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Global Call Center AI Market to reach USD 4.9 billion by 2028.

Global Call Center AI Market Size study, by Component (Solutions, Services ), by Mode of Channel ( Phone, Social Media, Chat, Email or Text, Website), By Application ( Workforce Optimization, Predictive Call Routing,Journey Orchestration , Agent Performance Management, Sentiment Analysis, Appointment Scheduling, Other Applications), By Deployment Mode (Cloud, On-premises), By Organization Size ( SMEs, Large Enterprises), by Verticals (BFSI, Media & entertainment, Retail & eCommerce, Healthcare & Life Sciences, Travel & Hospitality, IT & Telecom, Transportation & Logistics, Others) and Regional Forecasts 2022-2028

Product Code: ICTICTS-54476743
Publish Date: 31-05-2022
Page: 200

Global Call Center AI Market is valued approximately USD 1.3 billion in 2021 and is anticipated to grow with a healthy growth rate of more than 21% over the forecast period 2022-2028. AI can provide call centre agents with detailed historical data and insights about a customer, allowing them to provide cross-selling and up-selling opportunities. AI-enabled chatbots and virtual agents can also be used by businesses to automate repetitive and manual processes such as order placement, balance inquiries, general inquiries, technical assistance, and other customer services. Furthermore, call centre AI solutions enable businesses to strengthen and improve their contact centres without requiring deep AI expertise, all while lowering operational costs.The major factors driving the growth of the Call Center AI market are increased data and increased customer engagement via social media platforms. Some of the benefits of AI deployment mentioned by respondents were 24-hour service, quick responses to inquiries, and answers to simple questions.In some cases, dealing with customer inquiries in real time may be difficult for a customer service representative because customers may not understand the context of their inquiry. Better data analytics skills have been required as a result of this. However, unsupervised self-learning of chatbots, on the other hand, is a major impediment to the adoption of call centre AI solutions, as self-training of autonomous virtual agents through complex data and unsupervised learning algorithms is a difficult task. Furthermore, advanced AI and ML systems are employed by call centres in a variety of industries, including BFSI, IT & telecom, healthcare, and retail, to forecast outcomes and automate subsequent procedures. End use industry are widely adopting such call center AI. For instance, Avaya engaged into a strategic alliance with Alcatel -Lucent in March 2022 to increase the availability of Avaya’s OneCloud CCaaS composable solutions to Alcatel -Lucent’s worldwide customer base while also making the digital networking solutions available to Avaya customers globally. Sprinklr and Google Cloud will collaborate in January 2022 to help organisations reinvent their customer experience management strategies. Sprinklr collaborated with Google Cloud to speed their go-to-market strategy and increase client awareness. Sprinklr will collaborate closely with the worldwide salesforce, leveraging deep ties with businesses who have chosen to develop on Google Cloud.

The key regions considered for the global Call Center AI Marketstudy includes Asia Pacific, North America, Europe, Latin America, and Rest of the World.North America is expected to hold the largest share of the call centre AI market because of the region’s call centres’ early adoption of call centre AI technologies,. Call centre AI solutions and services are highly effective in most organisations and verticals in North America, owing to the growing need to provide businesses with a way to operationalize and extract more value from data assets. Increased smartphone adoption, as well as technological advancements in call centres, drive adoption across North America.However, Asia-Pacific is expected to grow at the fastest rate during the forecast period. Massive investments in call centre solutions by countries such as China, Japan, Thailand, India, and Indonesia are propelling the market forward. Furthermore, factors such as the presence of a large customer base, a significant number of SMEs, the rise of the e-commerce industry, and government initiatives supporting business process automation fuel the call centre AI industry growth.

Major market player included in this report are:
IBM
Microsoft
Oracle
Aws
Google
Sap
Avaya
Nice
Nuance Communications
Genesys
The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:
ByComponent
Solutions
Services

By Mode of Channel:
Phone
Social Media
Chat
Email or Text
Website
By Application:
Workforce Optimization
Predictive Call Routing
Journey Orchestration
Agent Performance Management
Sentiment Analysis
Appointment Scheduling
Other Applications

By Deployment Mode:
Cloud
On-premises
By Organization Size:
SMEs
Large Enterprises
By Verticals:
BFSI
Media & entertainment
Retail & eCommerce
Healthcare & Life Sciences
Travel & Hospitality
IT & Telecom
Transportation & Logistics
Others

By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2018, 2019, 2020
Base year – 2021
Forecast period – 2022 to 2028

Target Audience of the Global Call Center AI Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers
Investors

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2028 (USD Billion)
1.2.1. Call Center AI Market , by Region, 2020-2028 (USD Billion)
1.2.2. Call Center AI Market , by Component ,2020-2028 (USD Billion)
1.2.3. Call Center AI Market , by Mode of Channel,2020-2028 (USD Billion)
1.2.4. Call Center AI Market , by Application ,2020-2028 (USD Billion)
1.2.5. Call Center AI Market , by Deployment Mode ,2020-2028 (USD Billion)
1.2.6. Call Center AI Market , by Organization Size,2020-2028 (USD Billion)
1.2.7. Call Center AI Market , by Verticals,2020-2028 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Call Center AI Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Call Center AI Market Dynamics
3.1. Call Center AI Market Impact Analysis (2020-2028)
3.1.1. Market Drivers
3.1.1.1. Advent of AI in call center to offer enhanced customer support services and better experience
3.1.1.2. Rising development in customer engagement through social media platforms
3.1.2. Market Challenges
3.1.2.1. Unsupervised learning
3.1.3. Market Opportunities
3.1.3.1. Advancements in AI and ML to facilitate real-time actionable insights
Chapter 4. Global Call Center AI Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2028)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
4.5. Top investment opportunity
4.6. Top winning strategies
Chapter 5. Risk Assessment: COVID-19 Impact
5.1.1. Assessment of the overall impact of COVID-19 on the industry
5.1.2. Pre COVID-19 and post COVID-19 Market scenario
Chapter 6. Global Call Center AI Market , by Component
6.1. Market Snapshot
6.2. Global Call Center AI Market by Component , Performance – Potential Analysis
6.3. Global Call Center AI Market Estimates & Forecasts by Component 2018-2028 (USD Billion)
6.4. Call Center AI Market , Sub Segment Analysis
6.4.1. Solutions
6.4.2. Services
Chapter 7. Global Call Center AI Market , by Mode of Channel
7.1. Market Snapshot
7.2. Global Call Center AI Market by Mode of Channel, Performance – Potential Analysis
7.3. Global Call Center AI Market Estimates & Forecasts by Mode of Channel2018-2028 (USD Billion)
7.3.1. Phone
7.3.2. Social Media
7.3.3. Chat
7.3.4. Email or Text
7.3.5. Website
Chapter 8. Global Call Center AI Market , by Application
8.1. Market Snapshot
8.2. Global Call Center AI Market by Application , Performance – Potential Analysis
8.3. Global Call Center AI Market Estimates & Forecasts by Application 2018-2028 (USD Billion)
8.4. Call Center AI Market , Sub Segment Analysis
8.4.1. Workforce Optimization
8.4.2. Predictive Call Routing
8.4.3. Journey Orchestration
8.4.4. Agent Performance Management
8.4.5. Sentiment Analysis
8.4.6. Appointment Scheduling
8.4.7. Other Applications
Chapter 9. Global Call Center AI Market , by Deployment Mode
9.1. Market Snapshot
9.2. Global Call Center AI Market by Deployment Mode , Performance – Potential Analysis
9.3. Global Call Center AI Market Estimates & Forecasts by Deployment Mode 2018-2028 (USD Billion)
9.4. Call Center AI Market , Sub Segment Analysis
9.4.1. Cloud
9.4.2. On-premises
Chapter 10. Global Call Center AI Market , by Organization Size
10.1. Market Snapshot
10.2. Global Call Center AI Market by Organzisation Size, Performance – Potential Analysis
10.3. Global Call Center AI Market Estimates & Forecasts by Organzisation Size 2018-2028 (USD Billion)
10.4. Call Center AI Market , Sub Segment Analysis
10.4.1. SMEs
10.4.2. Large Enterprises
Chapter 11. Global Call Center AI Market , by Verticals
11.1. Market Snapshot
11.2. Global Call Center AI Market by Verticals, Performance – Potential Analysis
11.3. Global Call Center AI Market Estimates & Forecasts by Verticals 2018-2028 (USD Billion)
11.4. Call Center AI Market , Sub Segment Analysis
11.4.1. BFSI
11.4.2. Media & entertainment
11.4.3. Retail & eCommerce
11.4.4. Healthcare & Life Sciences
11.4.5. Travel & Hospitality
11.4.6. IT & Telecom
11.4.7. Transportation & Logistics
11.4.8. Others

Chapter 12. Global Call Center AI Market , Regional Analysis
12.1. Call Center AI Market , Regional Market Snapshot
12.2. North America Call Center AI Market
12.2.1. U.S.CallCenter AI Market
12.2.1.1. Component breakdown estimates & forecasts, 2018-2028
12.2.1.2. Mode of Channelbreakdown estimates & forecasts, 2018-2028
12.2.1.3. Application breakdown estimates & forecasts, 2018-2028
12.2.1.4. Deployment Mode breakdown estimates & forecasts, 2018-2028
12.2.1.5. Organization Size breakdown estimates & forecasts, 2018-2028
12.2.1.6. Verticals breakdown estimates & forecasts, 2018-2028

12.2.2. CanadaCallCenter AI Market
12.3. Europe Call Center AI Market Snapshot
12.3.1. U.K. Call Center AI Market
12.3.2. Germany Call Center AI Market
12.3.3. France Call Center AI Market
12.3.4. Spain Call Center AI Market
12.3.5. Italy Call Center AI Market
12.3.6. Rest of EuropeCallCenter AI Market
12.4. Asia-PacificCallCenter AI Market Snapshot
12.4.1. China Call Center AI Market
12.4.2. India Call Center AI Market
12.4.3. JapanCallCenter AI Market
12.4.4. Australia Call Center AI Market
12.4.5. South Korea Call Center AI Market
12.4.6. Rest of Asia PacificCallCenter AI Market
12.5. Latin America Call Center AI Market Snapshot
12.5.1. Brazil Call Center AI Market
12.5.2. Mexico Call Center AI Market
12.6. Rest of The World Call Center AI Market

Chapter 13. Competitive Intelligence
13.1. Top Market Strategies
13.2. Company Profiles
13.2.1. IBM
13.2.1.1. Key Information
13.2.1.2. Overview
13.2.1.3. Financial (Subject to Data Availability)
13.2.1.4. Mode of ChannelSummary
13.2.1.5. Recent Developments
13.2.2. Microsoft
13.2.3. Oracle
13.2.4. Aws
13.2.5. Google
13.2.6. Sap
13.2.7. Avaya
13.2.8. Nice
13.2.9. Nuance Communications
13.2.10. Genesys
Chapter 14. Research Process
14.1. Research Process
14.1.1. Data Mining
14.1.2. Analysis
14.1.3. Market Estimation
14.1.4. Validation
14.1.5. Publishing
14.2. Research Attributes
14.3. Research Assumption

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Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
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Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
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