Miller, Thomas W. <br/><br/>
Marketing Data Science: Modeling techniques in predictive analytics with R and python 

 - Noida  Pearson India Education Services Pvt. Ltd.  2019 
 - 458 
<br/><br/><br/>Table of Content<br/><br/> "Preface    <br/> Figures   <br/> Tables    <br/> Exhibits   <br/> 1 Understanding Markets    <br/> 2 Predicting Consumer Choice    <br/> 3 Targeting Current Customers    <br/> 4 Finding New Customers    <br/> 5 Retaining Customers    <br/> 6 Positioning Products    <br/> 7 Developing New Products    <br/> 8 Promoting Products    <br/> 9 Recommending Products    <br/> 10 Assessing Brands and Prices    <br/> 11 Utilizing Social Networks    <br/> 12 Watching Competitors    <br/> 13 Predicting Sales    <br/> 14 Redefining Marketing Research    <br/> A Data Science Methods    <br/> B Marketing Data Sources    <br/> C Case Studies    <br/> D Code and Utilities    <br/> Bibliography   <br/> Index    <br/>   <br/><br/>Salient Features<br/><br/>The fully-integrated, expert, hands-on guide to predictive analytics and data science for marketing<br/><br/>Fully integrates everything you need to know to address real marketing challenges - including all relevant web analytics, network science, information technology, and programming techniques<br/> Covers analytics for segmentation, targeting, positioning, pricing, product development, site selection, recommender systems, forecasting, retention, lifetime value analysis, and much more<br/> Includes multiple examples demonstrated with Python and R<br/> By Thomas W. Miller, leader of Northwestern's pioneering predictive analytics program, and author of Modeling Techniques in Predictive Analytics"  
<br/><br/><label>ISBN: </label>978-93-530-6574-4 
<br/><br/><label>Publisher. No.: </label> Allied Informatics, Jaipur 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Data Science
<br/><br/><label>Dewey Class. No.: </label>658.800285  / MIL