Marketing Data Science: : Modeling Techniques in Predictive Analytics with R and Python / Miller, Thomas.

Format:

Book

Author/Creator:

Miller, Thomas, author.

Language:

English

Subjects (All):

Marketing--Data processing.

Marketing.

Marketing--Mathematical models.

R (Computer program language).

Python (Computer program language).

Data mining.

Physical Description:

1 online resource (225 pages)

Edition:

1st edition

Other Title:

Modeling techniques in predictive analytics with R and Python

Place of Publication:

Pearson, 2015.

System Details:

text file

Summary:

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.

Notes:

Online resource; Title from title page (viewed May 12, 2015)

Includes bibliographical references and index.

OCLC:

910352479