2025年全球機器學習旅遊市場報告

近年來，旅遊業機器學習市場規模迅速擴張，預計將從2024年的32.1億美元成長到2025年的37.8億美元，複合年成長率達17.9%。過去幾年的成長可歸因於人工智慧旅行助理的日益普及、預測分析在需求預測中的應用不斷增加、聊天機器人在客戶支援方面的應用日益廣泛、旅行提案的個性化程度不斷提高，以及預訂和定價系統的自動化程度不斷提高。

預計未來幾年，旅遊業機器學習市場將快速成長，到2029年市場規模將達到72.2億美元，複合年成長率（CAGR）為17.6%。預測期內的成長要素包括：機器學習在詐欺偵測領域的應用日益廣泛；人工智慧在動態定價中的應用不斷成長；情感分析工具在旅客回饋收集方面的應用日益廣泛；旅遊公司數據驅動決策的日益普及；以及人工智慧在路線和行程最佳化方面的應用日益廣泛。預測期內的關鍵趨勢包括：用於個人化旅行規劃的生成式人工智慧技術的進步；自主旅行管理系統的開發；人工智慧驅動的即時語言翻譯技術的創新；旅遊基礎設施預測性維護技術的進步；以及人工智慧驅動的虛擬旅行助理的開發。

由於消費者對個人化互動的期望不斷提高，對個人化客戶體驗的需求激增，推動了市場成長。這種對個人化客戶體驗日益成長的需求預計將推動機器學習在旅遊市場的發展。個人化客戶體驗是指透過數據驅動的洞察，提供根據個人偏好和需求量身定做的互動和服務，確保在每個接觸點都能提供相關且引人入勝的體驗。隨著消費者與數位化連結日益緊密，並期望品牌了解他們的偏好並提供客製化解決方案，這種需求也不斷成長。機器學習在旅遊業中透過分析旅行者的數據和行為，提供客製化的推薦、動態定價和個人化服務，從而提升整個旅程的滿意度和參與度，實現個人化體驗。例如，英國出版公司Marketing Tech News於2023年1月發布的報告顯示，約66%的全球旅客在預訂旅行時更傾向於接收個人化優惠，約61%的消費者願意為客製化的旅遊體驗支付額外費用。因此，對個人化客戶體驗日益成長的需求預計將推動機器學習在旅遊市場的發展。

旅遊市場機器學習領域的主要企業正致力於發展基於代理的人工智慧解決方案，以提升客戶參與、營運效率和個人化旅行體驗。基於代理的人工智慧解決方案是一種先進的人工智慧系統，能夠自主決策並自適應地採取行動，從而在最大限度減少人工干預的情況下有效實現預期目標。例如，2025年9月，美國科技公司Saber Corporation發布了一系列基於代理的人工智慧API，這些API由其專有的模型上下文通訊協定（MCP）伺服器提供支援。這些API整合於Saber Mosaic平台，並由Saber IQ層提供支援（該層利用超過Petabyte的旅遊數據），使旅行社能夠連接其人工智慧系統，並實現即時航班和酒店搜尋、預訂以及預訂後工作流程。這項創新表明，基於代理商的人工智慧在自動化複雜的旅行流程以及為旅行社和客戶提供無縫、個人化體驗方面正得到日益廣泛的應用。

目錄

第1章執行摘要

第2章 市場特徵

第3章 市場趨勢與策略

第4章 市場：宏觀經濟情景，包括利率、通貨膨脹、地緣政治、貿易戰和關稅，以及新冠疫情及其復甦對市場的影響

第5章 全球成長分析與策略分析框架

• 全球旅遊業中的機器學習：PESTEL 分析（政治、社會、技術、環境、法律因素、促進因素和限制因素）
• 終端用戶產業分析
• 全球機器學習旅遊業市場：成長率分析
• 全球機器學習旅遊市場表現：規模與成長，2019-2024 年
• 全球機器學習旅遊市場預測：規模與成長，2024-2029年，2034年預測
• 全球旅遊業的機器學習：潛在市場規模 (TAM)

第6章 市場細分

• 全球機器學習旅遊市場：依組成部分分類，實際值及預測值，2019-2024年、2024-2029年預測值、2034年預測值
• 軟體
• 硬體
• 服務
• 全球機器學習旅遊市場：按部署模式、結果和預測分類，2019-2024 年、2024-2029 年預測、2034 年預測
• 本地部署
• 雲
• 全球機器學習旅遊市場：按應用、性能和預測分類，2019-2024年、2024-2029年預測、2034年預測
• 個性化建議
• 動態定價
• 詐欺偵測
• 客戶服務
• 預測分析
• 其他用途
• 全球機器學習旅遊市場：依最終使用者分類，實際結果與預測，2019-2024年、2024-2029年預測、2034年預測
• 旅行社
• 航空
• 飯店
• 汽車租賃公司
• 線上旅遊平台
• 其他最終用戶
• 全球機器學習旅遊產業市場：依軟體、類型、實際值及預測值細分，2019-2024年、2024-2029年預測值、2034年預測值
• 人工智慧平台
• 預測分析工具
• 資料管理解決方案
• 機器學習框架
• 自然語言處理工具
• 全球機器學習旅遊市場：按硬體、類型、效能和預測細分，2019-2024 年、2024-2029 年預測、2034 年預測
• 伺服器
• 儲存裝置
• 圖形處理單元
• 網路裝置
• 邊緣運算設備
• 全球機器學習旅遊市場：按服務、類型、表現和預測細分，2019-2024 年、2024-2029 年預測、2034 年預測
• 專業服務
• 託管服務
• 諮詢服務
• 培訓和支援服務
• 系統整合服務

第7章 區域和國家分析

• 全球機器學習旅遊市場：區域表現與預測，2019-2024年、2024-2029年預測、2034年預測
• 全球機器學習旅遊市場：國家、績效及預測，2019-2024 年、2024-2029 年預測、2034 年預測

第8章 亞太市場

第9章：中國市場

第10章 印度市場

第11章 日本市場

第12章：澳洲市場

第13章 印尼市場

第14章 韓國市場

第15章 西歐市場

第16章英國市場

第17章：德國市場

第18章：法國市場

第19章：義大利市場

第20章：西班牙市場

第21章 東歐市場

第22章 俄羅斯市場

第23章 北美市場

第24章美國市場

第25章：加拿大市場

第26章 南美洲市場

第27章：巴西市場

第28章 中東市場

第29章：非洲市場

第30章：競爭格局與公司概況

• 機器學習在旅遊業的應用：競爭格局
• 旅遊業機器學習市場：公司概況
  • 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：其他領先和創新企業

• 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 全球市場競爭基準化分析與儀錶板

第33章 重大併購

第34章 近期市場趨勢

第35章：高潛力市場國家、細分市場與策略

• 2029年機器學習旅遊市場：一個充滿新機會的國家
• 2029年旅遊業機器學習市場：新興細分市場機會
• 2029年旅遊業機器學習市場：成長策略
  • 基於市場趨勢的策略
  • 競爭對手策略

第36章附錄

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