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

Miller, Thomas W.

ISBN 10: 0133886557 / ISBN 13: 9780133886559

Edité par Pearson FT Press, 2015

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Détails bibliographiques

• Titre: Marketing Data Science : Modeling Techniques in Predictive Analytics With R and Python
• Éditeur: Pearson FT Press
• Date de parution: 2015
• Langue: anglais
• ISBN à 10 chiffres: 0133886557
• ISBN à 13 chiffres: 9780133886559
• Reliure: Couverture rigide
• État de l'article: As New

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Synopsis

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.

À propos de l?auteur

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 more than forty other faculty members in delivering training in predictive analytics and data science.

Miller is owner of Research Publishers LLC and its ToutBay Division, a publisher and distributor of data science applications. He has consulted widely in the areas of retail site selection, product positioning, segmentation, and pricing in competitive markets and has worked with predictive models for more than 30 years.

Miller’s books include Web and Network Data Science, Modeling Techniques in Predictive Analytics, Data and Text Mining: A Business Applications Approach, Research and Information Services: An Integrated Approach for Business, and a book about predictive modeling in sports, Without a Tout: How to Pick a Winning Team.

Before entering academia, Miller spent nearly 15 years in business IT in the computer and transportation industries. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin-Madison. He holds a Ph.D. in psychology (psychometrics) and a master’s degree in statistics from the University of Minnesota and an MBA and master’s degree in economics from the University of Oregon.