000			01999nam a22002057a 4500
999			_c2451 _d2451
003			OSt
005			20200219160654.0
008			200117b ||||| |||| 00| 0 eng d
020			_a978-93-530-6574-4
028			_bAllied Informatics, Jaipur _c7084 _d13/01/2020 _q2019-20
040			_aBSDU _bEnglish _cBSDU
082			_a658.800285 _bMIL
100			_aMiller, Thomas W.
245			_aMarketing Data Science: Modeling techniques in predictive analytics with R and python
260			_aNoida _bPearson India Education Services Pvt. Ltd. _c2019
300			_a458
504			_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"
650			_aData Science
942			_2ddc _cBK