R for Marketing Research and Analytics

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This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis.

Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis.

With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.

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About the author

Chris Chapman is a Senior Quantitative Researcher at Google. He is also a member of the editorial board of Marketing Insights magazine and the Marketing Insights Council of the American Marketing Association, and has served as chair of the AMA Advanced Research Techniques Forum and AMA Analytics with Purpose conferences. He is an enthusiastic contributor to the quantitative marketing community, where he regularly presents innovations in strategic research and teaches workshops on R and analytic methods.

Elea McDonnell Feit is an Assistant Professor at the LeBow College of Business at Drexel University. Her research focuses on leveraging customer data to make better product design and advertising decisions, particularly when data is incomplete, unmatched or aggregated. Much of her career has focused on building bridges between academia and practice, most recently as a Fellow of the Wharton Customer Analytics Initiative. She enjoys making quantitative methods accessible to a broad audience and regularly gives popular practitioner tutorials on marketing analytics, in addition to teaching courses at LeBow in data-driven digital marketing and design of marketing experiments.

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Additional Information

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Published on
Mar 9, 2015
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Business & Economics / General
Business & Economics / Marketing / General
Business & Economics / Sales & Selling / General
Business & Economics / Statistics
Computers / Mathematical & Statistical Software
Mathematics / Probability & Statistics / General
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This content is DRM protected.
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

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Since I wrote the Foreword for the second edition of this book, risk management processes have become much more widely used, but controversy about what should be done and how best to do it has grown. Managing risk is a risky business. Chapman and Ward provide an in-depth explanation of why it is important to understand and manage underlying uncertainty in all its forms, in order to realise opportunities more fully and enhance corporate performance. They show what best practice should look like. The implications go well beyond the conventional wisdom of project risk management, providing an enlightening new perspective.
—Professor Tony M. Ridley
Imperial College London, Past President, Institution of Civil Engineers

Chris Chapman and Stephen Ward continue to educate the profession with this masterful exposition of the differences between, and the potentials for combinations of, risk, uncertainty and opportunity. Particularly welcome is the way they integrate this trio into the project lifecycle – the bedrock of project management control and organization.
—Peter W.G. Morris
Head of School and Professor of Construction and Project Management University College London

Chris Chapman and Stephen Ward’s books on Project Risk Management have been an essential part of my repertoire for twenty years, and they are top of my recommended reading for the courses I do on that subject. In this book they have enhanced their previous work to focus on uncertainty management and emphasise more strongly opportunities for improving project performance, rather then just identifying what can go wrong. A structured process is an essential part of managing project uncertainty, and their process is one of the most powerful. This book will be added to my repertoire.
—Rodney Turner
Professor of Project Management, SKEMA Business School Lille

A profoundly important book. With How to Manage Project Opportunity and Risk, Chris Chapman and Stephen Ward take a good thing and make it better. Members of the project management profession have been influenced for years by their insights into project risk management. With this latest instalment the authors demonstrate that risk and uncertainty needn’t be dreaded; in fact, the reverse side of the ‘risk coin’ has always been opportunity. My sincere appreciation to Chapman and Ward for turning this particular coin over and showing readers, academic and practitioner alike, the opportunity embedded in managing projects.
—Jeffrey K. Pinto
Andrew Morrow and Elizabeth Lee Black Chair in Management of Technology Sam and Irene Black School of Business, Penn State Erie

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