Description
Book SynopsisMachine Learning and Artificial Intelligence in Marketing and Sales explores the ideas, and the statistical and mathematical concepts, behind Artificial Intelligence (AI) and machine learning models, as applied to marketing and sales, without getting lost in the details of mathematical derivations and computer programming.
Bringing together the qualitative and the technological, and avoiding a simplistic broad overview, this book equips those in the field with methods to implement machine learning and AI models within their own organisations. Bridging the "Domain Specialist - Data Scientist Gap" (DS-DS Gap) is imperative to the success of this and chapters delve into this subject from a marketing practitioner and the data scientist perspective. Rather than a context-free introduction to AI and machine learning, data scientists implementing these methods for addressing marketing and sales problems will benefit most if they are exposed to how AI and machine learning have been applied specifically in the marketing and sales contexts.
Marketing and sales practitioners who want to collaborate with data scientists can be much more effective when they expand their understanding across boundaries to include machine learning and AI.
Trade Review'If you're applying machine learning to marketing or sales, this book is a must-have. It uniquely blends the theory with the practice, each chapter covering a machine learning algorithm and then illustrating its use for a commercially viable scenario. Seriously, that's not something you'll find in any other book.' -- Eric Siegel, PhD, Coursera Instructor of Machine Learning for Everyone, Founder of Deep Learning World, Host of The Dr Data Show, and Executive Editor of The Machine Learning Times
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Machine Learning and Artificial Intelligence in Marketing and Sales: Essential Reference for Practitioners and Data Scientists strikes a, difficult to achieve, balance between providing sufficient information on commonly used but complex machine learning and AI tools, and yet keeping the book accessible and applicable to business practitioners with technical orientation. This is a great introduction book for those who wish to know not only about machine learning and AI, but also what it really is, and how to apply it in marketing and sales settings.' -- Oded Netzer, Professor of Business, Columbia University
'For readers well-versed in the Support Vector Machine, artificial neural nets, and deep learning, the book will be immediately useful. For readers new to these topics, the authors' accessible style lowers entry barriers. The book is required reading for managers, analysts, professors, and consultants involved in marketing and sales.' -- David J. Curry, Professor of Marketing, University of Cincinnati
'Syam and Kaul's book is a comprehensive treatise on data science of marketing, a rich and deeply informative dive into the next generation of marketing analytics solutions. The work comprehensively integrates the theoretical concepts of Machine Learning with practical applications of marketing, making it essential for either ML Engineers solving marketing problems or marketing analysts looking to get a rigorous treatment of the nascent science.' -- Alex Vayner, Data science and AI expert, Partner, PA Consulting
'The authors have skillfully tailored the content to a wide audience. I found this book as a solid reference guide for students and a reference for data science practitioners alike. While the book covers the most important Machine Learning topics in lucid detail, it also provides insightful executive summaries, and, most importantly, showcases applications of each model in the practical world of Sales and Marketing. I will wholeheartedly recommend this book to anyone interested in learning Machine Learning and Artificial Intelligence.' -- Sunish Mittal, Vice President, Data and Analytics, Aramark
Table of ContentsChapter 1. Training and Performance AssessmentChapter 2. Neural Networks Chapter 3. Overfitting and Regulation Chapter 4. Support Vector Machines Chapter 5. Random Forest, Bagging and Boosting of Decision Trees
