세계의 소매 엣지 컴퓨팅 시장 - 산업규모, 점유율, 동향, 기회, 예측 : 컴포넌트별, 용도별, 조직 규모별, 지역별 부문 및 경쟁(2020-2030년)
Retail Edge Computing Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Component, By Application, By Organization Size, By Region & Competition, 2020-2030F
세계의 소매 엣지 컴퓨팅 시장 규모는 2024년에 48억 7,000만 달러로, 2030년까지 CAGR 20.88%로 확대되어, 2030년에는 151억 9,000만 달러에 달할 것으로 예측되고 있습니다.
소매 엣지 컴퓨팅이란 먼 데이터센터나 클라우드 플랫폼에만 의존하는 것이 아니라, 소매점이나 배송 센터의 현장 등 데이터가 발생하는 장소 근처에서 데이터를 처리하는 것을 말합니다. 센서, 카메라, IoT(사물인터넷) 시스템 등의 엣지 디바이스를 활용해, 리얼타임으로 데이터를 수집, 처리, 분석하는 것으로, 소매업체는 데이터에 근거한 신속한 의사 결정을 할 수 있습니다. 재고 관리의 개선, 개인화된 쇼핑 체험, 업무 효율의 개선 등이 가능하기 때문에 소매 업계에서는 엣지 컴퓨팅의 도입이 진행되고 있습니다. 소비자 행동을 예측하고 심지어 고급 보안 시스템으로 도난을 줄일 수 있습니다.
| 시장 개요 |
| 예측 기간 |
2026-2030년 |
| 시장 규모 : 2024년 |
48억 7,000만 달러 |
| 시장 규모 : 2030년 |
151억 9,000만 달러 |
| CAGR : 2025-2030년 |
20.88% |
| 급성장 부문 |
중소기업 |
| 최대 시장 |
북미 |
소매 엣지 컴퓨팅 시장은 몇 가지 주요 촉진요인에 의해 크게 성장할 것으로 예상됩니다. 이러한 장치가 생성하는 대량의 데이터를 처리하는 분산형 컴퓨팅의 필요성이 높아지고 있습니다. 지원이 가능한 원활하고 응답성이 높은 시스템이 요구되고 있습니다. 디컴퓨팅을 통해 보다 빠르고 현지 처리가 가능하므로 소매업체는 업무를 효율화하고 고객 참여도를 높일 수 있으며 혼잡한 시장에서 경쟁 우위를 높일 수 있습니다. 따라서 소매 엣지 컴퓨팅 시장은 기술의 진보, 업무 효율화의 요구, 개인화된 실시간 고객 경험의 추진에 의해 급속하게 성장할 것으로 보입니다.
시장 성장 촉진요인
실시간 데이터 처리 및 의사 결정에 대한 수요
주요 시장 과제
기존 인프라와의 통합 복잡성
주요 시장 동향
엣지에서 인공지능과 머신러닝 채택 증가
목차
제1장 솔루션 개요
제2장 조사 방법
제3장 주요 요약
제4장 고객의 목소리
제5장 세계 소매 엣지 컴퓨팅 시장 개요
제6장 세계 소매 엣지 컴퓨팅 시장 전망
- 시장 규모와 예측
- 시장 점유율 및 예측
- 컴포넌트별(하드웨어, 소프트웨어, 서비스)
- 용도별(스마트 시티, 산업용 IoT, 원격 감시, 컨텐츠 전달, 증강현실, 가상현실, 기타)
- 조직 규모별(중소기업, 대기업)
- 지역별(북미, 유럽, 남미, 중동 및 아프리카, 아시아태평양)
- 기업별(2024년)
- 시장 맵
제7장 북미의 소매 엣지 컴퓨팅 시장 전망
- 시장 규모와 예측
- 시장 점유율 및 예측
- 북미: 국가별 분석
제8장 유럽의 소매 엣지 컴퓨팅 시장 전망
- 시장 규모와 예측
- 시장 점유율 및 예측
- 유럽: 국가별 분석
제9장 아시아태평양의 소매 엣지 컴퓨팅 시장 전망
- 시장 규모와 예측
- 시장 점유율 및 예측
- 아시아태평양: 국가별 분석
제10장 남미의 소매 엣지 컴퓨팅 시장 전망
- 시장 규모와 예측
- 시장 점유율 및 예측
- 남미 : 국가별 분석
제11장 중동 및 아프리카의 소매 엣지 컴퓨팅 시장 전망
- 시장 규모와 예측
- 시장 점유율 및 예측
- 중동 및 아프리카: 국가별 분석
- 사우디아라비아
- 아랍에미리트(UAE)
- 남아프리카
- 튀르키예
- 이스라엘
제12장 시장 역학
제13장 시장 동향과 발전
제14장 기업 프로파일
- Amazon.com, Inc.
- Microsoft Corporation
- IBM Corporation
- Intel Corporation
- Cisco Systems, Inc.
- Hewlett Packard Enterprise Company
- NVIDIA Corporation
- Google LLC
- Oracle Corporation
- Qualcomm Incorporated
제15장 전략적 제안
제16장 기업 소개와 면책사항
JHS
The Global Retail Edge Computing Market was valued at USD 4.87 billion in 2024 and is expected to reach USD 15.19 billion by 2030 with a CAGR of 20.88% through 2030. Retail Edge Computing refers to the practice of processing data closer to the location where it is generated, such as on-site at retail stores or distribution centers, rather than relying solely on distant data centers or cloud platforms. This technology leverages edge devices like sensors, cameras, and IoT (Internet of Things) systems to collect, process, and analyze data in real time, enabling retailers to make faster, data-driven decisions. The retail sector has been increasingly adopting edge computing as it allows for quicker responses to customer needs, better inventory management, personalized shopping experiences, and improved operational efficiency. For example, real-time analytics from in-store cameras can optimize store layouts, predict consumer behavior, and even reduce theft through advanced security systems. Edge computing enhances supply chain management by providing near-instantaneous feedback on inventory levels and customer preferences.
| Market Overview |
|---|
| Forecast Period | 2026-2030 |
| Market Size 2024 | USD 4.87 Billion |
| Market Size 2030 | USD 15.19 Billion |
| CAGR 2025-2030 | 20.88% |
| Fastest Growing Segment | Small & Medium Enterprises |
| Largest Market | North America |
The market for retail edge computing is expected to rise significantly due to several key drivers. The growing demand for hyper-personalized shopping experiences, driven by customer expectations for instant and tailored services, is pushing retailers to adopt technologies that can provide real-time insights. As the number of IoT devices and sensors in retail environments continues to increase, the need for decentralized computing grows to handle the massive volume of data these devices generate. The ongoing expansion of 5G networks further accelerates this shift, as 5G enables high-speed, low-latency communication, making edge computing more effective in handling real-time data processing. The rise of omnichannel retail, where consumers interact with brands through both physical stores and digital platforms, demands seamless and responsive systems that edge computing can support. Security concerns and the need for reducing data latency in processing transactions also play a role in the adoption of edge computing, as retailers seek to ensure customer data is handled efficiently and securely. The increasing importance of automation in retail operations, such as smart shelves, automated checkout, and personalized promotions, is another factor driving the market's growth. As edge computing enables faster, local processing, retailers can streamline operations and enhance customer engagement, leading to more competitive advantages in a crowded market. Therefore, the retail edge computing market is poised to grow rapidly, driven by advancements in technology, the need for operational efficiency, and the push for personalized, real-time customer experiences.
Key Market Drivers
Demand for Real-Time Data Processing and Decision Making
One of the primary drivers of the retail edge computing market is the increasing demand for real-time data processing and decision making within retail environments. The modern retail landscape is becoming increasingly data-driven, with retailers collecting vast amounts of information from in-store sensors, cameras, point-of-sale systems, and online interactions. These data points include customer behavior, inventory levels, and transaction details. For retail businesses, the ability to process this information as it is generated, without having to send it to a centralized cloud or data center, has become a critical factor in staying competitive. Retailers are under constant pressure to improve customer experiences, optimize operations, and stay ahead of market trends. Real-time data processing allows them to gain immediate insights into their operations, whether it is for analyzing customer foot traffic, adjusting pricing, or making stock replenishment decisions. Edge computing enables data to be processed closer to the point of origin, reducing latency and enabling quicker decision-making, which is especially crucial during peak hours or sales events. For instance, by leveraging real-time data at the edge, a retailer can adjust promotions, manage store layouts, and even optimize staff allocation instantly based on customer behavior patterns, thereby enhancing operational efficiency and improving customer experience. This ability to make informed decisions promptly is a major factor driving the retail edge computing market's growth. By the end of 2025, it is estimated that 80% of all enterprise data will need to be processed in real-time or near real-time to drive critical decision-making.
Key Market Challenges
Complexity of Integration with Existing Infrastructure
One of the primary challenges for the retail edge computing market is the complexity of integrating edge computing solutions with existing retail infrastructure. Many retailers, particularly legacy businesses, already have established systems in place for their operations, such as centralized data centers, cloud-based applications, and traditional point-of-sale systems. Implementing edge computing requires significant changes to this infrastructure, which can be costly, time-consuming, and technically challenging. Retailers must ensure that their edge computing solutions are seamlessly integrated with these legacy systems to maintain smooth operations and avoid disruptions. This can involve substantial investments in both hardware and software, as well as training personnel to manage and operate new systems. Many edge computing solutions require specialized hardware, such as local data processing units, sensors, or specialized network equipment, which may not be compatible with older retail technologies. Integrating such diverse systems can lead to compatibility issues, data silos, or inefficiencies that hinder the desired performance improvements. The process of integration may involve significant customization to align with the specific needs of a retail business. Retailers must work closely with technology vendors and service providers to ensure that edge computing solutions are tailored to their particular operational requirements, which can increase project timelines and costs. For businesses with a wide range of store formats or a diverse product offering, integrating edge computing at scale can be particularly challenging. A lack of standardized solutions or processes across different retail environments can create inconsistencies in performance and operational challenges, delaying the expected benefits of edge computing. Thus, retailers face considerable challenges in ensuring that edge computing solutions can be effectively incorporated into their existing infrastructure while maintaining operational continuity.
Key Market Trends
Increased Adoption of Artificial Intelligence and Machine Learning at the Edge
One of the significant trends in the retail edge computing market is the increasing integration of artificial intelligence and machine learning technologies directly at the edge. Traditionally, artificial intelligence and machine learning models required heavy processing power in centralized cloud environments, resulting in latency and bandwidth challenges. However, with the advancement of edge computing technologies, retailers are now able to deploy these advanced algorithms at the edge, closer to where data is generated. This enables real-time analysis of customer behavior, inventory management, and store operations. For example, edge devices equipped with artificial intelligence can instantly analyze video feeds from in-store cameras to recognize customer actions, detect patterns, and even predict future purchasing behavior. Retailers can leverage this data to offer personalized promotions, optimize store layouts, or detect shoplifting in real-time. Machine learning algorithms can be used to predict inventory needs based on in-store data, reducing stockouts and overstocking. The ability to run these sophisticated models locally ensures quicker response times and minimizes the need for constant cloud communication, which enhances overall system efficiency. The growing reliance on artificial intelligence and machine learning at the edge is transforming how retailers operate, providing them with enhanced insights and decision-making capabilities that drive business success.
Key Market Players
- Amazon.com, Inc.
- Microsoft Corporation
- IBM Corporation
- Intel Corporation
- Cisco Systems, Inc.
- Hewlett Packard Enterprise Company
- NVIDIA Corporation
- Google LLC
- Oracle Corporation
- Qualcomm Incorporated
Report Scope:
In this report, the Global Retail Edge Computing Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:
Retail Edge Computing Market, By Component:
- Hardware
- Software
- Services
Retail Edge Computing Market, By Application:
- Smart Cities
- Industrial Internet of Things
- Remote Monitoring
- Content Delivery
- Augmented Reality
- Virtual Reality
- Others
Retail Edge Computing Market, By Organization Size:
- Small & Medium Enterprises
- Large Enterprises
Retail Edge Computing Market, By Region:
- North America
- United States
- Canada
- Mexico
- Europe
- Germany
- France
- United Kingdom
- Italy
- Spain
- Belgium
- Asia Pacific
- China
- India
- Japan
- South Korea
- Australia
- Indonesia
- Vietnam
- South America
- Brazil
- Colombia
- Argentina
- Chile
- Middle East & Africa
- Saudi Arabia
- UAE
- South Africa
- Turkey
- Israel
Competitive Landscape
Company Profiles: Detailed analysis of the major companies present in the Global Retail Edge Computing Market.
Available Customizations:
Global Retail Edge Computing Market report with the given market data, TechSci 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).
Table of Contents
1. Solution Overview
- 1.1. Market Definition
- 1.2. Scope of the Market
- 1.2.1. Markets Covered
- 1.2.2. Years Considered for Study
- 1.2.3. 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 Retail Edge Computing Market Overview
6. Global Retail Edge Computing Market Outlook
- 6.1. Market Size & Forecast
- 6.2. Market Share & Forecast
- 6.2.1. By Component (Hardware, Software, Services)
- 6.2.2. By Application (Smart Cities, Industrial Internet of Things, Remote Monitoring, Content Delivery, Augmented Reality, Virtual Reality, Others)
- 6.2.3. By Organization Size (Small & Medium Enterprises, Large Enterprises)
- 6.2.4. By Region (North America, Europe, South America, Middle East & Africa, Asia Pacific)
- 6.3. By Company (2024)
- 6.4. Market Map
7. North America Retail Edge Computing Market Outlook
- 7.1. Market Size & Forecast
- 7.2. Market Share & Forecast
- 7.2.1. By Component
- 7.2.2. By Application
- 7.2.3. By Organization Size
- 7.2.4. By Country
- 7.3. North America: Country Analysis
- 7.3.1. United States Retail Edge Computing Market Outlook
- 7.3.1.1. Market Size & Forecast
- 7.3.1.2. Market Share & Forecast
- 7.3.1.2.1. By Component
- 7.3.1.2.2. By Application
- 7.3.1.2.3. By Organization Size
- 7.3.2. Canada Retail Edge Computing Market Outlook
- 7.3.2.1. Market Size & Forecast
- 7.3.2.2. Market Share & Forecast
- 7.3.2.2.1. By Component
- 7.3.2.2.2. By Application
- 7.3.2.2.3. By Organization Size
- 7.3.3. Mexico Retail Edge Computing Market Outlook
- 7.3.3.1. Market Size & Forecast
- 7.3.3.2. Market Share & Forecast
- 7.3.3.2.1. By Component
- 7.3.3.2.2. By Application
- 7.3.3.2.3. By Organization Size
8. Europe Retail Edge Computing Market Outlook
- 8.1. Market Size & Forecast
- 8.2. Market Share & Forecast
- 8.2.1. By Component
- 8.2.2. By Application
- 8.2.3. By Organization Size
- 8.2.4. By Country
- 8.3. Europe: Country Analysis
- 8.3.1. Germany Retail Edge Computing Market Outlook
- 8.3.1.1. Market Size & Forecast
- 8.3.1.2. Market Share & Forecast
- 8.3.1.2.1. By Component
- 8.3.1.2.2. By Application
- 8.3.1.2.3. By Organization Size
- 8.3.2. France Retail Edge Computing Market Outlook
- 8.3.2.1. Market Size & Forecast
- 8.3.2.2. Market Share & Forecast
- 8.3.2.2.1. By Component
- 8.3.2.2.2. By Application
- 8.3.2.2.3. By Organization Size
- 8.3.3. United Kingdom Retail Edge Computing Market Outlook
- 8.3.3.1. Market Size & Forecast
- 8.3.3.2. Market Share & Forecast
- 8.3.3.2.1. By Component
- 8.3.3.2.2. By Application
- 8.3.3.2.3. By Organization Size
- 8.3.4. Italy Retail Edge Computing Market Outlook
- 8.3.4.1. Market Size & Forecast
- 8.3.4.2. Market Share & Forecast
- 8.3.4.2.1. By Component
- 8.3.4.2.2. By Application
- 8.3.4.2.3. By Organization Size
- 8.3.5. Spain Retail Edge Computing Market Outlook
- 8.3.5.1. Market Size & Forecast
- 8.3.5.2. Market Share & Forecast
- 8.3.5.2.1. By Component
- 8.3.5.2.2. By Application
- 8.3.5.2.3. By Organization Size
- 8.3.6. Belgium Retail Edge Computing Market Outlook
- 8.3.6.1. Market Size & Forecast
- 8.3.6.2. Market Share & Forecast
- 8.3.6.2.1. By Component
- 8.3.6.2.2. By Application
- 8.3.6.2.3. By Organization Size
9. Asia Pacific Retail Edge Computing Market Outlook
- 9.1. Market Size & Forecast
- 9.2. Market Share & Forecast
- 9.2.1. By Component
- 9.2.2. By Application
- 9.2.3. By Organization Size
- 9.2.4. By Country
- 9.3. Asia Pacific: Country Analysis
- 9.3.1. China Retail Edge Computing Market Outlook
- 9.3.1.1. Market Size & Forecast
- 9.3.1.2. Market Share & Forecast
- 9.3.1.2.1. By Component
- 9.3.1.2.2. By Application
- 9.3.1.2.3. By Organization Size
- 9.3.2. India Retail Edge Computing Market Outlook
- 9.3.2.1. Market Size & Forecast
- 9.3.2.2. Market Share & Forecast
- 9.3.2.2.1. By Component
- 9.3.2.2.2. By Application
- 9.3.2.2.3. By Organization Size
- 9.3.3. Japan Retail Edge Computing Market Outlook
- 9.3.3.1. Market Size & Forecast
- 9.3.3.2. Market Share & Forecast
- 9.3.3.2.1. By Component
- 9.3.3.2.2. By Application
- 9.3.3.2.3. By Organization Size
- 9.3.4. South Korea Retail Edge Computing Market Outlook
- 9.3.4.1. Market Size & Forecast
- 9.3.4.2. Market Share & Forecast
- 9.3.4.2.1. By Component
- 9.3.4.2.2. By Application
- 9.3.4.2.3. By Organization Size
- 9.3.5. Australia Retail Edge Computing Market Outlook
- 9.3.5.1. Market Size & Forecast
- 9.3.5.2. Market Share & Forecast
- 9.3.5.2.1. By Component
- 9.3.5.2.2. By Application
- 9.3.5.2.3. By Organization Size
- 9.3.6. Indonesia Retail Edge Computing Market Outlook
- 9.3.6.1. Market Size & Forecast
- 9.3.6.2. Market Share & Forecast
- 9.3.6.2.1. By Component
- 9.3.6.2.2. By Application
- 9.3.6.2.3. By Organization Size
- 9.3.7. Vietnam Retail Edge Computing Market Outlook
- 9.3.7.1. Market Size & Forecast
- 9.3.7.2. Market Share & Forecast
- 9.3.7.2.1. By Component
- 9.3.7.2.2. By Application
- 9.3.7.2.3. By Organization Size
10. South America Retail Edge Computing Market Outlook
- 10.1. Market Size & Forecast
- 10.2. Market Share & Forecast
- 10.2.1. By Component
- 10.2.2. By Application
- 10.2.3. By Organization Size
- 10.2.4. By Country
- 10.3. South America: Country Analysis
- 10.3.1. Brazil Retail Edge Computing Market Outlook
- 10.3.1.1. Market Size & Forecast
- 10.3.1.2. Market Share & Forecast
- 10.3.1.2.1. By Component
- 10.3.1.2.2. By Application
- 10.3.1.2.3. By Organization Size
- 10.3.2. Colombia Retail Edge Computing Market Outlook
- 10.3.2.1. Market Size & Forecast
- 10.3.2.2. Market Share & Forecast
- 10.3.2.2.1. By Component
- 10.3.2.2.2. By Application
- 10.3.2.2.3. By Organization Size
- 10.3.3. Argentina Retail Edge Computing Market Outlook
- 10.3.3.1. Market Size & Forecast
- 10.3.3.2. Market Share & Forecast
- 10.3.3.2.1. By Component
- 10.3.3.2.2. By Application
- 10.3.3.2.3. By Organization Size
- 10.3.4. Chile Retail Edge Computing Market Outlook
- 10.3.4.1. Market Size & Forecast
- 10.3.4.2. Market Share & Forecast
- 10.3.4.2.1. By Component
- 10.3.4.2.2. By Application
- 10.3.4.2.3. By Organization Size
11. Middle East & Africa Retail Edge Computing Market Outlook
- 11.1. Market Size & Forecast
- 11.2. Market Share & Forecast
- 11.2.1. By Component
- 11.2.2. By Application
- 11.2.3. By Organization Size
- 11.2.4. By Country
- 11.3. Middle East & Africa: Country Analysis
- 11.3.1. Saudi Arabia Retail Edge Computing Market Outlook
- 11.3.1.1. Market Size & Forecast
- 11.3.1.2. Market Share & Forecast
- 11.3.1.2.1. By Component
- 11.3.1.2.2. By Application
- 11.3.1.2.3. By Organization Size
- 11.3.2. UAE Retail Edge Computing Market Outlook
- 11.3.2.1. Market Size & Forecast
- 11.3.2.2. Market Share & Forecast
- 11.3.2.2.1. By Component
- 11.3.2.2.2. By Application
- 11.3.2.2.3. By Organization Size
- 11.3.3. South Africa Retail Edge Computing Market Outlook
- 11.3.3.1. Market Size & Forecast
- 11.3.3.2. Market Share & Forecast
- 11.3.3.2.1. By Component
- 11.3.3.2.2. By Application
- 11.3.3.2.3. By Organization Size
- 11.3.4. Turkey Retail Edge Computing Market Outlook
- 11.3.4.1. Market Size & Forecast
- 11.3.4.2. Market Share & Forecast
- 11.3.4.2.1. By Component
- 11.3.4.2.2. By Application
- 11.3.4.2.3. By Organization Size
- 11.3.5. Israel Retail Edge Computing Market Outlook
- 11.3.5.1. Market Size & Forecast
- 11.3.5.2. Market Share & Forecast
- 11.3.5.2.1. By Component
- 11.3.5.2.2. By Application
- 11.3.5.2.3. By Organization Size
12. Market Dynamics
- 12.1. Drivers
- 12.2. Challenges
13. Market Trends and Developments
14. Company Profiles
- 14.1. Amazon.com, Inc.
- 14.1.1. Business Overview
- 14.1.2. Key Revenue and Financials
- 14.1.3. Recent Developments
- 14.1.4. Key Personnel/Key Contact Person
- 14.1.5. Key Product/Services Offered
- 14.2. Microsoft Corporation
- 14.2.1. Business Overview
- 14.2.2. Key Revenue and Financials
- 14.2.3. Recent Developments
- 14.2.4. Key Personnel/Key Contact Person
- 14.2.5. Key Product/Services Offered
- 14.3. IBM Corporation
- 14.3.1. Business Overview
- 14.3.2. Key Revenue and Financials
- 14.3.3. Recent Developments
- 14.3.4. Key Personnel/Key Contact Person
- 14.3.5. Key Product/Services Offered
- 14.4. Intel Corporation
- 14.4.1. Business Overview
- 14.4.2. Key Revenue and Financials
- 14.4.3. Recent Developments
- 14.4.4. Key Personnel/Key Contact Person
- 14.4.5. Key Product/Services Offered
- 14.5. Cisco Systems, Inc.
- 14.5.1. Business Overview
- 14.5.2. Key Revenue and Financials
- 14.5.3. Recent Developments
- 14.5.4. Key Personnel/Key Contact Person
- 14.5.5. Key Product/Services Offered
- 14.6. Hewlett Packard Enterprise Company
- 14.6.1. Business Overview
- 14.6.2. Key Revenue and Financials
- 14.6.3. Recent Developments
- 14.6.4. Key Personnel/Key Contact Person
- 14.6.5. Key Product/Services Offered
- 14.7. NVIDIA Corporation
- 14.7.1. Business Overview
- 14.7.2. Key Revenue and Financials
- 14.7.3. Recent Developments
- 14.7.4. Key Personnel/Key Contact Person
- 14.7.5. Key Product/Services Offered
- 14.8. Google LLC
- 14.8.1. Business Overview
- 14.8.2. Key Revenue and Financials
- 14.8.3. Recent Developments
- 14.8.4. Key Personnel/Key Contact Person
- 14.8.5. Key Product/Services Offered
- 14.9. Oracle Corporation
- 14.9.1. Business Overview
- 14.9.2. Key Revenue and Financials
- 14.9.3. Recent Developments
- 14.9.4. Key Personnel/Key Contact Person
- 14.9.5. Key Product/Services Offered
- 14.10. Qualcomm Incorporated
- 14.10.1. Business Overview
- 14.10.2. Key Revenue and Financials
- 14.10.3. Recent Developments
- 14.10.4. Key Personnel/Key Contact Person
- 14.10.5. Key Product/Services Offered
15. Strategic Recommendations
16. About Us & Disclaimer
