R For Marketing Research and Analytics (Use R!) - Softcover

Über die Autorin bzw. den Autor

Chris Chapman PhD is a Principal Quantitative Researcher at Google, and an author of Chapman & Feit, R for Marketing Research and Analytics (Springer, 2015). In the broader industry, he is chair of the 2017 Advanced Research Techniques Forum, past President of the American Marketing Association’s Practitioner Council, and a member of several other conference and industry committees. Chris is the internal “conjoint analysis evangelist” at Google, and an enthusiastic contributor to the quantitative marketing community outside, where he regularly presents research innovations and teaches workshops on R, conjoint analysis, strategic modeling, and other analytics topics.

Elea McDonnell Feit is an Assistant Professor of Marketing at Drexel University and a Senior Fellow of Marketing at The Wharton School. She enjoys making quantitative methods accessible to a broad audience and teaches workshops and courses on advertising measurement, marketing experiments, marketing analytics in R, discrete choice modeling and hierarchical Bayes methods.  She is active in the organizing committees for practitioner conferences including AMA Advanced Research Techniques Forum and INFORMS Business Analytics and is an author of Chapman & Feit, R for Marketing Research and Analytics (Springer, 2015).

Von der hinteren Coverseite

The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research. 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 modelswith 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.

The 2nd edition increases the book’s utility for students and instructors with the inclusion of exercises and classroom slides. At the same time, it retains all of the features that make it a vital resource for practitioners: non-mathematical exposition, examples modeled on real world marketing problems, intuitive guidance on research methods, and immediately applicable code.