Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)
Authors: Thomas W. Miller
ISBN-10: 0133886557
ISBN-13: 9780133886559
Edition 版本: 1
Released: 2015-05-22
Pages: 480 pages
Book Description
Now,a leader 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.
Building on Miller’s pioneering program,Marketing Data Science thoroughly addresses segmentation,target marketing,brand and product positioning,new product development,choice modeling,recommender systems,pricing research,retail site selection,demand estimation,sales forecasting,customer retention,and lifetime value analysis.
Starting where Miller’s widely-praised Modeling Techniques in Predictive Analytics left off,he integrates crucial information and insights that were previously segregated in texts on web analytics,network science,information technology,and programming. Coverage includes:
The role of analytics in delivering effective messages on the web
Understanding the web by understanding its hidden structures
Being recognized on the web – and watching your own competitors
Visualizing networks and understanding communities within them
Measuring sentiment and making recommendations
Leveraging key data science methods: databases/data preparation,classical/Bayesian statistics,regression/classification,machine learning,and text analytics
Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data,and more. This text’s extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R.
Marketing Data Science will be an invaluable resource for all students,faculty,and professional marketers who want to use business analytics to improve marketing performance.
Contents
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
Code Snippets
