Marketing Data Science -Modeling Techniques in Predictive Analytics with R and Python | First Edition | By Pearson (Paperback)  | Released: 01-Jan-18

In 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 AnalyticsPreface viiFigures xiTables xvExhibits xvii1 Understanding Markets 12 Predicting Consumer Choice 133 Targeting Current Customers 274 Finding New Customers 495 Retaining Customers 656 Positioning Products 877 Developing New Products 1118 Promoting Products 1219 Recommending Products 13910 Assessing Brands and Prices 15911 Utilizing Social Networks 19312 Watching Competitors 22113 Predicting Sales 23514 Redefining Marketing Research 247A Data Science Methods 257B Marketing Data Sources 291C Case Studies 353D Code and Utilities 397Bibliography 415Index 453.