2025年全球机器学习旅游市场报告

近年来，旅游业机器学习市场规模迅速扩张，预计将从2024年的32.1亿美元成长到2025年的37.8亿美元，复合年增长率达17.9%。过去几年的成长可归因于人工智慧旅行助理的日益普及、预测分析在需求预测中的应用不断增加、聊天机器人在客户支援方面的应用日益广泛、旅行提案的个性化程度不断提高，以及预订和定价系统的自动化程度不断提高。

预计未来几年，旅游业机器学习市场将快速成长，到2029年市场规模将达到72.2亿美元，复合年增长率（CAGR）为17.6%。预测期内的成长要素包括：机器学习在诈欺侦测领域的应用日益广泛；人工智慧在动态定价中的应用不断增长；情感分析工具在旅客回馈收集方面的应用日益广泛；旅游公司数据驱动决策的日益普及；以及人工智慧在路线和行程优化方面的应用日益广泛。预测期内的关键趋势包括：用于个人化旅行规划的生成式人工智慧技术的进步；自主旅行管理系统的开发；人工智慧驱动的即时语言翻译技术的创新；旅游基础设施预测性维护技术的进步；以及人工智慧驱动的虚拟旅行助理的开发。

由于消费者对个人化互动的期望不断提高，对个人化客户体验的需求激增，推动了市场成长。这种对个人化客户体验日益增长的需求预计将推动机器学习在旅游市场的发展。个人化客户体验是指透过数据驱动的洞察，提供根据个人偏好和需求量身定制的互动和服务，确保在每个接触点都能提供相关且引人入胜的体验。随着消费者与数位化连结日益紧密，并期望品牌了解他们的偏好并提供客製化解决方案，这种需求也不断增长。机器学习在旅游业中透过分析旅行者的数据和行为，提供客製化的推荐、动态定价和个人化服务，从而提升整个旅程的满意度和参与度，实现个人化体验。例如，英国出版公司Marketing Tech News于2023年1月发布的报告显示，约66%的全球旅客在预订旅行时更倾向于接收个人化优惠，约61%的消费者愿意为客製化的旅游体验支付额外费用。因此，对个人化客户体验日益增长的需求预计将推动机器学习在旅游市场的发展。

旅游市场机器学习领域的主要企业正致力于发展基于代理的人工智慧解决方案，以提升客户参与、营运效率和个人化旅行体验。基于代理的人工智慧解决方案是一种先进的人工智慧系统，能够自主决策并自适应地采取行动，从而在最大限度减少人工干预的情况下有效实现预期目标。例如，2025年9月，美国科技公司Saber Corporation发布了一系列基于代理的人工智慧API，这些API由其专有的模型上下文通讯协定（MCP）伺服器提供支援。这些API整合于Saber Mosaic平台，并由Saber IQ层提供支援（该层利用超过Petabyte的旅游数据），使旅行社能够连接其人工智慧系统，并实现即时航班和酒店搜寻、预订以及预订后工作流程。这项创新表明，基于代理商的人工智慧在自动化复杂的旅行流程以及为旅行社和客户提供无缝、个人化体验方面正得到日益广泛的应用。

目录

第一章执行摘要

第二章 市场特征

第三章 市场趋势与策略

第四章 市场：宏观经济情景，包括利率、通货膨胀、地缘政治、贸易战和关税，以及新冠疫情及其復苏对市场的影响

第五章 全球成长分析与策略分析框架

• 全球旅游业中的机器学习：PESTEL 分析（政治、社会、技术、环境、法律因素、驱动因素和限制因素）
• 终端用户产业分析
• 全球机器学习旅游业市场：成长率分析
• 全球机器学习旅游市场表现：规模与成长，2019-2024 年
• 全球机器学习旅游市场预测：规模与成长，2024-2029年，2034年预测
• 全球旅游业的机器学习：潜在市场规模 (TAM)

第六章 市场细分

• 全球机器学习旅游市场：依组成部分划分，实际值及预测值，2019-2024年、2024-2029年预测值、2034年预测值
• 软体
• 硬体
• 服务
• 全球机器学习旅游市场：按部署模式、结果和预测划分，2019-2024 年、2024-2029 年预测、2034 年预测
• 本地部署
• 云
• 全球机器学习旅游市场：按应用、性能和预测划分，2019-2024年、2024-2029年预测、2034年预测
• 个性化建议
• 动态定价
• 诈欺侦测
• 客户服务
• 预测分析
• 其他用途
• 全球机器学习旅游市场：依最终使用者划分，实际结果与预测，2019-2024年、2024-2029年预测、2034年预测
• 旅行社
• 航空
• 饭店
• 汽车租赁公司
• 线上旅游平台
• 其他最终用户
• 全球机器学习旅游产业市场：依软体、类型、实际值及预测值细分，2019-2024年、2024-2029年预测值、2034年预测值
• 人工智慧平台
• 预测分析工具
• 资料管理解决方案
• 机器学习框架
• 自然语言处理工具
• 全球机器学习旅游市场：按硬体、类型、效能和预测细分，2019-2024 年、2024-2029 年预测、2034 年预测
• 伺服器
• 储存装置
• 图形处理单元
• 网路装置
• 边缘运算设备
• 全球机器学习旅游市场：按服务、类型、表现和预测细分，2019-2024 年、2024-2029 年预测、2034 年预测
• 专业服务
• 託管服务
• 咨询服务
• 培训和支援服务
• 系统整合服务

第七章 区域和国家分析

• 全球机器学习旅游市场：区域表现与预测，2019-2024年、2024-2029年预测、2034年预测
• 全球机器学习旅游市场：国家、绩效及预测，2019-2024 年、2024-2029 年预测、2034 年预测

第八章 亚太市场

第九章：中国市场

第十章 印度市场

第十一章 日本市场

第十二章：澳洲市场

第十三章 印尼市场

第十四章 韩国市场

第十五章 西欧市场

第十六章英国市场

第十七章：德国市场

第十八章：法国市场

第十九章：义大利市场

第二十章：西班牙市场

第21章 东欧市场

第22章 俄罗斯市场

第23章 北美市场

第24章美国市场

第25章：加拿大市场

第26章 南美洲市场

第27章：巴西市场

第28章 中东市场

第29章：非洲市场

第三十章：竞争格局与公司概况

• 机器学习在旅游业的应用：竞争格局
• 旅游业机器学习市场：公司概况
  • Amazon.com Inc. Overview, Products and Services, Strategy and Financial Analysis
  • Microsoft Corporation Overview, Products and Services, Strategy and Financial Analysis
  • Hitachi Ltd. Overview, Products and Services, Strategy and Financial Analysis
  • Accenture plc Overview, Products and Services, Strategy and Financial Analysis
  • International Business Machines Corporation Overview, Products and Services, Strategy and Financial Analysis

第31章：其他领先和创新企业

• Oracle Corporation
• Salesforce Inc.
• SAP SE
• Tata Consultancy Services Limited
• NEC Corporation
• Booking Holdings Inc.
• Tencent Holdings Limited
• Infosys Limited
• DXC Technology Company
• Expedia Group Inc.
• Wipro Limited
• Trip.com Group Limited
• AMADEUS IT GROUP SOCIEDAD ANONIMA
• LG CNS Co. Ltd.
• Sabre Corporation

第32章 全球市场竞争基准化分析与仪錶板

第33章 重大併购

第34章 近期市场趋势

第35章：高潜力市场国家、细分市场与策略

• 2029年机器学习旅游市场：一个充满新机会的国家
• 2029年旅游业机器学习市场：新兴细分市场机会
• 2029年旅游业机器学习市场：成长策略
  • 基于市场趋势的策略
  • 竞争对手策略

第36章附录

Machine learning in the travel industry involves the application of advanced algorithms and data-driven models to process and analyze large volumes of travel-related information, identify patterns, and generate intelligent predictions or automated decisions without the need for explicit programming. It enables travel companies to better understand customer behavior, optimize pricing strategies, forecast travel demand, enhance operational efficiency, and deliver personalized experiences to travelers.

The key components of machine learning in travel include software, hardware, and services. This technology utilizes artificial intelligence and data analytics to improve travel operations, enhance customer experiences, and support strategic business decision-making. Deployment modes include on-premises and cloud-based solutions. Core applications encompass personalized recommendations, dynamic pricing, fraud detection, customer service optimization, and predictive analytics. The primary end users include travel agencies, airlines, car rental companies, online travel platforms, and other organizations operating within the travel ecosystem.

Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report's Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.

The rapid escalation of U.S. tariffs and the resulting trade tensions in spring 2025 are significantly impacting the information technology sector, particularly in hardware manufacturing, data infrastructure, and software deployment. Higher duties on imported semiconductors, circuit boards, and networking equipment have raised production and operational costs for tech firms, cloud service providers, and data centers. Companies relying on globally sourced components for laptops, servers, and consumer electronics are facing longer lead times and increased pricing pressures. In parallel, tariffs on specialized software tools and retaliatory measures from key international markets have disrupted global IT supply chains and reduced overseas demand for U.S.-developed technologies. To navigate these challenges, the sector is accelerating investments in domestic chip fabrication, diversifying supplier bases, and adopting AI-driven automation to enhance operational resilience and cost efficiency.

The machine learning in travel market research report is one of a series of new reports from The Business Research Company that provides machine learning in travel market statistics, including machine learning in travel industry global market size, regional shares, competitors with a machine learning in travel market share, detailed machine learning in travel market segments, market trends and opportunities, and any further data you may need to thrive in the machine learning in travel industry. This machine learning in travel market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

The machine learning in the travel market size has grown rapidly in recent years. It will grow from $3.21 billion in 2024 to $3.78 billion in 2025 at a compound annual growth rate (CAGR) of 17.9%. The growth in the historic period can be attributed to the increasing adoption of AI-based travel assistants, the growing use of predictive analytics for demand forecasting, the rising integration of chatbots for customer support, the increasing personalization in travel recommendations, and the growing automation in booking and pricing systems.

The machine learning in the travel market size is expected to see rapid growth in the next few years. It will grow to $7.22 billion in 2029 at a compound annual growth rate (CAGR) of 17.6%. The growth in the forecast period can be attributed to the rising use of machine learning for fraud detection, the growing implementation of AI in dynamic pricing, the increasing deployment of sentiment analysis tools for traveler feedback, the rise in data-driven decision-making by travel companies, and the growing utilization of AI for route and schedule optimization. Key trends in the forecast period include advancements in generative AI for personalized trip planning, the development of autonomous travel management systems, innovations in real-time language translation using AI, advancements in predictive maintenance for travel infrastructure, and the development of AI-driven virtual travel assistants.

The surge in demand for personalized customer experiences is fueling the growth of the market due to increasing customer expectations for tailored interactions. The growing demand for personalized customer experiences is expected to propel the growth of machine learning in the travel market going forward. Personalized customer experiences involve tailoring interactions and services to meet individual preferences and needs through data-driven insights that deliver relevant and engaging experiences across touchpoints. This demand is increasing as customers become more digitally connected and expect brands to understand their preferences and provide customized solutions. Machine learning in travel enables such personalization by analyzing traveler data and behavior to offer tailored recommendations, dynamic pricing, and customized services that enhance satisfaction and engagement throughout the journey. For instance, in January 2023, according to a report published by Marketing Tech News, a UK-based publishing company, about 66% of travelers globally preferred receiving personalized offers when booking trips, and around 61% of consumers worldwide were willing to pay extra for tailored travel experiences. Therefore, the growing demand for personalized customer experiences is expected to drive the growth of machine learning in the travel market.

Major companies operating in the machine learning in travel market are focusing on advancements in agentic AI solutions to enhance customer engagement, operational efficiency, and personalized travel experiences. Agentic AI solutions are advanced artificial intelligence systems capable of autonomous decision-making and adaptive behavior with minimal human intervention to achieve desired outcomes effectively. For instance, in September 2025, Sabre Corporation, a US-based technology company, launched a set of agentic AI-ready APIs powered by its proprietary Model Context Protocol (MCP) server. Integrated into the SabreMosaic platform and supported by the Sabre IQ layer leveraging over 50 petabytes of travel data, these APIs enable travel agencies to connect their AI systems for real-time shopping, booking, and post-booking workflows for flights and hotels. This innovation highlights the growing application of agentic AI in automating complex travel processes and delivering seamless, personalized experiences for agencies and customers.

In April 2023, Navan, Inc., a US-based technology company, acquired Tripeur for an undisclosed amount. This acquisition aimed to strengthen Navan's presence in the Indian business travel market by integrating Tripeur's advanced travel and expense management platform. It enhances Navan's localized offerings, leverages Tripeur's AI-driven automation capabilities, and provides a seamless, end-to-end travel experience for enterprises in the region. Tripeur is an India-based corporate travel management platform that provides machine learning solutions in the travel industry.

Major players in the machine learning in travel market are Amazon.com Inc., Microsoft Corporation, Hitachi Ltd., Accenture plc, International Business Machines Corporation, Oracle Corporation, Salesforce Inc. , SAP SE, Tata Consultancy Services Limited , NEC Corporation, Booking Holdings Inc., Tencent Holdings Limited , Infosys Limited, DXC Technology Company, Expedia Group Inc., Wipro Limited, Trip.com Group Limited, AMADEUS IT GROUP SOCIEDAD ANONIMA, LG CNS Co. Ltd., Sabre Corporation.

North America was the largest region in the machine learning in travel market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in machine learning in travel report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa.

The countries covered in the machine learning in travel market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

The machine learning in travel market consists of revenues earned by entities by providing services such as revenue management services, voice and language translation services, automated customer segmentation services, operational efficiency and route optimization services, and automated baggage handling services. The market value includes the value of related goods sold by the service provider or contained within the service offering. The machine learning in the travel market also includes kayak AI platform, mindtrip, sabre travel AI, citymapper, and navan concierge. Values in this market are 'factory gate' values; that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

Machine Learning In Travel Global Market Report 2025 from The Business Research Company provides strategists, marketers and senior management with the critical information they need to assess the market.

This report focuses on machine learning in travel market which is experiencing strong growth. The report gives a guide to the trends which will be shaping the market over the next ten years and beyond.

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Where is the largest and fastest growing market for machine learning in travel ? How does the market relate to the overall economy, demography and other similar markets? What forces will shape the market going forward, including technological disruption, regulatory shifts, and changing consumer preferences? The machine learning in travel market global report from the Business Research Company answers all these questions and many more.

The report covers market characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends and strategies for this market. It traces the market's historic and forecast market growth by geography.

• The market characteristics section of the report defines and explains the market.
• The market size section gives the market size ($b) covering both the historic growth of the market, and forecasting its development.
• The forecasts are made after considering the major factors currently impacting the market. These include:

The forecasts are made after considering the major factors currently impacting the market. These include the technological advancements such as AI and automation, Russia-Ukraine war, trade tariffs (government-imposed import/export duties), elevated inflation and interest rates.

• Market segmentations break down the market into sub markets.
• The regional and country breakdowns section gives an analysis of the market in each geography and the size of the market by geography and compares their historic and forecast growth.
• The competitive landscape chapter gives a description of the competitive nature of the market, market shares, and a description of the leading companies. Key financial deals which have shaped the market in recent years are identified.
• The trends and strategies section analyses the shape of the market as it emerges from the crisis and suggests how companies can grow as the market recovers.

Scope

• Markets Covered:1) By Component: Software; Hardware; Services
• 2) By Deployment Mode: On-Premises; Cloud
• 3) By Application: Personalized Recommendations; Dynamic Pricing; Fraud Detection; Customer Service; Predictive Analytics; Other Applications
• 4) By End-User: Travel Agencies; Airlines; Car Rental Companies; Online Travel Platforms; Other End-Users
• Subsegments:
• 1) By Software: Artificial Intelligence Platforms; Predictive Analytics Tools; Data Management Solutions; Machine Learning Frameworks; Natural Language Processing Tools
• 2) By Hardware: Servers; Storage Devices; Graphics Processing Units; Network Equipment; Edge Computing Devices
• 3) By Services: Professional Services; Managed Services; Consulting Services; Training And Support Services; System Integration Services
• Companies Mentioned: Amazon.com Inc.; Microsoft Corporation; Hitachi Ltd.; Accenture plc; International Business Machines Corporation; Oracle Corporation; Salesforce Inc. ; SAP SE; Tata Consultancy Services Limited ; NEC Corporation; Booking Holdings Inc.; Tencent Holdings Limited ; Infosys Limited; DXC Technology Company; Expedia Group Inc.; Wipro Limited; Trip.com Group Limited; AMADEUS IT GROUP SOCIEDAD ANONIMA; LG CNS Co. Ltd.; Sabre Corporation
• Countries: Australia; Brazil; China; France; Germany; India; Indonesia; Japan; Russia; South Korea; UK; USA; Canada; Italy; Spain.
• Regions: Asia-Pacific; Western Europe; Eastern Europe; North America; South America; Middle East; Africa
• Time Series: Five years historic and ten years forecast.
• Data: Ratios of market size and growth to related markets, GDP proportions, expenditure per capita,
• Data Segmentations: country and regional historic and forecast data, market share of competitors, market segments.
• Sourcing and Referencing: Data and analysis throughout the report is sourced using end notes.
• Delivery Format: PDF, Word and Excel Data Dashboard.

Table of Contents

1. Executive Summary

2. Machine Learning In Travel Market Characteristics

3. Machine Learning In Travel Market Trends And Strategies

4. Machine Learning In Travel Market - Macro Economic Scenario Including The Impact Of Interest Rates, Inflation, Geopolitics, Trade Wars and Tariffs, And Covid And Recovery On The Market

• 4.1. Supply Chain Impact from Tariff War & Trade Protectionism

5. Global Machine Learning In Travel Growth Analysis And Strategic Analysis Framework

• 5.1. Global Machine Learning In Travel PESTEL Analysis (Political, Social, Technological, Environmental and Legal Factors, Drivers and Restraints)
• 5.2. Analysis Of End Use Industries
• 5.3. Global Machine Learning In Travel Market Growth Rate Analysis
• 5.4. Global Machine Learning In Travel Historic Market Size and Growth, 2019 - 2024, Value ($ Billion)
• 5.5. Global Machine Learning In Travel Forecast Market Size and Growth, 2024 - 2029, 2034F, Value ($ Billion)
• 5.6. Global Machine Learning In Travel Total Addressable Market (TAM)

6. Machine Learning In Travel Market Segmentation

• 6.1. Global Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• Software
• Hardware
• Services
• 6.2. Global Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• On-Premises
• Cloud
• 6.3. Global Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• Personalized Recommendations
• Dynamic Pricing
• Fraud Detection
• Customer Service
• Predictive Analytics
• Other Applications
• 6.4. Global Machine Learning In Travel Market, Segmentation By End-User, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• Travel Agencies
• Airlines
• Hotels
• Car Rental Companies
• Online Travel Platforms
• Other End-Users
• 6.5. Global Machine Learning In Travel Market, Sub-Segmentation Of Software, By Type, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• Artificial Intelligence Platforms
• Predictive Analytics Tools
• Data Management Solutions
• Machine Learning Frameworks
• Natural Language Processing Tools
• 6.6. Global Machine Learning In Travel Market, Sub-Segmentation Of Hardware, By Type, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• Servers
• Storage Devices
• Graphics Processing Units
• Network Equipment
• Edge Computing Devices
• 6.7. Global Machine Learning In Travel Market, Sub-Segmentation Of Services, By Type, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• Professional Services
• Managed Services
• Consulting Services
• Training And Support Services
• System Integration Services

7. Machine Learning In Travel Market Regional And Country Analysis

• 7.1. Global Machine Learning In Travel Market, Split By Region, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 7.2. Global Machine Learning In Travel Market, Split By Country, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

8. Asia-Pacific Machine Learning In Travel Market

• 8.1. Asia-Pacific Machine Learning In Travel Market Overview
• Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
• 8.2. Asia-Pacific Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 8.3. Asia-Pacific Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 8.4. Asia-Pacific Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

9. China Machine Learning In Travel Market

• 9.1. China Machine Learning In Travel Market Overview
• 9.2. China Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F,$ Billion
• 9.3. China Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F,$ Billion
• 9.4. China Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F,$ Billion

10. India Machine Learning In Travel Market

• 10.1. India Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 10.2. India Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 10.3. India Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

11. Japan Machine Learning In Travel Market

• 11.1. Japan Machine Learning In Travel Market Overview
• 11.2. Japan Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 11.3. Japan Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 11.4. Japan Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

12. Australia Machine Learning In Travel Market

• 12.1. Australia Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 12.2. Australia Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 12.3. Australia Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

13. Indonesia Machine Learning In Travel Market

• 13.1. Indonesia Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 13.2. Indonesia Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 13.3. Indonesia Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

14. South Korea Machine Learning In Travel Market

• 14.1. South Korea Machine Learning In Travel Market Overview
• 14.2. South Korea Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 14.3. South Korea Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 14.4. South Korea Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

15. Western Europe Machine Learning In Travel Market

• 15.1. Western Europe Machine Learning In Travel Market Overview
• 15.2. Western Europe Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 15.3. Western Europe Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 15.4. Western Europe Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

16. UK Machine Learning In Travel Market

• 16.1. UK Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 16.2. UK Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 16.3. UK Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

17. Germany Machine Learning In Travel Market

• 17.1. Germany Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 17.2. Germany Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 17.3. Germany Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

18. France Machine Learning In Travel Market

• 18.1. France Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 18.2. France Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 18.3. France Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

19. Italy Machine Learning In Travel Market

• 19.1. Italy Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 19.2. Italy Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 19.3. Italy Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

20. Spain Machine Learning In Travel Market

• 20.1. Spain Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 20.2. Spain Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 20.3. Spain Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

21. Eastern Europe Machine Learning In Travel Market

• 21.1. Eastern Europe Machine Learning In Travel Market Overview
• 21.2. Eastern Europe Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 21.3. Eastern Europe Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 21.4. Eastern Europe Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

22. Russia Machine Learning In Travel Market

• 22.1. Russia Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 22.2. Russia Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 22.3. Russia Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

23. North America Machine Learning In Travel Market

• 23.1. North America Machine Learning In Travel Market Overview
• 23.2. North America Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 23.3. North America Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 23.4. North America Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

24. USA Machine Learning In Travel Market

• 24.1. USA Machine Learning In Travel Market Overview
• 24.2. USA Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 24.3. USA Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 24.4. USA Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

25. Canada Machine Learning In Travel Market

• 25.1. Canada Machine Learning In Travel Market Overview
• 25.2. Canada Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 25.3. Canada Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 25.4. Canada Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

26. South America Machine Learning In Travel Market

• 26.1. South America Machine Learning In Travel Market Overview
• 26.2. South America Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 26.3. South America Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 26.4. South America Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

27. Brazil Machine Learning In Travel Market

• 27.1. Brazil Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 27.2. Brazil Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 27.3. Brazil Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

28. Middle East Machine Learning In Travel Market

• 28.1. Middle East Machine Learning In Travel Market Overview
• 28.2. Middle East Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 28.3. Middle East Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 28.4. Middle East Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

29. Africa Machine Learning In Travel Market

• 29.1. Africa Machine Learning In Travel Market Overview
• 29.2. Africa Machine Learning In Travel Market, Segmentation By Component, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 29.3. Africa Machine Learning In Travel Market, Segmentation By Deployment Mode, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
• 29.4. Africa Machine Learning In Travel Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

30. Machine Learning In Travel Market Competitive Landscape And Company Profiles

• 30.1. Machine Learning In Travel Market Competitive Landscape
• 30.2. Machine Learning In Travel Market Company Profiles
  • 30.2.1. Amazon.com Inc. Overview, Products and Services, Strategy and Financial Analysis
  • 30.2.2. Microsoft Corporation Overview, Products and Services, Strategy and Financial Analysis
  • 30.2.3. Hitachi Ltd. Overview, Products and Services, Strategy and Financial Analysis
  • 30.2.4. Accenture plc Overview, Products and Services, Strategy and Financial Analysis
  • 30.2.5. International Business Machines Corporation Overview, Products and Services, Strategy and Financial Analysis

31. Machine Learning In Travel Market Other Major And Innovative Companies

• 31.1. Oracle Corporation
• 31.2. Salesforce Inc.
• 31.3. SAP SE
• 31.4. Tata Consultancy Services Limited
• 31.5. NEC Corporation
• 31.6. Booking Holdings Inc.
• 31.7. Tencent Holdings Limited
• 31.8. Infosys Limited
• 31.9. DXC Technology Company
• 31.10. Expedia Group Inc.
• 31.11. Wipro Limited
• 31.12. Trip.com Group Limited
• 31.13. AMADEUS IT GROUP SOCIEDAD ANONIMA
• 31.14. LG CNS Co. Ltd.
• 31.15. Sabre Corporation

32. Global Machine Learning In Travel Market Competitive Benchmarking And Dashboard

33. Key Mergers And Acquisitions In The Machine Learning In Travel Market

34. Recent Developments In The Machine Learning In Travel Market

35. Machine Learning In Travel Market High Potential Countries, Segments and Strategies

• 35.1 Machine Learning In Travel Market In 2029 - Countries Offering Most New Opportunities
• 35.2 Machine Learning In Travel Market In 2029 - Segments Offering Most New Opportunities
• 35.3 Machine Learning In Travel Market In 2029 - Growth Strategies
  • 35.3.1 Market Trend Based Strategies
  • 35.3.2 Competitor Strategies

36. Appendix

• 36.1. Abbreviations
• 36.2. Currencies
• 36.3. Historic And Forecast Inflation Rates
• 36.4. Research Inquiries
• 36.5. The Business Research Company
• 36.6. Copyright And Disclaimer