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

About

Features

The fully-integrated, expert, hands-on guide to predictive analytics and data science for marketing 

• Fully integrates everything you need to know to address real marketing challenges – including all relevant web analytics, network science, information technology, and programming techniques
• Covers analytics for segmentation, targeting, positioning, pricing, product development, site selection, recommender systems, forecasting, retention, lifetime value analysis, and much more
• Includes multiple examples demonstrated with Python and R
• By Thomas W. Miller, leader of Northwestern's pioneering predictive analytics program, and author of Modeling Techniques in Predictive Analytics

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.

Downloads

Downloads

Download supporting files for this book at https://github.com/mtpa/mds.

Or download individual code files by chapter.

Source Code

Access the code files from this book.

Sample Content

Table of Contents

• Preface   
• Figures   
• Tables   
• Exhibits   
• 1 Understanding Markets   
• 2 Predicting Consumer Choice   
• 3 Targeting Current Customers   
• 4 Finding New Customers   
• 5 Retaining Customers   
• 6 Positioning Products   
• 7 Developing New Products   
• 8 Promoting Products   
• 9 Recommending Products   
• 10 Assessing Brands and Prices
• 11 Utilizing Social Networks   
• 12 Watching Competitors   
• 13 Predicting Sales   
• 14 Redefining Marketing Research   
• A Data Science Methods   
• B Marketing Data Sources   
• C Case Studies   
• D Code and Utilities   
• Bibliography   
• Index   

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