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Marketing Data Science -Modeling Techniques in Predictive Analytics with R and Python


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  • ISBN13:9789353065744
  • ISBN10:9353065747
  • Publisher:Pearson Education
  • Language:English
  • Author:Thomas W. Miller
  • View all item details
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Highlights

  • ISBN13:9789353065744
  • ISBN10:9353065747
  • Publisher:Pearson Education
  • Language:English
  • Author:Thomas W. Miller
  • Binding:Paperback
  • Publishing Year:2018
  • Pages:599
  • Edition:1
  • SUPC: SDL514558861

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Country of Origin or Manufacture or Assembly India
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Description

In Marketing Data Science, a top faculty member of Northwestern 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

About the Author

Thomas W. Miller is faculty director of the Predictive Analytics program at Northwestern University. He has designed courses for the program, including Marketing Analytics, Advanced Modeling Techniques, Data Visualization, Web and Network Data Science, and the capstone course. He has taught extensively in the program and works with more than forty other faculty members in delivering training in predictive analytics and data science.

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

Table Content

Preface vii

Figures xi

Tables xv

Exhibits xvii

1 Understanding Markets 1

2 Predicting Consumer Choice 13

3 Targeting Current Customers 27

4 Finding New Customers 49

5 Retaining Customers 65

6 Positioning Products 87

7 Developing New Products 111

8 Promoting Products 121

9 Recommending Products 139

10 Assessing Brands and Prices 159

11 Utilizing Social Networks 193

12 Watching Competitors 221

13 Predicting Sales 235

14 Redefining Marketing Research 247

A Data Science Methods 257

B Marketing Data Sources 291

C Case Studies 353

D Code and Utilities 397

Bibliography 415

Index 453

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Marketing Data Science -Modeling Techniques in Predictive Analytics with R and Python

Marketing Data Science -Modeling Techniques in Predictive Analytics with R and Python

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