AI Shopping Assistant Market was valued at USD 3.65 billion in 2024 and is expected to reach USD 24.90 billion by 2032, growing at a CAGR of 27.22% from 2025-2032.
The AI Shopping Assistant Market is experiencing rapid growth due to increasing consumer demand for personalized and seamless shopping experiences.
According to Salesforce, 70% of consumers now expect AI-enhanced personalized retail experiences, and 63% are more likely to purchase from retailers offering them.
Retailers and e-commerce platforms are adopting AI-driven solutions to enhance customer engagement, improve conversion rates, and streamline product discovery through voice, text, and visual interfaces.
A McKinsey study shows that AI-driven personalization can boost revenues by up to 40%.
The rise of omnichannel retail, advancements in natural language processing and machine learning, and growing smartphone and internet penetration are further fueling adoption.
Salesforce also reports that 36% of retail employees currently use generative AI, with adoption projected to reach 45% by the end of 2025. Additionally, 93% of retailers use AI for personalization, and 81% have dedicated AI budgets.
These capabilities significantly improve ROI, making AI shopping assistants a strategic investment for digital commerce growth.
U.S. AI Shopping Assistant Market was valued at USD 1.00 billion in 2024 and is expected to reach USD 6.s billion by 2032, growing at a CAGR of 27.00% from 2025-2032.
The U.S. AI Shopping Assistant Market is growing due to rising demand for personalized shopping experiences, increasing adoption of AI by retailers to boost customer engagement, and the widespread use of smartphones and digital channels driving automation and convenience in e-commerce.
Drivers
Expanding integration of AI assistants with voice commerce and smart devices is boosting market growth and reshaping digital retail engagement.
As voice-enabled commerce gains momentum, AI shopping assistants integrated with smart speakers, mobile apps, and IoT devices are seeing surging adoption. These integrations offer users a hands-free, conversational shopping experience, allowing them to search, compare, and buy products using voice commands. Retailers are embedding AI into devices like Alexa, Siri, and Google Assistant, bridging the gap between virtual assistance and retail conversion. This seamless, real-time interaction boosts customer convenience and brand loyalty. With the rise in smart home devices and mobile-first shopping behaviors, the relevance and deployment of AI-powered shopping tools are poised to expand substantially.
According to Synup, 26% of voice assistant users have made a purchase via voice search, while 43% use voice to research products.
Furthermore, 54% of U.S. voice assistant users employ them for retail tasks such as product searches and simple purchases.
PwC reports that 71% of users favor voice assistants for quick searches, highlighting the growing demand for hands-free, efficient shopping interactions.
Restraints
Concerns over consumer data privacy and ethical use of AI are slowing adoption across regulatory-sensitive markets and user segments.
AI shopping assistants require vast amounts of personal data—ranging from search behavior to purchase history—to function effectively. However, rising concerns over data misuse, surveillance, and non-transparent algorithms have triggered consumer hesitation and legal scrutiny. Stricter regulations like GDPR and CCPA limit how businesses collect and process this data, creating compliance challenges. Users are wary of AI models tracking their digital footprint, and brands risk backlash if privacy breaches occur. These growing apprehensions around data protection may hinder wider acceptance, especially in markets where trust in digital ecosystems remains fragile and regulation enforcement is intensifying.
An Omnisend survey of 1,026 U.S. consumers found that 58% were worried about AI's use of their personal data, while 42% believed current AI systems function more as upselling tools than helpful assistants.
GDPR enforcement is also tightening in January 2025, Ireland’s Data Protection Commission fined Meta approximately USD 1.31 billion for unlawful data transfers to the U.S., and in 2024, TikTok was fined around USD 377 million for violating children's data privacy rules.
Opportunities
Rising consumer preference for 24/7 virtual shopping support offers a significant opportunity to expand AI assistant adoption across global retail sectors.
Consumers increasingly expect round-the-clock assistance, especially in online retail environments. AI shopping assistants can deliver this continuous support, providing instant responses, troubleshooting, and personalized suggestions anytime. This capability not only enhances customer satisfaction but also reduces dependency on human agents, lowering operational costs for businesses. Retailers can deploy AI to manage high volumes of queries during peak sales seasons or across different time zones. As more customers value immediate, consistent support in their shopping journey, the potential to embed AI assistants across diverse retail formats and regions continues to grow significantly.
According to Zendesk, 62% of customer experience (CX) leaders admit they’re behind in offering the instant support customers now expect, while 51% of consumers say they prefer interacting with bots over humans when needing immediate service.
More than 50% of consumers expect businesses to offer 24/7 availability, with AI chatbots addressing this demand.
Klarna’s AI assistant managed 2.3 million conversations roughly two-thirds of all customer chats in its first month, handling the equivalent workload of 700 full-time agents and resolving issues in under 2 minutes compared to 11 minutes previously.
Challenges
Challenges in standardizing AI performance across different retail platforms hinder consistent consumer experiences and brand cohesion.
Retailers use various tech stacks, APIs, and customer data systems, making it difficult to deploy a unified AI shopping assistant experience across platforms. Inconsistencies in response quality, visual interface, or product recommendations across mobile apps, websites, and social media channels can confuse users and damage brand trust. Moreover, maintaining synchronized inventory, pricing, and personalization across multiple touchpoints requires seamless integration. Without standardization, businesses face difficulty scaling AI solutions across regions or brands. Achieving a consistent, reliable AI assistant experience remains a significant operational and technological hurdle in the market's broader adoption.
By Offering
The Solution segment dominated the AI Shopping Assistant Market in 2024 due to growing enterprise adoption of proprietary AI tools for personalized product recommendations, virtual try-ons, and customer engagement. Retailers prefer integrated platforms offering scalability, advanced analytics, and seamless omnichannel support. These standalone or embedded AI solutions drive efficiency, elevate user experience, and generate higher ROI, making them the top revenue-generating segment in the market landscape.
The Services segment is projected to grow at the fastest CAGR of 28.72% from 2025 to 2032 owing to rising demand for AI consulting, deployment, training, and support services. Businesses seek tailored service models to integrate AI tools effectively across customer touchpoints. Service providers assist in real-time optimization, reduce deployment friction, and help non-tech-savvy retailers adopt AI shopping assistants, especially in developing markets and mid-sized enterprises.
By End-use
The Retail & E-Commerce segment dominated the AI Shopping Assistant Market in 2024 with a 48% revenue share, driven by surging online shopping volumes and digital transformation in retail. E-commerce leaders are investing heavily in AI-powered product recommendations, virtual assistants, and dynamic pricing engines to improve customer satisfaction and retention. The growing consumer expectation for real-time, personalized experiences also accelerates adoption in this sector more than any other.
The Healthcare segment is expected to grow at the fastest CAGR of 29.80% from 2025 to 2032 as healthcare providers increasingly adopt AI shopping assistants for recommending products like supplements, insurance plans, wellness tools, and medication reminders. AI aids in streamlining patient self-service, reducing wait times, and enhancing retail pharmacy operations. Personalized health product suggestions based on user data fuel demand for AI tools in digital healthcare commerce.
By Technology
The Natural Language Processing (NLP) segment dominated the AI Shopping Assistant Market in 2024 with a 39% revenue share due to its ability to interpret user intent, support multilingual conversations, and enhance query accuracy. NLP empowers chatbots and voice assistants to deliver human-like interaction, enabling seamless and contextual responses. Its maturity and integration across platforms make it the foundation of most AI shopping experiences today.
The Computer Vision (CV) segment is expected to grow at the fastest CAGR of 30.43% from 2025 to 2032 as retailers leverage visual AI to power virtual try-ons, product search by image, and in-store analytics. With growing demand for immersive and visual shopping experiences, CV technologies enhance decision-making and user engagement. Its application in AR/VR shopping environments drives future adoption in both digital and physical retail.
By Type
The Text segment dominated the AI Shopping Assistant Market in 2024 with a 41% revenue share due to its widespread use in chatbots, recommendation systems, and customer support tools. Text-based interactions remain cost-effective, language-flexible, and easy to deploy across web and mobile platforms. The reliability of text interfaces, especially in customer acquisition and problem resolution, has ensured sustained dominance in user-facing AI tools.
The Multimodal segment is projected to grow at the fastest CAGR of 30.53% from 2025 to 2032 as brands shift toward richer customer engagement involving voice, text, images, and gestures. Multimodal assistants improve personalization and comprehension by processing multiple data types simultaneously, creating more intuitive and natural interactions. Rising consumer expectations for seamless cross-modal shopping experiences fuel investment in these advanced AI capabilities across platforms.
North America dominated the AI Shopping Assistant Market in 2024 with a 38% revenue share due to its strong technological infrastructure, high digital adoption rates, and early investment by major retail and tech companies. The presence of leading AI developers, mature e-commerce ecosystems, and consumer readiness for personalized digital experiences further contributed to the region's market leadership in deploying AI-based retail and customer engagement solutions.
The United States is dominating the AI Shopping Assistant Market due to advanced AI adoption, strong retail infrastructure, and high consumer demand for personalized experiences.
Asia Pacific is projected to grow at the fastest CAGR of 29.84% from 2025 to 2032 driven by rapid smartphone penetration, expanding e-commerce platforms, and increasing digital literacy among consumers. Emerging markets like India, China, and Southeast Asia are seeing heavy investment in AI-driven retail innovation. Local businesses and global players are leveraging AI shopping assistants to tap into the region’s large, youthful, and digitally engaged consumer base.
China is dominating the AI Shopping Assistant Market in Asia Pacific due to massive e-commerce scale, advanced AI integration, and strong government support for digital innovation.
Europe is experiencing steady growth in the AI Shopping Assistant Market, driven by rising e-commerce adoption, digital transformation in retail, and increasing consumer demand for personalized shopping. Countries like the UK, Germany, and France are leading in AI integration and deployment.
The United Kingdom is dominating the AI Shopping Assistant Market in Europe due to strong e-commerce growth, AI innovation, and high digital consumer engagement.
The Middle East & Africa and Latin America are emerging markets in the AI Shopping Assistant sector, driven by growing smartphone usage, expanding online retail, and rising interest in automation. Local retailers are gradually adopting AI to enhance customer engagement and efficiency.
Alibaba Group Holding Limited, Shopify Inc., Salesforce, Inc., eBay Inc., Google LLC, Amazon.com, Inc., Meta Platforms, Inc., Microsoft Corporation, Adobe Inc., IBM Corporation, Perfect Corp., Sherpa.ai, The Yes, Outfittery, Caper AI, Uniphore, Manifest AI, Constructor, Yalo, SleekFlow.
2025: Alibaba Cloud unveiled upgraded AI models (Qwen-Max, QWQ-Plus, QVQ-Max, Qwen2.5-Omni-7b) with enhanced PAI platform, PolarDB in-database inference, and Model Studio integration for international generative AI customers.
2024: Alibaba made these 10 AI tools available to all Taobao and Tmall merchants; during 11.11, they were used 1.5 billion+ times, increasing AI-enhanced image click-through rates by 25%.
2023: Taobao and Tmall launched 10 AI-powered tools including Wenwen, Ali Xiaomi chatbot, and Business Advisor in October 2023, aimed at enhancing product discovery, personalization, and merchant analytics through generative AI.
| Report Attributes | Details |
|---|---|
| Market Size in 2024 | USD 3.65 Billion |
| Market Size by 2032 | USD 24.90 Billion |
| CAGR | CAGR of 27.22% From 2025 to 2032 |
| Base Year | 2024 |
| Forecast Period | 2025-2032 |
| Historical Data | 2021-2023 |
| Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
| Key Segments | • By Offering (Solution, Services) • By Technology (Natural Language Processing (NLP), Machine Learning (ML), Computer Vision (CV), Others) • By Type (Voice, Text, Visual, Multimodal) • By End-use (BFSI, Retail & E-Commerce, Healthcare, Travel & Hospitality, Media & Entertainment, Others) |
| Regional Analysis/Coverage | North America (US, Canada), Europe (Germany, UK, France, Italy, Spain, Russia, Poland, Rest of Europe), Asia Pacific (China, India, Japan, South Korea, Australia, ASEAN Countries, Rest of Asia Pacific), Middle East & Africa (UAE, Saudi Arabia, Qatar, South Africa, Rest of Middle East & Africa), Latin America (Brazil, Argentina, Mexico, Colombia, Rest of Latin America). |
| Company Profiles | Alibaba Group Holding Limited, Shopify Inc., Salesforce, Inc., eBay Inc., Google LLC, Amazon.com, Inc., Meta Platforms, Inc., Microsoft Corporation, Adobe Inc., IBM Corporation, Perfect Corp., Sherpa.ai, The Yes, Outfittery, Caper AI, Uniphore, Manifest AI, Constructor, Yalo, SleekFlow |
Ans: The AI Shopping Assistant Market is expected to grow at a CAGR of 27.22% from 2025 to 2032, indicating strong global demand.
Ans: The AI Shopping Assistant Market was valued at USD 3.65 billion in 2024, reflecting the growing role of AI in retail and e-commerce.
Ans: Increasing consumer demand for personalized shopping experiences and omnichannel retail adoption are driving the market’s strong global expansion.
Ans: The Text segment dominated the AI Shopping Assistant Market in 2024, accounting for 41% of total revenue share.
Ans: North America dominated the AI Shopping Assistant Market in 2024, holding a 38% revenue share due to strong AI and retail integration.
Table Of Contents
1. Introduction
1.1 Market Definition & Scope
1.2 Research Assumptions & Abbreviations
1.3 Research Methodology
2. Executive Summary
2.1 Market Snapshot
2.2 Market Absolute $ Opportunity Assessment & Y-o-Y Analysis, 2021–2032
2.3 Market Size & Forecast, By Segmentation, 2021–2032
2.3.1 Market Size By Offering
2.3.2 Market Size By Technology
2.3.3 Market Size By Type
2.3.4 Market Size By End-use
2.4 Market Share & Bps Analysis By Region, 2024
2.5 Industry Growth Scenarios – Conservative, Likely & Optimistic
2.6 Industry CxO’s Perspective
3. Market Overview
3.1 Market Dynamics
3.1.1 Drivers
3.1.2 Restraints
3.1.3 Opportunities
3.1.4 Key Market Trends
3.2 Industry PESTLE Analysis
3.3 Key Industry Forces (Porter’s) Impacting Market Growth
3.4 Industry Supply Chain Analysis
3.4.1 Raw Material Suppliers
3.4.2 Manufacturers
3.4.3 Distributors/Suppliers
3.4.4 Customers/End-Users
3.5 Industry Life Cycle Assessment
3.6 Parent Market Overview
3.7 Market Risk Assessment
4. Statistical Insights & Trends Reporting
4.1 Return on Investment (ROI) Metrics
4.1.1 Overview
4.1.2 Average ROI (%) for Businesses Deploying AI Shopping Assistants
4.1.3 Cost Savings (%) from Automation (e.g., Reduced Customer Service Costs)
4.1.4 Conversion Rate Improvements (%) After AI Assistant Implementation
4.2 Consumer Behavior Metrics
4.2.1 Overview
4.2.2 Average Increase in Basket Size (%) Due to AI Recommendations
4.2.3 Cart Abandonment Rate Reduction (%) After AI Integration
4.2.4 Time Spent Per Session (in Minutes) with AI Assistant Guidance
4.2.5 Product Discovery Uplift (%) Using Visual/Voice Shopping Tools
4.3 Technology Performance Metrics
4.3.1 Overview
4.3.2 AI Assistant Accuracy (%) in Understanding Natural Language Queries
4.3.3 Response Time (Milliseconds) of Real-Time Assistants
4.3.4 Recommendation Relevance Score (%) Based on Personalization Success
4.4 Data & Privacy Metrics
4.4.1 Overview
4.4.2 % of Platforms Offering Data Anonymization and GDPR/CCPA Compliance
4.4.3 Security Incidents Reported (%) Involving AI Shopping Bots
4.5 Investment & Funding Statistics
4.5.1 Overview
4.5.2 Annual Global Investment (USD Million) in AI Shopping Assistant Startups
4.5.3 Top-Funded Startups and Investment Rounds (2020–2024)
4.5.4 % Increase in AI R&D Spending by Retail Tech Companies
5. AI Shopping Assistant Market Segmental Analysis & Forecast, By Offering, 2021 – 2032, Value (USD Billion) & Volume (Million Users)
5.1 Introduction
5.2 Solution
5.2.1 Key Trends
5.2.2 Market Size & Forecast, 2021 – 2032
5.3 Services
5.3.1 Key Trends
5.3.2 Market Size & Forecast, 2021 – 2032
6. AI Shopping Assistant Market Segmental Analysis & Forecast, By Technology, 2021 – 2032, Value (USD Billion) & Volume (Million Users)
6.1 Introduction
6.2 Natural Language Processing (NLP)
6.2.1 Key Trends
6.2.2 Market Size & Forecast, 2021 – 2032
6.3 Machine Learning (ML)
6.3.1 Key Trends
6.3.2 Market Size & Forecast, 2021 – 2032
6.4 Computer Vision (CV)
6.4.1 Key Trends
6.4.2 Market Size & Forecast, 2021 – 2032
6.5 Others
6.5.1 Key Trends
6.5.2 Market Size & Forecast, 2021 – 2032
7. AI Shopping Assistant Market Segmental Analysis & Forecast, By Type, 2021 – 2032, Value (USD Billion) & Volume (Million Users)
7.1 Introduction
7.2 Voice
7.2.1 Key Trends
7.2.2 Market Size & Forecast, 2021 – 2032
7.3 Text
7.3.1 Key Trends
7.3.2 Market Size & Forecast, 2021 – 2032
7.4 Visual
7.4.1 Key Trends
7.4.2 Market Size & Forecast, 2021 – 2032
7.5 Multimodal
7.5.1 Key Trends
7.5.2 Market Size & Forecast, 2021 – 2032
8. AI Shopping Assistant Market Segmental Analysis & Forecast, By End-use, 2021 – 2032, Value (USD Billion) & Volume (Million Users)
8.1 Introduction
8.2 BFSI
8.2.1 Key Trends
8.2.2 Market Size & Forecast, 2021 – 2032
8.3 Retail & E-Commerce
8.3.1 Key Trends
8.3.2 Market Size & Forecast, 2021 – 2032
8.4 Healthcare
8.4.1 Key Trends
8.4.2 Market Size & Forecast, 2021 – 2032
8.5 Travel & Hospitality
8.5.1 Key Trends
8.5.2 Market Size & Forecast, 2021 – 2032
8.6 Media & Entertainment
8.6.1 Key Trends
8.6.2 Market Size & Forecast, 2021 – 2032
8.7 Others
8.7.1 Key Trends
8.7.2 Market Size & Forecast, 2021 – 2032
9. AI Shopping Assistant Market Segmental Analysis & Forecast By Region, 2021 – 2025, Value (USD Billion) & Volume (Million Users)
9.1 Introduction
9.2 North America
9.2.1 Key Trends
9.2.2 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.2.3 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.2.4 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.2.5 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.2.6 AI Shopping Assistant Market Size & Forecast, By Country, 2021 – 2032
9.2.6.1 USA
9.2.6.1.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.2.6.1.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.2.6.1.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.2.6.1.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.2.6.2 Canada
9.2.6.2.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.2.6.2.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.2.6.2.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.2.6.2.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.3 Europe
9.3.1 Key Trends
9.3.2 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.3.3 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.3.4 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.3.5 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.3.6 AI Shopping Assistant Market Size & Forecast, By Country, 2021 – 2032
9.3.6.1 Germany
9.3.6.1.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.3.6.1.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.3.6.1.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.3.6.1.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.3.6.2 UK
9.3.6.2.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.3.6.2.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.3.6.2.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.3.6.2.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.3.6.3 France
9.3.6.3.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.3.6.3.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.3.6.3.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.3.6.3.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.3.6.4 Italy
9.3.6.4.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.3.6.4.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.3.6.4.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.3.6.4.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.3.6.5 Spain
9.3.6.5.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.3.6.5.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.3.6.5.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.3.6.5.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.3.6.6 Russia
9.3.6.6.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.3.6.6.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.3.6.6.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.3.6.6.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.3.6.7 Poland
9.3.6.7.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.3.6.7.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.3.6.7.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.3.6.7.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.3.6.8 Rest of Europe
9.3.6.8.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.3.6.8.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.3.6.8.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.3.6.8.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.4 Asia-Pacific
9.4.1 Key Trends
9.4.2 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.4.3 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.4.4 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.4.5 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.4.6 AI Shopping Assistant Market Size & Forecast, By Country, 2021 – 2032
9.4.6.1 China
9.4.6.1.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.4.6.1.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.4.6.1.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.4.6.1.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.4.6.2 India
9.4.6.2.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.4.6.2.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.4.6.2.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.4.6.2.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.4.6.3 Japan
9.4.6.3.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.4.6.3.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.4.6.3.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.4.6.3.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.4.6.4 South Korea
9.4.6.4.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.4.6.4.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.4.6.4.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.4.6.4.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.4.6.5 Australia
9.4.6.5.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.4.6.5.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.4.6.5.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.4.6.5.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.4.6.6 ASEAN Countries
9.4.6.6.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.4.6.6.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.4.6.6.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.4.6.6.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.4.6.7 Rest of Asia-Pacific
9.4.6.7.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.4.6.7.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.4.6.7.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.4.6.7.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.5 Latin America
9.5.1 Key Trends
9.5.2 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.5.3 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.5.4 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.5.5 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.5.6 AI Shopping Assistant Market Size & Forecast, By Country, 2021 – 2032
9.5.6.1 Brazil
9.5.6.1.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.5.6.1.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.5.6.1.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.5.6.1.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.5.6.2 Argentina
9.5.6.2.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.5.6.2.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.5.6.2.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.5.6.2.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.5.6.3 Mexico
9.5.6.3.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.5.6.3.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.5.6.3.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.5.6.3.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.5.6.4 Colombia
9.5.6.4.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.5.6.4.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.5.6.4.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.5.6.4.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.5.6.5 Rest of Latin America
9.5.6.5.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.5.6.5.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.5.6.5.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.5.6.5.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.6 Middle East & Africa
9.6.1 Key Trends
9.6.2 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.6.3 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.6.4 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.6.5 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.6.6 AI Shopping Assistant Market Size & Forecast, By Country, 2021 – 2032
9.6.6.1 UAE
9.6.6.1.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.6.6.1.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.6.6.1.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.6.6.1.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.6.6.2 Saudi Arabia
9.6.6.2.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.6.6.2.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.6.6.2.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.6.6.2.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.6.6.3 Qatar
9.6.6.3.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.6.6.3.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.6.6.3.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.6.6.3.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.6.6.4 Egypt
9.6.6.4.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.6.6.4.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.6.6.4.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.6.6.4.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.6.6.5 South Africa
9.6.6.5.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.6.6.5.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.6.6.5.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.6.6.5.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
9.6.6.6 Rest of Middle East & Africa
9.6.6.6.1 AI Shopping Assistant Market Size & Forecast, By Offering, 2021 – 2032
9.6.6.6.2 AI Shopping Assistant Market Size & Forecast, By Technology, 2021 – 2032
9.6.6.6.3 AI Shopping Assistant Market Size & Forecast, By Type, 2021 – 2032
9.6.6.6.4 AI Shopping Assistant Market Size & Forecast, By End-use, 2021 – 2032
10. Competitive Landscape
10.1 Key Players' Positioning
10.2 Competitive Developments
10.2.1 Key Strategies Adopted (%), By Key Players, 2024
10.2.2 Year-Wise Strategies & Development, 2021 – 2025
10.2.3 Number Of Strategies Adopted By Key Players, 2024
10.3 Market Share Analysis, 2024
10.4 Product/Service & Application Benchmarking
10.4.1 Product/Service Specifications & Features By Key Players
10.4.2 Product/Service Heatmap By Key Players
10.4.3 Application Heatmap By Key Players
10.5 Industry Start-Up & Innovation Landscape
10.6 Key Company Profiles
10.6 Key Company Profiles
10.6.1 Alibaba Group Holding Limited
10.6.1.1 Company Overview & Snapshot
10.6.1.2 Product/Service Portfolio
10.6.1.3 Key Company Financials
10.6.1.4 SWOT Analysis
10.6.2 Shopify Inc.
10.6.2.1 Company Overview & Snapshot
10.6.2.2 Product/Service Portfolio
10.6.2.3 Key Company Financials
10.6.2.4 SWOT Analysis
10.6.3 Salesforce, Inc.
10.6.3.1 Company Overview & Snapshot
10.6.3.2 Product/Service Portfolio
10.6.3.3 Key Company Financials
10.6.3.4 SWOT Analysis
10.6.4 eBay Inc.
10.6.4.1 Company Overview & Snapshot
10.6.4.2 Product/Service Portfolio
10.6.4.3 Key Company Financials
10.6.4.4 SWOT Analysis
10.6.5 Google LLC
10.6.5.1 Company Overview & Snapshot
10.6.5.2 Product/Service Portfolio
10.6.5.3 Key Company Financials
10.6.5.4 SWOT Analysis
10.6.6 Amazon.com, Inc.
10.6.6.1 Company Overview & Snapshot
10.6.6.2 Product/Service Portfolio
10.6.6.3 Key Company Financials
10.6.6.4 SWOT Analysis
10.6.7 Meta Platforms, Inc.
10.6.7.1 Company Overview & Snapshot
10.6.7.2 Product/Service Portfolio
10.6.7.3 Key Company Financials
10.6.7.4 SWOT Analysis
10.6.8 Microsoft Corporation
10.6.8.1 Company Overview & Snapshot
10.6.8.2 Product/Service Portfolio
10.6.8.3 Key Company Financials
10.6.8.4 SWOT Analysis
10.6.9 Adobe Inc.
10.6.9.1 Company Overview & Snapshot
10.6.9.2 Product/Service Portfolio
10.6.9.3 Key Company Financials
10.6.9.4 SWOT Analysis
10.6.10 IBM Corporation
10.6.10.1 Company Overview & Snapshot
10.6.10.2 Product/Service Portfolio
10.6.10.3 Key Company Financials
10.6.10.4 SWOT Analysis
10.6.11 Perfect Corp.
10.6.11.1 Company Overview & Snapshot
10.6.11.2 Product/Service Portfolio
10.6.11.3 Key Company Financials
10.6.11.4 SWOT Analysis
10.6.12 Sherpa.ai
10.6.12.1 Company Overview & Snapshot
10.6.12.2 Product/Service Portfolio
10.6.12.3 Key Company Financials
10.6.12.4 SWOT Analysis
10.6.13 The Yes
10.6.13.1 Company Overview & Snapshot
10.6.13.2 Product/Service Portfolio
10.6.13.3 Key Company Financials
10.6.13.4 SWOT Analysis
10.6.14 Outfittery
10.6.14.1 Company Overview & Snapshot
10.6.14.2 Product/Service Portfolio
10.6.14.3 Key Company Financials
10.6.14.4 SWOT Analysis
10.6.15 Caper AI
10.6.15.1 Company Overview & Snapshot
10.6.15.2 Product/Service Portfolio
10.6.15.3 Key Company Financials
10.6.15.4 SWOT Analysis
10.6.16 Uniphore
10.6.16.1 Company Overview & Snapshot
10.6.16.2 Product/Service Portfolio
10.6.16.3 Key Company Financials
10.6.16.4 SWOT Analysis
10.6.17 Manifest AI
10.6.17.1 Company Overview & Snapshot
10.6.17.2 Product/Service Portfolio
10.6.17.3 Key Company Financials
10.6.17.4 SWOT Analysis
10.6.18 Constructor
10.6.18.1 Company Overview & Snapshot
10.6.18.2 Product/Service Portfolio
10.6.18.3 Key Company Financials
10.6.18.4 SWOT Analysis
10.6.19 Yalo
10.6.19.1 Company Overview & Snapshot
10.6.19.2 Product/Service Portfolio
10.6.19.3 Key Company Financials
10.6.19.4 SWOT Analysis
10.6.20 SleekFlow
10.6.20.1 Company Overview & Snapshot
10.6.20.2 Product/Service Portfolio
10.6.20.3 Key Company Financials
10.6.20.4 SWOT Analysis
11. Analyst Recommendations
11.1 SNS Insider Opportunity Map
11.2 Industry Low-Hanging Fruit Assessment
11.3 Market Entry & Growth Strategy
11.4 Analyst Viewpoint & Suggestions On Market Growth
12. Assumptions
13. Disclaimer
14. Appendix
14.1 List Of Tables
14.2 List Of Figures
An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.
Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.
The 5 steps process:
Step 1: Secondary Research:
Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.
Step 2: Primary Research
When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data. This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.
We at SNS Insider have divided Primary Research into 2 parts.
Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.
This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.
Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.
Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.
Step 3: Data Bank Validation
Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.
Step 4: QA/QC Process
After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.
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Key Segments:
By Offering
Solution
Services
By Technology
Natural Language Processing (NLP)
Machine Learning (ML)
Computer Vision (CV)
Others
By Type
Voice
Text
Visual
Multimodal
By End-use
BFSI
Retail & E-Commerce
Healthcare
Travel & Hospitality
Media & Entertainment
Others
Request for Segment Customization as per your Business Requirement: Segment Customization Request
Regional Coverage:
North America
US
Canada
Europe
Germany
France
UK
Italy
Spain
Poland
Russia
Rest of Europe
Asia Pacific
China
India
Japan
South Korea
Australia
ASEAN Countries
Rest of Asia Pacific
Middle East & Africa
UAE
Saudi Arabia
Qatar
South Africa
Rest of Middle East & Africa
Latin America
Brazil
Argentina
Mexico
Colombia
Rest of Latin America
Request for Country Level Research Report: Country Level Customization Request
Available Customization
With the given market data, SNS Insider offers customization as per the company’s specific needs. The following customization options are available for the report:
Detailed Volume Analysis
Criss-Cross segment analysis (e.g. Product X Application)
Competitive Product Benchmarking
Geographic Analysis
Additional countries in any of the regions
Customized Data Representation
Detailed analysis and profiling of additional market players