01999nam a22002057a 4500 2451 2451 OSt 20200219160654.0 200117b ||||| |||| 00| 0 eng d 978-93-530-6574-4 Allied Informatics, Jaipur 7084 13/01/2020 2019-20 BSDU English BSDU 658.800285 MIL Miller, Thomas W. Marketing Data Science: Modeling techniques in predictive analytics with R and python Noida Pearson India Education Services Pvt. Ltd. 2019 458 Table of Content "Preface Figures Tables Exhibits 1 Understanding Markets 2 Predicting Consumer Choice 3 Targeting Current Customers 4 Finding New Customers 5 Retaining Customers 6 Positioning Products 7 Developing New Products 8 Promoting Products 9 Recommending Products 10 Assessing Brands and Prices 11 Utilizing Social Networks 12 Watching Competitors 13 Predicting Sales 14 Redefining Marketing Research A Data Science Methods B Marketing Data Sources C Case Studies D Code and Utilities Bibliography Index Salient Features 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 Northwestern's pioneering predictive analytics program, and author of Modeling Techniques in Predictive Analytics" Data Science ddc BK 0 0 ddc 0 0 BSDU BSDU GEN 2020-01-17 619.00 0 658.800285 MIL 018005 2020-02-12 00:00:00 619.00 2020-01-17 BK