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1776718

2032 年零售市場人工智慧個人化購物預測：按零售類型、產品、部署模式、技術、應用、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球零售 AI 個人化購物市場預計在 2025 年達到 417 億美元，到 2032 年將達到 3,235 億美元，預測期內的複合年成長率為 34.0%。

零售業中的人工智慧 - 個人化購物是指利用人工智慧技術來增強和客製化個人消費者的購物體驗。這涉及分析客戶數據，例如瀏覽歷史、購買行為、偏好和人口統計資訊，以提供客製化的產品推薦、有針對性的促銷和動態定價。機器學習、自然語言處理和電腦視覺等人工智慧工具可幫助零售商即時了解和預測客戶需求。這實現了跨各種管道（包括網路商店、行動應用程式和商店自助服務終端）的無縫、引人入勝且高效的互動，最終提高客戶滿意度、客戶維繫和零售業的銷售業績。

對個人化客戶體驗的需求不斷增加

對個人化客戶體驗日益成長的需求是零售業人工智慧個人化購物市場的主要驅動力。消費者越來越期待跨通路的建議和個人化互動。機器學習和自然語言處理等人工智慧技術使零售商能夠分析大量資料集並即時提供客製化的購物體驗。這種轉變提高了顧客滿意度和轉換率，鼓勵更多零售商投資人工智慧主導的個人化工具。因此，市場成長正在加速，零售業正在轉型為一個數據主導、以客戶為中心的生態系統。

資料隱私和安全問題

對資料隱私和安全的擔憂是中小企業在零售領域採用人工智慧個人化購物的一大障礙。有限的資源和技術專長使得實施強力的資料保護措施變得困難，阻礙了依賴敏感客戶資訊的人工智慧技術的應用。對資料外洩和不合規的擔憂進一步阻礙了投資，限制了中小企業實施人工智慧個人化購物的能力，最終減緩了該領域的市場成長和創新。

電子商務和全通路零售的成長

電子商務和全通路零售的成長，為個人化消費者互動創造了廣闊的數位環境，積極推動了零售業人工智慧個人化購物市場的發展。隨著消費者在線上和線下接觸點之間不斷切換，零售商越來越依賴人工智慧來統一客戶資料、預測偏好，並為每個管道提供客製化的體驗。這種無縫整合提高了客戶滿意度和忠誠度，從而帶來了更高的轉換率和銷售額，從而推動了零售業對人工智慧個人化購物解決方案的需求。

中小企業實施成本高

高昂的實施成本是中小企業在零售領域採用人工智慧個人化購物的主要障礙。這些企業通常缺乏整合人工智慧所需的資金和技術專長，包括資料基礎設施、軟體和熟練的人力。因此，中小企業難以與大型零售商競爭，限制了市場包容性，並減緩了整體成長。這樣的成本負擔阻礙了整個零售業的應用和創新。

COVID-19的影響

新冠疫情顯著加速了人工智慧在零售——個人化購物市場的應用。由於實體店面臨限制，零售商轉向數位通路和人工智慧驅動的工具來提升客戶參與。為了滿足不斷變化的消費者期望，人工智慧驅動的個人化建議、虛擬試穿和聊天機器人援助變得日益流行。這場危機凸顯了敏捷性的重要性，促使零售商投資人工智慧技術，以確保業務連續性，並在不確定性的環境中提供客製化的體驗。

服裝業預計將成為預測期內最大的產業

由於客製化、款式推薦和虛擬試穿的需求，服裝業預計將在預測期內佔據最大的市場佔有率。隨著消費者尋求個人化的時尚體驗，電腦視覺和機器學習等人工智慧技術將使零售商能夠提供客製化提案、尺寸指導和趨勢分析。這將提高客戶滿意度、提升轉換率並減少退貨。服裝產業的擴張將加速人工智慧的應用，將購物之旅轉變為高度個人化的體驗。

預測期內，機器學習將以最高複合年成長率成長

在預測期內，機器學習領域預計將實現最高成長率，因為它能夠根據即時消費行為和偏好提供超個人化體驗。透過進階數據分析，機器學習演算法可以預測購買模式、提案客製化產品並增強客戶參與。這將帶來更高的轉換率、更高的客戶滿意度和更高的品牌忠誠度。透過自學習能力不斷改進演算法，確保動態個人化，使機器學習成為個人化零售體驗成長的重要催化劑。

佔比最大的地區：

在預測期內，由於數位轉型的快速推進、智慧型手機普及率的不斷提升以及電子商務的日益普及，亞太地區預計將佔據最大的市場佔有率。零售商正在利用人工智慧，透過即時產品推薦、動態定價和預測分析，提供高度個人化的購物體驗。中國、日本和印度等國家正引領技術創新，並不斷增加對人工智慧基礎設施的投資。這種技術轉變提升了顧客滿意度，推動了銷售成長，並增強了不同消費群的品牌忠誠度。

複合年成長率最高的地區：

預計北美地區在預測期內的複合年成長率最高，這得益於技術應用和消費者對客製化體驗的需求。零售商正在利用人工智慧主導的工具，例如建議引擎、客戶行為分析和虛擬助手，來提供高度個人化的購物體驗。這反過來又提高了客戶滿意度、轉換率和品牌忠誠度。北美在個人化零售創新方面處於領先地位，其先進的數位基礎設施和高智慧型手機普及率進一步推動了人工智慧的融合。

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  • 區域細分
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    • 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 研究範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球零售業人工智慧個人化購物市場（依零售業類型）

  • 電子商務
  • 全通路
  • 實體店面

6. 全球零售業人工智慧個人化購物市場，透過提供

  • 解決方案
  • 服務

7. 全球零售業AI個人化購物市場（依部署模式）

  • 本地
  • 雲

8. 全球零售業人工智慧個人化購物市場（按技術）

  • 機器學習
  • 預測分析
  • 自然語言處理（NLP）
  • 電腦視覺
  • 聊天機器人和虛擬助手

9. 全球零售業人工智慧個人化購物市場（按應用）

  • 個性化產品推薦
  • 庫存管理
  • 動態定價
  • 客戶行為分析
  • 視覺搜尋
  • 虛擬試衣間
  • 其他用途

10. 全球零售業人工智慧個人化購物市場（按最終用戶）

  • 衣服
  • 鞋類
  • 雜貨
  • 家具
  • 美容和個人護理
  • 電子產品
  • 其他最終用戶

11. 全球零售業人工智慧個人化購物市場（按地區）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第12章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第13章：企業概況

  • 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.