Applied AI in Retail & E-commerce Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented, By Technology (Machine Learning, Natural Language Processing (NLP), Computer Vision, Speech Recognition, and Predictive Analytic), By Application (Customer Service & Support, Sales & Marketing, Supply Chain Management, Price Optimization, Payment Processing, and Product Search & Discovery), By Deployment (On-premises, and Cloud-Based), By End-User (Retailers, E-commerce Platforms, Consumer Goods Manufacturers, Logistics & Supply Chain Companies), By Region, By Competition, 2019-2029F

  • Industry : ICT
  • Pages : NA
  • Published Date : NA
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Summary

Report Description

Forecast Period

2025-2029

Market Size (2023)

USD 34.21 Billion

Market Size (2029)

USD 81.44 Billion

CAGR (2024-2029)

15.38%

Fastest Growing Segment

Natural Language Processing (NLP)

Largest Market

North America

Market Overview

Global Applied AI in Retail & E-commerce Market was valued at USD 34.21 billion in 2023 and is expected to reach USD 81.44 billion by 2029 with a CAGR of 15.38% during the forecast period. The Global Applied AI in Retail & E-commerce Market refers to the integration and utilization of advanced artificial intelligence technologies specifically tailored to enhance various aspects of the retail & e-commerce sector. This market encompasses a wide range of AI-driven applications and tools designed to optimize business processes, improve customer experience, and drive operational efficiency in online retail environments. These applications include personalized recommendation systems, predictive analytic, chatbots, virtual assistants, dynamic pricing models, inventory management solutions, and supply chain optimization, among others. The adoption of AI in retail & e-commerce is driven by the increasing demand for personalized shopping experiences, the need for real-time data analytic, and the growing importance of operational efficiency in a highly competitive market. 

Key Market Drivers

Enhancing Customer Personalization through Applied AI in Retail & E-Commerce

The application of applied AI in retail & e-commerce has revolutionized customer personalization, significantly enhancing the online shopping experience. In an increasingly competitive market, personalization has become a key differentiator, enabling retailers to cater to the unique preferences and behaviors of individual customers. AI-driven algorithms analyze vast amounts of customer data, including browsing history, purchase patterns, and even social media activity, to create highly personalized shopping experiences. By leveraging machine learning models, retailers can predict customer preferences and recommend products with a high degree of accuracy. This level of personalization not only improves customer satisfaction but also drives higher conversion rates. For example, recommendation engines powered by AI can suggest products that a customer is likely to purchase based on their past behavior, increasing the likelihood of a sale. Moreover, AI can personalize marketing messages, ensuring that customers receive targeted offers and promotions that are relevant to their interests. This reduces the likelihood of customers feeling overwhelmed by irrelevant content, which can often lead to disengagement. AI-driven personalization also extends to pricing strategies, where dynamic pricing models can adjust prices in real-time based on customer behavior, competitor pricing, and market demand. This level of personalization helps retailers optimize revenue while providing customers with competitive pricing. Furthermore, AI enables retailers to offer personalized customer service through chatbots and virtual assistants, which can handle queries and provide recommendations in real-time, enhancing the overall customer experience. As consumers increasingly expect personalized experiences, the ability of AI to deliver these experiences at scale has become a crucial driver of growth in the retail & e-commerce market. Retailers who effectively harness AI for personalization are not only able to attract and retain customers more effectively but also differentiate themselves in a crowded market, ultimately driving higher sales and customer loyalty.

Optimizing Supply Chain and Inventory Management through AI in Retail & E-Commerce

The application of applied AI in retail & e-commerce has become a critical driver in optimizing supply chain and inventory management, which are essential components for maintaining efficiency and profitability in the highly competitive e-commerce landscape. Traditional supply chain management processes often struggle to keep up with the dynamic and fast-paced nature of online retail, where consumer demands and market conditions can change rapidly. AI-driven technologies address these challenges by providing retailers with advanced predictive analytic and real-time insights into their supply chains. By analyzing historical sales data, market trends, and external factors such as weather patterns or economic conditions, AI algorithms can accurately forecast demand, allowing retailers to optimize their inventory levels. This ensures that popular products are always in stock while minimizing excess inventory that ties up capital and space. Additionally, AI enhances the efficiency of order fulfillment by automating warehouse operations and optimizing logistics. For instance, AI-powered robots and automated picking systems can streamline the process of order assembly, reducing the time and cost associated with manual labor. In logistics, AI algorithms can optimize delivery routes based on real-time traffic data, weather conditions, and delivery priorities, ensuring that customers receive their orders as quickly as possible. This level of optimization not only improves customer satisfaction by reducing delivery times but also lowers operational costs for retailers. Furthermore, AI helps in identifying potential disruptions in the supply chain, such as supplier delays or transportation issues, allowing retailers to take proactive measures to mitigate these risks. In an industry where customer expectations for fast and reliable delivery are constantly rising, the ability to optimize supply chain and inventory management through AI is a significant competitive advantage. Retailers who leverage AI to streamline their operations can meet customer demands more effectively, reduce operational costs, and ultimately drive higher profitability in the retail & e-commerce market.

Enhancing Customer Engagement and Retention through AI in Retail & E-Commerce

AI has become a transformative force in enhancing customer engagement and retention in the retail & e-commerce sector, playing a crucial role in driving business growth. In a market where consumers have numerous options at their fingertips, retaining customers and fostering long-term loyalty has become increasingly challenging. AI-driven tools and technologies are addressing this challenge by enabling retailers to engage with customers in more meaningful and personalized ways. One of the primary ways AI enhances customer engagement is through the use of chatbots and virtual assistants, which provide instant, 24/7 customer support. These AI-powered tools can handle a wide range of customer queries, from product information to order tracking, without the need for human intervention. This not only improves the customer experience by providing quick and accurate responses but also frees up human resources for more complex tasks. Moreover, AI helps in analyzing customer feedback and sentiment across various channels, including social media, reviews, and direct interactions. By understanding customer sentiment, retailers can identify areas for improvement and proactively address any issues before they escalate, thereby improving customer satisfaction and loyalty. AI also plays a vital role in creating personalized marketing campaigns that resonate with individual customers. By analyzing customer behavior and preferences, AI can help retailers craft targeted email campaigns, personalized offers, and tailored content that appeal to specific segments of their customer base. This targeted approach increases the likelihood of customer engagement and conversion, as customers are more likely to respond positively to content that aligns with their interests. Additionally, AI-driven loyalty programs can be designed to reward customers based on their unique shopping habits, further incentivizing repeat purchases. In a competitive market, the ability to engage and retain customers through personalized, AI-driven interactions is a key driver of success. Retailers who effectively use AI to enhance customer engagement are better positioned to build strong customer relationships, reduce churn, and ultimately increase lifetime customer value in the retail & e-commerce market.


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Key Market Challenges

Data Privacy and Security Concerns

The implementation of applied AI in retail & e-commerce market brings significant challenges related to data privacy and security, which is a critical concern for businesses and consumers alike. As AI technologies increasingly rely on vast amounts of consumer data to optimize personalization, inventory management, and customer service, the risk of data breaches and misuse escalates. The collection and analysis of sensitive personal information, such as purchasing habits, payment details, and browsing history, expose consumers to potential privacy violations if not properly managed. Moreover, the introduction of AI-driven systems necessitates robust cybersecurity measures to prevent unauthorized access and protect against sophisticated cyber-attacks targeting these valuable data repositories. Retailers face the challenge of ensuring compliance with stringent data protection regulations like the General Data Protection Regulation (GDPR) in Europe, which imposes heavy fines for non-compliance. Implementing AI while maintaining transparency in data usage is also essential to building and retaining consumer trust. However, achieving this balance is difficult as AI models often operate as "black boxes," making it challenging to explain how decisions are made. This lack of transparency can lead to consumer skepticism and resistance to AI adoption, hindering the technology's full potential in the market. Additionally, the dynamic nature of e-commerce, where consumer preferences and behaviors are constantly evolving, necessitates continuous updates to AI algorithms to avoid bias and discrimination. These updates, if not handled correctly, can inadvertently introduce new vulnerabilities, making the system susceptible to manipulation or unintended consequences. Retailers must invest in cutting-edge security technologies, workforce training, and ethical AI practices to address these challenges. Failure to do so could result in severe reputational damage, loss of consumer trust, and significant financial penalties, all of which pose substantial barriers to the broader adoption of AI in retail & e-commerce.

Integration Complexity and Operational Disruption

Integrating applied AI into the existing retail & e-commerce infrastructure poses a significant challenge due to the complexity and potential for operational disruption. Retailers, especially those with established systems and processes, face difficulties in seamlessly incorporating AI-driven solutions into their operations without causing disruptions. The challenge begins with the integration of AI technologies with legacy systems, which may not be designed to handle the advanced computational requirements of AI algorithms. This can lead to compatibility issues, where data from different sources must be harmonized to feed into AI models accurately. The process of migrating to AI-powered systems requires significant time, resources, and expertise, often involving a complete overhaul of existing IT infrastructure. Additionally, AI adoption may necessitate rethinking organizational workflows, as traditional processes may no longer align with AI-driven operations. This realignment can be met with resistance from employees who may feel threatened by the technology, fearing job displacement or a significant change in their roles. Furthermore, AI systems often require large volumes of high-quality data to function optimally, but data inconsistencies, silos, or lack of real-time data access can hinder AI performance. Retailers must also grapple with the challenge of scaling AI solutions across multiple channels, ensuring that the AI system can handle the demands of an omnichannel retail environment without causing operational slowdowns or inefficiencies. Another layer of complexity arises from the need for continuous monitoring and maintenance of AI systems to ensure they adapt to changing market conditions and consumer behaviors. Without regular updates and fine-tuning, AI models can become obsolete or produce inaccurate results, leading to poor decision-making. Retailers must, therefore, invest in ongoing AI management and ensure that their workforce is adequately trained to work alongside AI tools, which can strain resources and disrupt normal business operations. Balancing the benefits of AI with the challenges of integration and potential operational disruptions remains a significant hurdle for retailers looking to leverage AI in the competitive e-commerce landscape.

Key Market Trends

AI-Powered Visual Search and Augmented Reality in Retail & E-Commerce

Another major trend in the applied AI in retail & e-commerce market is the adoption of AI-powered visual search and augmented reality (AR) technologies, which are transforming the online shopping experience. Visual search allows customers to search for products using images rather than text, significantly improving the accuracy and relevance of search results. This technology is particularly beneficial in fashion and home decor, where customers often have a specific style or design in mind but may struggle to describe it in words. AI algorithms analyze the uploaded images, identify the objects or patterns within them, and match them with similar products available in the retailer’s catalog. This enhances the customer’s ability to find exactly what they are looking for, reducing friction in the shopping journey. Augmented reality, on the other hand, allows customers to virtually try on products, such as clothing, accessories, or even furniture, in their own space before making a purchase. This interactive experience not only increases customer confidence in their purchasing decisions but also reduces return rates, a significant challenge in e-commerce. Retailers are increasingly integrating AI and AR technologies into their platforms to create immersive and engaging shopping experiences that bridge the gap between online and in-store shopping. As these technologies become more sophisticated and accessible, they are likely to become a standard feature in e-commerce, reshaping how customers discover, interact with, and purchase products online. The trend towards AI-powered visual search and AR is not only enhancing customer satisfaction but also providing retailers with valuable insights into consumer preferences and behavior, further driving the adoption of AI in retail & e-commerce.

Segmental Insights

Technology Insights

The Machine Learning segment held the largest Market share in 2023. The application of applied AI in retail & e-commerce market, particularly in the Machine Learning (ML) segment, is driving significant market growth due to its transformative impact on customer experience, operational efficiency, and revenue optimization. Machine Learning algorithms enable e-commerce platforms to deliver highly personalized shopping experiences by analyzing vast amounts of customer data, including browsing behavior, purchase history, and social media activity. This capability allows retailers to create tailored product recommendations, targeted marketing campaigns, and dynamic pricing strategies that resonate with individual customers, thereby increasing conversion rates and customer loyalty. Additionally, ML-driven demand forecasting and inventory management systems help retailers optimize their supply chains, reduce stockouts, and minimize excess inventory, leading to cost savings and improved profitability. The ability to predict customer demand with greater accuracy also enables more efficient allocation of resources and better management of logistics, which is crucial in the highly competitive e-commerce landscape.

Machine Learning enhances fraud detection and prevention in online transactions, safeguarding both retailers and consumers from potential threats. As e-commerce continues to grow, the integration of ML tools in managing large volumes of transactions and customer interactions will be essential in scaling operations without compromising service quality. The ongoing advancements in Machine Learning, coupled with its ability to process and learn from unstructured data, such as images and text, are also paving the way for innovative applications like visual search and AI-driven customer service through chatbots and virtual assistants. These innovations not only improve the customer journey but also enable retailers to gather deeper insights into consumer preferences and trends. The rising demand for seamless and personalized shopping experiences, along with the need for operational efficiency and robust security measures, is expected to drive the adoption of Machine Learning in the applied AI in retail & e-commerce market, further fueling its growth in the coming years.

 

 


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Regional Insights

North America region held the largest market share in 2023.  The applied AI in retail & e-commerce market in North America is driven by several key factors that are reshaping the landscape of online retail. One of the primary drivers is the increasing consumer demand for personalized shopping experiences, which AI can deliver through advanced data analytic and machine learning algorithms. Retailers are leveraging AI to analyze vast amounts of customer data, enabling them to tailor product recommendations, optimize pricing strategies, and enhance customer engagement. This personalized approach not only improves customer satisfaction but also increases conversion rates and boosts sales.  AI-powered chatbots and virtual assistants are transforming customer service by providing instant, 24/7 support, resolving queries, and guiding customers through their shopping journeys. The rapid growth of mobile commerce in North America further fuels the adoption of AI, as retailers seek to offer seamless, intuitive, and user-friendly experiences across multiple platforms.

AI-driven inventory management systems are becoming crucial for retailers to optimize their supply chains, reduce costs, and ensure timely delivery of products, which is particularly important in a highly competitive market. The integration of AI in e-commerce platforms also supports dynamic pricing, enabling retailers to adjust prices in real-time based on market demand, competitor pricing, and other external factors, thus maximizing profitability. Furthermore, the rise of voice commerce, driven by the proliferation of smart speakers and voice-activated assistants, is pushing retailers to adopt AI technologies that enhance voice search capabilities and improve the overall shopping experience. In the context of cybersecurity, AI plays a pivotal role in protecting e-commerce platforms from fraud and cyber-attacks by detecting suspicious activities and anomalies in real-time, thereby safeguarding both retailers and consumers.

The North American market is also influenced by significant investments in AI research and development, with tech giants and startups alike driving innovation in AI applications for retail & e-commerce. Government support for AI initiatives and favorable regulatory environments further contribute to the market's growth. The COVID-19 pandemic has accelerated the digital transformation of retail, with AI becoming an essential tool for retailers to adapt to changing consumer behaviors, manage disruptions in supply chains, and enhance operational efficiency. As consumer expectations continue to evolve, the need for hyper-personalized experiences, efficient operations, and robust security measures will continue to drive the adoption of applied AI in North American retail & e-commerce market, positioning it as a critical component of the industry's future.


Recent Developments

  • In August 2024, Amazon introduced Rufus, an AI-driven shopping assistant chatbot, for Indian shoppers. Rufus provides personalized product recommendations, offers advice, delivers updates, and helps users understand their specific needs while shopping on Amazon. 
  • In April 2024, SAP Emarsys unveiled its AI Product Finder, responding to a surge in demand for AI-driven solutions in the retail sector. This innovative tool leverages advanced artificial intelligence to enhance the shopping experience, allowing retailers to offer more personalized product recommendations and streamline customer interactions. As businesses increasingly recognize the value of AI in optimizing sales and improving customer satisfaction, the AI Product Finder positions SAP Emarsys at the forefront of this retail transformation, empowering brands to meet evolving consumer expectations with greater efficiency and insight.

Key Market Players

  • NVIDIA Corporation
  • Alphabet Inc.
  • Microsoft Corporation
  • IBM Corporation
  • Salesforce Inc.
  • Oracle Corporation
  • SAP SE
  • Adobe Inc.,
  • Alibaba Cloud International
  • Clarifai, Inc.

 

 

By Technology

By Application

By Deployment

By End-User

By Region
  • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Speech Recognition
  • Predictive Analytic
  • Customer Service & Support
  • Sales & Marketing
  • Supply Chain Management
  • Price Optimization
  • Payment Processing
  • Product Search & Discovery
  • On-premises
  • Cloud-Based
  • Retailers
  • E-commerce Platforms
  • Consumer Goods Manufacturers
  • Logistics & Supply Chain Companies
  • North America
  • Europe
  • Asia Pacific
  • South America
  • Middle East & Africa

 

Report Scope:

In this report, the Global Applied AI in Retail & E-commerce Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

  • Applied AI in Retail & E-commerce Market, By Technology:

o   Machine Learning

o   Natural Language Processing (NLP)

o   Computer Vision

o   Speech Recognition

o   Predictive Analytic   

  • Applied AI in Retail & E-commerce Market, By Application:

o   Customer Service & Support

o   Sales & Marketing

o   Supply Chain Management

o   Price Optimization

o   Payment Processing

o   Product Search & Discovery

  • Applied AI in Retail & E-commerce Market, By Deployment:

o   On-Premises

o   Cloud-Based

  • Applied AI in Retail & E-commerce Market, By End-User:

o    Retailers

o   E-commerce Platforms

o   Consumer Goods Manufacturers

o   Logistics & Supply Chain Companies

  • Applied AI in Retail & E-commerce Market, By Region:

o   North America

§  United States

§  Canada

§  Mexico

o   Europe

§  France

§  United Kingdom

§  Italy

§  Germany

§  Spain

o   Asia-Pacific

§  China

§  India

§  Japan

§  Australia

§  South Korea

o   South America

§  Brazil

§  Argentina

§  Colombia

o   Middle East & Africa

§  South Africa

§  Saudi Arabia

§  UAE

§  Kuwait

§  Turkey

Competitive Landscape

Company Profiles: Detailed analysis of the major companies presents in the Global Applied AI in Retail & E-commerce Market.

Available Customizations:

Global Applied AI in Retail & E-commerce Market report with the given Market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional Market players (up to five).

Global Applied AI in Retail & E-commerce Market is an upcoming report to be released soon. If you wish an early delivery of this report or want to confirm the date of release, please contact us at [email protected]  

Table of content

1.     Product Overview

1.1.   Market Definition

1.2.   Scope of the Market

1.2.1. Markets Covered

1.2.2. Years Considered for Study

1.3.   Key Market Segmentations

2.     Research Methodology

2.1.   Objective of the Study

2.2.   Baseline Methodology

2.3.   Formulation of the Scope

2.4.   Assumptions and Limitations

2.5.   Sources of Research

2.5.1. Secondary Research

2.5.2. Primary Research

2.6.   Approach for the Market Study

2.6.1. The Bottom-Up Approach

2.6.2. The Top-Down Approach

2.7.   Methodology Followed for Calculation of Market Size & Market Shares

2.8.   Forecasting Methodology

2.8.1. Data Triangulation & Validation

3.     Executive Summary

4.     Voice of Customer

5.     Global Applied AI in Retail & E-commerce Market Outlook

5.1.   Market Size & Forecast

5.1.1. By Value

5.2.   Market Share & Forecast

5.2.1. By Technology (Machine Learning, Natural Language Processing (NLP), Computer Vision, Speech Recognition, and Predictive Analytic)

5.2.2. By Application (Customer Service & Support, Sales & Marketing, Supply Chain Management, Price Optimization, Payment Processing, and Product Search & Discovery)

5.2.3. By Deployment (On-Premises, and Cloud-Based)

5.2.4. By End-User (Retailers, E-commerce Platforms, Consumer Goods Manufacturers, Logistics & Supply Chain Companies)

5.2.5. By Company (2023)

5.2.6. By Region

5.3. Market Map

6.     North America Applied AI in Retail & E-commerce Market Outlook

6.1.   Market Size & Forecast    

6.1.1. By Value

6.2.   Market Share & Forecast

6.2.1. By Technology

6.2.2. By Application

6.2.3. By Deployment

6.2.4. By End-User

6.2.5. By Country

6.3.   North America: Country Analysis

6.3.1. United States Applied AI in Retail & E-commerce Market Outlook

6.3.1.1.               Market Size & Forecast

6.3.1.1.1.                By Value

6.3.1.2.               Market Share & Forecast

6.3.1.2.1.                By Technology

6.3.1.2.2.                By Application

6.3.1.2.3.                By Deployment

6.3.1.2.4.                By End-User

6.3.2. Canada Applied AI in Retail & E-commerce Market Outlook

6.3.2.1.               Market Size & Forecast

6.3.2.1.1.                By Value

6.3.2.2.               Market Share & Forecast

6.3.2.2.1.                By Technology

6.3.2.2.2.                By Application

6.3.2.2.3.                By Deployment

6.3.2.2.4.                By End-User

6.3.3. Mexico Applied AI in Retail & E-commerce Market Outlook

6.3.3.1.               Market Size & Forecast

6.3.3.1.1.                By Value

6.3.3.2.               Market Share & Forecast

6.3.3.2.1.                By Technology

6.3.3.2.2.                By Application

6.3.3.2.3.                By Deployment

6.3.3.2.4.                By End-User

7.     Europe Applied AI in Retail & E-commerce Market Outlook

7.1.   Market Size & Forecast    

7.1.1. By Value

7.2.   Market Share & Forecast

7.2.1. By Technology

7.2.2. By Application

7.2.3. By Deployment

7.2.4. By End-User

7.2.5. By Country

7.3.   Europe: Country Analysis

7.3.1. Germany Applied AI in Retail & E-commerce Market Outlook

7.3.1.1.               Market Size & Forecast

7.3.1.1.1.                By Value

7.3.1.2.               Market Share & Forecast

7.3.1.2.1.                By Technology

7.3.1.2.2.                By Application

7.3.1.2.3.                By Deployment

7.3.1.2.4.                By End-User

7.3.2. United Kingdom Applied AI in Retail & E-commerce Market Outlook

7.3.2.1.               Market Size & Forecast

7.3.2.1.1.                By Value

7.3.2.2.               Market Share & Forecast

7.3.2.2.1.                By Technology

7.3.2.2.2.                By Application

7.3.2.2.3.                By Deployment

7.3.2.2.4.                By End-User

7.3.3. Italy Applied AI in Retail & E-commerce Market Outlook

7.3.3.1.               Market Size & Forecast

7.3.3.1.1.                By Value

7.3.3.2.               Market Share & Forecast

7.3.3.2.1.                By Technology

7.3.3.2.2.                By Application

7.3.3.2.3.                By Deployment

7.3.3.2.4.                By End-User

7.3.4. France Applied AI in Retail & E-commerce Market Outlook

7.3.4.1.               Market Size & Forecast

7.3.4.1.1.                By Value

7.3.4.2.               Market Share & Forecast

7.3.4.2.1.                By Technology

7.3.4.2.2.                By Application

7.3.4.2.3.                By Deployment

7.3.4.2.4.                By End-User

7.3.5. Spain Applied AI in Retail & E-commerce Market Outlook

7.3.5.1.               Market Size & Forecast

7.3.5.1.1.                By Value

7.3.5.2.               Market Share & Forecast

7.3.5.2.1.                By Technology

7.3.5.2.2.                By Application

7.3.5.2.3.                By Deployment

7.3.5.2.4.                By End-User

8.     Asia-Pacific Applied AI in Retail & E-commerce Market Outlook

8.1.   Market Size & Forecast    

8.1.1. By Value

8.2.   Market Share & Forecast

8.2.1. By Technology

8.2.2. By Application

8.2.3. By Deployment

8.2.4. By End-User

8.2.5. By Country

8.3.   Asia-Pacific: Country Analysis

8.3.1. China Applied AI in Retail & E-commerce Market Outlook

8.3.1.1.               Market Size & Forecast

8.3.1.1.1.                By Value

8.3.1.2.               Market Share & Forecast

8.3.1.2.1.                By Technology

8.3.1.2.2.                By Application

8.3.1.2.3.                By Deployment

8.3.1.2.4.                By End-User

8.3.2. India Applied AI in Retail & E-commerce Market Outlook

8.3.2.1.               Market Size & Forecast

8.3.2.1.1.                By Value

8.3.2.2.               Market Share & Forecast

8.3.2.2.1.                By Technology

8.3.2.2.2.                By Application

8.3.2.2.3.                By Deployment

8.3.2.2.4.                By End-User

8.3.3. Japan Applied AI in Retail & E-commerce Market Outlook

8.3.3.1.               Market Size & Forecast

8.3.3.1.1.                By Value

8.3.3.2.               Market Share & Forecast

8.3.3.2.1.                By Technology

8.3.3.2.2.                By Application

8.3.3.2.3.                By Deployment

8.3.3.2.4.                By End-User

8.3.4. South Korea Applied AI in Retail & E-commerce Market Outlook

8.3.4.1.               Market Size & Forecast

8.3.4.1.1.                By Value

8.3.4.2.               Market Share & Forecast

8.3.4.2.1.                By Technology

8.3.4.2.2.                By Application

8.3.4.2.3.                By Deployment

8.3.4.2.4.                By End-User

8.3.5. Australia Applied AI in Retail & E-commerce Market Outlook

8.3.5.1.               Market Size & Forecast

8.3.5.1.1.                By Value

8.3.5.2.               Market Share & Forecast

8.3.5.2.1.                By Technology

8.3.5.2.2.                By Application

8.3.5.2.3.                By Deployment

8.3.5.2.4.                By End-User

9.     South America Applied AI in Retail & E-commerce Market Outlook

9.1.   Market Size & Forecast    

9.1.1. By Value

9.2.   Market Share & Forecast

9.2.1. By Technology

9.2.2. By Application

9.2.3. By Deployment

9.2.4. By End-User

9.2.5. By Country

9.3.   South America: Country Analysis

9.3.1. Brazil Applied AI in Retail & E-commerce Market Outlook

9.3.1.1.               Market Size & Forecast

9.3.1.1.1.                By Value

9.3.1.2.               Market Share & Forecast

9.3.1.2.1.                By Technology

9.3.1.2.2.                By Application

9.3.1.2.3.                By Deployment

9.3.1.2.4.                By End-User

9.3.2. Argentina Applied AI in Retail & E-commerce Market Outlook

9.3.2.1.               Market Size & Forecast

9.3.2.1.1.                By Value

9.3.2.2.               Market Share & Forecast

9.3.2.2.1.                By Technology

9.3.2.2.2.                By Application

9.3.2.2.3.                By Deployment

9.3.2.2.4.                By End-User

9.3.3. Colombia Applied AI in Retail & E-commerce Market Outlook

9.3.3.1.               Market Size & Forecast

9.3.3.1.1.                By Value

9.3.3.2.               Market Share & Forecast

9.3.3.2.1.                By Technology

9.3.3.2.2.                By Application

9.3.3.2.3.                By Deployment

9.3.3.2.4.                By End-User

10.  Middle East and Africa Applied AI in Retail & E-commerce Market Outlook

10.1.    Market Size & Forecast 

10.1.1.         By Value

10.2.    Market Share & Forecast

10.2.1.         By Technology

10.2.2.         By Application

10.2.3.         By Deployment

10.2.4.         By End-User

10.2.5.         By Country

10.3.    Middle East and Africa: Country Analysis

10.3.1.         South Africa Applied AI in Retail & E-commerce Market Outlook

10.3.1.1.             Market Size & Forecast

10.3.1.1.1.             By Value

10.3.1.2.             Market Share & Forecast

10.3.1.2.1.             By Technology

10.3.1.2.2.             By Application

10.3.1.2.3.             By Deployment

10.3.1.2.4.             By End-User

10.3.2.         Saudi Arabia Applied AI in Retail & E-commerce Market Outlook

10.3.2.1.             Market Size & Forecast

10.3.2.1.1.             By Value

10.3.2.2.             Market Share & Forecast

10.3.2.2.1.             By Technology

10.3.2.2.2.             By Application

10.3.2.2.3.             By Deployment

10.3.2.2.4.             By End-User

10.3.3.         UAE Applied AI in Retail & E-commerce Market Outlook

10.3.3.1.             Market Size & Forecast

10.3.3.1.1.             By Value

10.3.3.2.             Market Share & Forecast

10.3.3.2.1.             By Technology

10.3.3.2.2.             By Application

10.3.3.2.3.             By Deployment

10.3.3.2.4.             By End-User

10.3.4.         Kuwait Applied AI in Retail & E-commerce Market Outlook

10.3.4.1.             Market Size & Forecast

10.3.4.1.1.             By Value

10.3.4.2.             Market Share & Forecast

10.3.4.2.1.             By Technology

10.3.4.2.2.             By Application

10.3.4.2.3.             By Deployment

10.3.4.2.4.             By End-User

10.3.5.         Turkey Applied AI in Retail & E-commerce Market Outlook

10.3.5.1.             Market Size & Forecast

10.3.5.1.1.             By Value

10.3.5.2.             Market Share & Forecast

10.3.5.2.1.             By Technology

10.3.5.2.2.             By Application

10.3.5.2.3.             By Deployment

10.3.5.2.4.             By End-User

11.  Market Dynamics

11.1.    Drivers

11.2.    Challenges

12.  Market Trends & Developments

13.  Company Profiles

13.1.    NVIDIA Corporation

13.1.1.         Business Overview

13.1.2.         Key Revenue and Financials 

13.1.3.         Recent Developments

13.1.4.         Key Personnel/Key Contact Person

13.1.5.         Key Product/Services Offered

13.2.    Alphabet Inc.

13.2.1.         Business Overview

13.2.2.         Key Revenue and Financials 

13.2.3.         Recent Developments

13.2.4.         Key Personnel/Key Contact Person

13.2.5.         Key Product/Services Offered

13.3.    Microsoft Corporation

13.3.1.         Business Overview

13.3.2.         Key Revenue and Financials 

13.3.3.         Recent Developments

13.3.4.         Key Personnel/Key Contact Person

13.3.5.         Key Product/Services Offered

13.4.    IBM Corporation

13.4.1.         Business Overview

13.4.2.         Key Revenue and Financials 

13.4.3.         Recent Developments

13.4.4.         Key Personnel/Key Contact Person

13.4.5.         Key Product/Services Offered

13.5.    Salesforce Inc.

13.5.1.         Business Overview

13.5.2.         Key Revenue and Financials 

13.5.3.         Recent Developments

13.5.4.         Key Personnel/Key Contact Person

13.5.5.         Key Product/Services Offered

13.6.    Oracle Corporation

13.6.1.         Business Overview

13.6.2.         Key Revenue and Financials 

13.6.3.         Recent Developments

13.6.4.         Key Personnel/Key Contact Person

13.6.5.         Key Product/Services Offered

13.7.    SAP SE  

13.7.1.         Business Overview

13.7.2.         Key Revenue and Financials 

13.7.3.         Recent Developments

13.7.4.         Key Personnel/Key Contact Person

13.7.5.         Key Product/Services Offered

13.8.    Adobe Inc.

13.8.1.         Business Overview

13.8.2.         Key Revenue and Financials 

13.8.3.         Recent Developments

13.8.4.         Key Personnel/Key Contact Person

13.8.5.         Key Product/Services Offered

13.9.    Alibaba Cloud International

13.9.1.         Business Overview

13.9.2.         Key Revenue and Financials 

13.9.3.         Recent Developments

13.9.4.         Key Personnel/Key Contact Person

13.9.5.         Key Product/Services Offered

13.10.   Clarifai, Inc.  

13.10.1.      Business Overview

13.10.2.      Key Revenue and Financials 

13.10.3.      Recent Developments

13.10.4.      Key Personnel/Key Contact Person

13.10.5.      Key Product/Services Offered

14.  Strategic Recommendations

15.  About Us & Disclaimer

Figures and Tables

Frequently asked questions

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The Market size of the Global Applied AI in Retail & E-commerce Market was USD 34.21 billion in 2023.

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Machine Learning segment dominated the market in 2023 due to its ability to personalize customer experiences, optimize pricing strategies, and enhance inventory management. Machine learning algorithms analyze vast datasets to predict consumer behavior, improve targeting, and streamline operations, leading to increased sales and customer satisfaction.

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North America region dominated the market in 2023 owing to AI's ability to analyze consumer behavior and optimize pricing strategies significantly boosts sales and customer satisfaction, making it a critical component for e-commerce growth in the region.

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The applied AI in retail & e-commerce market is driven by the increasing demand for personalized shopping experiences, enhanced customer service through chatbots, and efficient inventory management. Advanced AI algorithms enable precise product recommendations and dynamic pricing, boosting customer engagement and operational efficiency, and driving overall market growth.

Related Reports

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Sakshi Bajaal

Business Consultant
Press Release

Applied AI in Retail & E-commerce Market is expected to Grow with a CAGR of 15.38% through 2029

Oct, 2024

The applied AI in the retail & e-commerce market is expected to grow owing to the need for personalized shopping experiences, enhanced customer service through chatbots, data-driven inventory managem

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