Machine Learning and Artificial Intelligence in Marketing and Sales

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.

Table of Contents

1. Introduction and Machine Learning Preliminaries: Training and Performance Assessment

2. Neural Networks in Marketing and Sales

3. Overfitting and Regularization in Machine Learning Models

4. Support Vector Machines in Marketing and Sales

5. Random Forest, Bagging, and Boosting of Decision Trees

About the Authors

Niladri Syam is Director of the Center for Sales and Customer Development (CSCD), and the Robert J. Trulaske Sr. Associate Professor of marketing at the Trulaske College of Business at the University of Missouri, Columbia, USA. His research is in AI and Machine Learning, Pricing, Sales Management and Competitive Marketing Strategy.

Rajeeve Kaul has held executive roles at multiple Fortune 500 companies such as McDonalds, OfficeMax, G4S, Cardinal Health and Essendant leading pricing, advanced analytics and strategy organizations across multiple industries to deliver sustained P&L impact.