AI and Machine Learning Applications and Implications in Customer Support and Analytics
Editors
Md Shamim Hossain, Hajee Mohammad Danesh Science and Technology University, Bangladesh
Ree Ho, Taylor’s University, Malaysia
Goran Trajkovski, Western Governors University, United States
Call for Chapters
Proposals Submission Deadline: September 2, 2022
Full Chapters Due: December 1, 2022
Submission Date: December 1, 2022
Introduction
In the modern data-driven era, artificial intelligence (AI) and machine learning (ML) technologies that allow a computer to mimic intelligent human behavior are essential for organizations to achieve business excellence and assist organizations in extracting useful information from raw data. For decades, AI and ML have existed, but in the age of big data, this sort of analysis is in higher demand than ever, especially for customer support and analytics. Consequently, this book investigated the applications of AI and ML and how they can be implemented to enhance customer support and analytics at various levels of organizations.
Objective
AI and Machine Learning Applications and Implications in Customer Support and Analytics explores various artificial intelligence and machine learning models and methods for business applications, as well as algorithmic approaches for customer support and analytics in a variety of fields and applications in the modern data-driven era where data is arriving in greater variety and with more velocity. This book is ideal for marketing professionals, managers, business owners, researchers, practitioners, academicians, instructors, university libraries, and students, and covers topics such as artificial intelligence, machine learning, supervised learning, unsupervised learning, deep learning, customer sentiment analysis, customer emotional analysis, natural language processing, data mining, neural networks, ensemble learning, business analytics and analytical geared toward.
Target Audience
Academic and business leaders who want to use machine learning and artificial intelligence approaches from data science, machine learning, business intelligence, Big Data, data mining, statistics, and other related disciplines to put the data they've collected into action via insights and prescriptions are the primary target audience for this proposed publication. The topics discussed in the publication will be used by marketing professionals, managers, business owners, researchers, practitioners, academics, instructors, university libraries, and students to develop settings for data-driven decision-making in their respective fields. By evaluating the use of methodologies in the business sector, data scientists will contribute to and learn from the publication. Finally, this book may be considered as a reference book for data science and business analytics courses and training for graduate and postgraduate students at various universities and institutes.
Recommended Topics
* Artificial Intelligence * Machine Learning * Business analytics * Business Intelligence * Business Applications * Deep Learning * Using machine learning models to solve business problems * Managing the ML and AI functions in organizations * Customer Engagement * Customer Retention * Informing customer personas with data * Marketing Analytics approaches * Scaling up customer support with AI and ML * Integrating business rules with ML models for prescriptive intelligence
Submission Procedure
Researchers and practitioners are invited to submit on or before September 2, 2022, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by September 4, 2022 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by December 1, 2022, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.
Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, AI and Machine Learning Applications and Implications in Customer Support and Analytics. All manuscripts are accepted based on a double-blind peer review editorial process.
All proposals should be submitted through the eEditorial Discovery® online submission manager.
Publisher
This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2023.
Important Dates
September 2, 2022: Proposal Submission Deadline
September 4, 2022: Notification of Acceptance
December 1, 2022: Full Chapter Submission
January 29, 2023: Review Results Returned
March 12, 2023: Final Acceptance Notification
March 26, 2023: Final Chapter Submission
Inquiries
Md Shamim Hossain
Hajee Mohammad Danesh Science and Technology University
shamimuibe@yahoo.com
Ree Ho
Taylor’s University
reechanho@gmail.com
Goran Trajkovski
Western Governors University
goran.trajkovski@wgu.edu
Classifications Business and Management; Business and Management; Business and Management; Computer Science and Information Technology; Computer Science and Information Technology; Computer Science and Information Technology