مشخصات کتاب

R for Marketing Research and Analytics

ویرایش: 2nd 
نویسندگان: Chris Chapman. Elea McDonnell Feit  
سری: Use R! 
ISBN (شابک) : 9783030143152 
ناشر: Springer 
سال نشر: 2019 
تعداد صفحات: 492 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 15 مگابایت 

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توضیحاتی درمورد کتاب به خارجی

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. 1, 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: • It is organized around marketing research tasks. Instead of generic examples, we put methods into the context of marketing questions. • We presume only basic statistics knowledge and use a minimum of mathematics. This book is designed to be approachable for practitioners and does not dwell on equations or mathematical details of statistical models (although we give references to those texts). • This is a didactic book that explains statistical concepts and the R code. We want you to understand what we’re doing and learn how to avoid common problems in both statistics and R. We intend the book to be readable and to fulfill a different need than references and cookbooks available elsewhere. • The applied chapters demonstrate progressive model building. We do not present “the answer” but instead show how an analyst might realistically conduct analyses in successive steps where multiple models are compared for statistical strength and practical utility. • The chapters include visualization as a part of core analyses. We don’t regard visualization as a standalone topic; rather, we believe it is an integral part of data exploration and model building. • You will learn more than just R. In addition to core models, we include topics such as structural models and transaction analysis that may be new and useful even for experienced analysts. • The book reflects both traditional and Bayesian approaches. Core models are presented with traditional (frequentist) methods, while later sections introduce Bayesian methods for linear models and conjoint analysis. • Most of the analyses use simulated data, which provides practice in the R language along with additional insight into the structure of marketing data. If you are inclined, you can change the data simulation and see how the statistical models are affected. • Where appropriate, we call out more advanced material on programming or models so that you may either skip it or read it, as you find appropriate. These sections are indicated by * in their titles (such as This is an advanced section*). 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. 1.5) 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, you’ll be well equipped to apply R in many areas of marketing. Why are we the right teachers? We’ve used R and its predecessor S for a combined 35 years since 1997, and it is our primary analytics platform. We perform marketing analyses of all kinds in R, ranging from simple data summaries to complex analyses involving thousands of lines of custom code and newly created models. We’ve also taught R to many people. This book grew from courses the authors have presented at American Marketing Association (AMA) events including the Academy of Marketing Analytics at Emory University and several years of the Advanced Research Techniques Forum (ART Forum). As noted in our Acknowledgements below, we have taught R to students in many workshops at universities and firms. At last count, more than 40 universities used the first edition in their marketing analytics courses. All of these students’ and instructors’ experiences have helped to improve the book. This second edition focuses on making the book more useful for students, self-learners, and instructors. The code has proven to be very stable. Except for one line (updated at the book’s Web site), all of the code and examples from the first edition still work more than four years later. We have added one chapter, and otherwise, the marketing topics and statistical models are the same as in the first edition. The primary changes in this edition are: • New exercises appear at the end of each chapter. Several of these use real-world data, and there are example solutions at the book’s Web site. • A new chapter discusses analysis of behavior sequences (Chap. 14) using Markov chains. These methods are applicable to many sources of behavioral and other data comprising sequences of discrete events, such as application usage, purchases, and life events, as well as non-marketing data including physical processes and genomic sequences. We use a published Web server log file to demonstrate the methods applied to real data. • Classroom slides are available for instructors and self-learners at the book’s Web site. These include the slides themselves, the raw code that they discuss, and Rmarkdown and LaTeX files that generate the slides and may be edited for your own use. • For our various data sets, we present additional details about how such data might be acquired. For example, when a data set represents consumer survey data, we describe how the data might be gathered and a brief description of typical survey items. • A new appendix describes options for reproducible research in R and explains the basics of R Notebooks (Appendix B). R Notebooks are a simple yet powerful way to create documents in R with integrated code, graphics, and formatted text. They may be used to create documents as simple as homework exercises, or as complex as final deliverable reports for clients, with output in HTML, PDF, or Microsoft Word formats. • We have updated other content as needed. This includes additional explanations, code, and charts where warranted; up-to-date references; and correction of minor errors.

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