Marketing Data Science: Modeling Techniques In Predictive Analytics With R And Python

Hardcover | May 12, 2015

byThomas W. Miller

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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.

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From the Publisher

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 rea...

From the Jacket

To solve real marketing problems with predictive analytics, you need to master concepts, theory, skills, and tools.Now, one authoritative guide covers them all. Marketing Data Science brings together the knowledge you need to model consumer and buyer preferences and predict marketplace behavior, so you can make informed business deci...

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 mo...

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Format:HardcoverDimensions:480 pages, 9.4 × 7.2 × 1.4 inPublished:May 12, 2015Publisher:Pearson EducationLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0133886557

ISBN - 13:9780133886559

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Extra Content

Table of Contents

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