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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:
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.
Download supporting files for this book at https://github.com/mtpa/mds.
Or download individual code files by chapter.
1 Understanding Markets 1
2 Predicting Consumer Choice 13
3 Targeting Current Customers 27
4 Finding New Customers 49
5 Retaining Customers 65
6 Positioning Products 87
7 Developing New Products 111
8 Promoting Products 121
9 Recommending Products 139
10 Assessing Brands and Prices 159
11 Utilizing Social Networks 193
12 Watching Competitors 221
13 Predicting Sales 235
14 Redefining Marketing Research 247
A Data Science Methods 257
B Marketing Data Sources 291
C Case Studies 353
D Code and Utilities 397