Artificial Intelligence in Retail Market

Report Code TC 5669
Published in Oct, 2024, By MarketsandMarkets™
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Artificial Intelligence in Retail Market by Solution (Personalized Product Recommendation, Visual Search, Virtual Stores, Virtual Customer Assistant, CRM), Type (Generative AI, Other AI), End-user (Online, Offline) - Global Forecast to 2030

 

Overview

The AI in retail market is projected to expand from USD 31.12 billion in 2024 to USD 164.74 billion by 2030, at a CAGR of 32.0% during the forecast period.

The rapid growth of eCommerce and the increasing adoption of omnichannel retail strategies drive AI implementation in the sector. AI enhances the integration of online and offline channels, enabling retailers to provide a consistent and seamless shopping experience. AI tools support activities, such as dynamic pricing, real-time inventory tracking, and personalized promotions across channels, essential for staying competitive in a digital-first retail landscape. As more consumers engage with multiple touchpoints, retailers are turning to AI to optimize their operations and meet the growing demands of omnichannel shoppers.

Artificial Intelligence in Retail Market

Attractive Opportunities in the Artificial Intelligence in Retail Market

ASIA PACIFIC

Market growth in Asia Pacific can be attributed to the presence of key players in this region and the high growth in eCommerce and mobile shopping, particularly in China, India, and Japan.

Rapid economic expansion in countries such as China, India, and Southeast Asian nations has bolstered the adoption of AI in retail.

Increased funding from governments, venture capitalists, and corporations fuel the development and implementation of AI solutions in retail.

The primary factors driving the growth of the Asia Pacific AI in retail market are the region’s rapid economic growth and favorable government policies, funding, and initiatives.

Tech-savvy consumer base drives demand for AI-enhanced services, such as virtual assistants and recommendation systems, in Asia Pacific.

Use Cases of AI/ Generative AI on Artificial Intelligence in Retail Market

Generative AI is significantly changing the paradigm of AI in retail by driving innovation in personalized customer experiences and operational efficiency. Gen AI’s ability to generate new content, such as product descriptions, marketing campaigns, and visual designs, enables retailers to customize interactions, enhance engagement, and increase sales. Gen AI makes monitoring processes automated and more efficient. Al algorithms can diagnose problems from historical data and current performances and find solutions without human interference. This shift toward predictive management makes systems more reliable, operationally efficient, and less expensive. Al has become a critical tool for retailers to understand better and cater to evolving behaviors with consumer preferences shifting toward digital and on-demand experiences. Generative AI also plays a critical role in customer service by using AI-powered chatbots that offer real-time, context-aware responses, improving service delivery and satisfaction. Furthermore, retailers leverage generative AI for demand forecasting, supply chain optimization, and dynamic pricing strategies, enabling more accurate inventory management and cost savings.

Artificial Intelligence in Retail Market Impact

Global Artificial Intelligence in Retail Market Dynamics

Driver: Increasing adoption of conversational AI in retail for advice and recommendations

Advancements in technology have increasingly facilitated more natural and seamless communication, leading consumers to seek out conversational AI in retail for guidance actively. Conversational AI, such as chatbots and virtual assistants, enables retailers to take offers and shopping experiences at their fingertips and provide shoppers with personalized recommendations. AI-powered retail chatbots have proven to significantly reduce customer care costs, enhance the customer experience and the experience of customer service agents, and build stronger customer engagement. AI-driven chatbots can handle a large volume of customer inquiries, from answering common questions to providing personalized product recommendations. Retailers use conversational AI to streamline customer service, enhance the shopping experience, and improve overall operational efficiency. Conversational Al gives detailed advice, such as recommending the best fashion trends to go with or recommending shoes for a particular dress. Brands such as Sephora and H&M already use these tools to offer virtual assistance when buying a product.

Restraint: High implementation costs

The implementation of AI in retail often requires substantial investment, which remains a major barrier, particularly for smaller retailers. Al solutions often require hardware, software, and data management structures in even modest applications. The costs are further added because cloud computing services are required to process the large volumes of data required in training Al models. Moreover, using Al requires highly skilled labor, leading to higher employment and training costs. Despite the initial investment, the long-term benefits of AI implementation outweigh the costs for many retailers.

The ongoing operational costs associated with AI, such as system maintenance, updates, and data storage, add another layer of financial burden. Large enterprises are driven by long-term Return On Investment (ROI). At the same time, the sustainable route to profitability for small retail stores is not well highlighted, which may slow down the adoption of Al technology.

 

Opportunity: AI-powered customer engagement

Al technologies offer retailers a lucrative opportunity to enhance customer engagement. By utilizing Al-driven chatbots, recommendation engines, and virtual assistants, retailers can provide personalized, real-time support that significantly boosts customer satisfaction. Al-driven customer service tools, such as Sephora's Virtual Artist and H&M's chatbot, help guide customers through product selections. They provide customers with tailored suggestions based on personal preferences and previous shopping behavior. These Al systems are available 24/7, enabling retailers to engage with customers outside regular business hours and across multiple platforms, enhancing the customer journey.

H&M recently expanded its chatbot capabilities to improve customer interaction by providing personalized recommendations and fashion tips, showcasing how Al can transform retail engagement. Similarly, Amazon uses Al-driven personalization to offer customized product recommendations, contributing to its sales growth.

Challenge: Rising theft and fraud issues

AI technologies are transforming how retailers address theft and fraud, providing advanced tools to enhance security and minimize losses. Real-time surveillance, behavioral pattern analysis, and fraud detection systems powered by AI are used to prevent these issues. According to the 2023 National Retail Security Survey, retailers face an alarming increase in shrinkage, with losses amounting to USD 112.10 billion in 2022, up from USD 93.9 billion the previous year. Organized Retail Crime (ORC) is a major contributor, escalating by 26.5% in the same period. This surge has imposed pressure on Al-based loss prevention and fraud detection systems.

Al tools help enhance surveillance, identify theft patterns, and predict future incidents. However, it is challenging to balance effectiveness with privacy concerns and ensure that All systems do not result in false positives. Misidentification in theft prevention could lead to negative customer experiences, further complicating Al's role in security. Moreover, the National Retail Security Survey report suggests that 66.1% of retailers are allocating more resources to fight crime, signaling increased demand for AI-driven solutions. Yet, implementing Al for fraud detection and asset protection requires noteworthy upfront investment, ongoing training, and fine-tuning of algorithms to adapt to new theft strategies.

Global Artificial Intelligence in Retail Market Ecosystem Analysis

The AI in retail market is highly competitive and comprises many vendors who offer solutions to a specific or niche segment of the market. Several changes in the AI in retail market have occurred in recent years. Currently, the vendors are involved in various partnerships and collaborations to develop comprehensive solutions that address a wide range of requirements. Microsoft (US), IBM (US), Google (US), Amazon (US), Oracle (US), Salesforce (US), and NVIDIA (US) are some of the major players operating in this ecosystem.

Top Companies in Artificial Intelligence in Retail Market
 

Personalized product recommendations segment to account for largest market share during forecast period

Al-driven personalized product recommendations are essential for improving customer experience and increasing sales. By analyzing customer behavior, past purchases, and preferences, Al algorithms suggest products tailored to individual preferences. This helps retailers offer a more engaging and relevant shopping experience, increasing customer satisfaction and loyalty. Accurate product recommendations will increase the probability of repeated sales from the same customer.

Offline end users segment to register higher CAGR during forecast period

Al elevates functionality and customer experience of offline stores through the efficient layout of stores, integrated checkout processes, and order management in-store. Applications such as Amazon Go and Smart robotic services guide a customer in-store with smart checkout or help restock an item on the shelf. Such companies as Target and Carrefour are using Al to analyze customer behavior and stock management with the help of predictive analytics. Al has enhanced Point-Of-Sale (POS) systems that integrate various data points to recommend personalized in-store promotions. Supermarkets & hypermarkets such as Walmart and Carrefour use computer vision and predictive analytics to manage stock and minimize shortages. Al is also used in specialty and convenience stores to provide a better customer experience. In other offline channels, such as the department and the discount stores, Al applications comprise facial identification and customer flow analytics, which help manage foot traffic and improve store layouts for better shopping experiences.

By region, North America to account for largest market share during forecast period

North America holds the largest revenue share in the AI in retail market. Enterprises in this region are the early adopters of technologies. The region is experiencing rapid expansion due to several drivers, including increased awareness of AI's potential, the need for enhanced customer experiences, and productivity boosts through automation and data analytics. This region is expected to lead the AI retail sector, driven by heavy investments from tech giants and government support. The strong AI ecosystem in North America and innovation attract significant venture capital, boosting economic growth. Retailers are also forming key partnerships, such as Macy's collaboration with Rokt in August 2024, to improve customer engagement using AI solutions. Local governments, such as the US, with its "Partnership for Global Inclusivity on AI," aim to promote ethical AI practices, further accelerating AI adoption in retail. The presence of major AI vendors, such as Google, IBM, and Microsoft, is accelerating innovation in retail AI in the region.

HIGHEST MARKET SHARE IN 2024
CANADA FASTEST GROWING MARKET IN THE REGION
Artificial Intelligence in Retail Market Size and Share

Recent Developments of Artificial Intelligence in Retail Market

  • In October 2024, Microsoft and Rezolve AI partnered to enhance retail innovation through AI-powered commerce solutions. Rezolve's Brain Suite will be integrated with Microsoft Azure, providing retailers with advanced capabilities for digital engagement and operational efficiency.
  • In September 2024, Salesforce and Nvidia partnered to develop advanced AI capabilities, transforming customer and employee experiences. They aim to develop a new generation of AI agents and avatars that can operate autonomously, understand complex business contexts, and interact with humans more naturally and intuitively.
  • In June 2024, Bath & Body Works collaborated with Accenture to modernize, transform, and simplify its core digital and technology platforms. This multi-year program is part of Bath & Body Works’ strategy to elevate the brand and leverage the latest technologies in digital, MarTech, AI, and gen AI to drive growth.
  • In April 2024, Microsoft and Coca-Cola Company entered a five-year strategic partnership to advance Coca-Cola's technology strategy. The partnership leverages Microsoft Cloud and generative AI.

Key Market Players

List of Top Artificial Intelligence in Retail Market Companies

The Artificial Intelligence in Retail Market is dominated by a few major players that have a wide regional presence. The major players in the Artificial Intelligence in Retail Market are

  • Microsoft (US)
  • IBM (US)
  • Google (US)
  • Amazon (US)
  • Oracle (US)
  • Salesforce (US)
  • NVIDIA (US)
  • SAP (Germany)
  • ServiceNow (US)
  • Accenture (Ireland)
  • Infosys (India)
  • Alibaba (China)
  • Intel (US)
  • AMD (US)
  • Fujitsu (Japan)
  • Capgemini (France)
  • TCS (India)
  • Talkdesk (US)
  • Symphony AI (US)
  • Bloomreach (US)
  • C3.AI (US)
  • Visenze (Singapore)
  • Pathr.ai (US)
  • Vue.AI (US)
  • Nextail (Spain)
  • Daisy Intelligence (Canada)
  • Cresta (US)
  • Mason (US)
  • Syte (Israel)
  • Trax (Singapore)
  • Feedzai (US)
  • Shopic (Israel)

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Scope of the Report

Report Attribute Details
Market size available for years 2019-2030
Base year considered 2023
Forecast period 2024–2030
Forecast units Value (USD) Million/Billion
Segments Covered By Offering, Type, Business Function, End-user and Region
Regions covered North America, Europe, Asia Pacific, Middle East & Africa, and Latin America

 

Key Questions Addressed by the Report

What is the definition of the AI in retail market?
AI in retail is the use of AI algorithms and technologies, such as computer vision, NLP, and ML, in various aspects of the retail industry. Using AI helps improve the operations’ efficiency, enhance decision-making processes, drive business growth, and improve overall customer experience. AI helps retailers understand customer behavior, optimize supply chains, and improve operational efficiency.
What is the size of the AI in retail market?
The AI in retail market is projected to grow from USD 31.12 billion in 2024 to USD 164.74 billion by 2030, at a CAGR of 32.0% during the forecast period.
What are the major drivers in the AI in retail market?
Major drivers in AI in retail market are increasing adoption of conversational AI in retail for advice and recommendations, evolving consumer expectations and social commerce integration, enhancing checkout experiences with AI-powered automation, and data-driven decision-making.
Who are the key players operating in the AI in retail market?
The major players in the AI in retail market are Microsoft (US), IBM (US), Google (US), Amazon (US), Oracle (US), Salesforce (US), NVIDIA (US), SAP (Germany), ServiceNow (US), Accenture (Ireland), Infosys (India), Alibaba (China), Intel (US), AMD (US), Fujitsu (Japan), Capgemini (France), TCS (India), Talkdesk (US), Symphony AI (US), Bloomreach (US), C3.AI (US), Visenze (Singapore), Pathr.ai (US), Vue.AI (US), Nextail (Spain), Daisy Intelligence (Canada), Cresta (US), Mason (US), Syte (Israel), Trax (Singapore), Feedzai (US), and Shopic (Israel). These players have adopted various growth strategies, such as partnerships, agreements and collaborations, product launches/enhancements, and acquisitions to expand their footprint in the AI in retail market.
What are the opportunities for new entrants in the AI in retail market?
There are many opportunities for new players in AI in retail market as there is an increased need for a unique shopping experience, automation, and efficiency in retail operations. New players can focus on AI-driven customer service solutions encompassing chatbots, virtual assistants, and personalized marketing tools, which AI enables to provide recommendations. Furthermore, the adoption of AI solutions in supply chain optimization and demand forecasting spaces with smart inventory management will create growth opportunities. Retailers are also seeking AI-based visual search, smart checkout systems, and advanced fraud detection for retailers, which are growing areas where players can make investments.

 

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Table of Contents

Exclusive indicates content/data unique to MarketsandMarkets and not available with any competitors.

TITLE
PAGE NO
INTRODUCTION
1
RESEARCH METHODOLOGY
2
EXECUTIVE SUMMARY
3
PREMIUM INSIGHTS
4
MARKET OVERVIEW AND INDUSTRY TRENDS
5
  • 5.1 INTRODUCTION
  • 5.2 MARKET DYNAMICS
    DRIVERS
    RESTRAINTS
    OPPORTUNITIES
    CHALLENGES
  • 5.3 BRIEF HISTORY OF ARTIFICIAL INTELLIGENCE IN RETAIL
  • 5.4 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET: ECOSYSTEM ANALYSIS/MARKET MAP
  • 5.5 CASE STUDY ANALYSIS
  • 5.6 SUPPLY CHAIN ANALYSIS
  • 5.7 TARIFF AND REGULATORY LANDSCAPE
    TARIFF RELATED TO PROCESSORS AND CONTROLLERS (HSN: 854231)
    REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
    - NORTH AMERICA
    - EUROPE
    - ASIA PACIFIC
    - MIDDLE EAST & AFRICA
    - LATIN AMERICA
    KEY REGULATIONS
  • 5.8 PRICING ANALYSIS
    AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY SOLUTIONS
    INDICATIVE PRICING ANALYSIS, BY TYPE
  • 5.9 TRADE ANALYSIS
    EXPORT SCENARIO OF PROCESSORS AND CONTROLLERS
    IMPORT SCENARIO OF PROCESSORS AND CONTROLLERS
  • 5.10 TECHNOLOGY ANALYSIS
    KEY TECHNOLOGIES
    - AUTONOMOUS AI AND AUTONOMOUS AGENTS
    - CASUAL AI
    COMPLIMENTARY TECHNOLOGIES
    - EDGE COMPUTING
    - BIG DATA ANALYTICS
    ADJACENT TECHNOLOGIES
    - BLOCKCHAIN
    - CYBERSECURITY
  • 5.11 PATENT ANALYSIS
    LIST OF MAJOR PATENTS
  • 5.12 PORTERS FIVE FORCES ANALYSIS
    THREAT OF NEW ENTRANTS
    THREAT OF SUBSTITUTES
    BARGAINING POWER OF SUPPLIERS
    BARGAINING POWER OF BUYERS
    INTENSITY OF COMPETITIVE RIVALRY
  • 5.13 TRENDS/DISRUPTIONS IMPACTING CUSTOMER’S BUSINESS
  • 5.14 KEY STAKEHOLDERS AND BUYING CRITERIA
    KEY STAKEHOLDERS IN THE BUYING PROCESS
    BUYING CRITERIA
  • 5.15 KEY CONFERENCES & EVENTS, 2024-2025
  • 5.16 TECHNOLOGY ROADMAP FOR ARTIFICIAL INTELLIGENCE IN RETAIL MARKET
    SHORT-TERM ROADMAP (2023 – 2025)
    MID-TERM ROADMAP (2026 – 2028)
    LONG-TERM ROADMAP (2028 – 2030)
  • 5.17 BEST PRACTICES TO IMPLEMENT ARTIFICIAL INTELLIGENCE IN RETAIL
  • 5.18 CURRENT AND EMERGING BUSINESS MODELS
  • 5.19 TOOLS, FRAMEWORKS, AND TECHNIQUES USED IN ARTIFICIAL INTELLIGENCE IN RETAIL
  • 5.20 INVESTMENT AND FUNDING SCENARIO
  • 5.21 IMPACT OF AI/GEN AI ON ARTIFICIAL INTELLIGENCE IN RETAIL MARKET
ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY OFFERING
6
  • 6.1 INTRODUCTION
    OFFERING: MARKET DRIVERS
  • 6.2 SOLUTIONS
  • 6.3 SERVICES
    - SUPPORT & MAINTENANCE
ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE
7
  • 7.1 INTRODUCTION
    TYPE: MARKET DRIVERS
  • 7.2 GENERATIVE AI
  • 7.3 OTHER AI
    MACHINE LEARNING
    NATURAL LANGUAGE PROCESSING
    COMPUTER VISION
    PREDICTIVE ANALYTICS
ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY BUSINESS FUNCTIONS
8
  • 8.1 INTRODUCTION
    BUSINESS FUNCTIONS: MARKET DRIVERS
  • 8.2 MARKETING AND SALES
  • 8.3 HUMAN RESOURCE
  • 8.4 FINANCE AND ACCOUNTING
  • 8.5 OPERATIONS
  • 8.6 CYBERSECURITY
ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY END-USER
9
  • 9.1 INTRODUCTION
    END-USER: MARKET DRIVERS
  • 9.2 END-USER BY CHANNEL TYPE
    ONLINE
    OFFLINE
    - CONVENIENCE STORE
    - OTHER OFFLINE STORES (DEPARTMENT STORES AND DISCOUNT STORES)
    ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION
ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION
10
  • 10.1 INTRODUCTION
  • 10.2 NORTH AMERICA
    MACROECONOMIC OUTLOOK FOR NORTH AMERICA
    UNITED STATES
    CANADA
  • 10.3 EUROPE
    MACROECONOMIC OUTLOOK FOR EUROPE
    UK
    GERMANY
    FRANCE
    ITALY
    SPAIN
    NORDICS
    REST OF EUROPE
  • 10.4 ASIA PACIFIC
    MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
    CHINA
    JAPAN
    INDIA
    SOUTH KOREA
    AUSTRALIA & NEW ZEALAND
    REST OF ASIA PACIFIC
  • 10.5 MIDDLE EAST AND AFRICA
    MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA
    UAE
    KSA
    KUWAIT
    BAHRAIN
    SOUTH AFRICA
    REST OF MIDDLE EAST & AFRICA
  • 10.6 LATIN AMERICA
    MACROECONOMIC OUTLOOK FOR LATIN AMERICA
    BRAZIL
    MEXICO
    AGENTINA
    REST OF LATIN AMERICA
COMPETITVE LANDSCAPE
11
  • 11.1 KEY PLAYER STRATEGIES/RIGHT TO WIN
  • 11.2 MARKET SHARE ANALYSIS
    MARKET RANKING ANALYSIS
  • 11.3 REVENUE ANALYSIS
  • 11.4 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
    STARS
    EMERGING LEADERS
    PERVASIVE PLAYERS
    PARTICIPANTS
    COMPANY FOOTPRINT: KEY PLAYERS, 2023
    - COMPANY FOOTPRINT
    - REGION FOOTPRINT
    - OFFERING FOOTPRINT
    - TYPE FOOTPRINT
    - BUSINESS FUNCTION FOOTPRINT
    - END-USER FOOTPRINT
  • 11.5 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
    PROGRESSIVE COMPANIES
    RESPONSIVE COMPANIES
    DYNAMIC COMPANIES
    STARTING BLOCKS
    COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
    - DETAILED LIST OF KEY STARTUPS/SMES
    - COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
  • 11.6 COMPETITIVE SCENARIO AND TRENDS
    PRODUCT LAUNCHES
    - DEALS
    - OTHERS
  • 11.7 BRAND/PRODUCT COMPARISON ANALYSIS
  • 11.8 COMPANY VALUATION AND FINANCIAL METRICS OF KEY ARTIFICIAL INTELLIGENCE IN RETAIL PROVIDERS
COMPANY PROFILES
12
  • 12.1 INTRODUCTION
  • 12.2 KEY PLAYERS
    MICROSOFT
    - Business and Financial overview
    - Recent developments
    - MNM View
    IBM
    - Business and Financial Overview
    - Recent developments
    - MNM View
    GOOGLE
    - Business and Financial Overview
    - Recent developments
    - MNM View
    AWS
    - Business and Financial Overview
    - Recent Developments
    - MNM View
    ORACLE
    - Business and Financial Overview
    - Recent Developments
    - MnM View
    SALESFORCE
    - Recent developments
    NVIDIA
    - Business and Financial Overview
    - Recent developments
    SAP
    - Business and Financial Overview
    - Recent developments
    SERVICENOW
    - Business and Financial Overview
    - Recent developments
    ACCENTURE
    - Business and Financial Overview
    - Recent developments
    INFOSYS
    - Business and Financial Overview
    - Recent developments
    ALIBABA
    INTEL
    AMD
    FUJITSU
    CAPGEMINI
    TCS
    TALKDESK
    SYMPHONY AI
    BLOOMREACH
    C3.AI
  • 12.3 STARTUPS/SMES
    VISENZE
    PATHR.AI
    VUE.AI
    NEXTAIL
    DAISY INTELLIGENCE
    CRESTA
    MASON
    SYTE
    TRAX
    FEEDZAI
    SHOPIC
ADJACENT AND RELATED MARKETS
13
  • 13.1 ADJACENT AND RELATED MARKETS
  • 13.2 AI MARKET
    MARKET DEFINITION
    MARKET OVERVIEW
  • 13.3 RETAIL ANALYTICS MARKET
    MARKET DEFINITION
    MARKET OVERVIEW
APPENDIX
14
  • 14.1 ADJACENT REPORTS
  • 14.2 DISCUSSION GUIDE
  • 14.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
  • 14.4 AVAILABLE CUSTOMIZATIONS
  • 14.5 RELATED REPORTS
  • 14.6 AUTHOR DETAILS

The research study involved four major activities in estimating the AI in retail market size. Exhaustive secondary research has been done to collect important information about the market and peer markets. The next step has been to validate these findings and assumptions and size them with the help of primary research with industry experts across the value chain. Both top-down and bottom-up approaches have been used to estimate the market size. Post which the market breakdown and data triangulation have been adopted to estimate the market sizes of segments and sub-segments.

Secondary Research

The market size of the companies offering AI in retail solutions to various end users was arrived at based on the secondary data available through paid and unpaid sources and by analyzing the product portfolios of major companies in the ecosystem and rating the companies based on their performance and quality. In the secondary research process, various sources were referred to identify and collect information for the study. The secondary sources included annual reports, press releases, investor presentations of companies, white papers, journals, certified publications, and articles from recognized authors, directories, and databases.

Secondary research was mainly used to obtain critical information about industry insights, the market’s monetary chain, the overall pool of key players, market classification and segmentation according to industry trends to the bottom-most level, regional markets, and key developments from both market-oriented and technology-oriented perspectives.

Primary Research

In the primary research process, various sources from the supply and demand sides were interviewed to obtain qualitative and quantitative information for the report, Such as Chief Experience Officers (CXOs), Vice Presidents (VPs), directors from business development, marketing, and product development/innovation teams, and related key executives from AI in retail solutions vendors, system integrators, professional and managed service providers, industry associations, independent consultants, and key opinion leaders.

Primary interviews were conducted to gather insights, such as market statistics, data on revenue collected from platforms and services, market breakups, market size estimations, market forecasts, and data triangulation. Stakeholders from the demand side, such as Chief Information Officers (CIOs), Chief Finance Officers (CFOs), Chief Strategy Officers (CSOs), and the installation team of end users who use AI in retail solutions, were interviewed to understand buyers’ perspectives on suppliers, products, service providers, and their current usage of AI in retail solutions which is expected to affect the overall AI in retail market growth.

Artificial Intelligence in Retail Market Size, and Share

Note 1: Tier 1 companies have revenues over USD 1 billion, Tier 2 companies range between USD 500 million and 1 billion in overall revenues, and Tier 3 companies have revenues less than USD 500 million.
Other designations include sales managers, marketing managers, and product managers.
Source: Industry Experts

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

Both top-down and bottom-up approaches were used to estimate and validate the total size of the AI in retail market. These methods were also used extensively to estimate the size of various subsegments in the market. The research methodology used to estimate the market size includes the following:

Artificial Intelligence in Retail Market : Top-Down and Bottom-Up Approach

Artificial Intelligence in Retail Market Top Down and Bottom Up Approach

Data Triangulation

After arriving at the overall market size from the above estimation process, the AI in retail market has been split into several segments and sub-segments. To complete the overall market engineering process and arrive at the exact statistics for all segments and sub-segments, data triangulation and market breakdown procedures have been used, wherever applicable. The data has been triangulated by studying various factors and trends from both the demand and supply sides. The AI in retail market size has been validated using top-down and bottom-up approaches.

Market Definition

AI in retail is the use of artificial intelligence algorithms and technologies, such as computer vision, natural language processing, and machine learning, in various aspects of the retail industry. Using artificial intelligence helps to improve the operations’ efficiency, enhance decision-making processes, drive business growth, and improve overall customer experience. AI helps retailers understand customer behavior, optimize supply chains, and improve overall operational efficiency.

Stakeholders

  • Artificial intelligence software developers
  • Cloud service providers
  • Consulting service providers
  • Enterprise end users
  • Distributors and Value-added Resellers (VARs)
  • Government agencies
  • Independent Software Vendors (ISV)
  • Managed service providers
  • Support & maintenance service providers
  • System Integrators (SIs)/Migration service providers
  • Technology providers

Report Objectives

  • To determine, segment, and forecast the AI in Retail market based on offering, type, business functions, end-user, and region
  • To forecast the size of the market segments with respect to five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
  • To provide detailed information about the major factors (drivers, restraints, opportunities, and challenges) influencing the growth of the market
  • To study the complete value chain and related industry segments and perform a value chain analysis of the market landscape
  • To strategically analyze the macro and micromarkets with respect to individual growth trends, prospects, and contributions to the total market
  • To analyze the industry trends, pricing data, patents, and innovations related to the market
  • To analyze the opportunities for stakeholders by identifying the high-growth segments of the market
  • To profile the key players in the market and comprehensively analyze their market share/ranking and core competencies
  • To track and analyze competitive developments, such as mergers & acquisitions, product launches & developments, partnerships, and collaborations, in the market

Available Customizations

With the given market data, MarketsandMarkets offers customizations per the company’s specific needs. The following customization options are available for the report:

Geographic Analysis

  • Further break-up of the Asia Pacific market into countries contributing 75% to the regional market size
  • Further break-up of the North American market into countries contributing 75% to the regional market size
  • Further break-up of the Latin American market into countries contributing 75% to the regional market size
  • Further break-up of the Middle East African market into countries contributing 75% to the regional market size
  • Further break-up of the European market into countries contributing 75% to the regional market size

Company Information

  • Detailed analysis and profiling of additional market players (up to 5)

Previous Versions of this Report

Artificial Intelligence in Retail Market by Solution (Personalized Product Recommendation, Visual Search, Virtual Stores, Virtual Customer Assistant, CRM), Type (Generative AI, Other AI), End-user (Online, Offline) - Global Forecast to 2030

Report Code TC 5669
Published in Oct, 2024, By MarketsandMarkets™

Artificial Intelligence in Retail Market by Type (Online, Offline), Technology (Machine Learning and Deep Learning, NLP), Solution, Service (Professional, Managed), Deployment Mode (Cloud, On-Premises), Application, Region - Global Forecast to 2022

Report Code TC 5669
Published in Oct, 2017, By MarketsandMarkets™

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