02104nam a22002177a 4500999001500000003000400015005001700019008004100036020002200077028005800099040002400157082002000181100002200201245009000223260006000313300000800373504134700381650001701728942001201745952012901757 c2451d2451OSt20200219160654.0200117b ||||| |||| 00| 0 eng d a978-93-530-6574-4 bAllied Informatics, Jaipurc7084d13/01/2020q2019-20 aBSDUbEnglishcBSDU a658.800285bMIL aMiller, Thomas W. aMarketing Data Science: Modeling techniques in predictive analytics with R and python aNoidabPearson India Education Services Pvt. Ltd.c2019 a458 a 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"  aData Science 2ddccBK 00102ddc4070aBSDUbBSDUcGENd2020-01-17g619.00l0o658.800285 MILp018005r2020-02-12 00:00:00v619.00w2020-01-17yBK