Product Code: 2568
Title:
Artificial Intelligence (AI) in Retail
Market Size By Component (Solution [Chatbot, Customer Behavior Tracking, Customer Relationship Management (CRM), Inventory Management, Price Optimization, Recommendation Engine, Supply Chain Management, Visual Search], Service [Professional Service, Managed Service]), By Technology (Machine Learning, Natural Language Processing (NLP), Computer Vision), By Application (Automated Merchandising, Programmatic Advertising, Market Forecasting, In-Store AI & Location Optimization, Data Science), Industry Analysis Report, Regional Outlook, Growth Potential, Competitive Market Share & Forecast, 2021 - 2027.
Artificial intelligence (AI) in retail market is poised to grow at a strong rate over the coming time period owing to rising penetration of ecommerce, growing need to improve customer experience and the visibility of inventory across retailers, and rising adoption of social media.
AI (Artificial intelligence) covers a broad-spectrum branch of computer science and is related with the building of smart machines that are capable of performing several tasks that usually require human intelligence. The algorithms of artificial intelligence are specifically designed to make better decisions, generally using real-time data.
In the retail industry digital transformations have been going on for several years. It has gradually increased speed, accuracy, as well as efficiency across every branch of the retail sector, all thanks to the predictive analytics systems and advanced data that are aiding companies to make data-driven business decisions.
Artificial intelligence assists retailers in enhancing demand forecasting, optimizing product placement, and making better pricing decisions. Hence, customers connect with the right kind of products in the right place and time. In fact, predictive analytics can also aid in ordering the right quantity of sock so that the stores do not end up with too little or too much. Artificial intelligence can further track data from numerous online channels, thus informing much better E-commerce strategies. In addition, novel types of artificial intelligence also help in recognizing the customer intent as well as optimizing the journey of shoppers.
Artificial intelligence (AI) in retail market is segmented in terms of component, technology, application, and regional landscape.
With respect to component, the AI in retail market is divided into service and solution. Visual search & visual Listen, supply chain management, recommendation engines, price optimization, inventory management, CRM, customer behavior tracking, chatbots and others. Among these, chatbots segment will see a CAGR of around 40% over the forecast time period owing to growing demand to improve customer experience.
Likewise, the inventory management segment will witness a respectable CAGR of around 30% over the forthcoming time period. This projected growth is attributed to the growing need to improve the visibility of inventory across retailers.
In terms of application, the overall Artificial intelligence (AI) in retail market is bifurcated into data science, in store AI & location optimization, market forecasting, programmatic advertising, automated merchandising, and others. Programmatic advertising segment held a market share over 20% in 2020 and will witness strong growth owing to rising need to attract more customers.
Meanwhile, in 2020, the others segment held around 20% market share and is expected to follow a remunerative growth over the coming years. This anticipated growth is accredited to the rising penetration of social media.
From a regional frame of reference, AI in retail market in Middle East & Africa will witness a CAGR of around 40% over the forthcoming time period due to rising penetration of ecommerce in the region.
Table of Contents
Chapter 1 Methodology & Scope
- 1.1 Scope and definition
- 1.1.1 Methodology & forecast parameters
- 1.2 Data Sources
- 1.2.1 Secondary
- 1.2.1.1 Paid sources
- 1.2.1.2 Public sources
- 1.2.2 Primary
Chapter 2 Executive Summary
- 2.1 AI in retail industry 360 degree synopsis, 2016 - 2027
- 2.1.1 Business trends
- 2.1.2 Regional trends
- 2.1.3 Component trends
- 2.1.4 Technology trends
- 2.1.5 Application trends
Chapter 3 AI in Retail Industry Insights
- 3.1 Introduction
- 3.2 Industry segmentation
- 3.3 Impact of COVID-19 outbreak
- 3.3.1 Impact by region
- 3.3.1.1 North America
- 3.3.1.2 Europe
- 3.3.1.3 Asia Pacific
- 3.3.1.4 Latin America
- 3.3.1.5 Middle East & Africa
- 3.3.2 Impact by value chain
- 3.3.3 Impact by competitive landscape
- 3.4 Technological evolution
- 3.5 Industry ecosystem analysis
- 3.6 Technology & innovation landscape
- 3.6.1 Gesture recognition
- 3.6.2 Virtual mirrors
- 3.6.3 Video analytics
- 3.6.4 Robots
- 3.7 Regulatory landscape
- 3.7.1 Health Insurance Portability and Accountability Act (HIPAA)
- 3.7.2 Payment Card Industry Data Security Standard (PCI DSS)
- 3.7.3 North American Electric Reliability Corp (NERC) Standards
- 3.7.4 Federal Information Security Management Act (FISMA)
- 3.7.5 The Gramma-Leach-Bliley Act (GLB) Act of 1999
- 3.7.6 Sarbanes-Oxley Act of 2002
- 3.7.7. General Data Protection Regulation (GDPR)
- 3.8 Use cases
- 3.8.1 Sales & CRM application
- 3.8.2 Customer recommendations
- 3.8.3 Logistics & delivery
- 3.8.4 Payment service
- 3.9 Industry impact forces
- 3.9.1 Growth drivers
- 3.9.1.1 Growing investment in AI
- 3.9.1.2 Increasingly empowered consumer
- 3.9.1.3 Rising disruptive technologies
- 3.9.1.4 Advent of new business models
- 3.9.1.5 Advancement in data science
- 3.9.2 Industry pitfalls & challenges
- 3.9.2.1 Limited public-private partnership to address social implications directly
- 3.9.2.2 Privacy issues associated with the use of AI Growth potential analysis
- 3.10 Porter's analysis
- 3.11 PESTEL analysis
- 3.12 Growth potential analysis
Chapter 4 Competitive Landscape,
- 4.1 Introduction
- 4.2 Company market share, 2020
- 4.3 Competive analysis of major market players, 2020
- 4.3.1 Google Inc
- 4.3.2 Microsoft Corporation
- 4.3.3 IBM Corporation
- 4.3.4 Amazon Web Services
- 4.3.5 Salesforce
- 4.4 Competive analysis of innovative players, 2020
- 4.4.1 Inbenta Technologies Inc
- 4.4.2 Lexalytics Inc
- 4.4.3 Interactions LLC
- 4.4.4 RetailNext Inc
- 4.4.5 Evolv Technology Solutions, Inc (Sentinent Technologies)
Chapter 5 AI in Retail Market, By Component
- 5.1 Key trends, by component
- 5.2 Solution
- 5.2.1 Market estimates and forecast, 2016 - 2027
- 5.2.2 Chatbot
- 5.2.2.1 Market estimates and forecast, 2016 - 2027
- 5.2.3 Customer behavior tracking
- 5.2.3.1 Market estimates and forecast, 2016 - 2027
- 5.2.4 CRM
- 5.2.4.1 Market estimates and forecast, 2016 - 2027
- 5.2.5 Inventory management
- 5.2.5.1 Market estimates and forecast, 2016 - 2027
- 5.2.6 Price optimization
- 5.2.6.1 Market estimates and forecast, 2016 - 2027
- 5.2.7 Recommendation engines
- 5.2.7.1 Market estimates and forecast, 2016 - 2027
- 5.2.8 Supply chain management
- 5.2.8.1 Market estimates and forecast, 2016 - 2027
- 5.2.9 Visual search
- 5.2.9.1 Market estimates and forecast, 2016 - 2027
- 5.2.10 Others
- 5.2.10.1 Market estimates and forecast, 2016 - 2027
- 5.3 Service
- 5.3.1 Market estimates and forecast, 2016 - 2027
- 5.3.2 Professional service
- 5.3.2.1 Market estimates and forecast, 2016 - 2027
- 5.3.3 Managed service
- 5.3.3.1 Market estimates and forecast, 2016 - 2027
Chapter 6 AI in Retail Market, By Technology
- 6.1 Key trends, by technology
- 6.2 Machine learning and deep learning
- 6.2.1 Market estimates and forecast, 2016 - 2027
- 6.3 Natural Language Processing
- 6.3.1 Market estimates and forecast, 2016 - 2027
- 6.4 Computer vision
- 6.4.1 Market estimates and forecast, 2016 - 2027
- 6.5 Others
- 6.5.1 Market estimates and forecast, 2016 - 2027
Chapter 7 AI in Retail Market, By Application
- 7.1 Key trends, application
- 7.2 Automated merchandizing
- 7.2.1 Market estimates and forecast, 2016 - 2027
- 7.3 Programmating advertising
- 7.3.1 Market estimates and forecast, 2016 - 2027
- 7.4 Market forecasting
- 7.4.1 Market estimates and forecast, 2016 - 2027
- 7.5 In-store AI and location optimization
- 7.5.1 Market estimates and forecast, 2016 - 2027
- 7.6 Data Science
- 7.6.1 Market estimates and forecast, 2016 - 2027
- 7.7 Others
- 7.7.1 Market estimates and forecast, 2016 - 2027
Chapter 8 AI in Retail Market, By Region
- 8.1 Key trends, by region
- 8.2 North America
- 8.2.1 Market estimates and forecast, 2016 - 2027
- 8.2.2 Market estimates and forecast, by component, 2016 - 2027
- 8.2.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.2.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.2.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.2.4 Market estimates and forecast, by application, 2016 - 2027
- 8.2.5 U.S.
- 8.2.5.1 Market estimates and forecast, 2016 - 2027
- 8.2.5.2 Market estimates and forecast, by component, 2016 - 2027
- 8.2.5.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.2.5.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.2.5.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.2.5.4 Market estimates and forecast, by application, 2016 - 2027
- 8.2.6 Canada
- 8.2.6.1 Market estimates and forecast, 2016 - 2027
- 8.2.6.2 Market estimates and forecast, by component, 2016 - 2027
- 8.2.6.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.2.6.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.2.6.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.2.6.4 Market estimates and forecast, by application, 2016 - 2027
- 8.3 Europe
- 8.3.1 Market estimates and forecast, 2016 - 2027
- 8.3.2 Market estimates and forecast, by component, 2016 - 2027
- 8.3.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.3.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.3.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.3.4 Market estimates and forecast, by application, 2016 - 2027
- 8.3.5 UK
- 8.3.5.1 Market estimates and forecast, 2016 - 2027
- 8.3.5.2 Market estimates and forecast, by component, 2016 - 2027
- 8.3.5.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.3.5.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.3.5.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.3.5.4 Market estimates and forecast, by application, 2016 - 2027
- 8.3.6 Germany
- 8.3.6.1 Market estimates and forecast, 2016 - 2027
- 8.3.6.2 Market estimates and forecast, by component, 2016 - 2027
- 8.3.6.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.3.6.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.3.6.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.3.6.4 Market estimates and forecast, by application, 2016 - 2027
- 8.3.7 France
- 8.3.7.1 Market estimates and forecast, 2016 - 2027
- 8.3.7.2 Market estimates and forecast, by component, 2016 - 2027
- 8.3.7.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.3.7.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.3.7.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.3.7.4 Market estimates and forecast, by application, 2016 - 2027
- 8.3.8 Spain
- 8.3.8.1 Market estimates and forecast, 2016 - 2027
- 8.3.8.2 Market estimates and forecast, by component, 2016 - 2027
- 8.3.8.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.3.8.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.3.8.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.3.8.4 Market estimates and forecast, by application, 2016 - 2027
- 8.3.9 Sweden
- 8.3.9.1 Market estimates and forecast, 2016 - 2027
- 8.3.9.2 Market estimates and forecast, by component, 2016 - 2027
- 8.3.9.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.3.9.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.3.9.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.3.9.4 Market estimates and forecast, by application, 2016 - 2027
- 8.3.10 Switzerland
- 8.3.10.1 Market estimates and forecast, 2016 - 2027
- 8.3.10.2 Market estimates and forecast, by component, 2016 - 2027
- 8.3.10.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.3.10.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.3.10.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.3.10.4 Market estimates and forecast, by application, 2016 - 2027
- 8.4 Asia Pacific
- 8.4.1 Market estimates and forecast, 2016 - 2027
- 8.4.2 Market estimates and forecast, by component, 2016 - 2027
- 8.4.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.4.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.4.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.4.4 Market estimates and forecast, by application, 2016 - 2027
- 8.4.5 China
- 8.4.5.1 Market estimates and forecast, 2016 - 2027
- 8.4.5.2 Market estimates and forecast, by component, 2016 - 2027
- 8.4.5.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.4.5.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.4.5.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.4.5.4 Market estimates and forecast, by application, 2016 - 2027
- 8.4.6 Japan
- 8.4.6.1 Market estimates and forecast, 2016 - 2027
- 8.4.6.2 Market estimates and forecast, by component, 2016 - 2027
- 8.4.6.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.4.6.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.4.6.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.4.6.4 Market estimates and forecast, by application, 2016 - 2027
- 8.4.7 India
- 8.4.7.1 Market estimates and forecast, 2016 - 2027
- 8.4.7.2 Market estimates and forecast, by component, 2016 - 2027
- 8.4.7.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.4.7.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.4.7.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.4.7.4 Market estimates and forecast, by application, 2016 - 2027
- 8.4.8 South Korea
- 8.4.8.1 Market estimates and forecast, 2016 - 2027
- 8.4.8.2 Market estimates and forecast, by component, 2016 - 2027
- 8.4.8.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.4.8.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.4.8.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.4.8.4 Market estimates and forecast, by application, 2016 - 2027
- 8.4.9 Australia
- 8.4.9.1 Market estimates and forecast, 2016 - 2027
- 8.4.9.2 Market estimates and forecast, by component, 2016 - 2027
- 8.4.9.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.4.9.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.4.9.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.4.9.4 Market estimates and forecast, by application, 2016 - 2027
- 8.4.10 Singapore
- 8.4.10.1 Market estimates and forecast, 2016 - 2027
- 8.4.10.2 Market estimates and forecast, by component, 2016 - 2027
- 8.4.10.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.4.10.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.4.10.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.4.10.4 Market estimates and forecast, by application, 2016 - 2027
- 8.5 Latin America
- 8.5.1 Market estimates and forecast, 2016 - 2027
- 8.5.2 Market estimates and forecast, by component, 2016 - 2027
- 8.5.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.5.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.5.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.5.4 Market estimates and forecast, by application, 2016 - 2027
- 8.5.5 Brazil
- 8.5.5.1 Market estimates and forecast, 2016 - 2027
- 8.5.5.2 Market estimates and forecast, by component, 2016 - 2027
- 8.5.5.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.5.5.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.5.5.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.5.5.4 Market estimates and forecast, by application, 2016 - 2027
- 8.5.6 Mexico
- 8.5.6.1 Market estimates and forecast, 2016 - 2027
- 8.5.6.2 Market estimates and forecast, by component, 2016 - 2027
- 8.5.6.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.5.6.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.5.6.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.5.6.4 Market estimates and forecast, by application, 2016 - 2027
- 8.6 MEA
- 8.6.1 Market estimates and forecast, 2016 - 2027
- 8.6.2 Market estimates and forecast, by component, 2016 - 2027
- 8.6.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.6.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.6.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.6.4 Market estimates and forecast, by application, 2016 - 2027
- 8.6.5 South Africa
- 8.6.5.1 Market estimates and forecast, 2016 - 2027
- 8.6.5.2 Market estimates and forecast, by component, 2016 - 2027
- 8.6.5.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.6.5.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.6.5.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.6.5.4 Market estimates and forecast, by application, 2016 - 2027
- 8.6.6 UAE
- 8.6.6.1 Market estimates and forecast, 2016 - 2027
- 8.6.6.2 Market estimates and forecast, by component, 2016 - 2027
- 8.6.6.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.6.6.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.6.6.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.6.6.4 Market estimates and forecast, by application, 2016 - 2027
- 8.6.7 Israel
- 8.6.7.1 Market estimates and forecast, 2016 - 2027
- 8.6.7.2 Market estimates and forecast, by component, 2016 - 2027
- 8.6.7.2.1 Market estimates and forecast, by solution, 2016 - 2027
- 8.6.7.2.2 Market estimates and forecast, by service, 2016 - 2027
- 8.6.7.3 Market estimates and forecast, by technology, 2016 - 2027
- 8.6.7.4 Market estimates and forecast, by application, 2016 - 2027
Chapter 9 Company Profiles
- 9.1 Amazon Web Services
- 9.1.1 Business Overview
- 9.1.2 Financial Data
- 9.1.3 Product Landscape
- 9.1.4 Go-to-Market Strategy
- 9.1.5 SWOT Analysis
- 9.2 Baidu Inc.
- 9.2.1 Business Overview
- 9.2.2 Financial Data
- 9.2.3 Product Landscape
- 9.2.4 Go-to-Market Strategy
- 9.2.5 SWOT Analysis
- 9.3 BloomReach Inc.
- 9.3.1 Business Overview
- 9.3.2 Financial Data
- 9.3.3 Product Landscape
- 9.3.4 Go-to-Market Strategy
- 9.3.5 SWOT Analysis
- 9.4 CognitiveScale Inc.
- 9.4.1 Business Overview
- 9.4.2 Financial Data
- 9.4.3 Product Landscape
- 9.4.4 Go-to-Market Strategy
- 9.4.5 SWOT Analysis
- 9.5 Google Inc.
- 9.5.1 Business Overview
- 9.5.2 Financial Data
- 9.5.3 Product Landscape
- 9.5.4 Go-to-Market Strategy
- 9.5.5 SWOT Analysis
- 9.6 IBM Corporation
- 9.6.1 Business Overview
- 9.6.2 Financial Data
- 9.6.3 Product Landscape
- 9.6.4 Go-to-Market Strategy
- 9.6.5 SWOT Analysis
- 9.7 Inbenta Technologies
- 9.7.1 Business Overview
- 9.7.2 Financial Data
- 9.7.3 Product Landscape
- 9.7.4 Go-to-Market Strategy
- 9.7.5 SWOT Analysis
- 9.8 Intel Corporation
- 9.8.1 Business Overview
- 9.8.2 Financial Data
- 9.8.3 Product Landscape
- 9.8.4 Go-to-Market Strategy
- 9.8.5 SWOT Analysis
- 9.9 Interactions LLC
- 9.9.1 Business Overview
- 9.9.2 Financial Data
- 9.9.3 Product Landscape
- 9.9.4 Go-to-Market Strategy
- 9.9.5 SWOT Analysis
- 9.10 Lexalytics Inc.
- 9.10.1 Business Overview
- 9.10.2 Financial Data
- 9.10.3 Product Landscape
- 9.10.4 Go-to-Market Strategy
- 9.10.5 SWOT Analysis
- 9.11 Microsoft Corporation
- 9.11.1 Business Overview
- 9.11.2 Financial Data
- 9.11.3 Product Landscape
- 9.11.4 Go-to-Market Strategy
- 9.11.5 SWOT Analysis
- 9.12 NEXT IT Corp.
- 9.12.1 Business Overview
- 9.12.2 Financial Data
- 9.12.3 Product Landscape
- 9.12.4 Go-to-Market Strategy
- 9.12.5 SWOT Analysis
- 9.13 Nvidia Corporation
- 9.13.1 Business Overview
- 9.13.2 Financial Data
- 9.13.3 Product Landscape
- 9.13.4 Go-to-Market Strategy
- 9.13.5 SWOT Analysis
- 9.14 Oracle Corporation
- 9.14.1 Business Overview
- 9.14.2 Financial Data
- 9.14.3 Product Landscape
- 9.14.4 Go-to-Market Strategy
- 9.14.5 SWOT Analysis
- 9.15 RetailNext Inc.
- 9.15.1 Business Overview
- 9.15.2 Financial Data
- 9.15.3 Product Landscape
- 9.15.4 Go-to-Market Strategy
- 9.15.5 SWOT Analysis
- 9.16 Salesforce.com Inc.
- 9.16.1 Business Overview
- 9.16.2 Financial Data
- 9.16.3 Product Landscape
- 9.16.4 Go-to-Market Strategy
- 9.16.5 SWOT Analysis
- 9.17 SAP SE
- 9.17.1 Business Overview
- 9.17.2 Financial Data
- 9.17.3 Product Landscape
- 9.17.4 Go-to-Market Strategy
- 9.17.5 SWOT Analysis
- 9.18 Sentient Technologies
- 9.18.1 Business Overview
- 9.18.2 Financial Data
- 9.18.3 Product Landscape
- 9.18.4 Go-to-Market Strategy
- 9.18.5 SWOT Analysis
- 9.19 Visenze
- 9.19.1 Business Overview
- 9.19.2 Financial Data
- 9.19.3 Product Landscape
- 9.19.4 Go-to-Market Strategy
- 9.19.5 SWOT Analysis
比較リスト
