The field of communications is traditionally built on precise mathematical models that are well understood and have been shown to work exceptionally well for many practical applications. Unfortunately, communication systems designers have been forced to push the boundaries to such an extent that in many applications conventional mathematical models and signal processing techniques are no longer sufficient to accurately describe the encountered complex scenarios. Specifically, there is an increasing number of cases where rigorous mathematical models are either not known or are entirely impractical from a computational perspective. Machine learning methods can come to the rescue as they do not require rigid pre-defined models and can extract meaningful structure from large amounts of data to provide useful results.
The global market for Machine Learning in Communication was estimated to be worth US$ million in 2023 and is forecast to a readjusted size of US$ million by 2030 with a CAGR of % during the forecast period 2024-2030.
The Global Mobile Economy Development Report 2023 released by GSMA Intelligence pointed out that by the end of 2022, the number of global mobile users would exceed 5.4 billion. The mobile ecosystem supports 16 million jobs directly and 12 million jobs indirectly.
According to our Communications Research Centre, in 2022, the global communication equipment was valued at US$ 100 billion. The U.S. and China are powerhouses in the manufacture of communications equipment. According to data from the Ministry of Industry and Information Technology of China, the cumulative revenue of telecommunications services in 2022 was 楼1.58 trillion, an increase of 8% over the previous year. The total amount of telecommunications business calculated at the price of the previous year reached 楼1.75 trillion, a year-on-year increase of 21.3%. In the same year, the fixed Internet broadband access business revenue was 楼240.2 billion, an increase of 7.1% over the previous year, and its proportion in the telecommunications business revenue decreased from 15.3% in the previous year to 15.2%, driving the telecommunications business revenue to increase by 1.1 percentage points.
Report Scope
This report aims to provide a comprehensive presentation of the global market for Machine Learning in Communication, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Machine Learning in Communication by region & country, by Type, and by Application.
The Machine Learning in Communication market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning in Communication.
Market Segmentation
By Company
Amazon
IBM
Microsoft
Google
Nextiva
Nexmo
Twilio
Dialpad
Cisco
RingCentral
Segment by Type:
Cloud-Based
On-Premise
Segment by Application
Network Optimization
Predictive Maintenance
Virtual Assistants
Robotic Process Automation (RPA)
By Region
North America
U.S.
Canada
Europe
Germany
France
U.K.
Italy
Russia
Asia-Pacific
China
Japan
South Korea
China Taiwan
Southeast Asia
India
Latin America
Mexico
Brazil
Argentina
Middle East & Africa
Turkey
Saudi Arabia
UAE
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Machine Learning in Communication manufacturers competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Revenue of Machine Learning in Communication in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of Machine Learning in Communication in country level. It provides sigmate data by Type, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.
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1 Market Overview
1.1 Machine Learning in Communication Product Introduction
1.2 Global Machine Learning in Communication Market Size Forecast
1.3 Machine Learning in Communication Market Trends & Drivers
1.3.1 Machine Learning in Communication Industry Trends
1.3.2 Machine Learning in Communication Market Drivers & Opportunity
1.3.3 Machine Learning in Communication Market Challenges
1.3.4 Machine Learning in Communication Market Restraints
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Competitive Analysis by Company
2.1 Global Machine Learning in Communication Players Revenue Ranking (2023)
2.2 Global Machine Learning in Communication Revenue by Company (2019-2024)
2.3 Key Companies Machine Learning in Communication Manufacturing Base Distribution and Headquarters
2.4 Key Companies Machine Learning in Communication Product Offered
2.5 Key Companies Time to Begin Mass Production of Machine Learning in Communication
2.6 Machine Learning in Communication Market Competitive Analysis
2.6.1 Machine Learning in Communication Market Concentration Rate (2019-2024)
2.6.2 Global 5 and 10 Largest Companies by Machine Learning in Communication Revenue in 2023
2.6.3 Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Machine Learning in Communication as of 2023)
2.7 Mergers & Acquisitions, Expansion
3 Segmentation by Type
3.1 Introduction by Type
3.1.1 Cloud-Based
3.1.2 On-Premise
3.2 Global Machine Learning in Communication Sales Value by Type
3.2.1 Global Machine Learning in Communication Sales Value by Type (2019 VS 2023 VS 2030)
3.2.2 Global Machine Learning in Communication Sales Value, by Type (2019-2030)
3.2.3 Global Machine Learning in Communication Sales Value, by Type (%) (2019-2030)
4 Segmentation by Application
4.1 Introduction by Application
4.1.1 Network Optimization
4.1.2 Predictive Maintenance
4.1.3 Virtual Assistants
4.1.4 Robotic Process Automation (RPA)
4.2 Global Machine Learning in Communication Sales Value by Application
4.2.1 Global Machine Learning in Communication Sales Value by Application (2019 VS 2023 VS 2030)
4.2.2 Global Machine Learning in Communication Sales Value, by Application (2019-2030)
4.2.3 Global Machine Learning in Communication Sales Value, by Application (%) (2019-2030)
5 Segmentation by Region
5.1 Global Machine Learning in Communication Sales Value by Region
5.1.1 Global Machine Learning in Communication Sales Value by Region: 2019 VS 2023 VS 2030
5.1.2 Global Machine Learning in Communication Sales Value by Region (2019-2024)
5.1.3 Global Machine Learning in Communication Sales Value by Region (2025-2030)
5.1.4 Global Machine Learning in Communication Sales Value by Region (%), (2019-2030)
5.2 North America
5.2.1 North America Machine Learning in Communication Sales Value, 2019-2030
5.2.2 North America Machine Learning in Communication Sales Value by Country (%), 2023 VS 2030
5.3 Europe
5.3.1 Europe Machine Learning in Communication Sales Value, 2019-2030
5.3.2 Europe Machine Learning in Communication Sales Value by Country (%), 2023 VS 2030
5.4 Asia Pacific
5.4.1 Asia Pacific Machine Learning in Communication Sales Value, 2019-2030
5.4.2 Asia Pacific Machine Learning in Communication Sales Value by Country (%), 2023 VS 2030
5.5 South America
5.5.1 South America Machine Learning in Communication Sales Value, 2019-2030
5.5.2 South America Machine Learning in Communication Sales Value by Country (%), 2023 VS 2030
5.6 Middle East & Africa
5.6.1 Middle East & Africa Machine Learning in Communication Sales Value, 2019-2030
5.6.2 Middle East & Africa Machine Learning in Communication Sales Value by Country (%), 2023 VS 2030
6 Segmentation by Key Countries/Regions
6.1 Key Countries/Regions Machine Learning in Communication Sales Value Growth Trends, 2019 VS 2023 VS 2030
6.2 Key Countries/Regions Machine Learning in Communication Sales Value
6.3 United States
6.3.1 United States Machine Learning in Communication Sales Value, 2019-2030
6.3.2 United States Machine Learning in Communication Sales Value by Type (%), 2023 VS 2030
6.3.3 United States Machine Learning in Communication Sales Value by Application, 2023 VS 2030
6.4 Europe
6.4.1 Europe Machine Learning in Communication Sales Value, 2019-2030
6.4.2 Europe Machine Learning in Communication Sales Value by Type (%), 2023 VS 2030
6.4.3 Europe Machine Learning in Communication Sales Value by Application, 2023 VS 2030
6.5 China
6.5.1 China Machine Learning in Communication Sales Value, 2019-2030
6.5.2 China Machine Learning in Communication Sales Value by Type (%), 2023 VS 2030
6.5.3 China Machine Learning in Communication Sales Value by Application, 2023 VS 2030
6.6 Japan
6.6.1 Japan Machine Learning in Communication Sales Value, 2019-2030
6.6.2 Japan Machine Learning in Communication Sales Value by Type (%), 2023 VS 2030
6.6.3 Japan Machine Learning in Communication Sales Value by Application, 2023 VS 2030
6.7 South Korea
6.7.1 South Korea Machine Learning in Communication Sales Value, 2019-2030
6.7.2 South Korea Machine Learning in Communication Sales Value by Type (%), 2023 VS 2030
6.7.3 South Korea Machine Learning in Communication Sales Value by Application, 2023 VS 2030
6.8 Southeast Asia
6.8.1 Southeast Asia Machine Learning in Communication Sales Value, 2019-2030
6.8.2 Southeast Asia Machine Learning in Communication Sales Value by Type (%), 2023 VS 2030
6.8.3 Southeast Asia Machine Learning in Communication Sales Value by Application, 2023 VS 2030
6.9 India
6.9.1 India Machine Learning in Communication Sales Value, 2019-2030
6.9.2 India Machine Learning in Communication Sales Value by Type (%), 2023 VS 2030
6.9.3 India Machine Learning in Communication Sales Value by Application, 2023 VS 2030
7 Company Profiles
7.1 Amazon
7.1.1 Amazon Profile
7.1.2 Amazon Main Business
7.1.3 Amazon Machine Learning in Communication Products, Services and Solutions
7.1.4 Amazon Machine Learning in Communication Revenue (US$ Million) & (2019-2024)
7.1.5 Amazon Recent Developments
7.2 IBM
7.2.1 IBM Profile
7.2.2 IBM Main Business
7.2.3 IBM Machine Learning in Communication Products, Services and Solutions
7.2.4 IBM Machine Learning in Communication Revenue (US$ Million) & (2019-2024)
7.2.5 IBM Recent Developments
7.3 Microsoft
7.3.1 Microsoft Profile
7.3.2 Microsoft Main Business
7.3.3 Microsoft Machine Learning in Communication Products, Services and Solutions
7.3.4 Microsoft Machine Learning in Communication Revenue (US$ Million) & (2019-2024)
7.3.5 Google Recent Developments
7.4 Google
7.4.1 Google Profile
7.4.2 Google Main Business
7.4.3 Google Machine Learning in Communication Products, Services and Solutions
7.4.4 Google Machine Learning in Communication Revenue (US$ Million) & (2019-2024)
7.4.5 Google Recent Developments
7.5 Nextiva
7.5.1 Nextiva Profile
7.5.2 Nextiva Main Business
7.5.3 Nextiva Machine Learning in Communication Products, Services and Solutions
7.5.4 Nextiva Machine Learning in Communication Revenue (US$ Million) & (2019-2024)
7.5.5 Nextiva Recent Developments
7.6 Nexmo
7.6.1 Nexmo Profile
7.6.2 Nexmo Main Business
7.6.3 Nexmo Machine Learning in Communication Products, Services and Solutions
7.6.4 Nexmo Machine Learning in Communication Revenue (US$ Million) & (2019-2024)
7.6.5 Nexmo Recent Developments
7.7 Twilio
7.7.1 Twilio Profile
7.7.2 Twilio Main Business
7.7.3 Twilio Machine Learning in Communication Products, Services and Solutions
7.7.4 Twilio Machine Learning in Communication Revenue (US$ Million) & (2019-2024)
7.7.5 Twilio Recent Developments
7.8 Dialpad
7.8.1 Dialpad Profile
7.8.2 Dialpad Main Business
7.8.3 Dialpad Machine Learning in Communication Products, Services and Solutions
7.8.4 Dialpad Machine Learning in Communication Revenue (US$ Million) & (2019-2024)
7.8.5 Dialpad Recent Developments
7.9 Cisco
7.9.1 Cisco Profile
7.9.2 Cisco Main Business
7.9.3 Cisco Machine Learning in Communication Products, Services and Solutions
7.9.4 Cisco Machine Learning in Communication Revenue (US$ Million) & (2019-2024)
7.9.5 Cisco Recent Developments
7.10 RingCentral
7.10.1 RingCentral Profile
7.10.2 RingCentral Main Business
7.10.3 RingCentral Machine Learning in Communication Products, Services and Solutions
7.10.4 RingCentral Machine Learning in Communication Revenue (US$ Million) & (2019-2024)
7.10.5 RingCentral Recent Developments
8 Industry Chain Analysis
8.1 Machine Learning in Communication Industrial Chain
8.2 Machine Learning in Communication Upstream Analysis
8.2.1 Key Raw Materials
8.2.2 Raw Materials Key Suppliers
8.2.3 Manufacturing Cost Structure
8.3 Midstream Analysis
8.4 Downstream Analysis (Customers Analysis)
8.5 Sales Model and Sales Channels
8.5.1 Machine Learning in Communication Sales Model
8.5.2 Sales Channel
8.5.3 Machine Learning in Communication Distributors
9 Research Findings and Conclusion
10 Appendix
10.1 Research Methodology
10.1.1 Methodology/Research Approach
10.1.2 Data Source
10.2 Author Details
10.3 Disclaimer
Amazon
IBM
Microsoft
Google
Nextiva
Nexmo
Twilio
Dialpad
Cisco
RingCentral
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*If Applicable.
