Predictive Analytics and Generative AI for Data-Driven Marketing Strategies (eBook, PDF)
Redaktion: K, Hemachandran; T, Revathi; Wise, Jorge A.; Rodriguez, Raul Villamarin; Choudhury, Debdutta
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Predictive Analytics and Generative AI for Data-Driven Marketing Strategies (eBook, PDF)
Redaktion: K, Hemachandran; T, Revathi; Wise, Jorge A.; Rodriguez, Raul Villamarin; Choudhury, Debdutta
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The book provides an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions.
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The book provides an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis
- Seitenzahl: 290
- Erscheinungstermin: 10. Dezember 2024
- Englisch
- ISBN-13: 9781040188767
- Artikelnr.: 72273926
- Verlag: Taylor & Francis
- Seitenzahl: 290
- Erscheinungstermin: 10. Dezember 2024
- Englisch
- ISBN-13: 9781040188767
- Artikelnr.: 72273926
Dr. Hemachandran Kannan is a professor in the department of Artificial Intelligence & Business Analytics at School of Business, Woxsen University, India and holds the Zita Zoltay Paprika of Decision Sciences and Business Economics and Course5i- Chair Professor of Business Analytics and Machine Learning. He has been a passionate teacher with 15 years of teaching experience and 5 years of research experience. A strong educational professional with a scientific bent of mind, highly skilled in AI (Artificial Intelligence) & Business Analytics. After receiving a PhD in embedded systems, He started focusing on Interdisciplinary research. He served as an effective resource person at various national and international scientific conferences and panel discussions. He also gave lectures on topics related to Artificial Intelligence & Business Analytics. He was bestowed as best faculty at Woxsen University in 2021-2022 and in Ashoka Institute of Engineering & Technology in 2019 - 2020. He has rich working experience in Natural Language Processing, Computer Vision, Building Video recommendation systems, Building Chatbots for HR (Human Rights) policies and Education Sector, Automatic Interview processes, and Autonomous Robots. Dr. Hemachandran is currently working on various real-time use cases and projects in collaboration with Industries such as Advert flair, Nosh Technologies, Course5i and Apstek Corp. He has organized many International Conferences, Hackathons and Ideathon. He owed four patents to his credentials. He has a life membership in estimable professional bodies. An open-ended positive person who has a stupendous peer-reviewed publication record with more than 35 journals and international conference publications. As of now, he has authored 3 Books and edited 7 Books in CRC Press Taylor and Francis and IGI Global. Professor Debdutta Choudhury is an academician with 18 years of corporate experience spanning Manufacturing, BFSI, Technology and Education domain. Debdutta has spent most of his career in sales and marketing with exposure to sales strategy, sales management, corporate strategy, integrated marketing communication, corporate finance and general management. Debdutta teaches Marketing courses and has a research interest in technology in marketing, sustainability and pedagogical innovations. He has published several book chapters, cases and presented them in research conferences. At Woxsen, Debdutta handles various accreditations and new initiatives. Dr. Raul Villamarin Rodriguez is the Vice President, Woxsen University and holds the Steven Pinker Professor of Cognitive Psychology and Classavo Chair Professorship in Integrative Research and Digital Learning. Dr. Rodríguez is an Adjunct Professor at Universidad del Externado, Colombia and member of the International Advisory Board at IBS Ranepa, Russian Federation, and a member of the IAB, University of Pécs Faculty of Business and Economics. He is also part of the PRME i5 Expert Pedagogy Group - India representative. He holds a Ph.D. in Artificial Intelligence and Robotics Process Automation applications in Human Resources. Dr. Rodriguez's specific areas of expertise and interest are Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, Multi-agent Systems, Knowledge Engineering, and Quantum Artificial Intelligence. He has the experience and feels comfortable using Prolog, Java, C++, Python, R/RStudio, Julia, Swift, Scala, MySQL, and Spark, among others. He is a registered expert in Artificial intelligence, Intelligent Systems, and Multi-agent Systems at the European Commission, a nominee for the Forbes 30 Under 30 Europe 2020 list, and awardee in the Europe India 40 under 40 Leaders. Alongside this, he is a member of the GRLI Deans and Directors cohort. He is a regular keynote speaker and panel moderator at various national and international conferences or summits such as ML (Machine Learning) Conference (Singapore). Additionally, he is a member of the Harvard Business Review Advisory Council, the ETS Business School Advisory Council (BSAC) in India, and the Institute for Robotics Process Automation & Artificial Intelligence. He has co-authored two reference books: "New Age Leadership: A Critical Insight" and "Retail Store'e" and has more than 70 publications to his credit. He is a weekly contributing writer to various magazines in the field of analytics and emerging technologies. Alongside this, he is a journal reviewer and associate editor in various publications such as IEEE (Institution of Electrical & Electronics Engineers). Dr. Jorge A. Wise got a PhD from EGADE Business School. He stands out in professional activities, from 1980-2000 working in national and international firms. In 1998 he joined the Monterrey Tech (ITESM) as professor of Marketing and International Business. In 2014 is appointed Director fo the Center or Excellence for Competitiveness and Entrepreneurship at CETYS University. Later, he is appointed as Dean of the brand new CETYS Graduate School of Business. In those years, he is recognized as a member of Mexican National Research System from CONACYT (The Mexican Council for Science and Technology). Dr. Wise is an international professor working in the USA, Latin America and Europe. His latest appointments are at CEIPA Business School, Colombia and at IESEG School of Management, France. He is also a visiting professor at WOXSEN University, Hyderabad, India and Pforzheim University, Germany. Recognized for his work in marketing, international business, strategic management, and family businesses. Revathi T is an academician with 7 years of research experience in computer vision and 3 years of teaching in universities. Revathi has spent most of his career in research with exposure to computer vision, applied mathematics, machine learning and deep learning.Revathi teaches data analytics courses and has a research interest in technology in artificial Intelligence. She has published several research papers in journals and conferences. At Woxsen, Revathi is one of the members in AI Research Centre.
Chapter 1: Introduction to Predictive Analysis and Generative AI. Chapter
2: Fundamentals of data-driven marketing. Chapter 3: Future trends in
Predictive Analytics and Generative AI for Marketing. Chapter 4: Analytical
Prospective on Fashion Industry: Data Driven Strategies. Chapter 5:
Predictive analytics techniques for marketing. Chapter 6: Customer
targeting and segmentation. Chapter 7: Personalised Marketing and
Recommendation Systems. Chapter 8: Pricing Optimization Using Predictive
Analytics. Chapter 9: Churn Prediction and Customer Retention. Chapter 10:
Marketing Campaign Optimization. Chapter 11: Customer Retention Using
Machine Learning Techniques in Human Resource Industry: A Systemic
Literature. Chapter 12: A Persona-Based Approach for Churn Prediction and
Retention Strategies Driven by Predictive Analytics and Generative AI.
Chapter 13: Sentiment Analysis and Social Media Marketing. Chapter 14:
Generative AI Techniques for Marketing. Chapter 15: Privacy and Ethical
Considerations in Data-Driven Marketing. Chapter 16: Price Prediction and
Optimization in Predictive Analytics and Generative AI for Data-Driven
Marketing Strategies. Chapter 17: Synthetic Data Generation for Marketing
Insights. Chapter 18: Measuring Marketing Effectiveness and Return on
Investment. Chapter 19: Emerging Technologies Shaping the Future of
Marketing. Chapter 20: Customer Segmentation Techniques in Predictive
Analytics and Generative AI for Data-Driven Marketing Strategies. Chapter
21: Case Studies in Data-Driven Marketing. Chapter 22: Case Studies in
Churn Prediction and Customer Retention. Chapter 23: Leveraging Generative
AI for Personal Branding.
2: Fundamentals of data-driven marketing. Chapter 3: Future trends in
Predictive Analytics and Generative AI for Marketing. Chapter 4: Analytical
Prospective on Fashion Industry: Data Driven Strategies. Chapter 5:
Predictive analytics techniques for marketing. Chapter 6: Customer
targeting and segmentation. Chapter 7: Personalised Marketing and
Recommendation Systems. Chapter 8: Pricing Optimization Using Predictive
Analytics. Chapter 9: Churn Prediction and Customer Retention. Chapter 10:
Marketing Campaign Optimization. Chapter 11: Customer Retention Using
Machine Learning Techniques in Human Resource Industry: A Systemic
Literature. Chapter 12: A Persona-Based Approach for Churn Prediction and
Retention Strategies Driven by Predictive Analytics and Generative AI.
Chapter 13: Sentiment Analysis and Social Media Marketing. Chapter 14:
Generative AI Techniques for Marketing. Chapter 15: Privacy and Ethical
Considerations in Data-Driven Marketing. Chapter 16: Price Prediction and
Optimization in Predictive Analytics and Generative AI for Data-Driven
Marketing Strategies. Chapter 17: Synthetic Data Generation for Marketing
Insights. Chapter 18: Measuring Marketing Effectiveness and Return on
Investment. Chapter 19: Emerging Technologies Shaping the Future of
Marketing. Chapter 20: Customer Segmentation Techniques in Predictive
Analytics and Generative AI for Data-Driven Marketing Strategies. Chapter
21: Case Studies in Data-Driven Marketing. Chapter 22: Case Studies in
Churn Prediction and Customer Retention. Chapter 23: Leveraging Generative
AI for Personal Branding.
Chapter 1: Introduction to Predictive Analysis and Generative AI. Chapter
2: Fundamentals of data-driven marketing. Chapter 3: Future trends in
Predictive Analytics and Generative AI for Marketing. Chapter 4: Analytical
Prospective on Fashion Industry: Data Driven Strategies. Chapter 5:
Predictive analytics techniques for marketing. Chapter 6: Customer
targeting and segmentation. Chapter 7: Personalised Marketing and
Recommendation Systems. Chapter 8: Pricing Optimization Using Predictive
Analytics. Chapter 9: Churn Prediction and Customer Retention. Chapter 10:
Marketing Campaign Optimization. Chapter 11: Customer Retention Using
Machine Learning Techniques in Human Resource Industry: A Systemic
Literature. Chapter 12: A Persona-Based Approach for Churn Prediction and
Retention Strategies Driven by Predictive Analytics and Generative AI.
Chapter 13: Sentiment Analysis and Social Media Marketing. Chapter 14:
Generative AI Techniques for Marketing. Chapter 15: Privacy and Ethical
Considerations in Data-Driven Marketing. Chapter 16: Price Prediction and
Optimization in Predictive Analytics and Generative AI for Data-Driven
Marketing Strategies. Chapter 17: Synthetic Data Generation for Marketing
Insights. Chapter 18: Measuring Marketing Effectiveness and Return on
Investment. Chapter 19: Emerging Technologies Shaping the Future of
Marketing. Chapter 20: Customer Segmentation Techniques in Predictive
Analytics and Generative AI for Data-Driven Marketing Strategies. Chapter
21: Case Studies in Data-Driven Marketing. Chapter 22: Case Studies in
Churn Prediction and Customer Retention. Chapter 23: Leveraging Generative
AI for Personal Branding.
2: Fundamentals of data-driven marketing. Chapter 3: Future trends in
Predictive Analytics and Generative AI for Marketing. Chapter 4: Analytical
Prospective on Fashion Industry: Data Driven Strategies. Chapter 5:
Predictive analytics techniques for marketing. Chapter 6: Customer
targeting and segmentation. Chapter 7: Personalised Marketing and
Recommendation Systems. Chapter 8: Pricing Optimization Using Predictive
Analytics. Chapter 9: Churn Prediction and Customer Retention. Chapter 10:
Marketing Campaign Optimization. Chapter 11: Customer Retention Using
Machine Learning Techniques in Human Resource Industry: A Systemic
Literature. Chapter 12: A Persona-Based Approach for Churn Prediction and
Retention Strategies Driven by Predictive Analytics and Generative AI.
Chapter 13: Sentiment Analysis and Social Media Marketing. Chapter 14:
Generative AI Techniques for Marketing. Chapter 15: Privacy and Ethical
Considerations in Data-Driven Marketing. Chapter 16: Price Prediction and
Optimization in Predictive Analytics and Generative AI for Data-Driven
Marketing Strategies. Chapter 17: Synthetic Data Generation for Marketing
Insights. Chapter 18: Measuring Marketing Effectiveness and Return on
Investment. Chapter 19: Emerging Technologies Shaping the Future of
Marketing. Chapter 20: Customer Segmentation Techniques in Predictive
Analytics and Generative AI for Data-Driven Marketing Strategies. Chapter
21: Case Studies in Data-Driven Marketing. Chapter 22: Case Studies in
Churn Prediction and Customer Retention. Chapter 23: Leveraging Generative
AI for Personal Branding.