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零售業人工智能

市場調查報告書

商品編碼

1360035

全球零售市場人工智慧 - 2023-2030

Global Artificial Intelligence In Retail Market - 2023-2030

出版日期: 2023年10月11日 | 出版商:

DataM Intelligence | 英文 197 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

概述：

2022年，全球人工智慧零售市場規模達55億美元，預計2030年將達到554億美元，2023-2030年預測期間複合年成長率為34.2%。

人工智慧使零售商能夠提供個人化的購物體驗，包括產品推薦、客戶服務聊天機器人和虛擬試穿，這提高了客戶滿意度和忠誠度。人工智慧驅動的系統可以最佳化供應鏈管理、庫存控制和需求預測，從而節省成本並提高營運效率。零售商可以利用人工智慧的力量來分析大量資料，深入了解客戶行為、市場趨勢和競爭情報。

例如，2023 年 9 月 25 日，亞馬遜與人工智慧新創公司 Anthropic 合作，投資 40 億美元開發生成式人工智慧模式。這種合作夥伴關係符合亞馬遜對人工智慧的日益關注，特別是在其面向消費者的設備和服務方面。最初，此次合作將支援 Anthropic 使用亞馬遜雲端服務和微晶片開發產生人工智慧模型的工作。這些模型將透過 Amazon Web Services 的 Amazon Bedrock 平台提供。

亞太地區是全球人工智慧在零售市場中不斷成長的地區之一，覆蓋了超過3/5的市場，該地區的特點是人口眾多且不斷成長，同時城市化程度不斷提高，這導致了更高的消費者基礎和對零售服務的需求不斷增加，推動了對人工智慧驅動解決方案的需求，以有效滿足這些需求。該地區產生大量結構化和非結構化資料。人工智慧依靠資料而蓬勃發展，亞太地區的零售商利用人工智慧來分析客戶行為、偏好和市場趨勢，以做出數據驅動的決策。

動態：

人工智慧在電子商務產業的應用

人工智慧演算法分析客戶資料，提供個人化的產品推薦和購物體驗，從而提高客戶滿意度並增加銷售。由人工智慧支援的聊天機器人和虛擬助理提供 24/7 客戶支持，提高回應時間和客戶參與度。人工智慧透過預測需求模式、減少庫存過剩和庫存不足情況以及最大限度地降低持有成本來幫助零售商最佳化庫存。

例如，2023 年 7 月 31 日，BigCommerce 透過與 Google Cloud 合作，在其電子商務平台上推出了新的人工智慧功能，這些工具將幫助企業商家提高營運效率、增強客戶體驗並促進銷售。一些關鍵的人工智慧功能包括人工智慧驅動的產品描述、高度個人化的店面和人工智慧驅動的資料分析，以更深入地了解業務績效。

擴大使用人工智慧驅動的聊天機器人來改善客戶體驗，推動市場發展

聊天機器人可以對客戶查詢提供快速、即時的回應，減少等待時間並改善整體客戶體驗，並且它們可以同時處理大量客戶查詢，使其能夠針對客戶互動率高的企業進行擴展。聊天機器人向所有客戶提供一致的回應和訊息，確保每個人都能獲得相同程度的服務。高級聊天機器人可以使用客戶資料來個性化交互，提供量身定做的建議和解決方案。

例如，滑雪和體育用品品牌Evo 計劃於2023 年7 月12 日在假期期間及時推出由ChatGPT 支援的客戶服務聊天機器人，該人工智慧驅動的聊天機器人可以處理輕觸式客戶服務查詢，並可能減少該品牌的成本。在繁忙的冬季需要聘請額外的代理商。在此期間，Evo 的客戶服務員工數量通常會增加一倍。

人工智慧驅動的協作徹底改變零售體驗

透過合作，零售商可以將其資料與人工智慧公司在資料分析方面的專業知識相結合，從而使零售商能夠更深入地了解客戶行為、偏好和趨勢，從而做出更明智的業務決策。人工智慧驅動的零售合作有助於創造高度個人化的購物體驗。零售商可以與人工智慧公司合作開發推薦引擎，根據個人客戶資料和過去的互動來推薦產品。

例如，2022年4月6日，聯合利華與零售行銷平台Perch合作，在華盛頓特區的Giant Food超市推出互動店內產品參與平台，該平台配備數位螢幕，可自動響應購物者的互動透過提供有關這些產品的影片和資訊來了解產品，所有這些都不需要二維碼、其他應用程式或螢幕觸控。

資料隱私和不準確的數據

人工智慧依賴大量客戶資料來實現個人化和洞察。然而，人們越來越擔心資料隱私以及零售商如何處理和保護敏感的客戶資訊。遵守 GDPR 等資料保護法規至關重要，但也具有挑戰性。對於零售商，尤其是小型企業來說，實施人工智慧技術（包括基礎設施、軟體和員工培訓）可能成本高昂。採用人工智慧所需的初始投資可能是一個障礙。

人工智慧系統依賴高品質的資料。不準確或不完整的資料可能會導致錯誤的預測和建議。整合零售組織內各種來源的資料也可能很複雜。人工智慧需要熟練的資料科學家、機器學習工程師和人工智慧專家來開發和維護系統，而缺乏具有人工智慧專業知識的專業人員，這使得零售商在建立和管理人工智慧團隊方面面臨挑戰。

AI enables retailers to offer personalized shopping experiences, including product recommendations, chatbots for customer service and virtual try-ons and this enhances customer satisfaction and loyalty. AI-powered systems can optimize supply chain management, inventory control and demand forecasting, which leads to cost savings and more efficient operations. Retailers can harness the power of AI to analyze huge volumes of data, gaining insights into customer behavior, market trends and competitive intelligence.

For instance, on 25 September 2023, Amazon is partnering with AI startup Anthropic in a $4 billion investment to develop generative AI models. This partnership aligns with Amazon's growing focus on AI, particularly in its consumer-facing devices and services. Initially, the collaboration will support Anthropic's work on generative AI models using Amazon's cloud services and microchips. These models will be available through Amazon Web Services' Amazon Bedrock platform.

Asia-Pacific is among the growing regions in the global artificial intelligence in retail market covering more than 3/5th of the market and the region is characterized by a large and growing population, along with increasing urbanization and this results in a higher consumer base and greater demand for retail services, driving the need for AI-powered solutions to meet these demands efficiently. The region generates vast amounts of data, both structured and unstructured. AI thrives on data and retailers in Asia-Pacific leverage AI to analyze customer behavior, preferences and market trends to make data-driven decisions.

Dynamics:

Adoption of AI in E-Commerce Industry

AI algorithms analyze customer data to provide personalized product recommendations and shopping experiences and this enhances customer satisfaction and increases sales. Chatbots and virtual assistants powered by AI provide 24/7 customer support, improving response times and customer engagement. AI helps retailers optimize their inventory by predicting demand patterns, reducing overstock and understock situations and minimizing carrying costs.

For instance, on 31 July 2023, BigCommerce launched new AI-powered features on its e-commerce platform, due to its partnership with Google Cloud and these AI tools will help enterprise merchants improve operational efficiency, enhance customer experiences and boost sales. Some of the key AI features include AI-powered product descriptions, highly personalized storefronts and AI-driven data analytics to gain deeper insights into business performance.

Increasing Use of AI-Powered ChatBots that Improve Customer Experience Drives the Market

Chatbots can provide quick and instant responses to customer queries, reducing wait times and improving the overall customer experience and they can handle a large volume of customer inquiries simultaneously, making them scalable for businesses with high customer interaction rates. Chatbots provide consistent responses and information to all customers, ensuring that everyone receives the same level of service. Advanced chatbots can use customer data to personalize interactions, providing tailored recommendations and solutions.

For instance, on 12 July 2023 Ski and sporting goods brand Evo plans to launch a customer service chatbot, powered by ChatGPT, in time for the holiday season and this AI-driven chatbot can handle light-touch customer service inquiries and may reduce the brand's need to hire additional agents during the busy winter season. Evo typically doubles its customer service employees during this period.

AI-Powered Collaborations Revolutionize Retail Experiences

Collaborations allow retailers to combine their data with AI companies' expertise in data analysis and this enables retailers to gain deeper insights into customer behavior, preferences and trends, leading to more informed business decisions. AI-driven retail collaborations facilitate the creation of highly personalized shopping experiences. Retailers can partner with AI companies to develop recommendation engines that suggest products based on individual customer profiles and past interactions.

For instance, on 6 April 2022, Unilever partnered with Perch, a retail marketing platform, to launch an interactive in-store product engagement platform at Giant Food supermarkets in the Washington DC area and this platform features digital screens that automatically respond to shoppers' interactions with products by providing videos and information about those products, all without the need for QR codes, additional apps or screen touching.

Data Privacy and Inaccurate Data

AI relies on huge volumes of customer data for personalization and insights. However, there are growing concerns about data privacy and how retailers handle and protect sensitive customer information. Compliance with data protection regulations, such as GDPR, is essential but challenging. Implementing AI technologies, including infrastructure, software and staff training, can be expensive for retailers, especially smaller businesses. The initial investment required for AI adoption can be a barrier.

AI systems depend on high-quality data. Inaccurate or incomplete data can lead to erroneous predictions and recommendations. Integrating data from various sources within a retail organization can also be complex. AI requires skilled data scientists, machine learning engineers and AI specialists to develop and maintain systems and there is a shortage of professionals with AI expertise, making it challenging for retailers to build and manage AI teams.

Segment Analysis:

The global artificial intelligence in retail market is segmented based on offerings, function, deployment type, application, technology and region.

Services Provided to Customers Boost the Market

AI enables retailers to analyze huge volumes of customer data to create personalized shopping experiences and this personalization includes product recommendations, targeted marketing and customized promotions, all of which enhance the overall shopping experience and drive sales. AI helps retailers optimize inventory levels by predicting demand, reducing overstock and understock situations and improving supply chain efficiency, this leads to cost savings and ensures that products are available when customers want them.

For instance, on 10 November 2022, Amazon introduced Sparrow, an intelligent robotic system designed to enhance the fulfillment process by handling individual products before they are packaged. Over the past decade, Amazon has invested heavily in robotics and advanced technology to automate various aspects of its operations. Sparrow represents a critical advancement in the handling of individual products within Amazon's vast inventory.

Geographical Penetration:

Personalized Recommendation Enhance Customer Engagement Boosts the Market

North America is dominating the global artificial intelligence in retail market and retailers in the region are increasingly using AI to improve the customer shopping experience. AI-powered chatbots, virtual shopping assistants and personalized recommendations enhance customer engagement and satisfaction. North American consumers expect personalized experiences and AI helps retailers analyze vast amounts of customer data to provide tailored product recommendations, marketing messages and pricing strategies.

For instance, on 16 August 2023, a survey conducted by Honeywell revealed that around 60% of retailers plan to adopt artificial intelligence, machine learning and computer vision technologies in the next year to enhance the shopping experience, both in physical stores and online. The survey involved 1,000 retail directors globally and found that 48% of respondents believe AI, ML and Computer Vision(CV) will have a significant impact on the retail industry in the next three to five years.

Competitive Landscape

The major global players in the market include: Amazon.com, Inc., IBM Corporation, Intel Corporation, Google LLC, Salesforce.com, Inc., SAP SE, Talkdesk, Inc., Microsoft Corporation, Nvidia Corporation and Oracle Corporation.

COVID-19 Impact Analysis

Lockdowns and social distancing measures in place, there was a surge in online shopping. Retailers turned to AI-powered recommendation engines, chatbots and virtual shopping assistants to enhance the online shopping experience and manage increased website traffic. COVID-19 disrupted supply chains globally. AI-powered predictive analytics became crucial for retailers to predict and manage supply chain disruptions, optimize inventory levels and ensure products were available when and where customers needed them.

The pandemic caused fluctuations in demand and supply. AI was used to adjust pricing strategies in real-time, helping retailers avoid overstocking and maintain profitability. Retailers implemented AI-driven technologies like self-checkout kiosks and touchless payment options to minimize physical contact between customers and store employees. The unpredictable nature of the pandemic made demand forecasting more challenging. AI models were adapted to account for sudden shifts in consumer behavior and preferences.

AI analytics helped retailers understand changing customer behaviors during the pandemic and this information was used to tailor marketing campaigns, optimize product offerings and enhance customer engagement. AI-powered solutions, such as thermal imaging cameras and facial recognition systems, were deployed to enforce health and safety protocols in stores and distribution centers.

AI Impact

AI-powered recommendation systems analyze customer data to provide personalized product recommendations and this enhances the shopping experience and increases the likelihood of customers making purchases. AI algorithms can optimize inventory levels by predicting demand, reducing overstock and stockouts and this results in cost savings and improved customer satisfaction.

Retailers use AI-driven chatbots and virtual assistants to provide real-time customer support, answer queries and assist with product searches and this reduces the workload on human customer service agents. AI can analyze market conditions, competitor pricing and customer behavior to adjust product prices in real-time for maximum profitability. Also, AI-powered video analytics and image recognition systems boost the market.

For instance, on 13 September 2023, According to Amazon, amazon leveraged generative artificial intelligence to enhance the product listing creation and management process for sellers and these AI capabilities simplified the process of creating product titles, descriptions and listing details, making it faster and easier for sellers to create and enrich their product listings and this approach streamlines the listing creation process, reduces the need for manual data entry and ensures that customers receive more comprehensive, consistent and engaging product information.

Russia- Ukraine War Impact

The conflict has disrupted supply chain management, especially in the technology sector. Many AI-related components, such as semiconductors and hardware, are manufactured in various parts of the world. Disruptions in the supply chain can lead to shortages or increased costs for AI technology, impacting its adoption in retail. Geopolitical conflicts can contribute to economic uncertainty, which affects consumer behavior. Retailers may become more cautious in their investments, including AI initiatives, during uncertain times.

The ripple effects of geopolitical tensions can impact the global economy, leading to fluctuations in currency exchange rates, trade restrictions and changes in consumer spending patterns and these factors can influence the pace and scale of AI adoption in retail. Retailers rely on AI for customer data analysis, personalization and cybersecurity. Geopolitical tensions can lead to increased concerns about data security and privacy, prompting retailers to reassess their AI strategies and data handling practices.

By Offerings

Services
Solutions

By Function

Operation-Focused
Customer-Facing

By Deployment Type

Cloud
On-Premise

By Technology

Computer Vision
Machine Learning
Natural Language Processing
Others

By Application

Predictive Analytics
In-Store Visual Monitoring & Surveillance
Customer Relationship Management
Market Forecasting
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 October 2021, AT&T and H2O.ai collaborated together that resulted in the development of an AI feature store that allows the organization and recycle data and machine learning engineering skills. Data scientists and developers employ the same features that AI features used for storage and distribution when creating AI models.
In January 2023, EY introduced the EY Retail Intelligence solution which leveraging the Microsoft Cloud and Cloud for Retail, that leads to enhance consumers' shopping experiences. As the retail landscape undergoes digital transformation, traditional retailers face challenges such as consumers searching for the best prices across various channels.
In November 2022, Fractal, a global provider of AI and advanced analytics solutions, launched Asper.ai, an interconnected AI solution designed for consumer goods, manufacturing and retail. Asper.ai aims to address the fragmentation within the AI ecosystem in these sectors by offering an end-to-end AI product that unifies demand planning, inventory optimization, strategic pricing and promotion

Why Purchase the Report?

To visualize the global artificial intelligence in retail market segmentation based on offerings, function, deployment type, application, technology 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 in retail 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 in retail market report would provide approximately 77 tables, 77 figures and 197 Pages.

Target Audience 2023

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

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 Offerings
3.2. Snippet by Function
3.3. Snippet By Deployment Type
3.4. Snippet by Application
3.5. Snippet by Technology
3.6. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Adoption of AI in E-Commerce Industry
  - 4.1.1.2. Increasing Use of AI-Powered ChatBots that Improve Customer Experience Drives the Market
  - 4.1.1.3. AI-Powered Collaborations Revolutionize Retail Experiences
- 4.1.2. Restraints
  - 4.1.2.1. Data Privacy and Inaccurate Data
- 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 Offerings

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offerings
- 7.1.2. Market Attractiveness Index, By Offerings
7.2. Services *
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Solutions

8. By Function

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Function
- 8.1.2. Market Attractiveness Index, By Function
8.2. Operation-Focused*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Customer-Facing

9. By Deployment Type

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 9.1.2. Market Attractiveness Index, By Deployment Type
9.2. Cloud*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. On-Premise

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. Predictive Analytics*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. In-Store Visual Monitoring & Surveillance
10.4. Customer Relationship Management
10.5. Market Forecasting
10.6. Others

11. By Technology

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.1.2. Market Attractiveness Index, By Technology
11.2. Computer Vision*
- 11.2.1. Introduction
- 11.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
11.3. Machine Learning
11.4. Natural Language Processing
11.5. 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 Offerings
- 12.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Function
- 12.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 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 Technology
- 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 Offerings
- 12.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Function
- 12.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 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 Technology
- 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 Offerings
- 12.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Function
- 12.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 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 Technology
- 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 Offerings
- 12.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Function
- 12.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 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 Technology
- 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 Offerings
- 12.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Function
- 12.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 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 Technology

13. Competitive Landscape

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

14. Company Profiles

14.1. Amazon.com, Inc.*
- 14.1.1. Company Overview
- 14.1.2. Product Portfolio and Description
- 14.1.3. Financial Overview
- 14.1.4. Key Developments
14.2. IBM Corporation
14.3. Intel Corporation
14.4. Google LLC
14.5. Salesforce.com, Inc.
14.6. SAP SE
14.7. Talkdesk, Inc.
14.8. Microsoft Corporation
14.9. Nvidia Corporation
14.10. Oracle Corporation

LIST NOT EXHAUSTIVE