Artificial Intelligence and Marketing

1. Auflage 2019
ISBN: 978-1-138-60618-0
Verlag: Taylor & Francis Ltd

Buch, Englisch, 298 Seiten, Format (B × H): 159 mm x 235 mm

The exponential growth of data has inspired companies to gather and process information using a systematic approach. Marketers now require a sound working knowledge of new technologies that can allow them to analyse large volumes of data and make decisions for competitive advantage. This book provides a comprehensive overview of the theory and practice of artificial intelligence (AI) and machine learning (ML) within a marketing context. Across two overarching sections, the reader is taken step-by-step through

The evolution of AI and ML, and the background to their use in marketing

The objectives and goals

The approaches and tools that can be utilised

The philosophical and ethical questions raised

With a unique combination of theory and practice, including numerous practical examples, this book is particularly suitable for advanced undergraduate and postgraduate students and academics with an interest in marketing research, strategic marketing management, Big Data and technology, and innovation. It will also be of interest to any marketing practitioners looking for a thorough grounding in the theory and applications.

Autoren/Hrsg.

Seligman, James

Inhaltsverzeichnis

About the Author, Preface, Objectives, SECTION ONE: Artificial Intelligence, Chapter 1 Overview, Chapter 2 Goals, 2.1 Reasoning, problem solving, 2.2 Knowledge representation, 2.3 Planning, 2.4 Learning, 2.5 Natural language processing, 2.6 Perception, 2.7 Motion and manipulation, 2.8 Social intelligence, 2.9 Creativity, 2.10 General intelligence, Chapter 3 Approaches, 3.1 Cybernetics and brain simulation, 3.2 Symbolic, 3.3 Sub-symbolic, 3.4 Statistical, 3.5 Integrating the approaches, Chapter 4 Tools, 4.1 Search and optimization, 4.2 Logic, 4.3 Probabilistic methods for uncertain reasoning, 4.4 Classifiers and statistical learning methods, 4.5 Neural networks, 4.6 Deep feedforward neural networks, 4.7 Deep recurrent neural networks, 4.8 Control theory, 4.9 Languages, 4.10 Evaluating progress, Chapter 5 Applications, 5.1 Competitions and prizes, 5.2 Healthcare, 5.3 Automotive, 5.4 Finance, 5.5 Video games, Chapter 6 Platforms, 6.1 Partnership on AI, Chapter 7 Philosophy and ethics, 7.1 The limits of artificial general intelligence, 7.2 Potential risks and moral reasoning, 7.3 Machine consciousness, sentience and mind, 7.4 Superintelligence, References, SECTION TWO: MACHINE LEARNING, Chapter 8 Overview, 8.1 Types of problems and tasks, Chapter 9 History and relationships to other fields, 9.1 Relation to statistics, Chapter 10 Theory, Chapter 11 Approaches, 11.1 Decision tree learning, 11.2 Association rule learning, 11.3 Artificial neural networks, 11.4 Deep learning, 11.5 Inductive logic programming, 11.6 Support vector machines, 11.7 Clustering, 11.8 Bayesian networks, 11.9 Reinforcement learning, 11.10 Representation learning, 11.11 Similarity and metric learning, 11.12 Sparse dictionary learning, 11.13 Genetic algorithms, 11.14 Rule-based machine learning, Chapter 12 Applications, Chapter 13 Model assessments, Chapter 14 Ethics, Chapter 15 Software, 15.1 Free and open-source software, 15.2 Proprietary software with free and open-source editions, 15.3 Proprietary software, 15.4 Machine Learning Future in Marketing, References, Bibliography and Reading, Index.

Über Autor(innen)

James Seligman is a retired Principal Fellow of the Marketing Subject Group at the Business School at Southampton University, UK, with over30 years of multinational brand experience. A fellow of the Chartered Institute Marketing, his research interests lie in management and marketing with a focus on insight, analytics, communications, brand and behaviour, and technology.