Marketing Data Science Modeling Techniques in Predictive Analytics with R and Python |
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| Author:
| Miller, Thomas |
| Series title: | FT Press Analytics Ser. |
| ISBN: | 978-0-13-388655-9 |
| Publication Date: | May 2015 |
| Publisher: | Pearson Higher Education & Professional Group
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| Book Format: | Hardback |
| List Price: | AUD $100.95 |
Book Description:
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In Marketing Data Science, a top faculty member 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.
More Description
In Marketing Data Science, a top faculty member 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 his predictive analytics program at Northwestern, Miller covers 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 his 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:
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The role of analytics in delivering effective messages on the web
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Understanding the web by understanding its hidden structures
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Being recognized on the web - and watching your own competitors
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Visualizing networks and understanding communities within them
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Measuring sentiment and making recommendations
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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.