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