Chris Chapman - R For Marketing Research and Analytics

This series of inexpensive and focused books on R will publish shorter books aimed at practitioners. Books can discuss the use of R in a particular subject area (e.g., epidemiology, econometrics, psychometrics) or as it relates to statistical topics (e.g., missing data, longitudinal data). In most cases, books will combine LaTeX and R so that the code for figures and tables can be put on a website. Authors should assume a background as supplied by Dalgaards Introductory Statistics with R or other introductory books so that each book does not repeat basic material.

More information about this series at http://www.springer.com/series/6991

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We are here to help you learn R for marketing research and analytics.

R is a great choice for marketing analysts. It offers unsurpassed capabilities for fitting statistical models. It is extensible and able to process data from many different systems, in a variety of forms, for both small and large data sets. The R ecosystem includes the widest available range of established and emerging statistical methods and visualization techniques. Yet its use in marketing lags other fields such as statistics, econometrics, psychology, and bioinformatics. With your help, we hope to change that!

This book is designed for two audiences: practicing marketing researchers and analysts who want to learn R and students or researchers from other fields who wish to review selected marketing topics in an R context.

What are the prerequisites? Simply that you are interested in R for marketing, are conceptually familiar with basic statistical models such as linear regression, and are willing to engage in hands-on learning. This book will be particularly helpful to analysts who have some degree of programming experience and wish to learn R. In Chap. , we describe additional reasons to use R (and a few reasons perhaps not to use R).

The hands-on part is important. We teach concepts gradually in a sequence across the first seven chapters and ask you to type our examples as you work; this book is not a cookbook-style reference. We spend some time (as little as possible) in Part I on the basics of the R language and then turn in Part II to applied, real-world marketing analytics problems. Part III presents a few advanced marketing topics. Every chapter shows the power of R, and we hope each one will teach you something new and interesting.

Specific features of this book are:

What do we not cover? For one, this book teaches R for marketing and does not teach marketing research in itself. We discuss many marketing topics but omit others that would repeat analytic methods. As noted above, we approach statistical models from a conceptual point of view and skip the mathematics. A few specialized topics have been omitted due to complexity and space; these include customer lifetime value models and econometric time series models. In the R language, we do not cover the tidyverse (Sect. ) because it is an optional part of the language and would complicate the learning process. Overall, we believe the topics here represent a great sample of marketing research and analytics practice. If you learn to perform these, youll be well equipped to apply R in many areas of marketing.