Marketing Data Science -Modeling Techniques in Predictive Analytics with R and Python | First Edition | By Pearson Paperback – 1 January 2018

n Marketing Data Science, a top faculty member of North-western University’s prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles and theory in the context of real-world applications. The fully-integrated, expert, hands-on guide to predictive analytics and data science for marketing. Fully integrates everything you need to know to address real marketing challenges – including all relevant web analytics, network science, information technology and programming techniques. Covers analytics for segmentation, targeting, positioning, pricing, product development, site selection, recommender systems, forecasting, retention, lifetime value analysis and much more Includes multiple examples demonstrated with Python and R By Thomas W. Miller, leader of North western’s pioneering predictive analytics program and author of Modelling Techniques in Predictive Analytics
Preface vii
Figures xi
Tables xv
Exhibits xvii
1 Understanding Markets 1
2 Predicting Consumer Choice 13
3 Targeting Current Customers 27
4 Finding New Customers 49
5 Retaining Customers 65
6 Positioning Products 87
7 Developing New Products 111
8 Promoting Products 121
9 Recommending Products 139
10 Assessing Brands and Prices 159
11 Utilizing Social Networks 193
12 Watching Competitors 221
13 Predicting Sales 235
14 Redefining Marketing Research 247
A Data Science Methods 257
B Marketing Data Sources 291
C Case Studies 353
D Code and Utilities 397
Bibliography 415
Index 453.