The global artificial intelligence in retail market is forecast to reach US$ 10.76 billion in 2023. The adoption of artificial intelligence in retail is expected to surpass US$ 127.09 billion by 2033. Future Market Insights forecasts the demand for artificial intelligence in retail to grow by 28% CAGR between 2023 and 2033.
Key Factors Propelling the Demand for AI in Retail
In the coming years, the retail industry is set for an important overhaul thanks to the advent of AI. This innovative technology has the potential to transform the industry from cost elements to shopping participation. With e-commerce and AI working hand in hand, and the recent coronavirus outbreak boosting e-commerce growth rates, sellers must adopt AI as soon as possible. Planning for the integration of AI must be done with both technology and company strategy in mind.
The main advantage of AI in the retail industry is its ability to take over tedious, repetitive tasks and help consumers. Just like how AI has increased productivity in the workplace, the usage of AI in retail leads to the same results. AI-driven logistics help determine optimal delivery routes, while robots can assist with order selection and packing, freeing employees to focus on other important tasks.
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Artificial intelligence (AI) has become a game-changer for various industries, including healthcare, automotive, and manufacturing. As Gen Z takes over as the dominant consumer base, their strong preference for online shopping has made AI a must-have tool in the retail market. With cashier-less checkouts powered by computer vision and big data analytics, retailers are revolutionizing the shopping experience.
The growing popularity of online shopping trends, driven by the tech-savvy and mobile-friendly Gen Z population, has created a huge demand for AI solutions and services in the retail market. AI is not only transforming the shopping experience with cashier-less checkouts, but it's also making retail operations more efficient and intelligent. The future of retail is looking brighter with the integration of AI.
Despite the continued investment in AI technology by leading retail companies, there are still numerous barriers to the widespread adoption of AI in the retail sector. Small and medium-sized businesses and start-ups may face challenges in terms of infrastructure and technological know-how, while high implementation costs present a significant challenge for small retailers. However, the potential benefits of AI in the retail market cannot be ignored, particularly with the increasing usage of IoT, Big Data analytics, and e-commerce marketing.
The retail industry is anticipated to experience a wave of growth thanks to the increasing popularity of AI. Advancements in computer vision and other technologies are paving the way for new retail opportunities in areas such as customer experience, demand forecasting, and inventory management. With AI focusing on planning and product recommendations, the growth of AI products and services across various industrial domains and verticals is fueled by big data analytics.
| Artificial Intelligence in Retail Market Estimated Year Value (2023) | US$ 10.76 billion |
|---|---|
| Artificial Intelligence in Retail Market Projected Year Value (2033) | US$ 127.09 billion |
| Value CAGR (2023 to 2033) | 28% |
Principal Consultant
The retail industry is undergoing a promising transformation with the adoption of artificial intelligence. This new technology is changing the way companies track their operations, improve their strategies, and engage with customers in the digital world.
The growth of the global AI in retail market is driven by factors such as the increasing number of internet users and smart devices, rising awareness about AI and big data & analytics, and government initiatives towards digitization. The adoption of multichannel or omnichannel retailing strategy, untapped opportunities to boost sales efficiency, and enterprises' need to streamline their processes. In addition, the growing desire to enhance the end-user experience and take advantage of market dynamics is also contributing to the growth of global AI in retail market.
During the forecast period, the market is projected to experience substantial growth compared to the period of 2017 to 2022. Artificial intelligence in retail market is likely to record a 28% CAGR from 2023 to 2033, in comparison to the 19% CAGR registered from 2017 to 2022.
| Year | Market Growth during 2023 to 2033 |
|---|---|
| 2025 | 17.64 US$ billion |
| 2028 | 36.99 US$ billion |
| 2032 | 99.29 US$ billion |
Short term (2022 to 2025): With the growing number of internet users and the widespread adoption of smart devices, there is a growing demand for AI in retail. This is because AI-powered applications can provide enhanced customer experiences through personalized recommendations, product searches, and intelligent pricing algorithms.
Medium term (2025 to 2028): The increasing awareness about the benefits of AI and big data & analytics is driving growth in the global artificial intelligence in retail market. Retailers are recognizing the potential of AI to streamline their operations, improve customer engagement, and drive business growth.
Long term (2028 to 2032): Governments around the world are investing in digitization initiatives to promote the adoption of AI in retail. This includes providing financial incentives and subsidies, setting up innovation centers, and promoting digital literacy. These initiatives are creating a supportive environment for the growth of AI in the retail sector, thereby contributing to the overall growth of global artificial intelligence in retail market.
North America is poised to lead the AI retail market, with the United States expected to grow at a CAGR of 5.8% during the forecast period and reach a value of US$ 64 billion by 2033. The growth is driven by the rising number of businesses adopting AI and the presence of key players in the region, along with increased adoption of cloud services and investments in new technology.
North America is leading the way in terms of AI in retail, with the region dominating the global revenue share. Retailers in the region are leveraging customer data to improve customer service and boost efficiency. The United States is at the forefront of AI adoption, with high levels of investment in technology and the emergence of new startups and small enterprises in response to growing demand.
The Asia Pacific region is poised for rapid growth in the AI in retail market, driven by the rapidly growing digitalization of the retail industry. The region is undergoing an important transition, which is fueling demand for advanced technologies to improve operations and customer experience. For example, China has secured a 23.4% share of AI investments in the commerce and retail industry, according to SAP SE analysis. India is expected to see a leading growth due to the increasing demand for automation tools to improve decision-making and operations.
China AI in the retail industry is projected to grow at a CAGR of 5.46% and reach a value of US$ 5.4 billion during the forecast period. The growth is driven by the expansion of the IT business, increasing industrial automation, and the growth of internet penetration and mobile devices.
Japan AI in the retail industry is expected to grow at a CAGR of 5.6% and reach a market value of US$ 6.3 billion during the forecast period, driven by increasing industrial production and the expansion of mobile technologies. South Korea is expected to see a CAGR of 4.8%, driven by the growing consumer shopping experience and the implementation of smart building infrastructures.
The United Kingdom is expected to grow at a CAGR of 4.66% during the forecast period, driven by the emergence of IoT and Machine-to-Machine technologies and the increasing demand for research and industrial capacity in the region. AI in retail has become an integral part of the growing IoT market in the United Kingdom, with the region focusing on digitization post-Covid-19 by using AI and 5G networks.
Europe is expected to rank second in terms of AI in retail market share, with key retailers in the cosmetics, fashion, and apparel sectors actively investing in advanced technologies to improve the customer experience. The European technology industry saw a 26.7% rise in the AI segment in Q1 of 2020, fueling demand for AI in the retail industry.
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In terms of market share, the solutions segment is projected to hold a 73 % share, accounting for a substantial portion of the global AI in retail market. Retailers are turning to automated solutions to tackle complex management challenges, streamline supply chain operations, improve logistics, and enhance the customer experience.
However, the services segment is not far behind, with a significant growth rate forecast over the next few years. The increase in demand for AI services is attributed to the increasing popularity of AI solutions and their ability to drive revenue growth, improve customer experience, reduce human error, speed up innovation, and create intelligent functions.
According to market research, machine learning (ML) has acquired a leading revenue share of over 32% among the different AI technologies, such as natural language processing, image & video analytics, chatbots, and swarm intelligence. The increased precision and flexibility of ML technology are contributing to its expanding growth. With its ability to rapidly and deeply serve data, ML is ideal for providing personalized experiences to customers. It also helps merchants streamline their supply chain strategies and demand projections to increase inventory productivity. Amazon Sage Maker, a fully managed service, enables the deployment of machine learning models for various activities ranging from customer experience to predictive analytics.
Natural language processing (NLP) is also on the rise, as the demand for data analysis and AI-powered chatbots increases. The market for NLP is expected to grow rapidly during the forecast period, with a market share estimated to be 15%. As AI technology continues to progress, NLP plays a critical role in providing more accurate and efficient communication for various applications.
The AI in the retail market is divided into various applications, including customer relationship management (CRM), inventory management, supply chain & logistics, product optimization, payment & pricing analytics, in-store navigation, virtual assistant (VA), and others. CRM dominates the revenue share and is expected to continue growing, with a pressing need to improve customer service and retention. With the use of chatbots, search engines, and other AI technologies, retailers are aiming to establish strong customer relationships and foster loyalty.
Virtual assistant technologies have enormous growth potential in the retail industry, offering solutions for streamlining the supply chain, invoicing, ordering inventory, and bookkeeping. As a result, virtual assistance is expected to see significant growth in the forecast period, solidifying its position as a key player in the AI retail market.
The global artificial intelligence in retail market is becoming increasingly competitive, with new players entering the arena and established companies investing in cutting-edge technology. Leading players such as Amazon, IBM, Microsoft, and Salesforce are dominating the market with their advanced AI solutions. These companies are making significant investments in R&D to stay ahead of the curve and maintain their dominant positions.
In addition, emerging players such as H2O.ai, Neurala, and Vicarious are disrupting the market with their innovative solutions. These start-ups are attracting investments from leading venture capital firms and making a significant impact on the industry with their ground-breaking technologies.
Established players in the retail sector, such as Walmart, Tesco, and Alibaba, are also making significant investments in AI technology to improve their customer experience and operations. These retailers are embracing AI to enhance their competitiveness and maintain their dominant positions in the market.
Overall, artificial intelligence in retail market is expected to continue its growth trajectory, with increasing competition among established and emerging players. As AI technology continues to evolve and new solutions emerge, companies must stay ahead of the curve to remain competitive in this rapidly changing market.
Recent Developments in the Market:
The market is valued at US$ 10.76 billion in 2023.
The market is estimated to reach US$ 127.09 billion by 2033.
The market is forecasted to register a CAGR of 28% through 2033.
AI’s potential to transform the retail industry may drives sales in retail market.
High implementation costs present a significant challenge to the growth of the market.
The market's growth potential in North America is expected to be 5.8% through 2033.
1. Executive Summary | Artificial Intelligence in Retail Market 1.1. Global Market Outlook 1.2. Demand-side Trends 1.3. Supply-side Trends 1.4. Technology Roadmap Analysis 1.5. Analysis and Recommendations 2. Market Overview 2.1. Market Coverage / Taxonomy 2.2. Market Definition / Scope / Limitations 3. Market Background 3.1. Market Dynamics 3.1.1. Drivers 3.1.2. Restraints 3.1.3. Opportunity 3.1.4. Trends 3.2. Scenario Forecast 3.2.1. Demand in Optimistic Scenario 3.2.2. Demand in Likely Scenario 3.2.3. Demand in Conservative Scenario 3.3. Opportunity Map Analysis 3.4. Investment Feasibility Matrix 3.5. PESTLE and Porter’s Analysis 3.6. Regulatory Landscape 3.6.1. By Key Regions 3.6.2. By Key Countries 3.7. Regional Parent Market Outlook 4. Global Market Analysis 2018 to 2022 and Forecast, 2023 to 2033 4.1. Historical Market Size Value (US$ Million) Analysis, 2018 to 2022 4.2. Current and Future Market Size Value (US$ Million) Projections, 2023 to 2033 4.2.1. Y-o-Y Growth Trend Analysis 4.2.2. Absolute $ Opportunity Analysis 5. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Channel 5.1. Introduction / Key Findings 5.2. Historical Market Size Value (US$ Million) Analysis By Channel, 2018 to 2022 5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Channel, 2023 to 2033 5.3.1. Omnichannel 5.3.2. Brick and Mortar 5.3.3. Pure-play Online Retailers 5.4. Y-o-Y Growth Trend Analysis By Channel, 2018 to 2022 5.5. Absolute $ Opportunity Analysis By Channel, 2023 to 2033 6. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Component 6.1. Introduction / Key Findings 6.2. Historical Market Size Value (US$ Million) Analysis By Component, 2018 to 2022 6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Component, 2023 to 2033 6.3.1. Software 6.3.2. Service 6.4. Y-o-Y Growth Trend Analysis By Component, 2018 to 2022 6.5. Absolute $ Opportunity Analysis By Component, 2023 to 2033 7. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Deployment 7.1. Introduction / Key Findings 7.2. Historical Market Size Value (US$ Million) Analysis By Deployment , 2018 to 2022 7.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Deployment , 2023 to 2033 7.3.1. Cloud 7.3.2. On-Premise 7.4. Y-o-Y Growth Trend Analysis By Deployment , 2018 to 2022 7.5. Absolute $ Opportunity Analysis By Deployment , 2023 to 2033 8. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Technology 8.1. Introduction / Key Findings 8.2. Historical Market Size Value (US$ Million) Analysis By Technology, 2018 to 2022 8.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Technology, 2023 to 2033 8.3.1. Machine Learning 8.3.2. Natural Language Processing 8.3.3. Chatbots 8.3.4. Image and Video Analytics 8.4. Y-o-Y Growth Trend Analysis By Technology, 2018 to 2022 8.5. Absolute $ Opportunity Analysis By Technology, 2023 to 2033 9. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Application 9.1. Introduction / Key Findings 9.2. Historical Market Size Value (US$ Million) Analysis By Application, 2018 to 2022 9.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Application, 2023 to 2033 9.3.1. Supply Chain and Logistics 9.3.2. Product Optimization 9.3.3. In-Store Navigation 9.3.4. Payment and Pricing Analytics 9.3.5. Inventory Management 9.3.6. Customer Relationship Management 9.4. Y-o-Y Growth Trend Analysis By Application, 2018 to 2022 9.5. Absolute $ Opportunity Analysis By Application, 2023 to 2033 10. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Region 10.1. Introduction 10.2. Historical Market Size Value (US$ Million) Analysis By Region, 2018 to 2022 10.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2023 to 2033 10.3.1. North America 10.3.2. Latin America 10.3.3. Europe 10.3.4. Asia Pacific 10.3.5. Middle East and Africa 10.4. Market Attractiveness Analysis By Region 11. North America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 11.2.1. By Country 11.2.1.1. USA 11.2.1.2. Canada 11.2.2. By Channel 11.2.3. By Component 11.2.4. By Deployment 11.2.5. By Technology 11.2.6. By Application 11.3. Market Attractiveness Analysis 11.3.1. By Country 11.3.2. By Channel 11.3.3. By Component 11.3.4. By Deployment 11.3.5. By Technology 11.3.6. By Application 11.4. Key Takeaways 12. Latin America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 12.2.1. By Country 12.2.1.1. Brazil 12.2.1.2. Mexico 12.2.1.3. Rest of Latin America 12.2.2. By Channel 12.2.3. By Component 12.2.4. By Deployment 12.2.5. By Technology 12.2.6. By Application 12.3. Market Attractiveness Analysis 12.3.1. By Country 12.3.2. By Channel 12.3.3. By Component 12.3.4. By Deployment 12.3.5. By Technology 12.3.6. By Application 12.4. Key Takeaways 13. Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 13.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 13.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 13.2.1. By Country 13.2.1.1. United Kingdom 13.2.1.2. Spain 13.2.1.3. Germany 13.2.1.4. Italy 13.2.1.5. France 13.2.1.6. Rest of Europe 13.2.2. By Channel 13.2.3. By Component 13.2.4. By Deployment 13.2.5. By Technology 13.2.6. By Application 13.3. Market Attractiveness Analysis 13.3.1. By Country 13.3.2. By Channel 13.3.3. By Component 13.3.4. By Deployment 13.3.5. By Technology 13.3.6. By Application 13.4. Key Takeaways 14. Asia Pacific Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 14.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 14.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 14.2.1. By Country 14.2.1.1. India 14.2.1.2. China 14.2.1.3. Japan 14.2.1.4. Australia 14.2.1.5. Rest of Asia Pacific 14.2.2. By Channel 14.2.3. By Component 14.2.4. By Deployment 14.2.5. By Technology 14.2.6. By Application 14.3. Market Attractiveness Analysis 14.3.1. By Country 14.3.2. By Channel 14.3.3. By Component 14.3.4. By Deployment 14.3.5. By Technology 14.3.6. By Application 14.4. Key Takeaways 15. Middle East and Africa Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 15.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 15.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 15.2.1. By Country 15.2.1.1. South Africa 15.2.1.2. GCC Countries 15.2.1.3. Rest of Middle East and Africa 15.2.2. By Channel 15.2.3. By Component 15.2.4. By Deployment 15.2.5. By Technology 15.2.6. By Application 15.3. Market Attractiveness Analysis 15.3.1. By Country 15.3.2. By Channel 15.3.3. By Component 15.3.4. By Deployment 15.3.5. By Technology 15.3.6. By Application 15.4. Key Takeaways 16. Key Countries Market Analysis 16.1. USA 16.1.1. Pricing Analysis 16.1.2. Market Share Analysis, 2022 16.1.2.1. By Channel 16.1.2.2. By Component 16.1.2.3. By Deployment 16.1.2.4. By Technology 16.1.2.5. By Application 16.2. Canada 16.2.1. Pricing Analysis 16.2.2. Market Share Analysis, 2022 16.2.2.1. By Channel 16.2.2.2. By Component 16.2.2.3. By Deployment 16.2.2.4. By Technology 16.2.2.5. By Application 16.3. Brazil 16.3.1. Pricing Analysis 16.3.2. Market Share Analysis, 2022 16.3.2.1. By Channel 16.3.2.2. By Component 16.3.2.3. By Deployment 16.3.2.4. By Technology 16.3.2.5. By Application 16.4. Mexico 16.4.1. Pricing Analysis 16.4.2. Market Share Analysis, 2022 16.4.2.1. By Channel 16.4.2.2. By Component 16.4.2.3. By Deployment 16.4.2.4. By Technology 16.4.2.5. By Application 16.5. United Kingdom 16.5.1. Pricing Analysis 16.5.2. Market Share Analysis, 2022 16.5.2.1. By Channel 16.5.2.2. By Component 16.5.2.3. By Deployment 16.5.2.4. By Technology 16.5.2.5. By Application 16.6. Spain 16.6.1. Pricing Analysis 16.6.2. Market Share Analysis, 2022 16.6.2.1. By Channel 16.6.2.2. By Component 16.6.2.3. By Deployment 16.6.2.4. By Technology 16.6.2.5. By Application 16.7. Germany 16.7.1. Pricing Analysis 16.7.2. Market Share Analysis, 2022 16.7.2.1. By Channel 16.7.2.2. By Component 16.7.2.3. By Deployment 16.7.2.4. By Technology 16.7.2.5. By Application 16.8. Italy 16.8.1. Pricing Analysis 16.8.2. Market Share Analysis, 2022 16.8.2.1. By Channel 16.8.2.2. By Component 16.8.2.3. By Deployment 16.8.2.4. By Technology 16.8.2.5. By Application 16.9. France 16.9.1. Pricing Analysis 16.9.2. Market Share Analysis, 2022 16.9.2.1. By Channel 16.9.2.2. By Component 16.9.2.3. By Deployment 16.9.2.4. By Technology 16.9.2.5. By Application 16.10. India 16.10.1. Pricing Analysis 16.10.2. Market Share Analysis, 2022 16.10.2.1. By Channel 16.10.2.2. By Component 16.10.2.3. By Deployment 16.10.2.4. By Technology 16.10.2.5. By Application 16.11. China 16.11.1. Pricing Analysis 16.11.2. Market Share Analysis, 2022 16.11.2.1. By Channel 16.11.2.2. By Component 16.11.2.3. By Deployment 16.11.2.4. By Technology 16.11.2.5. By Application 16.12. Japan 16.12.1. Pricing Analysis 16.12.2. Market Share Analysis, 2022 16.12.2.1. By Channel 16.12.2.2. By Component 16.12.2.3. By Deployment 16.12.2.4. By Technology 16.12.2.5. By Application 16.13. Australia & New Zealand 16.13.1. Pricing Analysis 16.13.2. Market Share Analysis, 2022 16.13.2.1. By Channel 16.13.2.2. By Component 16.13.2.3. By Deployment 16.13.2.4. By Technology 16.13.2.5. By Application 16.14. South Africa 16.14.1. Pricing Analysis 16.14.2. Market Share Analysis, 2022 16.14.2.1. By Channel 16.14.2.2. By Component 16.14.2.3. By Deployment 16.14.2.4. By Technology 16.14.2.5. By Application 16.15. GCC Countries 16.15.1. Pricing Analysis 16.15.2. Market Share Analysis, 2022 16.15.2.1. By Channel 16.15.2.2. By Component 16.15.2.3. By Deployment 16.15.2.4. By Technology 16.15.2.5. By Application 17. Market Structure Analysis 17.1. Competition Dashboard 17.2. Competition Benchmarking 17.3. Market Share Analysis of Top Players 17.3.1. By Regional 17.3.2. By Channel 17.3.3. By Component 17.3.4. By Deployment 17.3.5. By Technology 17.3.6. By Application 18. Competition Analysis 18.1. Competition Deep Dive 18.1.1. SAP SE 18.1.1.1. Overview 18.1.1.2. Product Portfolio 18.1.1.3. Profitability by Market Segments 18.1.1.4. Sales Footprint 18.1.1.5. Strategy Overview 18.1.1.5.1. Marketing Strategy 18.1.2. IBM Corporation 18.1.2.1. Overview 18.1.2.2. Product Portfolio 18.1.2.3. Profitability by Market Segments 18.1.2.4. Sales Footprint 18.1.2.5. Strategy Overview 18.1.2.5.1. Marketing Strategy 18.1.3. Microsoft Corporation 18.1.3.1. Overview 18.1.3.2. Product Portfolio 18.1.3.3. Profitability by Market Segments 18.1.3.4. Sales Footprint 18.1.3.5. Strategy Overview 18.1.3.5.1. Marketing Strategy 18.1.4. Google LLC 18.1.4.1. Overview 18.1.4.2. Product Portfolio 18.1.4.3. Profitability by Market Segments 18.1.4.4. Sales Footprint 18.1.4.5. Strategy Overview 18.1.4.5.1. Marketing Strategy 18.1.5. Salesforce.com Inc. 18.1.5.1. Overview 18.1.5.2. Product Portfolio 18.1.5.3. Profitability by Market Segments 18.1.5.4. Sales Footprint 18.1.5.5. Strategy Overview 18.1.5.5.1. Marketing Strategy 18.1.6. Oracle Corporation 18.1.6.1. Overview 18.1.6.2. Product Portfolio 18.1.6.3. Profitability by Market Segments 18.1.6.4. Sales Footprint 18.1.6.5. Strategy Overview 18.1.6.5.1. Marketing Strategy 18.1.7. ViSenze Pte Ltd 18.1.7.1. Overview 18.1.7.2. Product Portfolio 18.1.7.3. Profitability by Market Segments 18.1.7.4. Sales Footprint 18.1.7.5. Strategy Overview 18.1.7.5.1. Marketing Strategy 18.1.8. Amazon Web Services Inc. 18.1.8.1. Overview 18.1.8.2. Product Portfolio 18.1.8.3. Profitability by Market Segments 18.1.8.4. Sales Footprint 18.1.8.5. Strategy Overview 18.1.8.5.1. Marketing Strategy 18.1.9. BloomReach, Inc. 18.1.9.1. Overview 18.1.9.2. Product Portfolio 18.1.9.3. Profitability by Market Segments 18.1.9.4. Sales Footprint 18.1.9.5. Strategy Overview 18.1.9.5.1. Marketing Strategy 18.1.10. Symphony RetailAI 18.1.10.1. Overview 18.1.10.2. Product Portfolio 18.1.10.3. Profitability by Market Segments 18.1.10.4. Sales Footprint 18.1.10.5. Strategy Overview 18.1.10.5.1. Marketing Strategy 18.1.11. Daisy Intelligence 18.1.11.1. Overview 18.1.11.2. Product Portfolio 18.1.11.3. Profitability by Market Segments 18.1.11.4. Sales Footprint 18.1.11.5. Strategy Overview 18.1.11.5.1. Marketing Strategy 18.1.12. Conversica Inc. 18.1.12.1. Overview 18.1.12.2. Product Portfolio 18.1.12.3. Profitability by Market Segments 18.1.12.4. Sales Footprint 18.1.12.5. Strategy Overview 18.1.12.5.1. Marketing Strategy 19. Assumptions & Acronyms Used 20. Research Methodology
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Artificial Intelligence in Retail Market
