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零售业人工智能

市场调查报告书

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1360035

全球零售市场人工智慧 - 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: 约2个工作天内

价格

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简介目录

概述：

2022年，全球人工智慧零售市场规模达55亿美元，预计2030年将达到554亿美元，2023-2030年预测期间复合年增长率为34.2%。

人工智慧使零售商能够提供个人化的购物体验，包括产品推荐、客户服务聊天机器人和虚拟试穿，这提高了客户满意度和忠诚度。人工智慧驱动的系统可以优化供应链管理、库存控制和需求预测，从而节省成本并提高营运效率。零售商可以利用人工智慧的力量来分析大量资料，深入了解客户行为、市场趋势和竞争情报。

例如，2023 年 9 月 25 日，亚马逊与人工智慧新创公司 Anthropic 合作，投资 40 亿美元开发生成式人工智慧模式。这种合作伙伴关係符合亚马逊对人工智慧的日益关注，特别是在其面向消费者的设备和服务方面。最初，此次合作将支援 Anthropic 使用亚马逊云端服务和微晶片开发产生人工智慧模型的工作。这些模型将透过 Amazon Web Services 的 Amazon Bedrock 平台提供。

亚太地区是全球人工智慧在零售市场中不断增长的地区之一，覆盖了超过3/5的市场，该地区的特点是人口众多且不断增长，同时城市化程度不断提高，这导致了更高的消费者基础和对零售服务的需求不断增加，推动了对人工智慧驱动解决方案的需求，以有效满足这些需求。该地区产生大量结构化和非结构化资料。人工智慧依靠资料而蓬勃发展，亚太地区的零售商利用人工智慧来分析客户行为、偏好和市场趋势，以做出数据驱动的决策。

动态：

人工智慧在电子商务产业的应用

人工智慧演算法分析客户资料，提供个人化的产品推荐和购物体验，从而提高客户满意度并增加销售。由人工智慧支援的聊天机器人和虚拟助理提供 24/7 客户支持，提高回应时间和客户参与度。人工智慧透过预测需求模式、减少库存过剩和库存不足情况以及最大限度地降低持有成本来帮助零售商优化库存。

例如，2023 年 7 月 31 日，BigCommerce 透过与 Google Cloud 合作，在其电子商务平台上推出了新的人工智慧功能，这些工具将帮助企业商家提高营运效率、增强客户体验并促进销售。一些关键的人工智慧功能包括人工智慧驱动的产品描述、高度个人化的店面和人工智慧驱动的资料分析，以更深入地了解业务绩效。

越来越多地使用人工智慧驱动的聊天机器人来改善客户体验，推动市场发展

聊天机器人可以对客户查询提供快速、即时的回应，减少等待时间并改善整体客户体验，并且它们可以同时处理大量客户查询，使其能够针对客户互动率高的企业进行扩展。聊天机器人向所有客户提供一致的回应和讯息，确保每个人都能获得相同程度的服务。高级聊天机器人可以使用客户资料来个性化交互，提供量身定制的建议和解决方案。

例如，滑雪和体育用品品牌Evo 计划于2023 年7 月12 日在假期期间及时推出由ChatGPT 支援的客户服务聊天机器人，该人工智慧驱动的聊天机器人可以处理轻触式客户服务查询，并可能减少该品牌的成本。在繁忙的冬季需要聘请额外的代理商。在此期间，Evo 的客户服务员工数量通常会增加一倍。

人工智慧驱动的协作彻底改变零售体验

透过合作，零售商可以将其资料与人工智慧公司在资料分析方面的专业知识相结合，从而使零售商能够更深入地了解客户行为、偏好和趋势，从而做出更明智的业务决策。人工智慧驱动的零售合作有助于创造高度个人化的购物体验。零售商可以与人工智慧公司合作开发推荐引擎，根据个人客户资料和过去的互动来推荐产品。

例如，2022年4月6日，联合利华与零售行销平台Perch合作，在华盛顿特区的Giant Food超市推出互动店内产品参与平台，该平台配备数位萤幕，可自动响应购物者的互动透过提供有关这些产品的影片和资讯来了解产品，所有这些都不需要二维码、其他应用程式或萤幕触控。

资料隐私和不准确的数据

人工智慧依赖大量客户资料来实现个人化和洞察。然而，人们越来越担心资料隐私以及零售商如何处理和保护敏感的客户资讯。遵守 GDPR 等资料保护法规至关重要，但也具有挑战性。对于零售商，尤其是小型企业来说，实施人工智慧技术（包括基础设施、软体和员工培训）可能成本高昂。采用人工智慧所需的初始投资可能是一个障碍。

人工智慧系统依赖高品质的资料。不准确或不完整的资料可能会导致错误的预测和建议。整合零售组织内各种来源的资料也可能很复杂。人工智慧需要熟练的资料科学家、机器学习工程师和人工智慧专家来开发和维护系统，而缺乏具有人工智慧专业知识的专业人员，这使得零售商在建立和管理人工智慧团队方面面临挑战。

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

15. Appendix

15.1. About Us and Services
15.2. Contact Us

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