全球行销市场人工智慧 (AI) - 2023-2030

概述 ：

2022年，全球人工智慧（AI）行销市场规模达到127亿美元，预计到2030年将达到773亿美元，2023-2030年预测期间复合年增长率为25.1%。

由于行业数位化，数据不断增加。资料是人工智慧的核心基础，资料越多，人工智慧对行销就越有用。人工智慧系统可以轻鬆应对复杂的行销活动，包括消费者细分、客製化和预测分析。由于高效能运算资源易于获取，人工智慧可以快速有效地处理大量资料集，从而实现即时决策。

例如，2023 年 5 月 25 日，着名的人工智慧 (AI) 软体即服务公司 Appier 正在与东南亚领先的零售和电子商务品牌合作，转变他们的行销策略并提供高度个人化的购物体验跨数位平台。生成式人工智慧的兴起正在对零售业产生重大影响，使零售商能够实现任务自动化、扩大个人化行销工作、增强聊天机器人客户服务支援并产生可行的见解。

亚太地区是全球人工智慧（AI）行销市场成长的地区之一，覆盖了超过1/3的市场，并且拥有大量接入互联网的人口，该地区经历了大规模的数位化，这增强了资料收集并为人工智慧驱动的行销提供了有用的见解，并且由于中国、印度和东南亚国家等国家电子商务平台的扩张，对人工智慧驱动的推荐引擎、个人化和客户支援的需求。

动态：

对预测分析的需求不断增长

日常任务和流程的自动化使法律专业人员能够专注于更高价值的任务，例如法律分析和策略制定，从而提高律师事务所和法律部门的效率和生产力。自动化透过最大限度地减少文件审查、合约分析和法律研究等任务中对体力劳动的需求，有助于降低营运成本，这种成本降低对于寻求优化预算的法律组织来说很有吸引力。

据 Squarkai.com 称，人工智慧和预测分析使行销人员能够透过分析个人客户资料来创建高度个人化的行销活动，这种量身定制的方法可以提高客户参与度和转换率。预测分析可自动进行资料分析，进而节省时间和资源。行销人员可以将精力分配到更具策略性的任务上，从而提高整体效率。据 Spiralytics 称，到 2021 年，80% 的专业人士拥有基于人工智慧的解决方案，对资料保护产生重大影响。

公司间的合作推动市场发展

企业可以透过协作，结合AI演算法、资料分析、行销平台、产业认知等能力，提供更有效率的AI行销解决方案。透过鼓励思想交流和研究新技术，协作促进创新。公司可以合作创建尖端的人工智慧工具和方法，以扩大行销的潜力。

例如，2023 年8 月16 日，Langoor Digital 和Quilt AI 建立了策略合作伙伴关係，旨在利用先进的人工智慧(AI) 技术改变行销格局，此次合作将重新定义行销人员与受众互动和理解的方式。透过将 Langoor 的创新行销策略与 Quilt AI 在诊断、预测和生成人工智慧方面的专业知识相结合，此次合作旨在彻底改变行销人员在其工作中利用人工智慧潜力的方式。

AI演算法提升行销能力

更复杂的机器学习模型和演算法的创建极大地增强了AI的营销能力。这些模型和演算法可以分析庞大的资料集、发现趋势并做出极其精确的预测，从而使行销工作更加成功。随着大资料来源变得越来越容易获取，行销人员将有机会利用大量资料进行使用和评估，这些庞大的资料库可以透过人工智慧进行处理，这将有助于行销人员根据资料做出决策。

例如，2023年9月12日，可口可乐推出了一款名为Coca-Cola Y3000的新饮料，被誉为首款由人类和人工智慧（AI）共同创造的口味，该产品是可口可乐创意的一部分平台，旨在吸引年轻消费者，同时突出其招牌苏打水。可口可乐Y3000与Creations平台中的其他饮料一样，并不强调特定的口味，而是专注于提供独特的心情或体验。可口可乐利用人工智慧来了解人们如何透过情感、愿望、颜色和口味来展望未来。

不准确或有偏差的数据以及所需的维护

人工智慧严重依赖资料，所用资料的品质可以显着影响人工智慧的效能。不准确或有偏见的资料可能会导致有缺陷的预测和建议。此外，使用消费者资料进行人工智慧驱动的行销会产生隐私问题，并需要遵守 GDPR 和 CCPA 等资料保护法。人工智慧缺乏人类的创造力和情绪智商，但它可以评估资料并做出数据驱动的决策。它可能很难产生真正有创意且能引起情感共鸣的内容，从而深入吸引客户。

在行销中实施人工智慧是复杂且资源密集的。开发和维护人工智慧模型和系统需要专门的技能和专业知识。由于资源限制，中小企业在采用人工智慧方面可能面临挑战。仅依靠人工智慧演算法来做出行销决策可能会导致缺乏人工监督。人类行销人员仍然应该在解释人工智慧产生的见解和製定策略决策方面发挥作用。

目录

第 1 章：方法与范围

• 研究方法论
• 报告的研究目的和范围

第 2 章：定义与概述

第 3 章：执行摘要

• 按产品分类的片段
• 按部署类型分類的程式码片段
• 技术片段
• 按应用程式片段
• 最终使用者的片段
• 按地区分類的片段

第 4 章：动力学

• 影响因素
  • 司机
    • 对预测分析的需求不断增长
    • 公司间的合作推动市场发展
    • AI演算法提升行销能力
  • 限制
    • 不准确或有偏差的数据以及所需的维护机会
  • 影响分析

第 5 章：产业分析

• 波特五力分析
• 供应链分析
• 定价分析
• 监管分析
• 俄乌战争影响分析
• DMI 意见

第 6 章：COVID-19 分析

• COVID-19 分析
  • 新冠疫情爆发前的情景
  • 新冠疫情期间的情景
  • 新冠疫情后的情景
• COVID-19 期间的定价动态
• 供需谱
• 疫情期间政府与市场相关的倡议
• 製造商策略倡议
• 结论

第 7 章：透过奉献

• 硬体
• 软体
• 服务

第 8 章：按部署类型

• 云
• 本地

第 9 章：按技术

• 机器学习
• 上下文感知计算
• 自然语言处理
• 电脑视觉

第 10 章：按应用

• 社群媒体广告
• 搜寻广告
• 内容策划
• 销售行销自动化
• 分析平台
• 其他的

第 11 章：最终用户

• BFSI
• 零售
• 消费品
• 媒体娱乐
• 企业
• 其他的

第 12 章：按地区

• 北美洲
  • 我们
  • 加拿大
  • 墨西哥
• 欧洲
  • 德国
  • 英国
  • 法国
  • 义大利
  • 俄罗斯
  • 欧洲其他地区
• 南美洲
  • 巴西
  • 阿根廷
  • 南美洲其他地区
• 亚太
  • 中国
  • 印度
  • 日本
  • 澳洲
  • 亚太其他地区
• 中东和非洲

第13章：竞争格局

• 竞争场景
• 市场定位/份额分析
• 併购分析

第 14 章：公司简介

• IBM Corporation
• 公司简介
• 产品组合和描述
• 财务概览
• 主要进展
• Intel Corporation
• Alphabet Inc
• Microsoft Corporation
• Twitter, Inc.
• Samsung India Electronics Pvt. Ltd.
• Amazon.com, Inc.
• NVIDIA Corporation
• Albert Technologies Ltd.
• H2O.ai, Inc.

第 15 章：附录

Overview:

Global Artificial Intelligence (AI) in Marketing Market reached US$ 12.7 billion in 2022 and is expected to reach US$ 77.3 billion by 2030, growing with a CAGR of 25.1% during the forecast period 2023-2030.

Data is increasing as a result of industry digitization. As data is the core foundation of AI, the more data there is, the more useful AI can be for marketing. The sophisticated marketing activities that AI systems can tackle easily include consumer segmentation, customization and predictive analytics. Because high-performance computing resources are easily accessible, AI can process massive datasets rapidly and effectively, enabling real-time decision-making.

For instance, on 25 May 2023, Appier, a prominent artificial intelligence (AI) software-as-a-service company, is partnering with leading retail and e-commerce brands in Southeast Asia to transform their marketing strategies and provide highly personalized shopping experiences across digital platforms. The rise of Generative AI is significantly impacting the retail industry, enabling retailers to automate tasks, scale personalized marketing efforts, enhance chatbot customer service support and generate actionable insights.

Asia-Pacific is among the growing regions in the global artificial intelligence (AI) in marketing market covering more than 1/3rd of the market and with a huge population having access to the internet, the area experienced major digitalization, which has enhanced data collection and provided useful insights for AI-driven marketing and there is a demand for AI-powered recommendation engines, personalization and customer support due to the expansion of e-commerce platforms in nations like China, India and Southeast Asian countries.

Dynamics:

Rising Demand for Predictive Analysis

Automation of routine tasks and processes allows legal professionals to focus on higher-value tasks, such as legal analysis and strategy development and this leads to increased efficiency and productivity within law firms and legal departments. Automation helps reduce operational costs by minimizing the need for manual labor in tasks like document review, contract analysis and legal research, this cost reduction is appealing to legal organizations seeking to optimize their budgets.

According to Squarkai.com, AI and predictive analytics enable marketers to create highly personalized campaigns by analyzing individual customer data and this tailored approach increases customer engagement and conversion rates. Predictive analytics automates data analysis, saving time and resources. Marketers can allocate their efforts to more strategic tasks, improving overall efficiency. According to Spiralytics, in 2021, 80% of professionals have AI-based solutions that have a major impact on data protection.

Collaboration Between Companies Boosts the Market

Companies can combine their capabilities, such as AI algorithms, data analytics, marketing platforms and awareness of particular industries, through collaboration to provide more efficient AI marketing solutions. By encouraging the exchange of ideas and studying novel technologies, collaboration promotes innovation. Companies can collaborate to create cutting-edge AI tools and methodologies that expand the potential of marketing.

For instance, on 16 August 2023, Langoor Digital and Quilt AI entered into a strategic partnership with the aim of transforming the marketing landscape using advanced artificial intelligence (AI) technologies and this collaboration will redefine how marketers engage with and comprehend their audiences. By merging Langoor's innovative marketing strategies with Quilt AI's expertise in Diagnostic, Predictive and Generative AI, this partnership seeks to revolutionize how marketers harness the potential of AI in their endeavors.

Enhancing Marketing Capabilities with AI Algorithms

The creation of more sophisticated machine learning models and algorithms has greatly enhanced AI's marketing capabilities. These models and algorithms can analyze huge datasets, spot trends and make incredibly precise predictions, resulting in more successful marketing efforts. As big data sources become more accessible, marketers will have the opportunity to utilize an abundance of data to use and evaluate, these huge databases can be processed by AI, which will help marketers make decisions based on data.

For instance, on 12 September 2023, Coca-Cola launched a new beverage called Coca-Cola Y3000, which is touted as the first flavor co-created with both human and artificial intelligence (AI) and this product is part of Coca-Cola's Creations platform, which aims to appeal to younger consumers while highlighting its signature soda. Coca-Cola Y3000, like other beverages in the Creations platform, does not emphasize a specific flavor but focuses on providing a unique mood or experience. Coca-Cola used AI to understand how people envision the future through emotions, aspirations, colors and flavors.

Inaccurate or Biased Data and Required Maintenance

AI relies heavily on data and the quality of the data used can significantly impact AI's performance. Inaccurate or biased data can lead to flawed predictions and recommendations. Additionally, using consumer data for AI-driven marketing creates privacy issues and demands compliance with data protection laws like GDPR and CCPA. AI lacks human creativity and emotional intelligence, nevertheless, it can evaluate data and make data-driven decisions. It may struggle to generate genuinely creative and emotionally resonant content that engages customers on a deep level.

Implementing AI in marketing is complex and resource-intensive. It requires specialized skills and expertise to develop and maintain AI models and systems. Small and mid-sized businesses may face challenges in adopting AI due to resource constraints. Relying solely on AI algorithms to make marketing decisions can lead to a lack of human oversight. Human marketers should still play a role in interpreting AI-generated insights and making strategic decisions.

Segment Analysis:

The global artificial intelligence (AI) in marketing market is segmented based on offering, deployment type, technology, application, end-user and region.

Adoption of Cloud-Based Artificial Intelligence (AI) Platforms

The increasing volume of data generated by online activities provides a wealth of information for marketers. Cloud-based AI solutions can efficiently process and analyze this data to derive valuable insights and improve marketing strategies. Cloud-based AI platforms offer scalability, allowing businesses to easily expand their AI capabilities as their marketing needs grow and this scalability is crucial in handling large datasets and complex AI models.

For instance, on 8 May 2023, Salesforce introduced new AI-powered innovations for its Marketing Cloud, aimed as 78% of the marketers say that they drive the market and help companies to create more personalized and humanized interactions with customers. The new features include Einstein Engagement Scoring in Salesforce CDP, Einstein Designer, Interaction Studio Templates and Datorama Connectors. In today's digital-first world, companies need to deliver connected and relevant experiences to meet changing customer expectations.

Geographical Penetration:

Technological Infrastructure and AI-driven Campaign Decisions Boosts the Market

North America is dominating the global artificial intelligence (AI) in marketing market covering more than 1/3rd of the market and the region, particularly U.S., boasts advanced technological infrastructure that supports AI development and deployment and this includes robust cloud computing services, high-speed internet and access to cutting-edge hardware. North America generates huge amounts of data daily and this data serves as the lifeblood of AI, enabling machine learning algorithms to make data-driven marketing decisions.

For instance, on 14 June 2023, Scibids partnered with Tinuiti, a performance marketing agency, to launch the Scibids AI Insights Solution and this solution offers transparency and control over the ad decisioning process within Scibids' AI-powered algorithms, providing media buyers with insights into AI-driven campaign decisions. It analyzes variables such as URLs, creative elements, location and time of day to understand their impact on campaign performance.

Competitive Landscape

The major global players in the market include: IBM Corporation, Intel Corporation, Alphabet Inc, Microsoft Corporation, Twitter, Inc., Samsung India Electronics Pvt. Ltd., Amazon.com, Inc., NVIDIA Corporation, Albert Technologies Ltd. and H2O.ai, Inc.

COVID-19 Impact Analysis

The pandemic forced many businesses to expedite their digital transformation efforts, including the adoption of AI-powered marketing technologies. Physical stores closed and consumers spending more time online, companies turned to AI to enhance their digital marketing strategies. As in-person shopping declined, e-commerce experienced significant growth. AI-driven recommendation engines, chatbots and virtual shopping assistants became essential tools for online retailers to personalize the shopping experience and manage increased customer inquiries.

Content generation and curation tools powered by AI became crucial as companies needed to maintain an online presence and communicate with customers. AI helped create and distribute content at scale while minimizing the need for manual labor. Due to economic uncertainties, many businesses adjusted their marketing budgets. AI tools that provided cost-effective and measurable results gained favor, leading to an increased allocation of resources to AI-driven campaigns.

Consumer behavior changed rapidly during the pandemic. AI was used to analyze these shifts in real time, helping marketers adapt their strategies to meet evolving customer needs and preferences. AI was employed in supply chain and inventory management to predict demand fluctuations, optimize product availability and reduce disruptions caused by supply chain challenges.

AI Impact

AI-powered tools lead to processing a large amount of data in real-time, providing marketers with valuable insights into consumer behavior, preferences and trends, this data-driven approach enables more effective targeting and personalization of marketing campaigns. Marketers could produce highly targeted and relevant content for various audience categories using AI algorithms that can segment customers based on their demographics, behavior and goals, this segmentation boosts audience engagement and conversion rates.

AI enables dynamic content generation and personalized recommendations. Marketers can deliver tailored messages, product recommendations and offers to individual customers, enhancing the customer experience and driving sales. AI-powered chatbots and virtual assistants can provide instant customer support, answer queries and guide users through the purchase process and they offer 24/7 availability and can handle routine tasks, freeing up human agents for more complex issues.

For instance, on 13 September 2023, e-Core, a technology services partner specializing in digital transformation, introduced Orbit AI, a strategic approach to leverage artificial intelligence (AI) for business expansion and productivity enhancement and this initiative aims to boost the productivity of digital services and expedite project delivery times. It empowers e-Core's teams with AI Agents, resulting in significant milestones such as a 55% increase in code delivery speed and a 43% overall productivity improvement since its implementation.

Russia- Ukraine War Impact

The ongoing conflict has created economic uncertainty, both in the region and globally. Economic instability can affect marketing budgets and investment in AI technologies. Companies may become more cautious about adopting new AI marketing tools during uncertain times. The war has strained international relations, leading to increased geopolitical tensions. Such tensions can impact global trade and collaboration, which may affect the availability and accessibility of AI-powered marketing solutions.

The conflict has disrupted supply chains, especially in industries with ties to the region. AI hardware components, software development and data centers can be affected by these disruptions, potentially impacting the AI marketing ecosystem. Geopolitical tensions can lead to concerns about data privacy and security. Companies using AI for marketing must ensure the protection of customer data, especially if they have operations or customers in the affected regions.

By Offering

• Hardware
• Software
• Services

By Deployment Type

• Cloud
• On-Premise

By Deployment Type

• Machine Learning
• Context-Aware Computing
• Natural Language Processing
• Computer Vision

By Application

• Social Media Advertising
• Search Advertising
• Content Curation
• Sales Marketing Automation
• Analytics Platforms
• Others

By End-User

• BFSI
• Retail
• Consumer Goods
• Media Entertainment
• Enterprise
• Others

By Region

• North America
  • U.S.
  • Canada
  • Mexico

• Europe
  • Germany
  • UK
  • France
  • Italy
  • Russia
  • Rest of Europe

• South America
  • Brazil
  • Argentina
  • Rest of South America

• Asia-Pacific
  • China
  • India
  • Japan
  • Australia
  • Rest of Asia-Pacific

• Middle East and Africa

Key Developments

• In March 2023, HubSpot launched two new tools powered by artificial intelligence (AI) Content Assistant and ChatSpot.ai. These tools aim to help customers save time and improve audience engagement. Content Assistant and ChatSpot.ai leverage industry-leading AI systems from OpenAI to enhance efficiency for marketing, sales and customer service professionals.
• In July 2023, Interpublic Group (IPG) and its global creative network McCann Worldgroup joined the Partnership on AI to Benefit People and Society (PAI), becoming the first global marketing and advertising services company to join the group. PAI is a nonprofit partnership that works to advance responsible governance and best practices in artificial intelligence (AI).
• In July 2023, HCL Software launched HCL Marketing Cloud, an AI-powered SaaS solution designed to assist marketers in managing end-to-end marketing needs. It provides predictive and generative AI capabilities, allowing marketers to create tailored campaigns, address complexities across the organization, execute real-time customer behaviors, capitalize on revenue opportunities and deliver connected customer experiences.

Why Purchase the Report?

• To visualize the global artificial intelligence (AI) in marketing market segmentation based on offering, deployment type, technology, application, end-user and region, as well as understand key commercial assets and players.
• Identify commercial opportunities by analyzing trends and co-development.
• Excel data sheet with numerous data points of artificial intelligence (AI) in marketing market-level with all segments.
• PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
• Product mapping available as excel consisting of key products of all the major players.

The global artificial intelligence (AI) in marketing market report would provide approximately 77 tables, 83 figures and 199 Pages.

Target Audience 2023

• Manufacturers/ Buyers
• Industry Investors/Investment Bankers
• Research Professionals
• Emerging Companies

Table of Contents

1. Methodology and Scope

• 1.1. Research Methodology
• 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

• 3.1. Snippet by Offering
• 3.2. Snippet by Deployment Type
• 3.3. Snippet by Technology
• 3.4. Snippet by Application
• 3.5. Snippet by End-User
• 3.6. Snippet by Region

4. Dynamics

• 4.1. Impacting Factors
  • 4.1.1. Drivers
    • 4.1.1.1. Rising Demand for Predictive Analysis
    • 4.1.1.2. Collaboration Between Companies Boosts the Market
    • 4.1.1.3. Enhancing Marketing Capabilities with AI Algorithms
  • 4.1.2. Restraints
    • 4.1.2.1. Inaccurate or Baised Data and Required Maintenance Opportunity
  • 4.1.3. Impact Analysis

5. Industry Analysis

• 5.1. Porter's Five Force Analysis
• 5.2. Supply Chain Analysis
• 5.3. Pricing Analysis
• 5.4. Regulatory Analysis
• 5.5. Russia-Ukraine War Impact Analysis
• 5.6. DMI Opinion

6. COVID-19 Analysis

• 6.1. Analysis of COVID-19
  • 6.1.1. Scenario Before COVID
  • 6.1.2. Scenario During COVID
  • 6.1.3. Scenario Post COVID
• 6.2. Pricing Dynamics Amid COVID-19
• 6.3. Demand-Supply Spectrum
• 6.4. Government Initiatives Related to the Market During Pandemic
• 6.5. Manufacturers Strategic Initiatives
• 6.6. Conclusion

7. By Offering

• 7.1. Introduction
  • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 7.1.2. Market Attractiveness Index, By Offering
• 7.2. Hardware*
  • 7.2.1. Introduction
  • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 7.3. Software
• 7.4. Services

8. By Deployment Type

• 8.1. Introduction
  • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 8.1.2. Market Attractiveness Index, By Deployment Type
• 8.2. Cloud*
  • 8.2.1. Introduction
  • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 8.3. On Premises

9. By Technology

• 9.1. Introduction
  • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 9.1.2. Market Attractiveness Index, By Technology
• 9.2. Machine Learning*
  • 9.2.1. Introduction
  • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 9.3. Context-Aware Computing
• 9.4. Natural Language Processing
• 9.5. Computer Vision

10. By Application

• 10.1. Introduction
  • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 10.1.2. Market Attractiveness Index, By Application
• 10.2. Social Media Advertising*
  • 10.2.1. Introduction
  • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 10.3. Search Advertising
• 10.4. Content Curation
• 10.5. Sales Marketing Automation
• 10.6. Analytics Platforms
• 10.7. Others

11. By End-User

• 11.1. Introduction
  • 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 11.1.2. Market Attractiveness Index, By End-User
• 11.2. BFSI*
  • 11.2.1. Introduction
  • 11.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 11.3. Retail
• 11.4. Consumer Goods
• 11.5. Media Entertainment
• 11.6. Enterprise
• 11.7. Others

12. By Region

• 12.1. Introduction
  • 12.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
  • 12.1.2. Market Attractiveness Index, By Region
• 12.2. North America
  • 12.2.1. Introduction
  • 12.2.2. Key Region-Specific Dynamics
  • 12.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 12.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 12.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 12.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 12.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 12.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 12.2.8.1. U.S.
    • 12.2.8.2. Canada
    • 12.2.8.3. Mexico
• 12.3. Europe
  • 12.3.1. Introduction
  • 12.3.2. Key Region-Specific Dynamics
  • 12.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 12.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 12.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 12.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 12.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 12.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 12.3.8.1. Germany
    • 12.3.8.2. UK
    • 12.3.8.3. France
    • 12.3.8.4. Italy
    • 12.3.8.5. Russia
    • 12.3.8.6. Rest of Europe
• 12.4. South America
  • 12.4.1. Introduction
  • 12.4.2. Key Region-Specific Dynamics
  • 12.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 12.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 12.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 12.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 12.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 12.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 12.4.8.1. Brazil
    • 12.4.8.2. Argentina
    • 12.4.8.3. Rest of South America
• 12.5. Asia-Pacific
  • 12.5.1. Introduction
  • 12.5.2. Key Region-Specific Dynamics
  • 12.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 12.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 12.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 12.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 12.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 12.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 12.5.8.1. China
    • 12.5.8.2. India
    • 12.5.8.3. Japan
    • 12.5.8.4. Australia
    • 12.5.8.5. Rest of Asia-Pacific
• 12.6. Middle East and Africa
  • 12.6.1. Introduction
  • 12.6.2. Key Region-Specific Dynamics
  • 12.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 12.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 12.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 12.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 12.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

13. Competitive Landscape

• 13.1. Competitive Scenario
• 13.2. Market Positioning/Share Analysis
• 13.3. Mergers and Acquisitions Analysis

14. Company Profiles

• 14.1. IBM Corporation*
  • 14.1.1. Company Overview
  • 14.1.2. Product Portfolio and Description
  • 14.1.3. Financial Overview
  • 14.1.4. Key Developments
• 14.2. Intel Corporation
• 14.3. Alphabet Inc
• 14.4. Microsoft Corporation
• 14.5. Twitter, Inc.
• 14.6. Samsung India Electronics Pvt. Ltd.
• 14.7. Amazon.com, Inc.
• 14.8. NVIDIA Corporation
• 14.9. Albert Technologies Ltd.
• 14.10. H2O.ai, Inc.

LIST NOT EXHAUSTIVE

15. Appendix

• 15.1. About Us and Services
• 15.2. Contact Us