Global Artificial Intelligence in Retail Market Set to Reach USD 36.87 Billion by 2030, Driven by 23.87% CAGR Growth from 2024

Summary: The Global Artificial Intelligence in Retail Market, valued at USD 8.24 Billion in 2023, is poised for remarkable growth, with projections indicating a surge to nearly USD 36.87 Billion by 2030. This growth is fueled by a compound annual growth rate (CAGR) of 23.87% from 2024 to 2030. In an increasingly competitive landscape, businesses are leveraging AI-driven insights to enhance customer experiences, optimize operations, and stay ahead of market trends.

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A) Market Size

The Global Artificial Intelligence in Retail Market was valued at USD 8.24 Billion in 2023. With an expected CAGR of 23.87% from 2024 to 2030, the market is anticipated to reach approximately USD 36.87 Billion by 2030. This growth reflects the increasing adoption of AI technologies in the retail sector, enabling businesses to streamline operations, improve customer service, and enhance decision-making processes.

B) Scope and Research Methodology

This comprehensive report delves into the dynamics of the Global Artificial Intelligence in Retail Market, analyzing current trends, challenges, and opportunities. Utilizing both qualitative and quantitative research methodologies, the report draws on data from a range of reputable sources, ensuring a thorough understanding of the market landscape.

C) Report Coverage

The report provides a detailed examination of the Global Artificial Intelligence in Retail Market, covering aspects such as market size, growth trends, competitive landscape, and key players. It also explores regional market variations and the impact of economic and technological factors on market growth.

D) Overview

As retail continues to evolve, the integration of Artificial Intelligence has become crucial for businesses seeking to enhance operational efficiency and customer engagement. The market encompasses various applications, including predictive merchandising, programmatic advertising, and customer relationship management, contributing to its substantial growth potential.

E) Dynamics

The growth of the Global Artificial Intelligence in Retail Market is driven by several factors:

  • Increased Demand for Personalized Experiences: Retailers are adopting AI solutions to analyze customer data and provide tailored experiences, leading to higher customer satisfaction and retention.
  • Cost Efficiency: AI technologies help streamline operations, reduce costs, and optimize inventory management.
  • Technological Advancements: Continuous innovations in AI, such as machine learning and natural language processing, are enhancing the capabilities of retail businesses.

F) Segmentation

The market can be segmented as follows:

  • By Type: Online, Offline
  • By Solution: Product Recommendation & Planning, Customer Relationship Management, Visual Search, Virtual Assistant, Price Optimization, Payment Services Management, Supply Chain Management & Demand Planning, Others
  • By Technology: Machine Learning & Deep Learning, Natural Language Processing, Others
  • By Deployment Mode: Cloud, On-Premises
  • By Application: Predictive Merchandising, Programmatic Advertising, Market Forecasting, In-Store Visual Monitoring and Surveillance, Location-Based Marketing, Others

G) Key Players Include

Prominent players in the Global Artificial Intelligence in Retail market include:

  1. IBM Corporation
  2. Microsoft Corporation
  3. Amazon Web Services
  4. Oracle
  5. SAP
  6. Google Inc.
  7. NVIDIA Corporation
  8. Intel Corporation
  9. Sentient Technologies
  10. Numenta Inc.
  11. Salesforce
  12. ViSenze
  13. Inbenta Technologies
  14. Interactions LLC
  15. Lexalytics Inc.

H) Key Offerings

The report highlights key offerings from various companies, including innovative AI solutions tailored for the retail industry, enhancing product recommendations, optimizing supply chain operations, and improving customer engagement strategies.

I) Table of Contents

  1. Introduction
  2. Market Overview
  3. Market Dynamics
  4. Market Segmentation
  5. Competitive Landscape
  6. Regional Analysis
  7. Key Findings
  8. Conclusion

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