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

Marc Notes: Writing for both managers and students, Thomas W. Miller explains essential concepts, principles and theory in the context of real-world applications. Thoroughly addressing 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, many of the examples in this book are accompanied by solutions in Python and/or R. Biographical Note: 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."Jacket Description/Back: 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: Reveal hidden drivers of consumer choice Target likely purchasers Strengthen retention Position products to exploit marketplace gaps Evaluate promotions Build recommender systems Assess response to brand and price Model the diffusion of innovation Analyze consumer sentiment Build competitive intelligence Choose new retail locations Develop an efficient and rigorous marketing research program, drawing on a wide range of data sources, internal and external "Table of Contents: Preface viiFigures xiTables xvExhibits xvii1 Understanding Markets 12 Predicting Consumer Choice 133 Targeting Current Customers 274 Finding New Customers 495 Retaining Customers 656 Positioning Products 877 Developing New Products 1118 Promoting Products 1219 Recommending Products 13910 Assessing Brands and Prices 15911 Utilizing Social Networks 19312 Watching Competitors 22113 Predicting Sales 23514 Redefining Marketing Research 247A Data Science Methods 257B Marketing Data Sources 291C Case Studies 353D Code and Utilities 397Bibliography 415Index 453"Publisher Marketing: 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."