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

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

by Thomas W. Miller
Released May 2015
Publisher(s): Pearson
ISBN: 9780133887662

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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.

    Table of contents

    1. About This eBook
    2. Title Page
    3. Copyright Page
    4. Contents
    5. Preface
    6. Figures
    7. Tables
    8. Exhibits
    9. 1. Understanding Markets
    10. 2. Predicting Consumer Choice
    11. 3. Targeting Current Customers
    12. 4. Finding New Customers
    13. 5. Retaining Customers
    14. 6. Positioning Products
    15. 7. Developing New Products
    16. 8. Promoting Products
    17. 9. Recommending Products
    18. 10. Assessing Brands and Prices
    19. 11. Utilizing Social Networks
    20. 12. Watching Competitors
    21. 13. Predicting Sales
    22. 14. Redefining Marketing Research
    23. A. Data Science Methods
      1. A.1 Database Systems and Data Preparation
      2. A.2 Classical and Bayesian Statistics
      3. A.3 Regression and Classification
      4. A.4 Data Mining and Machine Learning
      5. A.5 Data Visualization
      6. A.6 Text and Sentiment Analysis
      7. A.7 Time Series and Market Response Models
    24. B. Marketing Data Sources
      1. B.1 Measurement Theory
      2. B.2 Levels of Measurement
      3. B.3 Sampling
      4. B.4 Marketing Databases
      5. B.5 World Wide Web
      6. B.6 Social Media
      7. B.7 Surveys
      8. B.8 Experiments
      9. B.9 Interviews
      10. B.10 Focus Groups
      11. B.11 Field Research
    25. C. Case Studies
      1. C.1 AT&T Choice Study
      2. C.2 Anonymous Microsoft Web Data
      3. C.3 Bank Marketing Study
      4. C.4 Boston Housing Study
      5. C.5 Computer Choice Study
      6. C.6 DriveTime Sedans
      7. C.7 Lydia E. Pinkham Medicine Company
      8. C.8 Procter & Gamble Laundry Soaps
      9. C.9 Return of the Bobbleheads
      10. C.10 Studenmund’s Restaurants
      11. C.11 Sydney Transportation Study
      12. C.12 ToutBay Begins Again
      13. C.13 Two Month’s Salary
      14. C.14 Wisconsin Dells
      15. C.15 Wisconsin Lottery Sales
      16. C.16 Wikipedia Votes
    26. D. Code and Utilities
    27. Bibliography
    28. Index
    29. Code Snippets

    Product information

    • Title: Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python
    • Author(s): Thomas W. Miller
    • Release date: May 2015
    • Publisher(s): Pearson
    • ISBN: 9780133887662