Artificial Intelligence in Marketing Market (By Offering: Hardware, Software, Services; By Deployment Mode: On-premise, Cloud; By Technology: Machine Learning, Natural Language Processing, Computer Vision, Context-Aware Computing; By Application: Social Media Advertising, Search Advertising, Content Curation Sales & Marketing Automation, Analytics Platform, Others; By End User) - Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023-2032
Artificial Intelligence in Marketing Market Size to Reach USD 145.42 Bn by 2032
The global artificial intelligence in marketing market size reached USD 14.79 billion in 2022 and it is projected to surpass around USD 145.42 billion by 2032, poised to grow at a CAGR of 25.68% during the forecast period from 2023 to 2032.
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Artificial intelligence (AI) in marketing refers to the integration of advanced algorithms and machine learning techniques into various marketing processes and strategies. This cutting-edge technology enables businesses to leverage data-driven insights, automate repetitive tasks, and deliver personalized experiences to their target audience, ultimately leading to enhanced customer engagement, improved efficiency, and increased ROI. The application of AI in marketing spans various facets of the field, from analyzing consumer behavior and predicting trends to optimizing ad campaigns and improving customer service.
By harnessing the power of AI, marketers can better understand their customer's preferences and needs, tailor their marketing efforts accordingly, and build stronger, more lasting relationships with their audience. The key factors propelling the market development are the expanding use of machine learning and artificial intelligence to offer increasing acceptance of social networking platforms, personalized customer experiences, and the rise of e-commerce.
Report Coverage | Details |
Market Size in 2023 | USD 18.59 Billion |
Market Size by 2032 | USD 145.42 Billion |
Growth Rate from 2023 to 2032 | CAGR of 25.68% |
Largest Market | North America |
Base Year | 2022 |
Forecast Period | 2023 to 2032 |
Segments Covered | By Offering, By Deployment Mode, By Technology, By Application, and By End User |
Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Driver:
Rising demand for predictive analysis
AI can forecast customer behavior, such as purchasing patterns and churn likelihood. This allows marketers to anticipate customer needs and preferences, enabling them to proactively address issues and offer relevant solutions. AI enables marketers to deliver highly personalized content and offers to individual customers based on their preferences, purchase history, and behavior. Personalization enhances customer engagement, satisfaction, and loyalty, leading to increased conversions and revenue. Thus, the rising requirements for personalization and predictive analysis drive the growth of the market.
Restraint:
Cost and data privacy concerns
Integrating AI technologies into marketing processes can be costly, especially for smaller businesses. The initial investment in AI infrastructure and skilled personnel can be a barrier for some companies. In addition, utilizing AI for data analysis and personalization raises concerns about data privacy and security. Marketers must ensure compliance with data protection regulations and address customer apprehensions about data handling. Thus, the cost and data privacy concerns are expected to hamper the market growth over the forecast period.
Opportunity:
Rapid utilization of AI in the healthcare industry
In several fields of medicine, artificial intelligence can be advantageous to the marketing of the healthcare business. By boosting on-site engagement and conversion or pointing potential patients to answers online, chatbots may help healthcare marketers improve the patient experience overall. By examining demographic patterns, predictive analytics enables the forecasting of the future behavior of certain demographic groups, the creation of marketing campaigns tailored to each demographic, and the analysis of spikes in patient visits by location or time. For instance, the healthcare technology business Youper is focused on providing mental health treatment. With the use of short, tailored chats and psychological approaches, Youper's AI monitors and improves users' emotional well-being. Therefore, this is expected to offer a lucrative opportunity for market growth over the forecast period.
Based on the deployment mode, the global AI in marketing market is segmented into on-premise and cloud. The cloud segment is expected to grow at the highest CAGR over the forecast period. Cloud-based AI in marketing refers to the integration of artificial intelligence (AI) capabilities into marketing processes and strategies, with the AI algorithms and tools hosted and accessed through cloud computing infrastructure. This approach offers several advantages to marketers, making AI adoption more accessible, scalable, and cost-effective.
On the other hand, the on-premise segment is expected to hold a significant market share over the forecast period. On-premise AI in marketing refers to the deployment of artificial intelligence (AI) solutions directly within an organization's infrastructure, rather than relying on cloud-based services. In this approach, the AI algorithms and tools are installed and operated locally on the organization's servers or data centers, allowing businesses to have full control and ownership over their AI implementation. On-premise AI offers several benefits and considerations that may make it a preferable choice for certain businesses in the marketing domain. The numerous benefits are data privacy and security, low latency, customization and control and others.
Based on the technology, the global AI in the marketing market is segmented into machine learning, natural language processing, computer vision and context-aware computing. The machine learning segment is expected to dominate the market over the forecast period. Machine learning algorithms can analyze large datasets, such as customer behavior, interactions, and preferences, to extract valuable insights. This data analysis aids marketers in understanding customer trends and optimizing marketing strategies.
Moreover, these technologies enable marketers to deliver personalized experiences by predicting customer preferences and recommending relevant products, services, or content based on individual behavior. Furthermore, these algorithms can predict future outcomes, such as customer churn, sales trends, and campaign performance, helping marketers make proactive decisions.
On the other hand, the computer vision segment is expected to grow at the fastest rate during the projected period. Specific facets of social media can be addressed by computer vision technologies. Concerning customer behavior, it can give advertisers real-time information. For instance, the American technology company Meta Platforms, Inc. trains its vision transformers (Vit) using a technique called a DINO. Furthermore, this model is capable of automatically identifying and categorizing items in both video and still images. Thereby, driving the segment growth over the forecast period.
Based on the application, the global AI in marketing market is segmented into social media advertising, search advertising, content curation, sales & marketing automation, analytics platform and others. The content curation segment is expected to dominate the market over the forecast period. AI-powered content curation is a valuable application of artificial intelligence in the marketing domain. Content curation involves the process of discovering, gathering, and organizing relevant and high-quality content from various sources to share with a target audience. AI plays a crucial role in streamlining this process by automating content discovery, analysis, and distribution, allowing marketers to deliver curated content tailored to individual interests and preferences.
On the other hand, social media advertising is expected to grow at a substantial rate over the forecast period. AI-powered social media advertising is a prominent and rapidly growing application of artificial intelligence in the marketing domain. Social media platforms offer an enormous amount of data and user interactions, making it an ideal environment for AI algorithms to analyze and optimize ad campaigns. AI enhances social media advertising by enabling precise targeting, personalized content delivery, real-time optimization, and improved return on investment (ROI).
Based on the end user, the global AI in marketing market is segmented into BFSI, retail, consumer goods, media & entertainment, enterprise and others. The media & entertainment segment is expected to dominate the market over the forecast period. This large percentage can be attributed to emerging AI advertising apps that have gained substantial momentum. For instance, a U.K.-based candy firm called Cadbury in January 2022 unveiled a program that let small business owners produce their advertising utilizing a celebrity's voice and facial traits for free. Moreover, AI-powered streaming platforms recommend movies, shows, and music based on user's viewing history and preferences, increasing user engagement and watch time.
North America is expected to dominate the market over the forecast period. In North America, AI is widely used in marketing for data analytics and insights, personalized customer experiences, programmatic advertising, chatbots, recommendation engines, and content curation, among other applications. Additionally, artificial intelligence in marketing is prevalent in a wide range of industries in North America, including retail and e-commerce, banking and financial services, technology, media and entertainment, healthcare, and more.
Furthermore, AI adoption in marketing is observed in both large enterprises and small-to-medium-sized businesses (SMEs) in North America. While larger companies may have more resources for AI implementation, AI technology is becoming more accessible to SMEs through various cloud-based solutions. Thus, this is expected to drive market expansion in the region.
Asia Pacific is expected to grow at the fastest rate over the forecast period. The rise of the e-commerce and online retail sectors, expanding investment in artificial intelligence, rising company awareness of and acceptance of AI-powered marketing solutions, and rapid technical progress and digitalization are significant drivers impacting growth in this area. Particularly, the AI sector in China is growing swiftly, and it is predicted that China will soon overtake other countries as the leader in AI technology. The Asia Pacific region's retail and e-commerce industries are an important industrial vertical for artificial intelligence in marketing due to the growing need for personalized and data-driven marketing solutions.
Segments Covered in the Report:
By Offering
By Deployment Mode
By Technology
By Application
By End User
By Geography
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology (Premium Insights)
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on Artificial Intelligence in Marketing Market
5.1. COVID-19 Landscape: Artificial Intelligence in Marketing Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global Artificial Intelligence in Marketing Market, By Offering
8.1. Artificial Intelligence in Marketing Market, by Offering, 2023-2032
8.1.1. Hardware
8.1.1.1. Market Revenue and Forecast (2020-2032)
8.1.2. Software
8.1.2.1. Market Revenue and Forecast (2020-2032)
8.1.3. Services
8.1.3.1. Market Revenue and Forecast (2020-2032)
Chapter 9. Global Artificial Intelligence in Marketing Market, By Deployment Mode
9.1. Artificial Intelligence in Marketing Market, by Deployment Mode, 2023-2032
9.1.1. On-premise
9.1.1.1. Market Revenue and Forecast (2020-2032)
9.1.2. Cloud
9.1.2.1. Market Revenue and Forecast (2020-2032)
Chapter 10. Global Artificial Intelligence in Marketing Market, By Technology
10.1. Artificial Intelligence in Marketing Market, by Technology, 2023-2032
10.1.1. Machine Learning
10.1.1.1. Market Revenue and Forecast (2020-2032)
10.1.2. Natural Language Processing
10.1.2.1. Market Revenue and Forecast (2020-2032)
10.1.3. Computer Vision
10.1.3.1. Market Revenue and Forecast (2020-2032)
10.1.4. Context-Aware Computing
10.1.4.1. Market Revenue and Forecast (2020-2032)
Chapter 11. Global Artificial Intelligence in Marketing Market, By Application
11.1. Artificial Intelligence in Marketing Market, by Application, 2023-2032
11.1.1. Social Media Advertising
11.1.1.1. Market Revenue and Forecast (2020-2032)
11.1.2. Search Advertising
11.1.2.1. Market Revenue and Forecast (2020-2032)
11.1.3. Content Curation
11.1.3.1. Market Revenue and Forecast (2020-2032)
11.1.4. Sales & Marketing Automation
11.1.4.1. Market Revenue and Forecast (2020-2032)
11.1.5. Analytics Platform
11.1.5.1. Market Revenue and Forecast (2020-2032)
11.1.6. Others
11.1.6.1. Market Revenue and Forecast (2020-2032)
Chapter 12. Global Artificial Intelligence in Marketing Market, By End User
12.1. Artificial Intelligence in Marketing Market, by End User, 2023-2032
12.1.1. BFSI
12.1.1.1. Market Revenue and Forecast (2020-2032)
12.1.2. Retail
12.1.2.1. Market Revenue and Forecast (2020-2032)
12.1.3. Consumer Goods
12.1.3.1. Market Revenue and Forecast (2020-2032)
12.1.4. Media & Entertainment
12.1.4.1. Market Revenue and Forecast (2020-2032)
12.1.5. Enterprise
12.1.5.1. Market Revenue and Forecast (2020-2032)
12.1.6. Others
12.1.6.1. Market Revenue and Forecast (2020-2032)
Chapter 13. Global Artificial Intelligence in Marketing Market, Regional Estimates and Trend Forecast
13.1. North America
13.1.1. Market Revenue and Forecast, by Offering (2020-2032)
13.1.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.1.3. Market Revenue and Forecast, by Technology (2020-2032)
13.1.4. Market Revenue and Forecast, by Application (2020-2032)
13.1.5. Market Revenue and Forecast, by End User (2020-2032)
13.1.6. U.S.
13.1.6.1. Market Revenue and Forecast, by Offering (2020-2032)
13.1.6.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.1.6.3. Market Revenue and Forecast, by Technology (2020-2032)
13.1.6.4. Market Revenue and Forecast, by Application (2020-2032)
13.1.6.5. Market Revenue and Forecast, by End User (2020-2032)
13.1.7. Rest of North America
13.1.7.1. Market Revenue and Forecast, by Offering (2020-2032)
13.1.7.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.1.7.3. Market Revenue and Forecast, by Technology (2020-2032)
13.1.7.4. Market Revenue and Forecast, by Application (2020-2032)
13.1.7.5. Market Revenue and Forecast, by End User (2020-2032)
13.2. Europe
13.2.1. Market Revenue and Forecast, by Offering (2020-2032)
13.2.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.2.3. Market Revenue and Forecast, by Technology (2020-2032)
13.2.4. Market Revenue and Forecast, by Application (2020-2032)
13.2.5. Market Revenue and Forecast, by End User (2020-2032)
13.2.6. UK
13.2.6.1. Market Revenue and Forecast, by Offering (2020-2032)
13.2.6.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.2.6.3. Market Revenue and Forecast, by Technology (2020-2032)
13.2.7. Market Revenue and Forecast, by Application (2020-2032)
13.2.8. Market Revenue and Forecast, by End User (2020-2032)
13.2.9. Germany
13.2.9.1. Market Revenue and Forecast, by Offering (2020-2032)
13.2.9.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.2.9.3. Market Revenue and Forecast, by Technology (2020-2032)
13.2.10. Market Revenue and Forecast, by Application (2020-2032)
13.2.11. Market Revenue and Forecast, by End User (2020-2032)
13.2.12. France
13.2.12.1. Market Revenue and Forecast, by Offering (2020-2032)
13.2.12.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.2.12.3. Market Revenue and Forecast, by Technology (2020-2032)
13.2.12.4. Market Revenue and Forecast, by Application (2020-2032)
13.2.13. Market Revenue and Forecast, by End User (2020-2032)
13.2.14. Rest of Europe
13.2.14.1. Market Revenue and Forecast, by Offering (2020-2032)
13.2.14.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.2.14.3. Market Revenue and Forecast, by Technology (2020-2032)
13.2.14.4. Market Revenue and Forecast, by Application (2020-2032)
13.2.15. Market Revenue and Forecast, by End User (2020-2032)
13.3. APAC
13.3.1. Market Revenue and Forecast, by Offering (2020-2032)
13.3.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.3.3. Market Revenue and Forecast, by Technology (2020-2032)
13.3.4. Market Revenue and Forecast, by Application (2020-2032)
13.3.5. Market Revenue and Forecast, by End User (2020-2032)
13.3.6. India
13.3.6.1. Market Revenue and Forecast, by Offering (2020-2032)
13.3.6.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.3.6.3. Market Revenue and Forecast, by Technology (2020-2032)
13.3.6.4. Market Revenue and Forecast, by Application (2020-2032)
13.3.7. Market Revenue and Forecast, by End User (2020-2032)
13.3.8. China
13.3.8.1. Market Revenue and Forecast, by Offering (2020-2032)
13.3.8.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.3.8.3. Market Revenue and Forecast, by Technology (2020-2032)
13.3.8.4. Market Revenue and Forecast, by Application (2020-2032)
13.3.9. Market Revenue and Forecast, by End User (2020-2032)
13.3.10. Japan
13.3.10.1. Market Revenue and Forecast, by Offering (2020-2032)
13.3.10.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.3.10.3. Market Revenue and Forecast, by Technology (2020-2032)
13.3.10.4. Market Revenue and Forecast, by Application (2020-2032)
13.3.10.5. Market Revenue and Forecast, by End User (2020-2032)
13.3.11. Rest of APAC
13.3.11.1. Market Revenue and Forecast, by Offering (2020-2032)
13.3.11.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.3.11.3. Market Revenue and Forecast, by Technology (2020-2032)
13.3.11.4. Market Revenue and Forecast, by Application (2020-2032)
13.3.11.5. Market Revenue and Forecast, by End User (2020-2032)
13.4. MEA
13.4.1. Market Revenue and Forecast, by Offering (2020-2032)
13.4.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.4.3. Market Revenue and Forecast, by Technology (2020-2032)
13.4.4. Market Revenue and Forecast, by Application (2020-2032)
13.4.5. Market Revenue and Forecast, by End User (2020-2032)
13.4.6. GCC
13.4.6.1. Market Revenue and Forecast, by Offering (2020-2032)
13.4.6.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.4.6.3. Market Revenue and Forecast, by Technology (2020-2032)
13.4.6.4. Market Revenue and Forecast, by Application (2020-2032)
13.4.7. Market Revenue and Forecast, by End User (2020-2032)
13.4.8. North Africa
13.4.8.1. Market Revenue and Forecast, by Offering (2020-2032)
13.4.8.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.4.8.3. Market Revenue and Forecast, by Technology (2020-2032)
13.4.8.4. Market Revenue and Forecast, by Application (2020-2032)
13.4.9. Market Revenue and Forecast, by End User (2020-2032)
13.4.10. South Africa
13.4.10.1. Market Revenue and Forecast, by Offering (2020-2032)
13.4.10.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.4.10.3. Market Revenue and Forecast, by Technology (2020-2032)
13.4.10.4. Market Revenue and Forecast, by Application (2020-2032)
13.4.10.5. Market Revenue and Forecast, by End User (2020-2032)
13.4.11. Rest of MEA
13.4.11.1. Market Revenue and Forecast, by Offering (2020-2032)
13.4.11.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.4.11.3. Market Revenue and Forecast, by Technology (2020-2032)
13.4.11.4. Market Revenue and Forecast, by Application (2020-2032)
13.4.11.5. Market Revenue and Forecast, by End User (2020-2032)
13.5. Latin America
13.5.1. Market Revenue and Forecast, by Offering (2020-2032)
13.5.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.5.3. Market Revenue and Forecast, by Technology (2020-2032)
13.5.4. Market Revenue and Forecast, by Application (2020-2032)
13.5.5. Market Revenue and Forecast, by End User (2020-2032)
13.5.6. Brazil
13.5.6.1. Market Revenue and Forecast, by Offering (2020-2032)
13.5.6.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.5.6.3. Market Revenue and Forecast, by Technology (2020-2032)
13.5.6.4. Market Revenue and Forecast, by Application (2020-2032)
13.5.7. Market Revenue and Forecast, by End User (2020-2032)
13.5.8. Rest of LATAM
13.5.8.1. Market Revenue and Forecast, by Offering (2020-2032)
13.5.8.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
13.5.8.3. Market Revenue and Forecast, by Technology (2020-2032)
13.5.8.4. Market Revenue and Forecast, by Application (2020-2032)
13.5.8.5. Market Revenue and Forecast, by End User (2020-2032)
Chapter 14. Company Profiles
14.1. NVIDIA Corporation
14.1.1. Company Overview
14.1.2. Product Offerings
14.1.3. Financial Performance
14.1.4. Recent Initiatives
14.2. Salesforce, Inc.
14.2.1. Company Overview
14.2.2. Product Offerings
14.2.3. Financial Performance
14.2.4. Recent Initiatives
14.3. Google LLC
14.3.1. Company Overview
14.3.2. Product Offerings
14.3.3. Financial Performance
14.3.4. Recent Initiatives
14.4. Intel Corporation
14.4.1. Company Overview
14.4.2. Product Offerings
14.4.3. Financial Performance
14.4.4. Recent Initiatives
14.5. International Business Machines Corporation
14.5.1. Company Overview
14.5.2. Product Offerings
14.5.3. Financial Performance
14.5.4. Recent Initiatives
14.6. Microsoft Corporation
14.6.1. Company Overview
14.6.2. Product Offerings
14.6.3. Financial Performance
14.6.4. Recent Initiatives
14.7. Amazon.com, Inc.
14.7.1. Company Overview
14.7.2. Product Offerings
14.7.3. Financial Performance
14.7.4. Recent Initiatives
14.8. Oracle Corporation
14.8.1. Company Overview
14.8.2. Product Offerings
14.8.3. Financial Performance
14.8.4. Recent Initiatives
14.9. Baidu, Inc.
14.9.1. Company Overview
14.9.2. Product Offerings
14.9.3. Financial Performance
14.9.4. Recent Initiatives
14.10. Twitter, Inc.
14.10.1. Company Overview
14.10.2. Product Offerings
14.10.3. Financial Performance
14.10.4. Recent Initiatives
Chapter 15. Research Methodology
15.1. Primary Research
15.2. Secondary Research
15.3. Assumptions
Chapter 16. Appendix
16.1. About Us
16.2. Glossary of Terms
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