by The global generative AI in e-commerce market size accounted for US$ 624.51 Mn in 2022 and is projected to reach around USD 2,530.89 Mn by 2032, growing at a CAGR of 15.02% from 2023 to 2032.
Key Takeaways:
- North America contributed more than 43% of revenue share in 2022.
- By deployment mode, the on-premises segment shows a leading growth in the generative AI in the e-commerce market.
- By technology, the machine learning segment accounted for more than 38% of revenue share in 2022.
- By application, the B2C segment is projected to share the maximum CAGR in the generative AI in e-commerce during the projection period.
Report Summary
The global generative AI in e-commerce market report provides a Point-by-Point and In-Depth analysis of global market size, regional and country-level market size, market share, segmentation market growth, competitive landscape, sales analysis, opportunities analysis, strategic market growth analysis, the impact of domestic and global market key players, value chain optimization, trade regulations, recent developments, product launches, area marketplace expanding, and technological innovations.
The study offers a comprehensive analysis on diverse features, including production capacities, demand, product developments, revenue generation, and sales in the generative AI in e-commerce market across the globe.
A comprehensive estimate on the generative AI in e-commerce market has been provided through an optimistic scenario as well as a conservative scenario, taking into account the sales of generative AI in e-commerce during the forecast period. Price point comparison by region with global average price is also considered in the study.
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Generative AI in E-Commerce Market Report Scope
| Report Coverage | Details |
| Market Size in 2023 | USD 718.31 Million |
| Market Size by 2032 | USD 2,530.89 Million |
| Growth Rate from 2023 to 2032 | CAGR of 15.02% |
| Largest Market | North America |
| Fastest Growing Market | Asia Pacific |
| Base Year | 2022 |
| Forecast Period | 2023 To 2032 |
| Segments Covered | By Deployment Mode, By Deployment Mode, and By Application |
| Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Key Highlights:
Reports Coverage: It incorporates key market sections, key makers secured, the extent of items offered in the years considered, worldwide containerized generative AI in e-commerce market and study goals. Moreover, it contacts the division study gave in the report based on the sort of item and applications.
Market Outline: This area stresses the key investigations, market development rate, serious scene, market drivers, patterns, and issues notwithstanding the naturally visible pointers.
Market Production by Region: The report conveys information identified with import and fare, income, creation, and key players of every single local market contemplated are canvassed right now.
Also Read: AR and VR Headsets Market Size To Cross USD 142.5 Bn By 2032
Generative AI in E-Commerce Market Players
The report includes the profiles of key generative AI in e-commerce market companies along with their SWOT analysis and market strategies. In addition, the report focuses on leading industry players with information such as company profiles, components and services offered, financial information, key development in past five years.
Major companies operating in this area
- Amazon, Inc
- Jingdong
- Meituan
- Suning.com
- Otto Group
- Shein
- Rakuten, Inc
- Wayfair
- eBay
- Zalando
- B2W Companhia Digital
- Groupon
Market Segmentation
By Deployment Mode
- Cloud-based
- On-premises
- Hybrid
By Technology
- Natural Language Processing
- Computer Vision
- Machine Learning
- Speech Recognition
- Augmented Reality
By Application
- B2B
- B2C
- Personal
- Business Use
- Seasonal Use
- Other Applications
Regional Segmentation
- North America (U.S., Canada, Mexico)
- Europe (Germany, France, U.K., Italy, Spain, Rest of Europe)
- Asia-Pacific (China, Japan, India, Southeast Asia and Rest of APAC)
- Latin America (Brazil and Rest of Latin America)
- Middle East and Africa (GCC, North Africa, South Africa, Rest of MEA)
Research Methodology
Secondary Research
It involves company databases such as Hoover’s: This assists us to recognize financial information, the structure of the market participants and industry’s competitive landscape.
The secondary research sources referred in the process are as follows:
- Governmental bodies, and organizations creating economic policies
- National and international social welfare institutions
- Company websites, financial reports and SEC filings, broker and investor reports
- Related patent and regulatory databases
- Statistical databases and market reports
- Corporate Presentations, news, press release, and specification sheet of Manufacturers
Primary Research
Primary research includes face-to-face interviews, online surveys, and telephonic interviews.
- Means of primary research: Email interactions, telephonic discussions and Questionnaire-based research etc.
- In order to validate our research findings and analysis, we conduct primary interviews of key industry participants. Insights from primary respondents help in validating the secondary research findings. It also develops Research Team’s expertise and market understanding.
TABLE OF CONTENT
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 Generative AI in E-Commerce Market
5.1. COVID-19 Landscape: Generative AI in E-Commerce 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 Generative AI in E-Commerce Market, By Deployment Mode
8.1. Generative AI in E-Commerce Market, by Deployment Mode, 2023-2032
8.1.1 Cloud-based
8.1.1.1. Market Revenue and Forecast (2020-2032)
8.1.2. On-premises
8.1.2.1. Market Revenue and Forecast (2020-2032)
8.1.3. Hybrid
8.1.3.1. Market Revenue and Forecast (2020-2032)
Chapter 9. Global Generative AI in E-Commerce Market, By Technology
9.1. Generative AI in E-Commerce Market, by Technology, 2023-2032
9.1.1. Natural Language Processing
9.1.1.1. Market Revenue and Forecast (2020-2032)
9.1.2. Computer Vision
9.1.2.1. Market Revenue and Forecast (2020-2032)
9.1.3. Machine Learning
9.1.3.1. Market Revenue and Forecast (2020-2032)
9.1.4. Speech Recognition
9.1.4.1. Market Revenue and Forecast (2020-2032)
9.1.5. Augmented Reality
9.1.5.1. Market Revenue and Forecast (2020-2032)
Chapter 10. Global Generative AI in E-Commerce Market, By Application
10.1. Generative AI in E-Commerce Market, by Application, 2023-2032
10.1.1. B2B
10.1.1.1. Market Revenue and Forecast (2020-2032)
10.1.2. B2C
10.1.2.1. Market Revenue and Forecast (2020-2032)
10.1.3. Personal
10.1.3.1. Market Revenue and Forecast (2020-2032)
10.1.4. Business Use
10.1.4.1. Market Revenue and Forecast (2020-2032)
10.1.5. Seasonal Use
10.1.5.1. Market Revenue and Forecast (2020-2032)
10.1.6. Other Applications
10.1.6.1. Market Revenue and Forecast (2020-2032)
Chapter 11. Global Generative AI in E-Commerce Market, Regional Estimates and Trend Forecast
11.1. North America
11.1.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.1.2. Market Revenue and Forecast, by Technology (2020-2032)
11.1.3. Market Revenue and Forecast, by Application (2020-2032)
11.1.4. U.S.
11.1.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.1.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.1.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.1.5. Rest of North America
11.1.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.1.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.1.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.2. Europe
11.2.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.2.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.4. UK
11.2.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.2.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.5. Germany
11.2.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.2.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.6. France
11.2.6.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.2.6.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.6.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.7. Rest of Europe
11.2.7.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.2.7.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.7.3. Market Revenue and Forecast, by Application (2020-2032)
11.3. APAC
11.3.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.3.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.3. Market Revenue and Forecast, by Application (2020-2032)
11.3.4. India
11.3.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.3.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.3.5. China
11.3.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.3.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.5.3. Market Revenue and Forecast, by Application (2020-2032)<犀利士
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11.3.6. Japan
11.3.6.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.3.6.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.6.3. Market Revenue and Forecast, by Application (2020-2032)
11.3.7. Rest of APAC
11.3.7.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.3.7.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.7.3. Market Revenue and Forecast, by Application (2020-2032)
11.4. MEA
11.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.4. GCC
11.4.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.4.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.5. North Africa
11.4.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.4.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.6. South Africa
11.4.6.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.4.6.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.6.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.7. Rest of MEA
11.4.7.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.4.7.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.7.3. Market Revenue and Forecast, by Application (2020-2032)
11.5. Latin America
11.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.5.4. Brazil
11.5.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.5.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.5.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.5.5. Rest of LATAM
11.5.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.5.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.5.5.3. Market Revenue and Forecast, by Application (2020-2032)
Chapter 12. Company Profiles
12.1. Amazon, Inc
12.1.1. Company Overview
12.1.2. Product Offerings
12.1.3. Financial Performance
12.1.4. Recent Initiatives
12.2. Jingdong
12.2.1. Company Overview
12.2.2. Product Offerings
12.2.3. Financial Performance
12.2.4. Recent Initiatives
12.3. Meituan
12.3.1. Company Overview
12.3.2. Product Offerings
12.3.3. Financial Performance
12.3.4. Recent Initiatives
12.4. Suning.com
12.4.1. Company Overview
12.4.2. Product Offerings
12.4.3. Financial Performance
12.4.4. Recent Initiatives
12.5. Otto Group
12.5.1. Company Overview
12.5.2. Product Offerings
12.5.3. Financial Performance
12.5.4. Recent Initiatives
12.6. Shein
12.6.1. Company Overview
12.6.2. Product Offerings
12.6.3. Financial Performance
12.6.4. Recent Initiatives
12.7. Rakuten, Inc
12.7.1. Company Overview
12.7.2. Product Offerings
12.7.3. Financial Performance
12.7.4. Recent Initiatives
12.8. Wayfair
12.8.1. Company Overview
12.8.2. Product Offerings
12.8.3. Financial Performance
12.8.4. Recent Initiatives
12.9. eBay
12.9.1. Company Overview
12.9.2. Product Offerings
12.9.3. Financial Performance
12.9.4. Recent Initiatives
12.10. Zalando
12.10.1. Company Overview
12.10.2. Product Offerings
12.10.3. Financial Performance
12.10.4. Recent Initiatives
Chapter 13. Research Methodology
13.1. Primary Research
13.2. Secondary Research
13.3. Assumptions
Chapter 14. Appendix
14.1. About Us
14.2. Glossary of Terms
Thanks for reading you can also get individual chapter-wise sections or region-wise report versions such as North America, Europe, or the Asia Pacific.
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