AI in E-commerce Market by Technology (ML, NLP, Computer Vision), Business Model, Deployment Mode, Product Offering (Beauty & Fashion, Pharmaceutical, Electronic), End User (B2B, B2C), and Geography

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AI in E-commerce Market by Technology (ML, NLP, Computer Vision), Business Model, Deployment Mode, Product Offering (Beauty & Fashion, Pharmaceutical, Electronic), End User (B2B, B2C), and Geography-Global Forecast to 2031.

The research report titled 'AI in E-commerce Market by Technology (ML, NLP, Computer Vision), Business Model, Deployment Mode, Product Offering (Beauty & Fashion, Pharmaceutical, Electronic), End User (B2B, B2C), and Geography-Global Forecast to 2031', provides an in-depth analysis of the AI in e-commerce market in five major geographies and emphasizes on the current market trends, market sizes, market shares, recent developments, and forecasts till 2031.

The AI in e-commerce market is projected to reach $11.1 billion by 2031, at a CAGR of 17.8% from 2024 to 2031.

The growth of the AI in e-commerce market is attributed to the increasing need for customer retention in the e-commerce industry, growing usage of social media and e-commerce, rising penetration of the internet and smartphones, and increasing need to improve and automate inventory management systems. However, the high initial investment restrains the market's growth.

Moreover, the increasing penetration of voice recognition technology offers significant opportunities for the growth of the AI in e-commerce market. However, data privacy concerns and a lack of skilled labor pose a challenge to the market's growth.

The AI in e-commerce market is segmented by technology (machine learning (ML), natural language processing (NLP), computer vision, deep learning, other technologies), business model (horizontal e-commerce, vertical e-commerce), deployment mode (on-premise deployment, cloud-based deployment), product offering (beauty & fashion products, travel & tourism products, electronic products, household products, pharmaceutical products, food & beverage products, sports/fitness products, automotive products, other products), and end user (B2B, B2C). The study also evaluates industry competitors and analyzes the market at the regional and country levels.

Based on technology, the AI in e-commerce market is segmented into machine learning (ML), natural language processing (NLP), computer vision, deep learning, and other technologies. In 2024, the machine learning (ML) segment is expected to account for the largest share of above 65% of the AI in e-commerce market. The large market share of this segment is attributed to the increasing use of machine learning technology in customer experience management, customer behavior tracking, and predictive merchandising to engage customers and increase productivity. It offers personalized product recommendations to enhance the customer shopping experience. It also helps design effective sales strategies through various pricing factors such as seasonality, supply, and demand to boost sales through real-time and dynamic price adjustments.

However, the computer vision segment is projected to register the highest CAGR during the forecast period. This segment's growth is attributed to organizations' increasing need to organize and monitor inventory on their websites and the increasing demand for better product discovery, reduced cost, better buying experience, and improved security.

Based on business model, the AI in e-commerce market is segmented into horizontal e-commerce and vertical e-commerce. In 2024, the horizontal e-commerce segment is expected to account for the larger share of AI in e-commerce market. The horizontal e-commerce model caters to a diverse product category and hence enjoys a large section of consumers and high sales, contributing to the segment's large share. Horizontal e-commerce platforms often use a marketplace model, where third-party sellers can also list and sell their products. This allows for even greater product variety and selection, as well as increased competition and potentially lower prices for customers.

However, the vertical e-commerce segment is projected to register the highest CAGR during the forecast period. The vertical e-commerce model allows businesses to offer customers a specialized and tailored shopping experience within a specific product category or industry. By focusing on a specific niche, businesses can differentiate themselves from larger, more generalized e-commerce platforms and establish a strong brand identity.

Based on deployment mode, the AI in e-commerce market is segmented into on-premise deployment and cloud-based deployment. In 2024, the on-premise deployment segment is expected to account for the larger share of above 60% of the AI in e-commerce market. This segment's large share is attributed to the high preference for on-premise deployments among large enterprises and security issues associated with cloud deployments.

However, the cloud-based deployment segment is projected to register the highest CAGR during the forecast period. This segment's growth is attributed to the rapid evolution of new avenues for cloud-based deployments, a decrease in the cost of cloud-enabling technology, the growing use of learning analytics, and the increasing incorporation of cloud-based solutions by small and medium-scale enterprises.

Based on product offering, the AI in e-commerce market is segmented into beauty & fashion products, travel & tourism products, electronic products, household products, pharmaceutical products, food & beverage products, sports/fitness products, automotive products, and other products. In 2024, the beauty & fashion products segment is expected to account for the largest share of above 25% of the AI in e-commerce market. The large market share of this segment is attributed to the rapidly changing customer behavior and preference towards online shopping, the high demand for cosmetics and clothing, and the rising popularity of mobile wallets.

However, the electronic products segment is projected to register the highest CAGR during the forecast period. This segment's growth is attributed to the high demand for electronic devices, including smartphones, and the incorporation of advanced technologies, including chatbots, face recognition payments, and image/voice search technologies, to enhance customer engagement in online shopping platforms.

Based on end user, the AI in e-commerce market is segmented into B2B and B2C. In 2024, the B2B segment is expected to account for the larger share of above 80% of the AI in e-commerce market. The large market share of this segment is attributed to the increasing need for B2B transaction security, the increasing focus of organizations to boost their brand name, and the increasing adoption of AI for price optimization, inventory management, improved efficiency, and increased profitability. Moreover, the segment is also projected to register the highest CAGR during the forecast period.

Based on geography, the AI in e-commerce market is segmented into North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa. In 2024, Asia-Pacific is expected to account for the largest share of above 40% of the AI in e-commerce market. Factors such as large consumer groups, growing penetration of smartphones and the internet, rising disposable income, and technological advancements in various countries such as South Korea and Japan drive the growth of AI in e-commerce market. Moreover, this region is also projected to register the highest CAGR of above 19% during the forecast period.

The key players operating in the AI in E-commerce market are Amazon Web Services, Inc. (U.S.), Google LLC (U.S.), IBM Corporation (U.S.), Alibaba Cloud (China), Intel Corporation (U.S.), Microsoft Corporation (U.S.), Shelf.AI (Portugal), Appier Inc. (Japan), NVIDIA Corporation (U.S.), Granify, Inc. (Canada), Oracle Corporation (U.S.), Manthan Software Services Pvt. Ltd. (U.S.), SAP SE (Germany), LivePerson, Inc. (U.S.), and Twiggle Ltd. (Israel).

Key Questions Answered in the Report:

What are the high-growth market segments in terms of technology, business model, deployment mode, product offering, and end user?
What is the historical market size for the AI in e-commerce market?
What are the market forecasts and estimates for 2024-2031?
What are the major drivers, restraints, opportunities, challenges, and trends in the AI in e-commerce market?
Who are the major players in the AI in e-commerce market, and what are their market shares?
What is the competitive landscape like?
What are the recent developments in the AI in e-commerce market?
What are the different strategies adopted by major market players?
What are the trends and high-growth countries?
Who are the local emerging players in the AI in e-commerce market, and how do they compete with other players?

Scope of the Report:

AI in E-commerce Market Assessment-by Technology

Machine Learning (ML)
- Product Recommendations
- Predictive Analytics
- Fraud Detection
- Customer Service
- Other Machine Learning (ML) Applications
Natural Language Processing (NLP)
- Chatbots
- Search and Recommendations
- Voice Assistants
- Other Natural Language Processing (NLP) Applications
Computer Vision
- Visual Search
- Product Categorization
- Facial Recognition
- Quality Control
- Security
- Other Computer Vision Applications
Deep Learning
- Supply Chain Optimization
- Fraud Detection
- Dynamic Pricing
- Personalized Product Recommendations
- Other Deep Learning Applications
Other Technologies

AI in E-commerce Market Assessment-by Business Model

Horizontal E-commerce
Vertical E-commerce

AI in E-commerce Market Assessment-by Deployment Mode

On-premise Deployment
Cloud-based Deployment

AI in E-commerce Market Assessment-by Product Offering

Beauty & Fashion Products
Travel & Tourism Products
Electronic Products
Household Products
Pharmaceutical Products
Food & Beverage Products
Sports / Fitness Products
Automotive Products
Other Products

AI in E-commerce Market Assessment-by End User

AI in E-commerce Market Assessment-by Geography

North America
- U.S.
- Canada
Europe
- Germany
- U.K.
- France
- Italy
- Spain
- Rest of Europe
Asia-Pacific
- Japan
- China
- India
- South Korea
- Rest of Asia-Pacific
Latin America
Middle East & Africa

Product Code: MRICT - 1041084

1. Introduction

1.1. Market Definition & Scope
1.2. Currency & Limitations
- 1.2.1. Currency
- 1.2.2. Limitations

2. Research Methodology

2.1. Research Approach
2.2. Process of Data Collection & Validation
- 2.2.1. Secondary Research
- 2.2.2. Primary Research/Interviews with Key Opinion Leaders from the Industry
2.3. Market Sizing and Forecast
- 2.3.1. Market Size Estimation Approach
- 2.3.2. Growth Forecast Approach
2.4. Assumptions for the Study

3. Executive Summary

4. Market Insights

4.1. Overview
4.2. Factors Affecting Market Growth
- 4.2.1. Drivers
  - 4.2.1.1. Increasing Need for Customer Retention in E-commerce Industry
  - 4.2.1.2. Growing Usage of Social Media and Social Commerce
  - 4.2.1.3. Rising Penetration of the Internet and Smartphones
  - 4.2.1.4. Increasing Need to Improve and Automate Inventory Management System
- 4.2.2. Restraints
  - 4.2.2.1. High Initial Investment
- 4.2.3. Opportunities
  - 4.2.3.1. Increasing Penetration of Voice Recognition Technology
- 4.2.4. Challenges
  - 4.2.4.1. Data Privacy Concerns
  - 4.2.4.2. Lack of Skilled Labour
4.3. Market Trends
4.4. Case Studies
4.5. Vendor Selection Criteria/Factors Affecting Buying Decision

5. AI in E-commerce Market Assessment-by Technology

5.1. Overview
5.2. Machine Learning (ML)
- 5.2.1. Product Recommendations
- 5.2.2. Predictive Analytics
- 5.2.3. Fraud Detection
- 5.2.4. Customer Service
- 5.2.5. Other Machine Learning (ML) Applications
5.3. Natural Language Processing (NLP)
- 5.3.1. Chatbots
- 5.3.2. Search and Recommendations
- 5.3.3. Voice Assistants
- 5.3.4. Other Natural Language Processing (NLP) Applications
5.4. Computer Vision
- 5.4.1. Visual Search
- 5.4.2. Product Categorization
- 5.4.3. Facial recognition
- 5.4.4. Quality control
- 5.4.5. Security
- 5.4.6. Other Computer Vision Applications
5.5. Deep Learning
- 5.5.1. Supply Chain Optimization
- 5.5.2. Fraud Detection
- 5.5.3. Dynamic Pricing
- 5.5.4. Personalized Product Recommendations
- 5.5.5. Other Deep Learning Applications
5.6. Other Technologies

6. AI in E-commerce Market Assessment-by Business Model

6.1. Overview
6.2. Horizontal E-commerce
6.3. Vertical E-commerce

7. AI in E-commerce Market Assessment-by Deployment Mode

7.1. Overview
7.2. On-premise Deployment
7.3. Cloud-based Deployment

8. AI in E-commerce Market Assessment-by Product Offering

8.1. Overview
8.2. Beauty & Fashion Products
8.3. Travel & Tourism Products
8.4. Electronic Products
8.5. Household Products
8.6. Pharmaceutical Products
8.7. Food & Beverage Products
8.8. Sports / Fitness Products
8.9. Automotive Products
8.10. Other Products

9. AI in E-commerce Market Assessment-by End User

9.1. Overview
9.2. B2B
9.3. B2C

10. AI in E-commerce Market Assessment-by Geography

10.1. Overview
10.2. North America
- 10.2.1. U.S.
- 10.2.2. Canada
10.3. Europe
- 10.3.1. Germany
- 10.3.2. U.K.
- 10.3.3. France
- 10.3.4. Italy
- 10.3.5. Spain
- 10.3.6. Rest of Europe
10.4. Asia-Pacific
- 10.4.1. Japan
- 10.4.2. China
- 10.4.3. India
- 10.4.4. South Korea
- 10.4.5. Rest of Asia-Pacific
10.5. Latin America
10.6. Middle East & Africa

11. Competition Analysis

11.1. Overview
11.2. Key Growth Strategies
11.3. Competitive Benchmarking
11.4. Competitive Dashboard
- 11.4.1. Industry Leaders
- 11.4.2. Market Differentiators
- 11.4.3. Vanguards
- 11.4.4. Emerging Companies
11.5. Market Share Analysis

12. Company Profiles (Company Overview, Financial Overview, Product Portfolio, and Strategic Developments)

12.1. Amazon Web Services, Inc.
12.2. Google LLC
12.3. IBM Corporation
12.4. Alibaba Cloud
12.5. Intel Corporation
12.6. Microsoft Corporation
12.7. Shelf.AI
12.8. Appier Inc.
12.9. NVIDIA Corporation
12.10. Granify, Inc.
12.11. Oracle Corporation
12.12. Manthan Software Services Pvt. Ltd.
12.13. SAP SE
12.14. LivePerson, Inc.
12.15. Twiggle Ltd.

(Note: SWOT Analysis of Top 5 Companies Will Be Provided)

13. Appendix

13.1. Available Customization
13.2. Related Reports