R for Marketing Research and Analytics

ISBN-13: 9783319144351 / Angielski / Miękka / 2015 / 454 str.

Chris Chapman; Elea McDonnell Feit

Opis

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.

Recenzje

"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)

Spis treści

Welcome to R.- The R Language.- Describing Data.- Relationships Between Continuous Variables.- Comparing Groups: Tables and Visualizations.- Comparing Groups: Statistical Tests.- Identifying Drivers of Outcomes: Linear Models.- Reducing Data Complexity.- Additional Linear Modeling Topics.- Confirmatory Factor Analysis and Structural Equation Modeling.- Segmentation: Clustering and Classification.- Association Rules for Market Basket Analysis.- Choice Modeling.- Conclusion.- Appendix: R Versions and Related Software.- Appendix: Scaling up.- Appendix: Packages Used.- Index.

O autorze

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.