予測分析のアメリカ市場2020–2027:コンポーネント別(ソリューション、サービス、接続技術)、展開別、企業規模別、産業別

【英語タイトル】U.S. Predictive Analytics Market by Component (Solution, Services, and Connectivity Technology), Deployment (On-premise and Cloud), Enterprise Size (Large Enterprises and Small & Medium-sized Enterprises), and Industry Vertical (BFSI, Retail, IT & Telecom, Healthcare, Government, Manufacturing, and Others): Country Opportunity Analysis and Industry Forecast, 2020–2027

Allied Market Researchが出版した調査資料(AMR20OC060)・商品コード:AMR20OC060
・発行会社(調査会社):Allied Market Research
・発行日:2020年8月
・ページ数:174
・レポート言語:英語
・レポート形式:PDF
・納品方法:Eメール(受注後24時間以内)
・調査対象地域:アメリカ
・産業分野:IT
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本調査レポートでは、予測分析のアメリカ市場を広く調査・分析し、イントロダクション、エグゼクティブサマリー、市場動向、コンポーネント別分析、展開別分析、企業規模別分析、産業別分析、競争状況、企業情報など、以下の構成でまとめております。
・イントロダクション
・エグゼクティブサマリー
・市場動向
・予測分析のアメリカ市場規模:コンポーネント別
・予測分析のアメリカ市場規模:展開別
・予測分析のアメリカ市場規模:企業規模別
・予測分析のアメリカ市場規模:産業別
・競争状況
・企業情報
【レポートの概要】

The U.S. predictive analytics market size was valued at $2.49 billion in 2019 and is projected to reach $8.27 billion by 2027, growing at a CAGR of 16.1% from 2020 to 2027. Predictive analytics is the practice of using statistics and modeling techniques to extract information from current and historical datasets to predict potential future outcomes and trends. Rise in awareness among organizations about massive volume of data generated to predict future outcomes by using predictive analysis solutions is driving the growth of the market. In addition, increase in usage of internet coupled with the availability of several sources of accessing the internet has led increase in data generation. Thus, leveraging this data to make accurate business strategies and decisions optimizes the revenue, which is driving the demand for predictive analytics solutions.

Increase in the adoption of predictive modeling tools, surge in investments for Big Data supporting initiatives by governments and rise in adoption of Big Data technologies are some of the major factors that are driving the global predictive analytics market growth. However, dearth of skilled IT staff and high implementation cost are anticipated to restrict the market growth. On the contrary, integration of IoT and AI in predictive analytics and rise in demand for predictive analytics by SMEs are anticipated to provide lucrative growth opportunities for the expansion of global predictive analytics market during the analysis period.
Based on connectivity technology, the Wi-Fi segment has dominated the U.S. predictive analytics market share in 2019 and is projected to maintain its dominance in the country. However, the 4G+ segment is expected to attain significant growth during the forecast period.

According to industry vertical, the BFSI segment accounted for the highest share in predictive analytics market share in 2019, as regulatory framework has become more complex across the world. Moreover, predictive analytics in BFSI facilitates capital planning, financial analytics, credit risk management, and insurance risk management, which further fuel the growth of this segment.
The report focuses on the growth prospects, restraints, and trends of the U.S. predictive analytics market analysis. The study provides Porter’s five forces analysis to understand the impact of various factors such as bargaining power of suppliers, competitive intensity of competitors, threat of new entrants, threat of substitutes, and bargaining power of buyers in the U.S. predictive analytics market.

Segment review
The U.S. predictive analytics market analysis is segmented based on component, deployment, enterprise size, and industry vertical. In terms of component, it is bifurcated into solution, services, and connectivity technology. The connectivity technology segment is further bifurcated into wired, cellular technology (2G & 3G and 4G+), Wi-Fi, Bluetooth, and others. Based on deployment, the market is divided into on-premise and cloud. Based on enterprise size, it is classified into large enterprises and small & medium-sized enterprises. According to industry vertical, it is segmented into BFSI, retail, IT & telecom, healthcare, government, manufacturing, and others.

Top impacting factors
Rise in adoption of predictive modeling tools
Predictive modeling tools help in accelerating the methods of developing and analyzing predictive models that are used by organizations for conducting operations such as customer analytics, risk reporting, threat management, and product innovation. Therefore, the adoption of predictive modeling tools has amplified in recent years supplemented with interactive visualization and automation across several industrial sectors. Furthermore, the use of predictive modeling tools for social media advertising, e-mail campaigns, and cognitive analysis of customers has led to growth in sales and increase in customer retention. Thus, as several industries such as BFSI, retail & e-commerce, and manufacturing are adopting predictive modelling tools, which is expected to contribute toward the predictive analytics market growth.

Increased adoption of Big Data technologies
Increase in need to store, process, and analyze large volume of structured as well as unstructured datasets has driven many organizations and individuals to adopt advanced & big data analytics, which is likely to drive the growth of the market. Moreover, the volume of data captured by organization is continuously increasing due to rise in trend of Internet of Things (IoT), social media, and multimedia, which has produced a prodigious flow of data. Furthermore, due to massive amount of data generation in different industry verticals, investment in Big Data will increase, which, in turn, is expected to fuel the growth of the predictive analytics market.

Advent of modern technologies such as IoT and AI
IoT environment is proliferating the adoption of predictive analytics, as it facilitates predictive maintenance leading to enhanced energy efficiency and higher levels of production uptime. In addition, numerous industries are demanding a solution that can predict when their machines are going to get damaged. They are implementing sensors that can analyze the patterns and predict the outcome or any anomaly in the operation of machines. This as a result enables industries to reduce the downtime of the machine and increase the productivity. This benefit is expected to provide lucrative opportunities to the predictive analytics industry in upcoming years.

Key benefits for stakeholders
• The study provides an in-depth analysis of the U.S. predictive analytics market share along with the current & future trends to elucidate the imminent investment pockets.
• Information about key drivers, restrains, and opportunities and their impact analysis on the market size is provided in the report.
• Porter’s five forces analysis illustrates the potency of the buyers and suppliers operating in the industry.
• An extensive analysis of the key segments of the industry helps to understand the U.S. predictive analytics market trends.
• The quantitative analysis of the U.S. predictive analytics market from 2020 to 2027 is provided to determine the market potential.

Key market segments
By Component
• Solution
• Customer Analytics
• Financial Analytics
• Risk Analytics
• Marketing & Sales Analytics
• Supply Chain Analytics
• Network Analytics
• Web & Social Media Analytics
• Others
• Services
• Connectivity Technology

By Deployment
• On-premise
• Cloud

By Enterprise Size
• Large Enterprises
• Small & Medium-sized Enterprises

By Industry Vertical
• BFSI
• Retail
• IT & Telecom
• Healthcare
• Government
• Manufacturing
• Others

【レポートの目次】

CHAPTER 1:INTRODUCTION

1.1.REPORT DESCRIPTION
1.2.KEY MARKET SEGMENTS
1.3.KEY BENEFITS
1.4.RESEARCH METHODOLOGY

1.4.1.Primary research
1.4.2.Secondary research
1.4.3.Analyst tools and models

CHAPTER 2:EXECUTIVE SUMMARY

2.1.CXO PERSPECTIVE

CHAPTER 3:MARKET LANDSCAPE

3.1.MARKET DEFINITION AND SCOPE
3.2.KEY FINDINGS

3.2.1.Top investment pockets
3.2.2.Top winning strategies

3.3.PORTER’S FIVE FORCES ANALYSIS

3.3.1.Bargaining power of suppliers
3.3.2.Threat of new entrants
3.3.3.Threat of substitutes
3.3.4.Competitive rivalry
3.3.5.Bargaining power among buyers

3.4.MARKET SHARE ANALYSIS/TOP PLAYER POSITIONING 2019
3.5.MARKET DYNAMICS

3.5.1.Drivers
3.5.2.Restraints
3.5.3.Opportunities

3.6.IMPACT ANALYSIS OF COVID-19

CHAPTER 4:PREDICTIVE ANALYTICS MARKET, BY COMPONENT

4.1.Component

4.1.1.Solution

4.1.1.1.Customer analytics
4.1.1.2.Financial analytics
4.1.1.3.Risk analytics
4.1.1.4.Marketing analytics
4.1.1.5.Supply chain analytics
4.1.1.6.Network analytics
4.1.1.7.Web & social media analytics

4.1.2.Service

4.1.2.1.Professional services
4.1.2.2.Managed services

4.1.3.Connectivity Technology

4.1.3.1.Wired
4.1.3.2.Cellular Technologies

4.1.3.2.1.3G
4.1.3.2.2.4G+

4.1.3.3.Wi-Fi
4.1.3.4.Bluetooth
4.1.3.5.Others

CHAPTER 5:PREDICTIVE ANALYTICS MARKET, BY DEPLOYMENT MODEL

5.1.1.On-Premise
5.1.2.Cloud

CHAPTER 6:PREDICTIVE ANALYTICS MARKET, BY ENTERPRISE SIZE

6.1.1.Large Enterprises
6.1.2.Small & Medium-sized Enterprises

CHAPTER 7:PREDICTIVE ANALYTICS MARKET, BY INDUSTRY VERTICAL

7.1.1.BFSI
7.1.2.Retail
7.1.3.IT & Telecom
7.1.4.Healthcare
7.1.5.Government
7.1.6.Manufacturing
7.1.7.Others



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