|Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python||
Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python
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:
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.
The fully-integrated, expert, hands-on guide to predictive analytics and data science for marketing
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
To solve real marketing problems with predictive analytics, you need to master concepts, theory, skills, and tools.
Now, one authoritative guide covers them all.
Marketing Data Science brings together the knowledge you need to model consumer and buyer preferences and predict marketplace behavior, so you can make informed business decisions. Using hands-on examples built with R, Python, and publicly available data sets, Thomas W. Miller shows how to solve a wide array of marketing problems with predictive analytics.
Building on the pioneering data science program at Northwestern University, Miller covers analytics for 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.
Miller brings together essential concepts, principles, and skills that were formerly scattered across multiple texts. You’ll gain realistic experience extending predictive analytics with powerful techniques from web analytics, network science, programming, and marketing research. As you practice, you’ll master data management and modeling skills you can apply in all markets, business-to-consumer and business-to-business alike.
All data sets, extensive R and Python code, and additional examples are available for download at www.ftpress.com/miller/.
In a world transformed by information and communication technology, marketing, sales, and research have merged--and data rule them all. Today, marketers must master a new data science and use it to uncover meaningful answers rapidly and inexpensively.
This book teaches marketing data science through real-world examples that integrate essential knowledge from the disciplines that have shaped it. Building on his pioneering courses at Northwestern University, Thomas W. Miller walks you through the entire process of modeling and answering marketing questions with R and Python, today’s leading open source tools for data science.
Using real data sets, Miller covers a full spectrum of marketing applications, from targeting new customers to improving retention, setting prices to quantifying brand equity.
Marketing professionals can use Marketing Data Science as a ready resource and reference for any project. For programmers, it offers an extensive foundation of working code for solving real problems--with step-by-step comments and expert guidance for taking your analysis even further.
ADDRESS IMPORTANT MARKETING PROBLEMS:
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.