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市场调查报告书
商品编码
1776718

2032 年零售市场人工智慧个人化购物预测：按零售类型、产品、部署模式、技术、应用、最终用户和地区进行的全球分析

AI in Retail - Personalized Shopping Market Forecasts to 2032 - Global Analysis By Retail Type (E-commerce, Omnichannel, Brick-and-Mortar), Offering, Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球零售 AI 个人化购物市场预计在 2025 年达到 417 亿美元，到 2032 年将达到 3,235 亿美元，预测期内的复合年增长率为 34.0%。

零售业中的人工智慧 - 个人化购物是指利用人工智慧技术来增强和客製化个人消费者的购物体验。这涉及分析客户数据，例如浏览历史、购买行为、偏好和人口统计信息，以提供客製化的产品推荐、有针对性的促销和动态定价。机器学习、自然语言处理和电脑视觉等人工智慧工具可帮助零售商即时了解和预测客户需求。这实现了跨各种管道（包括网路商店、行动应用程式和商店自助服务终端）的无缝、引人入胜且高效的互动，最终提高客户满意度、客户维繫和零售业的销售业绩。

对个人化客户体验的需求不断增加

对个人化客户体验日益增长的需求是零售业人工智慧个人化购物市场的主要驱动力。消费者越来越期待跨通路的建议和个人化互动。机器学习和自然语言处理等人工智慧技术使零售商能够分析大量资料集并即时提供客製化的购物体验。这种转变提高了顾客满意度和转换率，鼓励更多零售商投资人工智慧主导的个人化工具。因此，市场成长正在加速，零售业正在转型为一个数据主导、以客户为中心的生态系统。

资料隐私和安全问题

对资料隐私和安全的担忧是中小企业在零售领域采用人工智慧个人化购物的一大障碍。有限的资源和技术专长使得实施强有力的资料保护措施变得困难，阻碍了依赖敏感客户资讯的人工智慧技术的应用。对资料外洩和不合规的担忧进一步阻碍了投资，限制了中小企业实施人工智慧个人化购物的能力，最终减缓了该领域的市场成长和创新。

电子商务和全通路零售的成长

电子商务和全通路零售的成长，为个人化消费者互动创造了广阔的数位环境，积极推动了零售业人工智慧个人化购物市场的发展。随着消费者在线上和线下接触点之间不断切换，零售商越来越依赖人工智慧来统一客户资料、预测偏好，并为每个管道提供客製化的体验。这种无缝整合提高了客户满意度和忠诚度，从而带来了更高的转换率和销售额，从而推动了零售业对人工智慧个人化购物解决方案的需求。

中小企业实施成本高

高昂的实施成本是中小企业在零售领域采用人工智慧个人化购物的主要障碍。这些企业通常缺乏整合人工智慧所需的资金和技术专长，包括资料基础设施、软体和熟练的人力。因此，中小企业难以与大型零售商竞争，限制了市场包容性，并减缓了整体成长。这样的成本负担阻碍了整个零售业的应用和创新。

COVID-19的影响

新冠疫情显着加速了人工智慧在零售——个人化购物市场的应用。由于实体店面临限制，零售商转向数位通路和人工智慧驱动的工具来提升客户参与。为了满足不断变化的消费者期望，人工智慧驱动的个人化建议、虚拟试穿和聊天机器人援助变得日益流行。这场危机凸显了敏捷性的重要性，促使零售商投资人工智慧技术，以确保业务连续性，并在不确定性的环境中提供客製化的体验。

服装业预计将成为预测期内最大的产业

由于客製化、款式推荐和虚拟试穿的需求，服装业预计将在预测期内占据最大的市场占有率。随着消费者寻求个人化的时尚体验，电脑视觉和机器学习等人工智慧技术将使零售商能够提供客製化提案、尺寸指导和趋势分析。这将提高客户满意度、提升转换率并减少退货。服装产业的扩张将加速人工智慧的应用，将购物之旅转变为高度个人化的体验。

预测期内，机器学习将以最高复合年增长率成长

在预测期内，机器学习领域预计将实现最高成长率，因为它能够根据即时消费行为和偏好提供超个人化体验。透过进阶数据分析，机器学习演算法可以预测购买模式、提案客製化产品并增强客户参与。这将带来更高的转换率、更高的客户满意度和更高的品牌忠诚度。透过自学习能力不断改进演算法，确保动态个人化，使机器学习成为个人化零售体验成长的重要催化剂。

占比最大的地区：

在预测期内，由于数位转型的快速推进、智慧型手机普及率的不断提升以及电子商务的日益普及，亚太地区预计将占据最大的市场占有率。零售商正在利用人工智慧，透过即时产品推荐、动态定价和预测分析，提供高度个人化的购物体验。中国、日本和印度等国家正引领技术创新，并不断增加对人工智慧基础设施的投资。这种技术转变提升了顾客满意度，推动了销售成长，并增强了不同消费群的品牌忠诚度。

复合年增长率最高的地区：

预计北美地区在预测期内的复合年增长率最高，这得益于技术应用和消费者对客製化体验的需求。零售商正在利用人工智慧主导的工具，例如建议引擎、客户行为分析和虚拟助手，来提供高度个人化的购物体验。这反过来又提高了客户满意度、转换率和品牌忠诚度。北美在个人化零售创新方面处于领先地位，其先进的数位基础设施和高智慧型手机普及率进一步推动了人工智慧的融合。

免费客製化服务

此报告的订阅者可以从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他市场公司进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户兴趣对主要国家进行的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球零售业人工智慧个人化购物市场（依零售业类型）

  • 电子商务
  • 全通路
  • 实体店面

6. 全球零售业人工智慧个人化购物市场，透过提供

  • 解决方案
  • 服务

7. 全球零售业AI个人化购物市场（依部署模式）

  • 本地
  • 云

8. 全球零售业人工智慧个人化购物市场（按技术）

  • 机器学习
  • 预测分析
  • 自然语言处理（NLP）
  • 电脑视觉
  • 聊天机器人和虚拟助手

9. 全球零售业人工智慧个人化购物市场（按应用）

  • 个性化产品推荐
  • 库存管理
  • 动态定价
  • 客户行为分析
  • 视觉搜寻
  • 虚拟试衣间
  • 其他用途

10. 全球零售业人工智慧个人化购物市场（按最终用户）

  • 衣服
  • 鞋类
  • 杂货
  • 家具
  • 美容和个人护理
  • 电子产品
  • 其他最终用户

11. 全球零售业人工智慧个人化购物市场（按地区）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十二章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十三章：企业概况

  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • Amazon Web Services, Inc.
  • Salesforce, Inc.
  • SAP SE
  • Oracle Corporation
  • Adobe Inc.
  • Intel Corporation
  • NVIDIA Corporation
  • Infosys Limited
  • Cognizant Technology Solutions
  • Capgemini SE
  • Tata Consultancy Services（TCS）
  • Wipro Limited
  • Shopify Inc.
  • Sentient Technologies
  • ViSenze Pte Ltd.
  • Syte Visual Conception Ltd.
Product Code: SMRC30049

According to Stratistics MRC, the Global AI in Retail - Personalized ShoppingMarket is accounted for $41.7 billion in 2025 and is expected to reach $323.5 billion by 2032 growing at a CAGR of 34.0% during the forecast period. AI in Retail - Personalized Shopping refers to the use of artificial intelligence technologies to enhance and tailor the shopping experience for individual consumers. It involves analyzing customer data such as browsing history, purchase behavior, preferences, and demographics to deliver customized product recommendations, targeted promotions, and dynamic pricing. AI tools like machine learning, natural language processing, and computer vision help retailers understand and predict customer needs in real time. This enables seamless, engaging, and efficient interactions across various channels, including online stores, mobile apps, and in-store kiosks, ultimately boosting customer satisfaction, retention, and overall retail sales performance.

Market Dynamics:

Driver:

Rising Demand for Personalized Customer Experiences

The rising demand for personalized customer experiences is significantly driving the AI in Retail Personalized Shopping Market. Consumers increasingly expect tailored recommendations, and individualized engagement across channels. AI technologies like machine learning and natural language processing empower retailers to analyze vast datasets and deliver real-time, customized shopping experiences. This shift enhances customer satisfaction, and conversion rates, prompting more retailers to invest in AI-driven personalization tools. As a result, market growth is accelerating, transforming retail into a data-driven, customer-centric ecosystem.

Restraint:

Data Privacy and Security Concerns

Data privacy and security concerns pose a significant hindrance for SMEs adopting AI in retail personalized shopping. Limited resources and technical expertise make it challenging to implement robust data protection measures, deterring the use of AI technologies reliant on sensitive customer information. Fears of data breaches and regulatory non-compliance further discourage investment, restricting SMEs from leveraging AI-driven personalization and ultimately slowing market growth and innovation in this sector.

Opportunity:

Growth of E-commerce and Omnichannel Retailing

The growth of e-commerce and omnichannel retailing is positively driving the AI in Retail - Personalized Shopping Market by creating a vast digital landscape for personalized consumer engagement. With shoppers navigating between online and offline touchpoints, retailers increasingly rely on AI to unify customer data, predict preferences, and deliver tailored experiences across channels. This seamless integration enhances customer satisfaction and loyalty, while boosting conversion rates and sales, thereby propelling the demand for AI-driven personalized shopping solutions in the retail sector.

Threat:

High Implementation Costs for SMEs

High implementation costs pose a significant barrier for small and medium-sized enterprises (SMEs) in adopting AI in retail personalized shopping. These businesses often lack the financial resources and technical expertise required for AI integration, including data infrastructure, software, and skilled personnel. As a result, SMEs struggle to compete with larger retailers, limiting market inclusivity and slowing overall growth. This cost burden hinders widespread adoption and innovation across the retail sector.

Covid-19 Impact

The COVID-19 pandemic significantly accelerated the adoption of AI in the retail personalized shopping market. As physical stores faced restrictions, retailers turned to digital channels and AI-driven tools to enhance customer engagement. AI-enabled personalized recommendations, virtual try-ons, and chatbot assistance gained traction to meet evolving consumer expectations. The crisis highlighted the need for agility, prompting retailers to invest in AI technologies to ensure continuity and deliver tailored experiences amid uncertainty.

The apparel segment is expected to be the largest during the forecast period

The apparel segment is expected to account for the largest market share during the forecast period, due to demand for customization, style recommendations, and virtual try-ons. As consumers seek personalized fashion experiences, AI technologies such as computer vision and machine learning enable retailers to deliver tailored suggestions, sizing assistance, and trend analysis. This enhances customer satisfaction, boosts conversion rates, and reduces return rates. The apparel segment's expansion thus accelerates AI adoption, transforming the shopping journey into a highly individualized experience.

The machine learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the machine learning segment is predicted to witness the highest growth rate as it delivers hyper-personalized experiences based on real-time consumer behavior and preferences. Through advanced data analysis, machine learning algorithms can predict purchasing patterns, suggest tailored products, and enhance customer engagement. This leads to increased conversion rates, higher customer satisfaction, and brand loyalty. The continuous improvement of algorithms through self-learning capabilities ensures dynamic personalization, making machine learning a vital catalyst in the growth of personalized retail experiences.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share due to rapid digital transformation, increasing smartphone penetration, and growing e-commerce adoption. Retailers are leveraging AI to deliver hyper-personalized shopping experiences through real-time product recommendations, dynamic pricing, and predictive analytics. Countries like China, Japan, and India are leading innovation, supported by rising investments in AI infrastructure. This technological shift enhances customer satisfaction, drives sales growth, and strengthens brand loyalty across diverse consumer segments.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, owing to technological adoption and consumer demand for customized experiences. Retailers are leveraging AI-driven tools like recommendation engines, customer behavior analytics, and virtual assistants to deliver hyper-personalized shopping journeys. This enhances customer satisfaction, increases conversion rates, and boosts brand loyalty. The region's advanced digital infrastructure and high smartphone penetration further support AI integration, making North America a leader in personalized retail innovation.

Key players in the market

Some of the key players profiled in the AI in Retail - Personalized Shopping Market include IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services, Inc., Salesforce, Inc., SAP SE, Oracle Corporation, Adobe Inc., Intel Corporation, NVIDIA Corporation, Infosys Limited, Cognizant Technology Solutions, Capgemini SE, Tata Consultancy Services (TCS), Wipro Limited, Shopify Inc, Sentient Technologies, ViSenze Pte Ltd. and Syte Visual Conception Ltd.

Key Developments:

In May 2025, Finanz Informatik, has renewed and expanded its partnership with IBM. Under the new multi year agreement, Finanz Informatik will deploy state of the art IBM mainframe, Power, and storage systems-alongside AI-enabled software from the watsonx portfolio-within its own data centers.

In April 2025, IBM and Tokyo Electron (TEL) have signed a new five-year extension of their longstanding semiconductor R&D partnership, originally spanning over two decades. The renewed agreement centres on advancing next-generation semiconductor nodes, chiplet architectures, and High NA EUV patterning to meet the performance and energy-efficiency demands of generative AI.

Retail Types Covered:

  • E-commerce
  • Omnichannel
  • Brick-and-Mortar

Offerings Covered:

  • Solution
  • Services

Deployment Modes Covered:

  • On-premise
  • Cloud

Technologies Covered:

  • Machine Learning
  • Predictive Analytics
  • Natural Language Processing (NLP)
  • Computer Vision
  • Chatbots & Virtual Assistants

Applications Covered:

  • Personalized Product Recommendations
  • Inventory Management
  • Dynamic Pricing
  • Customer Behavior Analytics
  • Visual Search
  • Virtual Fitting Rooms
  • Other Applications

End Users Covered:

  • Apparel
  • Footwear
  • Grocery
  • Home Furnishing
  • Beauty & Personal Care
  • Electronics
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI in Retail - Personalized Shopping Market, By Retail Type

  • 5.1 Introduction
  • 5.2 E-commerce
  • 5.3 Omnichannel
  • 5.4 Brick-and-Mortar

6 Global AI in Retail - Personalized Shopping Market, By Offering

  • 6.1 Introduction
  • 6.2 Solution
  • 6.3 Services

7 Global AI in Retail - Personalized Shopping Market, By Deployment Mode

  • 7.1 Introduction
  • 7.2 On-premise
  • 7.3 Cloud

8 Global AI in Retail - Personalized Shopping Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning
  • 8.3 Predictive Analytics
  • 8.4 Natural Language Processing (NLP)
  • 8.5 Computer Vision
  • 8.6 Chatbots & Virtual Assistants

9 Global AI in Retail - Personalized Shopping Market, By Application

  • 9.1 Introduction
  • 9.2 Personalized Product Recommendations
  • 9.3 Inventory Management
  • 9.4 Dynamic Pricing
  • 9.5 Customer Behavior Analytics
  • 9.6 Visual Search
  • 9.7 Virtual Fitting Rooms
  • 9.8 Other Applications

10 Global AI in Retail - Personalized Shopping Market, By End User

  • 10.1 Introduction
  • 10.2 Apparel
  • 10.3 Footwear
  • 10.4 Grocery
  • 10.5 Home Furnishing
  • 10.6 Beauty & Personal Care
  • 10.7 Electronics
  • 10.8 Other End Users

11 Global AI in Retail - Personalized Shopping Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 IBM Corporation
  • 13.2 Microsoft Corporation
  • 13.3 Google LLC
  • 13.4 Amazon Web Services, Inc.
  • 13.5 Salesforce, Inc.
  • 13.6 SAP SE
  • 13.7 Oracle Corporation
  • 13.8 Adobe Inc.
  • 13.9 Intel Corporation
  • 13.10 NVIDIA Corporation
  • 13.11 Infosys Limited
  • 13.12 Cognizant Technology Solutions
  • 13.13 Capgemini SE
  • 13.14 Tata Consultancy Services (TCS)
  • 13.15 Wipro Limited
  • 13.16 Shopify Inc.
  • 13.17 Sentient Technologies
  • 13.18 ViSenze Pte Ltd.
  • 13.19 Syte Visual Conception Ltd.

List of Tables

  • Table 1 Global AI in Retail - Personalized Shopping Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Retail - Personalized Shopping Market Outlook, By Retail Type (2024-2032) ($MN)
  • Table 3 Global AI in Retail - Personalized Shopping Market Outlook, By E-commerce (2024-2032) ($MN)
  • Table 4 Global AI in Retail - Personalized Shopping Market Outlook, By Omnichannel (2024-2032) ($MN)
  • Table 5 Global AI in Retail - Personalized Shopping Market Outlook, By Brick-and-Mortar (2024-2032) ($MN)
  • Table 6 Global AI in Retail - Personalized Shopping Market Outlook, By Offering (2024-2032) ($MN)
  • Table 7 Global AI in Retail - Personalized Shopping Market Outlook, By Solution (2024-2032) ($MN)
  • Table 8 Global AI in Retail - Personalized Shopping Market Outlook, By Services (2024-2032) ($MN)
  • Table 9 Global AI in Retail - Personalized Shopping Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 10 Global AI in Retail - Personalized Shopping Market Outlook, By On-premise (2024-2032) ($MN)
  • Table 11 Global AI in Retail - Personalized Shopping Market Outlook, By Cloud (2024-2032) ($MN)
  • Table 12 Global AI in Retail - Personalized Shopping Market Outlook, By Technology (2024-2032) ($MN)
  • Table 13 Global AI in Retail - Personalized Shopping Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 14 Global AI in Retail - Personalized Shopping Market Outlook, By Predictive Analytics (2024-2032) ($MN)
  • Table 15 Global AI in Retail - Personalized Shopping Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 16 Global AI in Retail - Personalized Shopping Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 17 Global AI in Retail - Personalized Shopping Market Outlook, By Chatbots & Virtual Assistants (2024-2032) ($MN)
  • Table 18 Global AI in Retail - Personalized Shopping Market Outlook, By Application (2024-2032) ($MN)
  • Table 19 Global AI in Retail - Personalized Shopping Market Outlook, By Personalized Product Recommendations (2024-2032) ($MN)
  • Table 20 Global AI in Retail - Personalized Shopping Market Outlook, By Inventory Management (2024-2032) ($MN)
  • Table 21 Global AI in Retail - Personalized Shopping Market Outlook, By Dynamic Pricing (2024-2032) ($MN)
  • Table 22 Global AI in Retail - Personalized Shopping Market Outlook, By Customer Behavior Analytics (2024-2032) ($MN)
  • Table 23 Global AI in Retail - Personalized Shopping Market Outlook, By Visual Search (2024-2032) ($MN)
  • Table 24 Global AI in Retail - Personalized Shopping Market Outlook, By Virtual Fitting Rooms (2024-2032) ($MN)
  • Table 25 Global AI in Retail - Personalized Shopping Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 26 Global AI in Retail - Personalized Shopping Market Outlook, By End User (2024-2032) ($MN)
  • Table 27 Global AI in Retail - Personalized Shopping Market Outlook, By Apparel (2024-2032) ($MN)
  • Table 28 Global AI in Retail - Personalized Shopping Market Outlook, By Footwear (2024-2032) ($MN)
  • Table 29 Global AI in Retail - Personalized Shopping Market Outlook, By Grocery (2024-2032) ($MN)
  • Table 30 Global AI in Retail - Personalized Shopping Market Outlook, By Home Furnishing (2024-2032) ($MN)
  • Table 31 Global AI in Retail - Personalized Shopping Market Outlook, By Beauty & Personal Care (2024-2032) ($MN)
  • Table 32 Global AI in Retail - Personalized Shopping Market Outlook, By Electronics (2024-2032) ($MN)
  • Table 33 Global AI in Retail - Personalized Shopping Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.