by Chris Chapman (Author), Chris Chapman (Author), Elea McDonnell Feit (Author)
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.
Format: Paperback
Pages: 472
Edition: 2015
Publisher: Springer
Published: 10 Mar 2015
ISBN 10: 3319144359
ISBN 13: 9783319144351
Book Overview: R for Marketing Research and Analytics is the perfect book for those interested in driving success for their business and for students looking to get an introduction to R. While many books take a purely academic approach, Chapman (Google) and Feit (Formerly of GM and the Modellers) know exactly what is needed for practical marketing problem solving. I am an expert R user, yet had never thought about a textbook that provides the soup-to-nuts way that Chapman and Feit: show how to load a data set, explore it using visualization techniques, analyze it using statistical models, and then demonstrate the business implications. It is a book that I wish I had written. Eric Bradlow, K.P. Chao Professor, Chairperson, Wharton Marketing Department and Co-Director, Wharton Customer Analytics Initiative R for Marketing Research and Analytics provides an excellent introduction to the R statistical package for marketing researchers. This is a must-have book for anyone who seriously pursues analytics in the field of marketing. R is the software gold-standard in the research industry, and this book provides an introduction to R and shows how to run the analysis. Topics range from graphics and exploratory methods to confirmatory methods including structural equation modeling, all illustrated with data. A great contribution to the field! Greg Allenby, Helen C. Kurtz Chair in Marketing, Professor of Marketing and Professor of Statistics, Ohio State University Chris Chapman's and Elea Feit's engaging and authoritative book nicely fills a gap in the literature. At last we have an accessible book that presents core marketing research methods using the tools and vernacular of modern data science. The book will enable marketing researchers to up their game by adopting the R statistical computing environment. And data scientists with an interest in marketing problems now have a reference that speaks to them in their language. James Guszcza, Chief Data Scientist, Deloitte - US Finally a highly accessible guide for getting started with R. Feit and Chapman have applied years of lessons learned to developing this easy-to-use guide, designed to quickly build a strong foundation for applying R to sound analysis. The authors succeed in demystifying R by employing a likeable and practical writing style, along with sensible organization and comfortable pacing of the material. In addition to covering all the most important analysis techniques, the authors are generous throughout in providing tips for optimizing R's efficiency and identifying common pitfalls. With this guide, anyone interested in R can begin using it confidently in a short period of time for analysis, visualization, and for more advanced analytics procedures. R for Marketing Research and Analytics is the perfect guide and reference text for the casual and advanced user alike. Matt Valle, Executive Vice President, Global Key Account Management - GfK
The monograph presents various numerous illustrations for R language, with setting the data, applying R codes, and interpreting the results obtained. It is written in a very friendly attitude to readers, giving an immediate practical guide to solving real marketing research problems. (Stan Lipovetsky, Technometrics, Vol. 58 (3), August, 2016)
R for Marketing Research and Analytics is a clearly written, well-organized, comprehensive, and readable guide to using R ... for marketing research and analytics. ... For many readers-even for those who know R and have marketing research and analytics experience-this book can be a valuable resource. ... used as a reference by marketing researchers and analysts, by engineering and business practitioners who wish to learn more about the analyses of customer and marketing data ... . (R. Jean Ruth, Interfaces, Vol. 46 (3), May-June, 2016)
The authors take care to guide the reader through the difficult task of data analysis of marketing data with R. ... It is well written, in a colloquial and friendly tone. The reader often has the feeling that the authors talk directly to her. ... I find the book to be a very welcome addition to the Use R! series and the marketing research and business analytics world. I can wholeheartedly recommend it ... . (Thomas Rusch, Journal of Statistical Software, Vol. 67 (2), October, 2015)
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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