Artificial Intelligence in Retail Market Forecast 2020-2025, Latest Trends and Opportunities
Category: #technology |   By Market Study Report |   Date: 2021-02-09  |  Product ID: 1130968 Request Free Sample | 
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Artificial Intelligence in Retail Market Forecast 2020-2025, Latest Trends and Opportunities

Artificial Intelligence in Retail Market Trends covers the companies’ data including Growth potential analysis, Industry segmentation, Business trends, Growth drivers, Price trend analysis. The Report also Gives Detail Analysis on Market concern Like Artificial Intelligence in Retail Market share, CAGR Status, Market demand and up to date Market Trends with key Market segments.

AI in Retail Market is estimated to cross the USD 8 billion mark by the year 2024. The growth of the market is driven by the disruption of the technology in the retail sector. Enterprises are rapidly deploying new technologies to get an edge over competitors and provide a personalized shopping experience to the customers. The growing demand for the enhanced customer experience is also one of the major forces propelling the market growth. As the competition among the enterprises is increasing, companies have started focusing more on providing improved shopping experience to gain customers-loyalty. Furthermore, rising investments in the AI and the development of new business models along with the advances in the data science are some of the major factors that foster the market growth. However, the data privacy and the lack of public private partnership are hindering the growth of the AI in Retail market.

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Recommendation engine is the most used solution among the retailers accounting for more than 35% share in the revenue. The growth of the recommendation engine market is credited to the rising demand for the personalized shopping experience among the customers. Furthermore, the demand for the visual search solutions is estimated to grow at a CAGR of more than 45% during 2018-2024.

The solutions market is estimated to lead the AI in Retail market with over 85% share in the revenue. The growing demand for the advanced analytics solutions among the retailers to extract the consumer data is propelling the growth. Services market is anticipated to grow at a CAGR of over 45% during the forecast timeline. The increasing demand among the retailers for the third-party services is the driving the growth.

Natural Language Processing (NLP) leads AI in Retail market with over 40% share in the revenue. The growing demand to provide the enhanced customer experience is the major force augmenting the market growth. Machine learning and deep learning technology are anticipated to surpass the NLP technology during the forecast period with a CAGR of over 42%. The market is driven by the increasing investments in machine learning and deep learning technology.

North America accounts for over 35% share in the AI in Retail market. The increasing investments in the AI technology, the early adoption, and the presence of tech giants, such as AWS, Microsoft, Google and IBM, in the country also foster the market growth. Asia Pacific AI in Retail market is anticipated to grow significantly with a CAGR of more than 45%. The growth of the market is driven by e-commerce industry in the region. Furthermore, the investment in the AI by the Chinese technology giants Alibaba and Baidu also fuel the market growth.

The companies that serve AI-powered solutions in the retail sector are Google, Microsoft, IBM, AWS, Baidu, Intel, Oracle, SAP, Salesforce.com, Nvidia, Interactions, CognitiveScale, Lexalytics, Inbenta Technologies, NEXT IT, RetailNext, Sentient Technologies, Visenze, and BloomReach.

Report Content Chapter 1. Methodology & Scope 1.1. Methodology 1.1.1. Initial data exploration 1.1.2. Statistical model and forecast 1.1.3. Industry insights and validation 1.1.4. Scope, 1.1.5. Definition 1.1.6. Methodology and research parameters 1.2. Data Sources 1.2.1. Primary 1.2.2. Secondary Chapter 2. Executive Summary 2.1. AI in Retail industry 360º synopsis, 2013 - 2024 2.1.1. Business trends 2.1.2. Regional trends 2.1.3. Component trends 2.1.4. Solution trends 2.1.5. Service trends 2.1.6. Technology trends 2.1.7. Application trends Chapter 3. AI in Retail Industry Insights 3.1. Introduction 3.2. Industry segmentation 3.3. Industry landscape, 2013-2024 3.4. Industry ecosystem analysis 3.5. Industry evolution 3.6. Market news 3.7. Technology & innovation landscape 3.7.1. Gesture recognition 3.7.2. Virtual mirrors 3.7.3. Chatbots 3.7.4. Video analytics 3.7.5. Robots 3.8. Regulatory landscape 3.8.1. Health Insurance Portability and Accountability Act (HIPAA) 3.8.2. Payment Card Industry Data Security Standard (PCI DSS) 3.8.3. North American Electric Reliability Corp. (NERC) Standards 3.8.4. Federal Information Security Management Act (FISMA) 3.8.5. The Gramma-Leach-Bliley Act (GLB) Act of 1999 3.8.6. Sarbanes-Oxley Act of 2002 3.8.7. General Data Protection Regulation (GDPR) 3.9. Use cases 3.9.1. Sales & CRM application 3.9.2. Customer recommendations 3.9.3. Logistics & delivery 3.9.4. Payment service 3.10. Industry impact forces 3.10.1. Growth drivers 3.10.1.1. Growing investment in AI 3.10.1.2. Increasingly empowered consumer 3.10.1.3. Disruptive technologies 3.10.1.4. Advent of new business models 3.10.1.5. Advancement in data science 3.10.2. Industry pitfalls & challenges 3.10.2.1. Limited public-private partnership to address social implications directly 3.10.2.2. Privacy issues associated with the use of AI Growth potential analysis 3.11. Growth potential analysis 3.12. Porter's analysis 3.13. PESTEL analysis Chapter 4. Competitive Landscape 4.1. Introduction 4.2. Company analysis by major market players, 2017 4.2.1. Google Inc. 4.2.2. Microsoft Corporation 4.2.3. IBM Corporation 4.2.4. Amazon Web Services 4.2.5. Salesforce 4.3. Company analysis by innovation leaders, 2017 4.3.1. Inbenta Technologies Inc. 4.3.2. Lexalytics Inc. 4.3.3. Interactions LLC 4.3.4. RetailNext Inc. 4.3.5. Sentinent Technologies 4.4. Other prominent vendors Chapter 5. AI in Retail Market, By Component 5.1. Key trends, by component 5.2. Solution 5.2.1. Market estimates and forecast, 2013 - 2024 5.2.2. Market estimates and forecast, by region, 2013 - 2024 5.2.3. Chatbot 5.2.3.1. Market estimates and forecast, 2013 - 2024 5.2.3.2. Market estimates and forecast, by region, 2013 - 2024 5.2.4. Customer behavior tracking 5.2.4.1. Market estimates and forecast, 2013 - 2024 5.2.4.2. Market estimates and forecast, by region, 2013 - 2024 5.2.5. Customer relationship management (CRM) 5.2.5.1. Market estimates and forecast, 2013 - 2024 5.2.5.2. Market estimates and forecast, by region, 2013 - 2024 5.2.6. Inventory management 5.2.6.1. Market estimates and forecast, 2013 - 2024 5.2.6.2. Market estimates and forecast, by region, 2013 - 2024 5.2.7. Price optimization 5.2.7.1. Market estimates and forecast, 2013 - 2024 5.2.7.2. Market estimates and forecast, by region, 2013 - 2024 5.2.8. Recommendation engines 5.2.8.1. Market estimates and forecast, 2013 - 2024 5.2.8.2. Market estimates and forecast, by region, 2013 - 2024 5.2.9. Supply chain management 5.2.9.1. Market estimates and forecast, 2013 - 2024 5.2.9.2. Market estimates and forecast, by region, 2013 - 2024 5.2.10. Visual search 5.2.10.1. Market estimates and forecast, 2013 - 2024 5.2.10.2. Market estimates and forecast, by region, 2013 - 2024 5.2.11. Others 5.2.11.1. Market estimates and forecast, 2013 - 2024 5.2.11.2. Market estimates and forecast, by region, 2013 - 2024 5.3. Service 5.3.1. Market estimates and forecast, 2013 - 2024 5.3.2. Market estimates and forecast, by region, 2015 - 2024 5.3.3. Professional service 5.3.3.1. Market estimates and forecast, 2013 - 2024 5.3.3.2. Market estimates and forecast, by region, 2013 - 2024 5.3.4. Managed service 5.3.4.1. Market estimates and forecast, 2013 - 2024 5.3.4.2. Market estimates and forecast, by region, 2013 - 2024

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