Machine learning and artificial intelligence in marketing and sales : essential reference for practitioners and data scientists / Niladri Syam (University of Missouri, USA), Rajeeve Kaul (McDonald's Corporation, USA).

Includes index.

Includes bibliographical references.

Chapter 1. Training and performance assessment -- Chapter 2. Neural networks -- Chapter 3. Overfitting and regulation -- Chapter 4. Support vector machines -- Chapter 5. Random forest, bagging and boosting of decision trees.

'Machine 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.

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