish a data culture. With Alation, you break down data silos and establish a standardized vocabulary across the organization so everyone has access to only the data they need to provide business insights that reduce time-to-market.

Our AI/ML drives pattern recognition to give you insights into how people use data so that you can implement data governance policies and procedures aligned with user needs. Our natural language search enables you to reinforce your data governance strategy without compromising usability, ensuring that everyone has the best and most relevant data whenever they need it.

Curious to see Alation in action? Join a demo to learn how a data intelligence platform can revolutionize the data strategy at your organization.

Travel can be stressful and emotionally fraught. With data analytics, travel organizations can gain real-time insights about customers to make strategic decisions and improve their travel experience. By aggregating and analyzing data from multiple sources, these companies can understand customer behavior and market trends so they can provide the types of customer experiences that create brand loyalty.

The travel and tourism industry can use predictive, descriptive, and prescriptive analytics to make data-driven decisions that ultimately enhance revenue, mitigate risk, and increase efficiencies.

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How Is Data Analytics Used in Tourism?

Collating user demographics with more in-depth data analytics can supply the tourism industry with a far broader range of benefits. Analytics can inform funding decisions, tailor business development, and direct marketing campaigns based on in-depth user preferences.

Analytics involves far more than just data gathered from hotels and surveys; psychographic data captures information on transactions, device data, lifestyles, preferences, and the best marketing channels to reach your customers on. This detailed data helps to categorise and identify households with the same or similar characteristics for more tailored marketing strategies. Organised data also benefits the tourism companies, destination marketing organisations, and the destination city itself; as we will explore within this article.

5 Ways Data Analytics Can Improve Tourism

Data can be used in numerous ways, and its scalability means it can provide insights into almost anything, from a single business problem to the entire industry. Here are five of the key ways that Data Analytics is boosting the tourism industry.

1. Revenue Management

Financial optimisation can be assisted and informed by data analytics. Functions such as predicting demand, improving pricing availability, and optimising your inventory, are all informed decisions through the application of data analytics.

Incorporating data analytics into revenue management allows you to understand where your business is succeeding and where improvements can be made, thus, analytics is fast becoming a must-have business tool. The insights provided cannot be replicated by anything else with such speed, efficiency and organisation, so it is a key tool to utilise when competing with other businesses.

Multiple studies have shown the benefit of using Data Analytics in revenue management; one of which was shared by Kevin Hof, a Data Scientist at RoomPriceGenie. The study shows nine hotels experiencing a 22% average increase in revenue, alongside an average 4% increase in Average Daily Rate after implementing pricing optimisation.

2. Identify Top Origin Markets

Knowing which sources are successful in directing customers towards you is invaluable. It allows you to understand seasonal market fluctuations and increase marketing in areas of untapped markets – particularly in areas with similar consumer profiles to your key visitors.

Data analytics can pinpoint the markets that are most likely to yield results, so that you can concentrate your media buying there; saving money while attracting new customers. Specific groups can be targeted to reach more of your potential customers, and the effectiveness of these campaigns can be measured.

The efficiency and quality of services also improves through applying analytics; customers are responded to more quickly; their experience is analysed and improved, and personalised offers can be tailored to them and their needs. In addition to this, your decision-making process can be highly informed through the number-driven data of analytics, resulting in a more efficient company operation.

3. Route Optimisation

Admission volumes are commonly available, however, it is also valuable to find out which combination of tourist attractions your customers tend to visit. This helps in both market targeting and preparing promotional offers.

Conducting destination analysis through mobile data can build a more concise picture of your customer base and the places they like to visit. The tourists’ interests also help to inform route optimisation as this combined information can be used to create multi-destination packages that not only appeal to your customers but encourage longer visits too.

The above data can be calculated along with geographical, route and traffic information to plot the quickest tour route for maximum efficiency, providing a seamless experience for your visitors.

4. Predictive Analytics and Pricing Analysis

Predictive analytics use techniques such as data mining, predictive modelling and machine learning to predict future events through an analysis of current and historical data. This allows pricing analytics to enter the picture and inform financial decision-making.

Data analytics uses metrics and additional tools to collect and compare information on price points from a variety of sources. This allows you to analyse the profitability of price points, understand how pricing activities affect the business overall, and optimise your business pricing strategy to ensure maximum revenue.

Revenue can be further increased through using recommendation engines to upsell and cross-sell. This is taken into consideration in predictive analytics to ensure your marketing campaigns are targeted towards the groups most likely to convert.

Sentiment across social media networks can be analysed, providing valuable feedback for your service and establishing your online reputation. This can flag sentiment as positive, negative or neutral and allow you to quickly respond to customers who have had a less than desirable experience.

Many well-known companies are currently using predictive analytics, whether to guide their business or as a part of their service; these include Qantas, Booking.com and Kayak.

5. Automated Robots and Chat-Bots

Working in the tourism sector means dealing with customers across multiple time zones, and as such they should be able to access the information they need at a time convenient to them. The easiest way to ensure 24/7 access to this is through using an automated chat bot throughout the hours that customer service is not available.

The data generated from the usage of chat-bots can indicate what information needs to be clarified based on the frequency of questions being asked. This information can improve your marketing channels, communications and customer service.

Robots can also assist to provide a speedier service, such as when checking in to an airport, paying at the supermarket or using the traditional ATM. These technologies were particularly useful in the COVID-19 pandemic, and the trend is set to continue.

Benefits of Using Data Analytics in Tourism

The tourism industry can greatly benefit from making better use of the data analytics and opportunities now available. In doing so, better business decisions can be made that are based solely on facts, figures and analysis. The tourism marketing programmes can also improve through taking these steps, thereby bringing increased revenue to the city in question.

Modern analytics-led approaches are able to leverage new alternative datasets to depict your visitor profile, pinpoint the best areas to focus marketing efforts on, optimise your pricing structure and compete more effectively within the market.

To try out what data analytics can do for your tourism business; simply post your project on Pangaea X’s freelancer platform .

Get your data results fast and accelerate your business performance with the insights you need today.

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Data Analytics in Tourism Industry: What Is It, Benefits, How It’s Used, & Real-life Examples

What is data analytics in tourism?

Why is data analytics important for the tourism industry, what are the benefits of having data analytics in the tourism industry, how are data science and big data used in tourism industry analytics, real-life examples of data science usage in the tourism industry, how can descriptive analytics help travel agencies.

Big data and data analytics are most commonly tied with industries such as fintech and IT . However, a few years ago, they also made it into the travel sector. They didn’t just make it into the industry. The enormous adoption rates of data analytics in the sector have created a brand new market – the tourism industry and the big data analytics market. 

This market’s size reached $220 billion in 2022, and it’s projected to reach $350 billion by 2032 . We are talking about a CAGR of 15% here, which is huge!

If you are interested in whether big data and data analytics can benefit your business, you need to learn more about these concepts. Below you will find everything you need to assess their value, including real-life examples.

The best way to understand data analytics in tourism is to understand the concept of data analytics.  

Data analytics is a domain of data science . It refers to various processes and techniques developed to streamline raw data analysis . Its primary purpose is to help you make sense of data and use it to make informed conclusions and decisions.

Over the years, these processes and techniques have been successfully automated thanks to sophisticated algorithms. The travel sector can now efficiently utilize different types of data analytics. 

They can analyze data to see exactly what happened, called descriptive analytics. They can understand why something occurred thanks to diagnostic analytics . Alternatively, they can identify what will happen and what to do next, thanks to predictive and prescriptive analytics.

The data fed to data analytics algorithms is called big data. It only takes a quick literature review to find out the big data definition:

“Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. It enables companies to do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, and more.”

The role of data analytics in the tourism and hospitality industry is becoming increasingly important with every passing year. Thanks to new IT technologies, companies in the travel sector can now efficiently track, record, store, and process big data, which enables even small companies to benefit from cutting-edge solutions. 

Advances in cloud technologies and infrastructure that support big data and data analytics enabled service providers to decrease costs. It simply means that the travel sector can now use big data in a cost-efficient manner. 

Data analytics unlocks many opportunities for travel companies. First and foremost, it allows people who are not data science experts to quickly review large-scale volumes of data . That is important because most of the touch points consumers have with travel businesses are now online, and each one produces some data. 

Big data and analytics can finally equip travel companies with everything they need to understand their target customers and capture more profit – or, in other words, gain a competitive advantage. 

At the same time, your business also generates internal data. Data analytics is essential because you can truly understand your business processes and how your company interacts with partners and customers.

Many scientific papers are trying to put a finger on the benefits data analytics unlocks for the tourism industry. While there are some subtle differences, all papers find out that big data, data analytics, ML, and AI-enabled strategies help unlock the following benefits.

Thanks to data analytics tools, travel agencies and tour operators can now better understand their markets. They can gauge the market performance to adopt new strategies or refine new ones to achieve more optimal results. 

They can better understand their customers and prospects as well. They can identify customer behavior patterns and customize their offer to reflect the current demand to attract more consumers and boost sales.

Data analytics can also help companies put their operations under a microscope. They can analyze the data they generate internally. It can help them achieve operational excellence, reduce operational costs, and make informed customization decisions to facilitate work for their staff. 

Data analytics also enables companies to gauge their supply chains. It allows them to source their products in a smarter and more informed manner, thus potentially increasing profit margins while still staying competitive within their markets. 

They can ultimately adopt new strategies to generate new revenue streams, maximize profits, and establish a better positioning within a target market.

Data science and big data have found many use cases in the tourism industry analytics. Travel organizations of all sizes are collecting and storing copious amounts of data. Some do it passively as their processes generate data. 

For instance, their website can automatically collect data with Google Analytics to facilitate web analytics . They can also store interactions with customers and previous purchase history to personalize service and offer relevant products to consumers.

Others adopt a more active approach, engaging in data-gathering activities such as data mining . It can help them optimize pricing strategies or identify viable marketing channels for specific markets. 

The primary role of big data in tourism industry analytics is to enable an accurate decision-making process. Proving accurate, updated, and structured data is paramount in this instance, as the quality analytics outcomes depend on all these factors.

In one study , scientists decided to further examine the impact of the rise of big data and analytics in the travel sector . They found out that most companies that adopt these new technologies do it because access to actionable data enables them to increase sales revenue, improve marketing initiatives, and get a competitive advantage.

One of the recent studies provides a systematic review of the big data use cases in the tourism industry. According to this study, the companies in the sector are using big data to:

• Custom-tailor travel products to meet the needs of market/audience segments;
• Improve cyber-security to enhance the travel experience;
• Create new value by espousing different types of interest;
• Gauge customer sentiment and identify factors of positive and negative engagement on social media.

Understanding data science, data analytics, and big data can be challenging, given that these are all new concepts. The best way to do it is to review some of the use cases. Below you can find real-life examples of data science usage in the tourism industry.

Venice and Salzburg as perfect examples of smart tourism destination

Smart tourism destination is a brand new kind of destination. It refers to destinations that heavily rely on technology to enhance their competitiveness, favor tourism development projects, and improve tourism experiences. These destinations promote the use of technology and capture consumers’ data to fine-tune their offers to deliver delightful experiences to tourists. 

The two examples of smart tourism destinations in practice are Venice and Salzburg, as outlined in this study . Using data analytics to model the experience process while visiting Venice and Salzburg helped define destination boundaries and better manage destination stakeholders. 

These two destinations managed to improve the co-creation of tourism experiences, be more sustainable and competitive, and promote sustainable tourism and sector development.

Fareboom’s success with the fare predictor tool

Freboom is one of the world’s most popular online travel agencies. They decided that big data and analytics could help them improve their travel booking website by offering attractive prices. To do it, they implemented a fare predictor tool.

The fare predictor tool uses a self-learning algorithm and goes through millions of website fare search records. The algorithm used factors such as demand growth and seasonal trends to predict future price movements, with a confidence rate of 75%. The final results are outstanding as the tool increased the average time a person spends on the Fareboom site by 100% .

Booking.com uses predictive analytics across departments

Booking.com doesn’t require a formal introduction as it’s one of the world’s largest travel marketplaces. When it comes to data analytics, Booking.com might just be a perfect example. Why? Because the company uses it to enable all its departments to make timely and informed decisions.

In one of the recent interviews , Lukas Vermeer, a data scientist at Booking.com, revealed that the company uses data analytics in web marketing, attribution models, ROI predictions, recommendation systems, call volume predictions, and scheduling algorithms. 

Mini Cambodian hotel uses big data as a starting point for hotel management

Mini-hotel in Cambodia decided to step up the hotel management strategy by using big data and data analytics. Their goal was to develop a new, more competitive, and cost-effective hotel management strategy. They started using one of the world’s top analytics tools to collect valuable data, including sales statistics, the booker’s country of origin, travel purpose, the device to book, and cancelation details, among others.

The case study reveals that big data and data analytics can be used as a starting point for improving hotel management strategies . In this case, big data enabled the hotel owner to identify new opportunities, find out means to pursue them in the most efficient manner, identify new areas of potential growth, and monitor the effect of the new strategy.

Hyatt’s intelligent virtual assistant and data analytics save the company $4.4 million

Hyatt is one of the largest hospitality companies out there. It handles hundreds of thousands of calls per month. Since it became borderline impossible to provide a great customer experience using only human assistants and agents, the company decided to deploy an intelligent virtual assistant.

The virtual assistant’s role was to automate the new reservation process, transfer callers, and automate, confirm, and cancel reservations. More importantly, it could collect data and feed it to the data analytics engine. Thanks to automation and data analytics, Hyatt reduced contact center costs by 33% and saved 94% on fully automated interactions which roughly translates into $4.4 million. 

Qantas airline significantly reduced the number and length of delays thanks to data analytics

As one of the world’s most reputable airlines, Qantas wanted to improve its schedule recovery to provide nothing but the best transportation experience to consumers. To do it, the company decided to make use of data. They invested in a schedule recovery solution that uses real-time data analytics to provide instant insights.

The results were outstanding. Qantas was able to assess all aspects of the operational costs, reduce the number and length of delays, and significantly reduce the cost of managing flight disruptions , whether due to weather conditions, excessive traffic, or unforeseen operational delays.

As we’ve previously stated, descriptive analytics provides answers to the “What happened” question. It can include various historical data types, but it can also be applied to data streams in real-time. With this in mind, descriptive analytics can help travel agencies in many ways:

• Better understand customers – a travel agency can track numerous behavioral metrics to truly understand customers and their preferences. Thanks to these insights, you can easily personalize your offer in a cost-efficient manner;
• Improve brand image – descriptive analytics can help travel agencies manage their brand reputation. They can mine data from social media platforms to gauge customer sentiment, respond to 
• Dynamic pricing management – imagine being able to adjust prices in real-time based on the supply, demand, and competitors’ prices. That’s precisely what descriptive analytics can offer to travel agencies of all sizes;
• Smart route optimization – cross-referencing logistics with customer behavioral data can help travel agencies further delight customers and make traveling more convenient. Smart route optimization refers to identifying and offering the most optimal flight and travel routes to customers;
• Make travel safer for everyone – fintech companies are already using descriptive analytics to detect frauds and anomalies in their transaction data sets. Travel companies can do it too to offer additional protection to their customers. With predictive analytics, they can easily detect unauthorized payments and stolen accounts.

Data analytics has found many applications in the travel sector. It can offer actionable insights and streamline the decision-making process, and many organizations are already using solutions with built-in data analytics capabilities. 

They have various goals ranging from improving customers’ experience to maximizing profits. Given that the tourism industry and big data analytics market are projected to continue to grow, the chances are that we are going to see even more use cases of data analytics in the sector .

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Marc Truyols has a degree in Tourism from the University of the Balearic Islands. Marc has extensive experience in the leisure, travel and tourism industry. His skills in negotiation, hotel management, customer service, sales and hotel management make him a strong business development professional in the travel industry.

Mize is the leading hotel booking optimization solution in the world. With over 170 partners using our fintech products, Mize creates new extra profit for the hotel booking industry using its fully automated proprietary technology and has generated hundreds of millions of dollars in revenue across its suite of products for its partners. Mize was founded in 2016 with its headquarters in Tel Aviv and offices worldwide.

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Tourism Analytics Before and After COVID-19

Case Studies from Asia and Europe

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Nanyang Business School, Nanyang Technological University, Singapore, Singapore

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• Takes a data analytics approach to forging a path forward for the tourism industry badly impacted by COVID-19
• Brings together tourism case studies from Europe, Hong Kong, China, and Singapore
• Adopts machine learning predictive models and simulation models to provide holistic understanding

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Table of contents (14 chapters)

Front matter, hong kong tourism under covid-19.

• Cui Yuting, Gao Yinan, Ge Xinyi, Hao Junyi, Jiang Zhongyang, Yu Peichen

Tourism Analytics, the Case for Hainan China

• He Pan, Lu Hengyu, Wei Yuzhi, Wang Qi, Wu Meng, Zhang Qiqi

Impacts of COVID-19 on Food, Aviation, and Accommodation in Europe

• Chen Shijing, Chen Yuheng, Chen Ziyan, Gong Manlin, Lai Zijun, Lin Dazheng et al.

Tourism Rebounds Analysis—Lessons from Baltics Countries

• Long Zhaowen, Wei Kexian, Wu Mengran, Xiong Yike, Yang Yafeng, Zhao Chenxi et al.

Compare and Contrast the Impact of COVID-19 from Small to Large Country

• Hu Yubin, Ma Defeng, Qiu Zicong, Tang Manhong, Wang Lyu, Wang Yang

Tourism Analytics—The Case for South Africa

• Yong Heng Michael Tan, Yok Yen Nguwi

Hotel Booking Cancellation Analytics on Imbalanced Data

• Cai Yuxuan, Hsu Tuan-Chun, Jin Zhuofan, Tan Chian Wen Melvin, Vivek Goyal, Zheng Yijun

Tourism Prediction Analytics

• Chen Shuhua, Gao Yuan, Lin Desheng, Shen Yi, Wu Di

Marketing Segmentation and Targeted Marketing for Tourism

• Liu Ye Xin, Li Yiteng, Ritika Jain, Tran Thi Hong Van, William Lim, Zhao Yilin

Machine Learning for Tourism

• Chang Chai, Yanbo Chen, Taiying Kuang, Chun-Yu Lai, Jingyi Li, Jian Zhang

Data Visualization on Tourism

• Hanlin Xiao, Jie Cheng, Yunfan Lyu, Yuqing Ma, Dongxu Sun, Qian Wu

Sustaining Tourism Sector Through Domestic Tourism and Analytics

• Dingming Chen, Pou Ing Gan, Hoi Ming Lee, Ziye Li, Vadlamudi Santosh Krishna, Quanxin Wang

Tourism Analytics with Price and Room Booking Simulation

• Yile Cai, Ke Duan, Congcong Peng, Xiaodan Shao, Yichu Sun, Jiayi Wang et al.

Tourism Arrival Prediction

• Cao Wenfei, Gu Yichao, Wang Jingyi, Wang Yanan, Zhao Yifan, Zhu Haoxiang
• Data analytics for the Tourism Industry
• Data Analytics for COVID-19 Effects on Tourism
• Machine Learning Predictive Models for Tourism Management
• Recovery of Tourism After COVID-19
• Tourism and Big Data

About this book

This book is compilation of different analytics and machine learning techniques focusing on the tourism industry, particularly in measuring the impact of COVID-19 as well as forging a path ahead toward recovery. It includes case studies on COVID-19's effects on tourism in Europe, Hong Kong, China, and Singapore with the objective of looking at the issues through a data analytical lens and uncovering potential solutions. It adopts descriptive analytics, predictive analytics, machine learning predictive models, and some simulation models to provide holistic understanding.

Editors and Affiliations

Yok Yen Nguwi

About the editor

Yok-Yen is Senior Lecturer of Data Analytics in College of Business (Nanyang Business School). She obtained her B.Eng.(Computer) from the University of Newcastle, Australia, before completing her Ph.D. in Computer Engineering at Nanyang Technological University. Apart from that, she also received ACCA accountancy qualification.

Bibliographic Information

Book Title : Tourism Analytics Before and After COVID-19

Book Subtitle : Case Studies from Asia and Europe

Editors : Yok Yen Nguwi

DOI : https://doi.org/10.1007/978-981-19-9369-5

Publisher : Springer Singapore

eBook Packages : Business and Management , Business and Management (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023

Hardcover ISBN : 978-981-19-9368-8 Published: 09 March 2023

Softcover ISBN : 978-981-19-9371-8 Published: 09 March 2024

eBook ISBN : 978-981-19-9369-5 Published: 08 March 2023

Edition Number : 1

Number of Pages : VIII, 246

Number of Illustrations : 20 b/w illustrations, 223 illustrations in colour

Topics : Tourism Management , Computer Science, general , Statistics, general

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Tourism statistics, indicators and big data: a perspective article

Tourism Review

ISSN : 1660-5373

Article publication date: 7 January 2020

Issue publication date: 20 February 2020

This paper aims to discuss the evolution of tourism data and critically debates future perspective for producers and users of tourism data.

Design/methodology/approach

This paper provides a perspective on tourism data based on selected literature.

Industry developments, technological changes and novel methodologies have influenced tourism data sources. Closer attention to new data collection methods and novel analytics is required.

Research limitations/implications

A considerate and integrated system of tourism data (statistics, indicators, and big data) shall remain a priority for scholars and practitioners alike.

Practical implications

The thoughtful merging of tourists’ digital traces with industry data, the competences of data analysts and the theoretical strengths of tourism scholars will result in a redesign of the tourism data landscape.

Social implications

This perspective article provides a brief overview of the development and challenges related to the future use of tourism statistics, indicators and big data.

Originality/value

The paper offers a novel vision of tourism data by combining three different but complementary aspects of tourism data.

• Tourism statistics
• Data sources
• Tourism measurement

Volo, S. (2020), "Tourism statistics, indicators and big data: a perspective article", Tourism Review , Vol. 75 No. 1, pp. 304-309. https://doi.org/10.1108/TR-06-2019-0262

Emerald Publishing Limited

Copyright © 2019, Serena Volo.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Discussing the evolution of tourism data sources spanning from the traditional collection of tourism supply and demand statistics to the exploitation of big data; and

Presenting and debating future perspective for producers and users of tourism data.

Past perspective: 75 years of developments (1946-2020)

governments’ evaluation of tourism dimension and its significance to the national economy;

destinations’ forecasts of tourism arrivals; and

industry’s decision-makers use for strategic marketing purposes ( Wöber, 2000 ; Massieu, 2001 ; Volo, 2004 ).

the deficiencies in systematically collect the necessary elementary data; and

the difficulties in accounting for the complex nature of tourism which requires suitable operationalization and measurements of the investigated constructs ( Mazanec et al. , 2007 ; Volo, 2015 ; Mendola and Volo, 2017 ).

Thus, despite the recent developments, the contribution of composite indicators to comprehensive theoretical frameworks and the actual usage – by tourism stakeholders and operators – of the derived rankings remain often unclear and undocumented ( Volo, 2018 ).

Future perspective: 75 years of outlook (2020-2095)

exploit these powerful data as measurements of tourist flows in space and time ( Hallo et al. , 2012 ; Scaglione et al. , 2016 );

predict tourism demand (Bangwayo-Skeete and Skeete, 2015 ; Song and Liu, 2017 ); and

assess their validity as data sources ( Mariani and Borghi, 2018 ; Mariani et al. , 2019 ).

Along the same lines, a Eurostat task force has investigated the use of big data in complementing official statistics, including tourism statistics ( Eurostat, 2014 ). The full potential of big data is under investigation, clearly the potential to induce real-time marketing actions (Buhalis and Sinarta, 2019 ) is appealing to tourism stakeholders, while scholars acknowledge the need for proper data mining and ad hoc algorithms to enable accurate use of these digital traces ( Scaglione et al. , 2016 ; Mariani et al. , 2018 ).

Fast raising opportunities to use novel data sources impose few basic considerations for their successful integration as tourism data, as evident in Figure 1 .

A basic system of tourism statistics is lacking in several countries – mostly the underdeveloped – thus significant work is needed, as these countries are receiving an increasing share of international tourism. The recent developments in the creation of composite indicator for tourism will continue but will need further strengthening and integration between traditional statistics and big data. Creating shared databases and replicable methods will allow scholars across countries to apply indicators to destinations of different magnitude enabling policymakers to easily access and soundly use these indicators. Meanwhile, tourists traces have become an invaluable source of data, and the emerging smart disruptive innovations in tourism will allow even more gathering of tourism-related big data and thus the need for reciprocity and fairness in data and information exchanges will be paramount ( Buhalis et al. , 2019 ). Legal and ethical exploitation of these novel data sources ought to be investigated, and pathways for mutual beneficial usage of scholars and businesses shall be designed. The challenges of data sharing, data extraction and data analytics have been explored albeit in an incomplete and fragmented way; thus, conceptual frameworks ought to be developed to ensure theory building and enhance customization and intelligent service supply ( Mariani et al. , 2018 ). Improved data analytics will enable using big data for not only tourism online marketing, design and recommendations but also demand prediction, precaution and emergency studies ( Li et al. , 2018 ). At the twilight of traditional measurements, tourism private and public stakeholders should foresee the enormous opportunity to combine, in real-time, information obtained by tourists’ digital traces with that of tourism companies’ databases and information systems. An exciting time to come for the tourism industry.

Conclusions

The slow development of tourism statistics, followed by the methodological scrutiny of those interested in indicators have left space to the disruption of big data. The challenge remains however on shifting the attention from a “big” to a “smart” usage of these data, adding layers of information, facilitating real-time usage and appropriate dissemination of trends. The thoughtful merging of tourists’ digital traces with industry data, the competences of data analysts and the theoretical strengths of tourism scholars will results in a redesign of the tourism data landscape. A considerate and integrated system of tourism data (statistics, indicators and big data) shall remain a priority for scholars and practitioners alike.

Tourism data: future perspectives

Aroca , P. , Brida , J.G. and Volo , S. ( 2017 ), “ Tourism statistics: correcting data inadequacy ”, Tourism Economics , Vol. 23 No. 1 , pp. 99 - 112 .

Bangwayo-Skeete , P.F. and Skeete , R.W. ( 2015 ), “ Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach ”, Tourism Management , Vol. 46 No. 1 , pp. 454 - 464 .

Buhalis , D. and Sinarta , Y. ( 2019 ), “ Real-time co-creation and nowness service: lessons from tourism and hospitality ”, Journal of Travel & Tourism Marketing , Vol. 36 No. 5 , pp. 563 - 582 .

Buhalis , D. , Harwood , T. , Bogicevic , V. , Viglia , G. , Beldona , S. and Hofacker , C. ( 2019 ), “ Technological disruptions in services: lessons from tourism and hospitality ”, Journal of Service Management , Vol. 30 No. 4 , available at: https://dx.doi.org/10.1108/JOSM-12-2018-0398

Castellani , V. and Sala , S. ( 2010 ), “ Sustainable performance index for tourism policy development ”, Tourism Management , Vol. 31 No. 6 , pp. 871 - 880 .

Eurostat ( 2014 ), “ Feasibility Study on the Use of Mobile Positioning Data for Tourism Statistics ”, Eurostat .

Frechtling , D.C. ( 1976 ), Proposed Standard Definitions and Classifications for Travel Research , US Travel Data Center , New York, NY .

Frechtling , D.C. and Hara , T. ( 2016 ), “ State of the world’s tourism statistics and what to do about it ”, Tourism Economics , Vol. 22 No. 5 , pp. 995 - 1013 .

Gooroochurn , N. and Sugiyarto , G. ( 2005 ), “ Competitiveness indicators in the travel and tourism industry ”, Tourism Economics , Vol. 11 No. 1 , pp. 25 - 43 .

Hallo , J.C. , Beeco , J.A. , Goetcheus , C. , McGee , J. , McGehee , N.G. and Norman , W.C. ( 2012 ), “ GPS as a method for assessing spatial and temporal use distributions of nature-based tourists ”, Journal of Travel Research , Vol. 51 No. 5 , pp. 591 - 606 .

Hannigan , K. ( 1994 ), “ Developing European community tourism statistics ”, Annals of Tourism Research , Vol. 21 No. 2 , pp. 415 - 417 .

Laesser , C. , Bieger , T. , Keller , P. and Buhalis , D. ( 2019 ), “ 75 Years of tourism review: survival by transformation: a perspective article ”, Tourism Review , Vol. 1 , available at: https://doi.org/10.1108/TR-02-2019-0069

Latham , J. and Edwards , C. ( 2003 ), “ The statistical measurements of tourism ”, in Cooper C. (Ed.), Classic Reviews in Tourism , Channel View Publications , Clevedon .

Li , J. , Xu , L. , Tang , L. , Wang , S. and Li , L. ( 2018 ), “ Big data in tourism research: a literature review ”, Tourism Management , Vol. 68 No. 2 , pp. 301 - 323 .

Liang , C.J.H. , Chen , H.B. and Wang , M.Y. ( 2012 ), “ Units, populations, and valid analyses ”, International Journal of Culture, Tourism and Hospitality Research , Vol. 6 No. 1 , pp. 70 - 80 .

Lickorish , L.J. ( 1997 ), “ Travel statistics – the slow move forward ”, Tourism Management , Vol. 18 No. 8 , pp. 491 - 497 .

Mariani , M.M. and Borghi , M. ( 2018 ), “ Effects of the booking.com rating system: bringing hotel class into the picture ”, Tourism Management , Vol. 66 , pp. 47 - 52 .

Mariani , M.M. , Borghi , M. and Gretzel , U. ( 2019 ), “ Online reviews: differences by submission device ”, Tourism Management , Vol. 70 , pp. 295 - 298 .

Mariani , M.M. , Baggio , R. , Fuchs , M. and Höpken , W. ( 2018 ), “ Business intelligence and big data in hospitality and tourism: a systematic literature review ”, International Journal of Contemporary Hospitality Management , Vol. 30 No. 12 , pp. 3514 - 3554 .

Massieu , A. ( 2001 ), “ A system of tourism statistics (STS): scope and content ”, in Lennon , J.J. (Ed.), Tourism Statistics , Continuum , London , pp. 3 - 13 .

Mazanec , J.A. , Wöber , K. and Zins , A.H. ( 2007 ), “ Tourism destination competitiveness: from definition to explanation? ”, Journal of Travel Research , Vol. 46 No. 1 , pp. 86 - 95 .

Mendola , D. and Volo , S. ( 2017 ), “ Building composite indicators in tourism studies: measurements and applications in tourism destination competitiveness ”, Tourism Management , Vol. 59 No. 2 , pp. 541 - 553 .

Önder , I. , Koerbitz , W. and Hubmann-Haidvogel , A. ( 2016 ), “ Tracing tourists by their digital footprints: the case of Austria ”, Journal of Travel Research , Vol. 55 No. 5 , pp. 566 - 573 .

Pine , R.J. ( 1992 ), “ Towards a useful measure of tourism activity at individual country level ”, Tourism Management , Vol. 13 No. 1 , pp. 91 - 94 .

Pulido , I.J. and Sanchez , M. ( 2009 ), “ Measuring tourism sustainability: proposal for a composite index ”, Tourism Economics , Vol. 15 No. 2 , pp. 277 - 296 .

Ritchie , J.R.B. ( 1975 ), “ Some critical aspects of measurement theory and practice in travel research ”, Journal of Travel Research , Vol. 14 No. 1 , pp. 1 - 10 .

Scaglione , M. , Favre , P. and Trabichet , J.-P. ( 2016 ), “ Using mobile data and strategic tourism flows: pilot study monitour in Switzerland ”, Proceedings of the Big Data in Tourism & Business Intelligence in the Travel & Tourism Domain , Mid Sweden University , pp. 69 - 72 .

Shackleford , P. ( 1980 ), “ Keeping tabs on tourism: a manager’s guide to statistics ”, International Journal of Tourism Management , Vol. 1 No. 3 , pp. 148 - 157 .

Smith , S.L.J. ( 1988 ), “ Defining tourism a supply-side view ”, Annals of Tourism Research , Vol. 15 No. 2 , pp. 179 - 190 .

Song , H. and Liu , H. ( 2017 ), “ Predicting tourist demand using big data ”, in Xiang , Z. and Fesenmaier , D.R. (Eds), Analytics in Smart Tourism Design , Springer , Cham , pp. 13 - 29 .

UNWTO ( 1994 ), Recommendations on Tourism Statistics , 1994 Edition Madrid: World Tourism Organization , https://unstats.un.org/unsd/publication/Seriesm/SeriesM_83e.pdf .

UNWTO (2008). OECD (2008) , International Recommendations for Tourism Statistics 2008 (IRTS 2008) , New York , Madrid , United Nations, Statistical Office of the European Communities; Organisation for Economic Co-operation and Development; and World Tourism Organization .

Volo , S. ( 2004 ), “ A journey through tourism statistics: accuracy and comparability issues across local, regional and national levels ”, Proceedings of the 24th Biennial Conference on Regional and Urban Statistics: Understanding Change (SCORUS) , Minneapolis, MN , pp. 210 - 216 .

Volo , S. ( 2005 ), “ A consumer-based measurement of tourism innovation ”, Journal of Quality Assurance in Hospitality & Tourism , Vol. 6 Nos 3/4 , pp. 73 - 87 .

Volo , S. ( 2015 ), “ Indicator ”, in Cater , C. , Garrod , B. and Low T. (Eds), The Encyclopedia of Sustainable Tourism , CABI , Oxford , pp. 277 - 279 .

Volo , S. ( 2018 ), “ Tourism data sources: from official statistics to big data ”, in Cooper , C. , Volo , S. , Gartner , W.C. and Scott , N. (Eds), The SAGE Handbook of Tourism Management , SAGE , London , pp. 193 - 201 .

Volo , S. and Giambalvo , O. ( 2008 ), “ Tourism statistics: methodological imperatives and difficulties: the case of residential tourism in island communities ”, Current Issues in Tourism , Vol. 11 No. 4 , pp. 369 - 380 .

Wöber , K.W. ( 2000 ), “ Standardizing city tourism statistics ”, Annals of Tourism Research , Vol. 27 No. 1 , pp. 51 - 68 .

Corresponding author

About the author.

Serena Volo is based at the Faculty of Economics and Management, Free University of Bozen, Brunico, Italy. She is Associate Professor at the Faculty of Economics and Management, Free University of Bozen, Italy. Her research interests include consumer behavior in tourism, tourism statistics and indicators, tourism big data analytics, destination competitiveness and innovation.

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Big Data Analytics in the Travel Industry: Types, Uses

In order to succeed and compete in the travel industry, organizations need to deliver exceptional customer service, differentiate themself from competitors, and optimize their processes and offerings.

Learn how leveraging big data in travel and tourism can help your organization achieve those goals while increasing your revenue in the process.

Things to know about Travel Industry Data Analytics:

What is Data Analytics?

Types of big data analytics in the tourism industry, data that can be analyzed in the travel industry, 6 uses of data analytics in the travel industry.

Data analytics is the ability to leverage an organization’s data to help them gain deeper insights and enhanced decision-making capabilities. It helps organizations be more agile and proactive, ultimately gaining a competitive edge in the market.

There are four primary types of data analysis:

• Descriptive Analytics: Focuses on past events, leveraging historical data to identify trends and relationships
• Predictive Analytics: Uses data, modeling, and machine learning (ML) to analyze current and historical data to make predictions about future outcomes
• Diagnostic Analytics: Examines data to determine the causes of trends and relationships, answering the question, “Why did this happen?”
• Prescriptive Analytics: Analyzes data to provide recommendations on the best course of action to achieve a desired outcome

Organizations in the travel industry have a vast array of data available to analyze and leverage, including:

• Customer demographics
• Customer searching, browsing, and buying behaviors
• Customer reviews and feedback
• Customer preferences on flights, destinations, accommodations, transportation, and meals
• Data on internal operations
• Targeted marketing
• Pricing and revenue optimization
• Improved recommendations
• Enhanced customer experiences
• Reputation management
• Competitor research

Targeted marketing in the travel industry

Collecting and analyzing large amounts of data can provide you with valuable insights into your customers’ preferences and their browsing, searching, and purchasing behaviors.

This enables you to know your customers’ wants and needs on a deeper level and tailor your marketing campaigns accordingly, delivering the kind of relevant, strategic marketing content and offers that will drive engagement and revenue.

Pricing and revenue optimization in the travel industry

Organizations can leverage a combination of internal and external data, including occupancy rates, popular events occurring in specific destinations, upcoming holidays, flights, and historic booking and occupancy data to more accurately and proactively predict demand.

This enables travel organizations to manage their room rates, airline ticket costs, travel packages, and other products and services to increase costs during peak periods of high demand, maximizing the generated revenue.

Improved recommendations in the travel industry

Leveraging historical data and predictive analytics can allow your organization to better understand individual users’ behaviors and recommend the relevant products and services that are most likely to drive conversions.

For instance, an effective, data-driven recommendation system can upsell your customers by recognizing that they are searching for the most affordable flight options and, as a result, would probably be more interested in cheaper accommodations than high-end hotels.

You can use customer data to upsell by making effective, personalized, relevant recommendations that will ultimately drive more sales and increase revenue.

Enhanced customer experiences in the travel industry

More than 70% of customers today say they expect the companies they interact with to understand their unique needs, desires, and expectations.

Travel organizations can leverage big data analysis to understand what their customers prefer and expect from their travel experiences, enabling the potential for a deep level of personalization. This can include:

• Preselecting a customer’s preferred make and model of rental vehicle
• Creating and offering more desirable tour packages
• Offering travel deals and activities tailored to families with young children

Better reputation management

Big data analytics allows you to collect, process and analyze data from a variety of sources including complaint forms, AI chatbots, social media, and customer reviews.

This allows you to identify your greatest strengths that your customers are happy with, as well as your greatest weaknesses that customers frequently identify as pain points.

Your travel organization can leverage this information to adjust your processes, services and offerings, and internal training accordingly to ensure maximum levels of customer satisfaction, resulting in an improved brand reputation.

Competitor research in the travel industry

You can also conduct similar research on your business’s biggest competitors, collecting and analyzing data from social media and customer reviews.

This enables your brand to identify your competitors’ greatest strengths that you should strive to match, if not surpass, as well as their greatest weaknesses. This information is incredibly valuable, as it can empower your business leaders to identify gaps and opportunities in the market.

You can use this information to clearly differentiate yourself from other players in the space and give your organization a competitive advantage, ultimately driving greater revenue.

Leverage the Full Value of Your Travel Data

When you look to turn insights into action, it’s helpful to have experts in your corner.

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By Celeste Harms

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Enabling Tourism Data Analytics

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This blog is co-authored by Jaymin Darbari, Data Governance Lead, Abu Dhabi Department of Culture and Tourism and Hafdi Salah, Managing Partner at BBI Consultancy

The travel and tourism industry depends on tourism data analytics for a variety of reasons. It helps spot trends and understand what travelers are looking for. Data and analytics also enable organizations to make informed decisions about how to attract new visitors. But that data is often disconnected and locked up in various enterprise systems. Tourism boards and travel organizations need to solve the problem of getting tourism data from source to dashboard in a timely manner. So, they are turning to integration platform as a service ( iPaaS ) for cloud-native data integration solutions. The benefits of cloud modernization for tourism and visitors associations are significant. Imagine having a single view of visitors, aggregated from the data collected from multiple sources and delivered as trusted data.

Here’s how iPaaS services like cloud data integration, API management and others helped one tourism department meet its data maturity goals to become a trusted tourism data analytics provider of choice.

Bringing more visitors to Abu Dhabi using trusted data

Located on an island in the Persian Gulf, Abu Dhabi is a popular destination for tourists. Visitors enjoy 250 miles of coastline, four football stadiums, a vibrant art and music scene, world-class museums, myriad cultural sites and the region’s largest exhibition center. It receives approximately 11.3 million visitors every year.

The Abu Dhabi Department of Culture and Tourism (DCT) regulates, develops and promotes the emirate of Abu Dhabi. And it has ambitious goals for increasing visits to the emirate. But it faced a few tourism data analytics challenges:

• Increasing visits to the Emirate and tracking those statistics required integrating and aggregating tourism data from over 100 sources and stakeholders. This includes legacy systems and databases. It also pulls data from hotels, malls, tourist attractions, Wi-Fi hotspots, TripAdvisor and cultural sites.
• DCT met challenges to increase operational efficiency. But it lacked the necessary tools to innovate at scale. Until recently, compiling this data was a daily, manual process. It was based on spreadsheets, emails and flat files that consumed nearly 40 hours of employee time per week. As a result, DCT could only refresh its existing data warehouse on a monthly basis. This limited the usefulness of the data for reporting purposes.
• They lacked trusted data and data quality issues were only addressed reactively. DCT also lacked an enterprise taxonomy for KPIs and data elements. Most effort was spent on data prep and cleaning. Little effort was focused on providing insights and business recommendations. All these challenges were holding DCT back from delivering true business value and innovating at scale.

Improving time to market for tourism data

DCT needed an end-to-end, unified, scalable intelligent, cloud-native data management platform that automated data sourcing, metadata management and data quality. DCT’s business intelligence team worked with BBI, an Informatica Partner, to deploy an integrated Informatica Intelligent Data Management Cloud (IDMC) solution :

• Informatica Cloud Data Integration enabled DCT to rapidly onboard tourism data from disparate source systems into a Microsoft SQL Server data warehouse in just four months.
• DCT was able to consume and build APIs using Informatica Cloud API Manager. They relied on Informatica B2B Gateway to capture data coming in from hotels, cultural sites and other tourism partners. This data was captured from emails and files and uploaded it to a secure FTP site. DCT also uses Informatica Cloud API Manager to consume data from Wi-Fi hotspots and industry aggregators via APIs. The tool also makes tourism data available via APIs to government departments and stakeholders.
• Informatica Data Quality enabled DCT to deliver clean, trusted data. At the same time, Informatica Data Quality helps DCT detect anomalies and report any issues back to the business.

Quick results and ROI with IDMC

As Jaymin Darbari, Data Governance Lead, Abu Dhabi Department of Culture and Tourism, explains, “ Instead of reporting tourism data monthly, we are able to switch to daily reporting for important KPIs. And we can now report monthly data the day after the month ends, instead of waiting two weeks for manual reconciliation.”

DCT was also able to:

• Gain a 360-degree view of visitors . DCT was able to quickly collate and co-relate nationality, spend, seasonality, site visits, event visits, etc.
• Promote self-service and democratize access to data . DCT increased productivity by enabling business users to quickly discover data and define KPIs. They were also able to transform data to derive new actionable insights while saving workers 2,000+ hours of manual labor annually.
• Accelerate time to market by rapidly onboarding new sources of data . DCT was able to onboard new internal and external sources of data with minimal effort using a zero-code interface. • Enable full automation. DCT achieved full automation of manual tasks for source file collection and consolidation. This came with no human delays and errors, and they were able to break free from spreadsheets.
• Become a data provider to government and other stakeholders . DCT was able to expose its public data through a unified API layer. This enabled data exchange with the AD Executive Council, Miral and SCAD, etc. This realized the goal of becoming a trusted data provider for the government and other private organizations focused on culture and tourism in Abu Dhabi.
• Adopt advanced analytics . IDMC allowed DCT to apply advanced machine learning and AI algorithms to forecast hotel occupancy, event forecasts, etc.
• Improve trust in data across the organization . This was done by proactively addressing data quality issues and encouraging users to adopt analytics.

To learn more about the Abu Dhabi DCT Cloud Modernization journey, please watch the on-demand webinar – “ Data to Decisions: Getting results from Cloud Data Warehouse Modernization ,” where Jaymin Darbari, Data Governance Lead, Abu Dhabi Department of Culture and Tourism, and Hafdi Salah, Managing Partner at BBI Consultancy , discuss how DCT overcame its data management challenges and implemented new data strategies.

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Tourism Industry Big Data Analytics Market

Detailed Analysis of Tourism Industry Big Data Analytics Market by On-premise and Cloud Deployment Model, 2023 to 2033.

The Rise of Smart Tourism- Big Data Analytics Drives Personalized Experiences! FMI Highlights How Tourism Industry Big Data Analytics Blow up a Revolution in Travel Experiences

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Tourism Industry Big Data Analytics Market Outlook (2023 to 2033)

As per newly released data by Future Market Insights (FMI), the global tourism industry and big data analytics market is estimated at US$ 225.4 billion in 2023 and is projected to reach US$ 486.6 billion by 2033, at a CAGR of 8% from 2023 to 2033.

Big data analytics empowers tourism businesses to gather and analyze vast amounts of customer data, encompassing preferences, behaviors, and demographics. This invaluable information enables businesses to offer personalized recommendations, tailored travel packages and targeted marketing campaigns. The result is heightened customer satisfaction and loyalty.

Big data analytics facilitates precise demand forecasting by analyzing historical booking data, seasonal patterns, events, and other relevant factors. This foresight empowers tourism businesses to optimize pricing strategies, maximizing revenue during peak seasons and minimizing the risk of under-booking during periods of low demand.

The tourism industry heavily relies on social media platforms and online review sites for customer engagement. Big data analytics allows businesses to monitor these channels, gauge customer sentiment, identify emerging trends, and promptly address customer complaints. Companies can better understand customer feedback and adapt their marketing and service strategies by conducting sentiment analysis.

Big data analytics assists destination management organizations and tourism boards in identifying popular attractions, understanding visitor flows, and optimizing marketing efforts to attract more tourists to their regions. By leveraging data-driven insights, destinations can effectively promote their unique offerings and enhance overall tourism experiences.

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2018 to 2022 Global Tourism Industry Big Data Analytics Demand Outlook Compared to 2023 to 2033 Forecast

Over the last few years the advanced tools and technologies has evolved the tourism industry. The data analysis in tourism industry has made easy to understand the opportunity of markets, weaknesses of the company, consumer perception, preferences and better mode of connectivity among all the verticals of the market. Earlier it was difficult to conduct data analysis due to the massive and time consuming data collection process. The advanced tools have made it convenient due to the networking of the online platform. In the modern era many travelers and organizations are making use of modern devices, software for a convenient and smooth journey. The data analytic tools use this online to understand the market current scenario and develop a strategy accordingly. The technologies such as Hadoop and cloud provide ample amount of space for data storage and offer wide range of data sources for analysis in a structured manner. In the modern era of technology and advancement big data analysis act as a prime factor for tourism industry.

Rise In Efficiency of Tourism Industry Helps to Boost the Global Tourism Industry Big Data Analytics Market

Big data tools allow tour operator companies or travel agencies to understand the market performance. It helps to understand the demand and supply of service in the market, to estimate the demand and supply of service in near future, the competitor comparison, segment analysis, to optimize supply chain. Furthermore, it helps government agencies to understand the flow of tourism in the country and strategies the area of investment in tourism industry of a country. Hotel chain use data analysis to understand the consumer preference and plan marketing strategy to attract more number of customers. The tools help to create relevant packages and offers based on the historic data or on travel patterns. It also aids the customer loyalty program as the tools help to analyze the frequent travelers using the service. Hence, the big data tools help to rise the efficiency of all the verticals of tourism industry.

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Revenue Optimization To Increase The Demand For Global Tourism Industry Big Data Analytics Market

The big data technology helps to improve the tourism industry is various ways. Big data helps to analyze and manage the revenue. The revenue management refers to investment of right amount to a specific part of business to maximize the financial outcomes. The feature enables travel agencies to analyze the right price for the service based on the expenses of the company, competitor prices comparison, past and current occupancy rates in the market, etc. It also helps to analyze which service can be merged with other services such as tour packages which includes hotel bookings with flight travel and likewise. It helps agencies to save the optimum cost and brings opportunity for diversity in business.

Regional Analysis

The tourism industry in North America has been at the forefront of utilizing big data analytics. Prominent players, including travel agencies, hotels, and airlines, have made substantial investments in cutting-edge data analytics technologies. Their goal is to enhance customer satisfaction, personalize marketing efforts, and optimize their operations with data-driven precision.

Europe has witnessed remarkable growth in the adoption of big data analytics in the tourism sector. Countries like the United Kingdom, Germany, France, and Spain have wholeheartedly embraced data-driven approaches to bolster destination management, elevate tourist experiences, and execute highly effective marketing campaigns tailored to individual preferences.

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Country-wise Insights

What role does customer segmentation play in boosting united kingdom tourism businesses.

Big Data Analytics is Shaping Responsible Tourism in the United Kingdom

The United Kingdom tourism industry big data analytics market is experiencing significant growth and presents numerous opportunities for businesses in the country. The United Kingdom tourism industry has undergone a digital transformation driven by the increasing use of mobile devices, online bookings, and digital marketing channels. Cities like London, Edinburgh, and Manchester attract a significant number of tourists. The United Kingdom's scenic landscapes, national parks, and outdoor recreational activities attract nature and adventure tourists. Big data analytics can help identify popular destinations, track visitor activities, and analyze feedback, facilitating the development of sustainable and engaging experiences for travelers.

The tourism industry plays a crucial role in the United Kingdom's economy. According to the World Travel and Tourism Council (WTTC), the total contribution of travel and tourism to the country's GDP was US$ 233.14 billion in 2022, accounting for 8.9% of the total GDP. There is a growing emphasis on sustainable tourism practices in the United Kingdom. Big data analytics can help organizations track and analyze data related to environmental impact, resource consumption, and carbon emissions. As a result, they are better equipped to decide how to lessen their ecological imprint.

What are the Key Trends in China's Tourism Industry and how is Big Data Influencing Them?

Big Data Analytics is Redefining & Shaping the Future of China's Tourism Industry

The China tourism industry has experienced impressive growth in recent years. With a burgeoning middle class and improved infrastructure, domestic tourism has been on the rise in China. In 2022, the total contribution of travel and tourism to China's GDP was approximately 11.5%. The GDP contribution of China's travel and tourism industry is anticipated to increase by more than 150% this year, according to the World Travel & Tourism Council's (WTTC) 2023 Economic Impact Research (EIR).

Travelers from China have become a significant force in global tourism. Outbound tourism from China has grown consistently, driven by rising incomes, easing travel restrictions, and a growing appetite for international experiences. The application of big data analytics in the China tourism market has been transformative. Big data enables businesses to optimize their offerings, marketing strategies, and operational efficiency. Hence, the future outlook for the China tourism industry big data analytics market looks promising, with continued growth, advanced analytics adoption, and a focus on sustainability and personalization.

How Big Data Analytics is Driving the Market in India?

Big Data Analytic To Boost The India Tourism Industry

Traveler from all over the world travel India for various reason. One of the thing that travelers like to experience the most is Indian Railways. Indian Railways is one of the tourist attraction. Everyday millions of commuters travel in Indian railways. According to India Brand Equity Foundation (IBEF) report India has the fourth largest railway network with 22593 trains and approximately 24 million passengers travel in Indian railways every day. It is recognized as one of the largest railway system in the world. In 2014, T he India Railways government of India launched IRCTC e-ticketing application for ease and convenience of ticket booking. There are millions of user accounts available consisting all the information of travelers. The data consist of traveler’s demographics, along with the information about other travelers traveling with him, travel preference, etc. To channelize and track this information the railway ministry makes use of oracle database. The oracle server management help to store the data, track the data and analyze the data which help railways to make strategic decision and development of new packages and services for better services for its people. Meanwhile there are various other travel agencies and tour aggregators like Veena World, Kesari Tours, Yatra, others make use of big data analytics for smooth functioning and create better opportunity in market. This attracts other players in the market to generate demand for big data analytics in tourism industry.

How Big Data Analytics in Tourism Market is Progressing in United States?

The Big Data Analytics Use By Travel Agencies Drives The Tourism Industry In United States

Travelers travel United States throughout the year. People travel United States for job opportunities’, tourism, education. In United States traveler make use of online applications extensively. As many travelers travel United States across the world, there is a huge demand for airlines in United States. As United States Airline serves a huge audience they generate a massive amount of data. Hence, they use big data analytics extensively. The big data analytics not only help them analyze the consumer data segment but it also helps them to analyze and perform various other task. For example, Southwest airline use big data analytics to enhance their service to its customer, but the data also help them for smarter maintenance. The fuel efficiency report, airplane health management systems, the flight metrics data help them understand the defects in all the aircraft help to reduce repair cost and provide safe flight to its customers. Such big data analytics is used in all the different verticals in United States for better efficiency of their services.

What Role Does Big Data Play in Enhancing Tourist Experiences in Germany?

Germany's Innovative Approach to Managing Tourist Hotspots with Big Data Analytics

The tourism industry in Germany is a vital contributor to the country's economy, generating substantial revenue and employment opportunities. The current Economic Impact report from the World Travel & Tourism Council projects a rise in the sector's GDP by 1.3% annually on average between 2022 and 2032. This growth rate outpaces the overall economy's projected growth rate of 1.1% during the same period. The tourism sector's GDP is expected to reach over US$ 429.15 billion, which accounts for 9.7% of the total GDP in Germany.

The Germany tourism industry big data analytics market is witnessing steady growth and is expected to expand further in the coming years. As more tourism businesses recognize the importance of data-driven decision-making, the demand for big data analytics solutions and services is projected to increase. Germany has been at the forefront of leveraging big data to manage tourist destinations efficiently. From crowd monitoring to traffic management, big data analytics enables authorities to optimize resources, improve infrastructure, and provide a seamless experience for visitors.

What Does the Future Hold for Japan's Tourism Market with Big Data Analytics?

Big Data Analytics Fuels Tourism Innovation in Japan, Reshaping Travel Experiences

The Japan tourism industry big data analytics market is poised for significant growth. Japan has witnessed a steady rise in inbound tourism, with a record number of international visitors in recent years. The tourism industry in Japan is a significant contributor to the country's economy. According to the 2023 Economic Impact Research published by the World Travel & Tourism Council (WTTC), Japan's travel and tourism industry is expected to contribute US$ 285.5 billion to the country's GDP this year. More than US$ 257 billion, or 6.2% of the economy, was contributed to the GDP by the industry last year, an increase of 50.5%.

Japan is known for its technological advancements, and the tourism industry is no exception. Businesses are adopting technologies to enhance the tourist experience and improve operational efficiency. Big data analytics has had a profound impact on the tourism industry in Japan. Tourism businesses in Japan are likely to collaborate and form partnerships with data analytics firms, technology providers, and government agencies to harness the full potential of big data. Such collaborations are likely to lead to innovative solutions and a more holistic approach to data-driven decision-making.

Category-wise Insights

Which type of analytics is mainly used in global tourism industry big data analytics market.

Descriptive Analysis Is Used In Global Tourism Industry Big Data Analytics Market

The descriptive data analysis helps to develop strategies based on historical and real time data, predictive analytics help to forecast and develop long term strategies for the travel agencies and perspective analytics help to understand the market and customer perception towards the industry. Other analysis such as e-commerce data, user generated content, temporal spatial data, etc. help to understand and develop strategy based on various other aspects of the industry.

Descriptive analytics has been a fundamental and established approach to data analysis for a long time. Many tourism businesses have already incorporated basic descriptive analytics tools into their operations, making it easier for them to adopt more advanced solutions in this segment.

Descriptive analytics relies on historical data, and tourism businesses usually have vast amounts of historical data accumulated over time. This data is often readily available. This makes it easier to implement descriptive analytics tools without significant additional data collection efforts.

Descriptive analytics tools are generally easier to implement and use compared to more complex analytics methods like predictive or prescriptive analytics. This simplicity makes it more accessible to a wider range of tourism companies, including smaller businesses that may not have the resources or expertise to adopt more advanced analytics methods.

Global Tourism Industry Big Data Analytics is Used For Which Purpose?

Global Tourism Industry Big Data Analytics Is Mainly Use to Analyze Revenue Management

Revenue management holds a dominant position in the tourism industry's big data analytics market due to its ability to optimize pricing and inventory strategies and maximize revenue. Revenue management is focused on optimizing pricing and inventory strategies to maximize revenue and profitability for tourism businesses. Big data analytics plays a crucial role in this process by providing insights into customer behavior, market trends, and demand patterns. By leveraging data analytics, companies can make informed decisions to set optimal prices, allocate resources effectively, and maximize their overall revenue potential.

Travelers nowadays have high expectations when it comes to personalized experiences. Big data analytics enable businesses to gather and analyze customer data, such as preferences, past behaviors, and feedback, to offer tailored products and services. By personalizing offers, recommendations, and interactions, tourism businesses can enhance the customer experience, increase customer satisfaction and loyalty, and ultimately drive revenue growth.

Which End-Use Outlook Prefer The Use Of Global Tourism Industry Big Data Analytics Market?

Tourism Industry Big Data Analytics Market is More Preferred by The Travel Agencies

In terms of end-use outlook, the tourism industry big data analytics is more preferred by travel agencies. Travel agencies are mostly multimodal aggregators managing various verticals or types of services such as air travelling, train bookings, hotel bookings, others. With more number of services, they offer they generate a massive amount of data. The big data analytics allow them to store data on cloud database to conduct analysis and gain insightful information that help to frame strategies for their business.

Competitive Landscape

The leading players operating in the global market and are focusing on developing innovative systems that can help to measure their market impact, customer presentence, market opportunities and various other measure more effectively and efficiently.

For instance:

In June 2023, OTELZ emerged as a remarkable example of success by blending tourism and technology. OTELZ has a considerable 40% cost advantage because to the efficient use of Microsoft's Azure technologies. With continuous advancements in technologies such as artificial intelligence, big data, and the integration of the Internet of Things, OTELZ aims to offer services at a more sophisticated level.

In January 2022, Marriott revealed its partnership with IBM Cloud technology, and together, they aim to elevate Marriott's IT operations. This collaboration aims to enable Marriott to deliver quicker digital services to tech-savvy guests and gain valuable insights about this crucial group of travelers, benefiting over 4,000 properties worldwide.

In the year 2017, Southwest Airlines collaborated with EPAM to improve the in-airport customer experience. EPAM with the help of data analytics build a digital wayfinding system for trouble-free navigation at airport. This helped customers for easy navigation at airport resulted in increase in demand for Southwest Airlines.

Key Players

• Micro Focus
• Informatica
• Continuum Analytics
• Hitachi Data Systems
• Orchestra Networks
• Predixion Software
• Riversand Technologies
• Stibo Systems
• TIBCO Software

Global Tourism Industry Big Data Analytics Market by Category

By product types:.

• Descriptive Analytics
• Predictive Analytics
• Perspective Analytics

By End-Use Outlook:

• Accommodation
• Travel Agencies

By Deployment Outlook:

• Cloud Warehouse

By Enterprises:

• Large Enterprises

By Purpose:

• Revenue Management
• Reputation Management
• Strategic Management
• Customer Experience
• Market Research
• Target Marketing
• Market Intelligence
• North America
• Latin America

Frequently Asked Questions

What is the cagr from 2023 to 2033.

The CAGR for the market is 8% until 2033.

What is the market’s historical performance?

From 2018 to 2022, the market expanded at a 6.5% CAGR.

What is the market size in 2023?

The market is valued at US$ 225.4 in 2023.

What will the market size be in 2033?

The market will reach US$ 486.6 billion by 2033.

What was North America's Market share in 2022?

North America generated 23% revenue in 2022.

Table of Content

List of tables, list of charts.

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UN Tourism | Bringing the world closer

Tourism statistics database.

• 145 Key Tourism Statistics
• Economic Contribution and the SDGs

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145 key tourism statistics

Data are collected from countries by UN Tourism through a series of yearly questionnaires that are in line with the International Recommendations for Tourism Statistics (IRTS 2008 )  standard led by UN Tourism and approved by the United Nations.

The latest update took place in 24 November 2023.

Access the data by clicking on the sections below:

Inbound Tourism

Arrivals (in thousands) 2022 or latest available year

Total Arrivals in country over time Please select a country on the map above to display data below

Arrivals in country by region of origin *

Arrivals in country by Transportation Mode

Arrivals in country by Main Purpose of trip

Accommodation

Accommodation: Guests and Overnights Please select series on top 2022 or latest available year

Total Guests in country

Total Overnight stays in country

Inbound Expenditure

Inbound Expenditure (in million USD) 2022 or latest available year

Total Inbound Expenditure in country

Inbound Expenditure in country by type of expenditure

Total arrivals

Total international arrivals. Data disaggregated by: Overnight, Same-day (of which, cruise passengers)

Expenditure

Total inbound tourism expenditure, disaggregated by: Travel, Passenger Transport

Total arrivals by region

Total international arrivals, disaggregated by region of origin. Regions include: Africa, Americas, East Asia and the Pacific, Europe, Middle East, South Asia

Total arrivals by Main Purpose

Total international arrivals, disaggregated by Main Purpose of the trip. Main Purpose categories include: Personal (holiday and vacation, other personal), Business and professional

Total arrivals by Mode of Transport

Total international arrivals, disaggregated by Mode of Transport of the trip. Modes of Transport include: Air, Water, Land (railway, road, other)

Total arrivals by form of organization

Total international arrivals, disaggregated by form of organization. Forms of organization include: Package tour, Other

Accommodation: Guests and Overnights

Total number of Guests and Nights spent in accommodation establishments. Data disaggregated by: All establishments, Hotels and similar establishments.

Expenditure by Main Purpose

Total inbound travel expenditure, disaggregated by Main Purpose of the trip. Main Purpose categories include: Personal, Business and professional

Other indicators

Other relevant indicators related to inbound tourism. These include: Average size of party, Average length of stay, Average expenditure per day

Domestic Tourism

Domestic Trips (in thousands) 2022 or latest available year

Domestic Trips over time: Select Country on top

Domestic Trips vs International Arrivals: Select Type of Trip / Arrival on top

Domestic Tourism in Accommodation 2022 or latest available year

Domestic Tourism in Accommodation over time:

Variable displayed: Overnights Select Country on top

Variable displayed: Guests Select Country on top

Domestic vs Inbound Tourism in Accommodation

Overnights in Hotels and similar Select Country on top

Overnights in All Establishments Select Country on top

Guests in Hotels and similar Select Country on top

Guests in All establishments Select Country on top

Total trips

Total domestic tourism trips, disaggregated by: Overnight, Same-day

Total trips by Main Purpose

Total domestic tourism trips, disaggregated by Main Purpose of trip. Main Purpose categories include: Personal (holiday and vacation, other personal), Business and professional

Total trips by Mode of Transport

Total domestic tourism trips, disaggregated by Main Mode of Transport. Modes of Transport include: Air, Water, Land (railway, road, other).

Total trips by form of organization

Total domestic tourism trips, disaggregated by form of organization of the trip. Forms of organization include: Package tour, Other

Total number of Guests and Nights spent in accommodation establishments by domestic tourists. Data disaggregated by: All establishments, Hotels and similar establishments

Other relevant indicators related to domestic tourism. These include: Average size of party, Average length of stay, Average expenditure per day

Outbound Tourism

Departures (in thousands) 2022 or latest available year

Departures over time

• Overnights Visitors (tourists)
• Same-Day Visitors (excursionists)
• Total (Tourists + Excursionists)

Outbound Expenditure

Tourism outbound expenditure (in million USD) 2022 or latest available year

Oubound expenditure over time Select a Country on top

Outbound expenditure by type of expenditure Select Country on top

Total Inbound vs Outbound Tourism Expenditure Select Country on top

Total departures

Total international departures. Data disaggregated by: Overnight, Same-day

Total outbound tourism expenditure, disaggregated by: Travel, Passenger Transport

Total outbound travel expenditure, disaggregated by Main Purpose of the trip. Main Purpose categories include: Personal, Business and professional

Other relevant indicators related to outbound tourism. These include: Average length of stay, Average expenditure per day

Tourism Industries

2022 or latest available year

Total number of Bed-places Select Country on top

Total number of Hotels and similar establishments Select Country on top

Total number of Rooms in Hotels and similar Select Country on top

Occupation rate by bed-places and rooms Select Country on top

Average length of stay in hotels and similar Select Country on the top

Accommodation in hotels and similar establihsments

Monetary data related to accommodation activities. Indicators include: output, intermediate consumption, Gross Value Added, compensation of employees, Gross Fixed Capital Formation.

Non-monetary data related to accommodation activities. Indicators include: total number of establishments, total number of rooms, total number of bed-places.

Travel Agencies and other reservation services

Monetary data related to travel agencies and other reservation services activity. Indicators include: output, intermediate consumption, Gross Value Added, compensation of employees, Gross Fixed Capital Formation.

Non-monetary data related to travel agencies and other reservation services activity. Indicators include: total domestic trips , total inbound trips and total outbound trips.

Number of establishments by type

Total number of tourism establishments in the country, by type. Types of establishment include: Accommodation, Food and Beverage, Passenger transportation, Travel Agencies and reservation services, Other

Other relevant indicators related to performance of tourism industries. Indicators include: Occupancy rate (rooms, bed-places), Average length of stay, Available capacity

Total employees in tourism industries 2022 or latest available year Totals may include different industries depending on the reporting country

Total employees by tourism industries Dark red line indicates reported total Select Country on the top to display data

Number employees by tourism industry

Total number of employees, disaggregated by tourism industry. Tourism industries include: Accommodation, Food and Beverage, Passenger transportation, Travel Agencies and reservation services, Other

Number of jobs by status

Total number of jobs, disaggregated by: Employees, Self-employed

Number of full-time equivalent jobs

Total full-time equivalent jobs, as defined by the TSA:RMF 2008. Data disaggregated by status (employees, self-employed) and gender.

Macroeconomic Indicators

Based on additional data collected from several international sources, a number of macroeconomic indicators are compiled:

International Tourism Flows

Estimated international arrivals flows for 2018.

These regional and sub-regional flows visualizations are based on the following data reported by countries:

Arrivals: Border Statistics

Total arrivals of non-resident tourists and visitors at national borders, disaggregated by country of residence and citizenship

Arrivals: Statistics on accommodation establishments

Total arrivals of non-resident tourists in hotels and similar establishments and all types of accommodation, disaggregated by country of residence and citizenship

Overnight stays

Total overnight stays (total nights) of non-resident tourists in hotels and similar establishments and all types of accommodation, by country of origin

Detailed information on the metadata, country notes and methodologies used in the compilation of these data can be found in the Methodological Notes .

Publications on tourism statistics

UN Tourism´s flagship annual publications in the field of tourism statistics are the Compendium of Tourism Statistics and the Yearbook of Tourism Statistics . They contain the main data and indicators compiled by UN Tourism in e-book format (PDF).

Compendium of Tourism Statistics

The Compendium provides statistical data and indicators on inbound, outbound and domestic tourism, as well as on the number and types of tourism industries, the number of employees by tourism industries, and macroeconomic indicators related to international tourism.

Please click here to see the Index of indicators and basic data that form the Compendium. 

The 2023 edition presents data for 194 countries from 2017 to 2021, with methodological notes in English, French and Spanish.

Yearbook of Tourism Statistics

Understanding, for each country, where its inbound tourism is generated is essential for analyzing international tourism flows and devising marketing strategies, such as those related to the positioning of national markets abroad. The Yearbook focuses on data related to inbound tourism (total arrivals and overnight stays), broken down by country of origin.

The 2023 edition presents data for 187 countries from 2017 to 2021, with methodological notes in English, French and Spanish.

Country Fact Sheets

Country Factsheets include graphics on inbound tourism, domestic tourism, outbound tourism, tourism expenditure, tourism industries, tourism employment, Tourism Direct GDP and other macroeconomic indicators.

To download the latest Country Fact Sheet, please click on the country on the map below.