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R for Marketing Research and Analytics (Use R!), by Christopher N. Chapman, Elea McDonnell Feit
Download PDF R for Marketing Research and Analytics (Use R!), by Christopher N. Chapman, Elea McDonnell Feit
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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.
- Sales Rank: #329093 in Books
- Published on: 2015-03-10
- Released on: 2015-03-10
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.11" w x 6.10" l, .0 pounds
- Binding: Paperback
- 454 pages
Review
“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)
Review
"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
From the Back Cover
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.
Most helpful customer reviews
27 of 27 people found the following review helpful.
An excellent companion for data analysis and analytics for marketing researchers
By Dr. Chuck Chakrapani
There are many things to commend about this book.
First and foremost, this is the first major and successful attempt to present analytic techniques to marketing researchers from a modern perspective. It replaces the standard multivariate technique books used by marketing researchers (starting with Paul Green in the 1970s and ending with the currently in print Hair, Tatham et al.). There are some excellent contemporary books on analytics relevant to marketing researchers such as "An Introduction to Statistical Learning" by James, Witten, Hastie and Tibshirani, and "Applied Predictive Modeling" by Kuhn and Johnson. However, they are not directly designed to address marketing research issues. This book is. The advantage to this book being specific is that it can address the problems specific to marketing researchers rather than dealing with such issues tangentially.
Second, Chapman and Feit do not deal with marketing research problems from an academic perspective with artificial scenarios. Their examples are of the type a marketing researcher would deal with on a day-to-day basis. When I first saw that the authors use generated data as opposed to real-life data to illustrate the techniques, I had misgivings. It is easy enough to create contrived datasets to solve imagined problems; in real life, datasets are not always that cooperative. The authors have not fallen into this trap and they have generated datasets skillfully to illustrate the points they are trying to make.
Third, as a modern take on traditional bivariate and multivariate techniques, Chapman and Feit present Bayesian methods, which are becoming increasingly popular. I believe Bayesian methods (especially with the advent of R) will soon be part of mainstream data analysis in marketing research. The book includes sections on many relatively newer (in any case, less frequently used) techniques such as random forest and na�ve Bayes.
Fourth, in several places Chapman and Feit explore the implications and extensions of basic techniques, which I have not found in other comparable texts. As an example, while discussing factor analysis, they discuss how to use factor analysis to create perceptual maps.. Such extensions are seldom discussed explicitly in other texts dealing with factor analysis.
Fifth, the book is comprehensive. It covers all aspects of analysis a beginning or intermediate marketing researcher or analyst is likely to encounter. Although initially I wondered if it was necessary to devote a third of the book to basic statistics and R, it does provide a good foundation for data manipulation.
Sixth, the writing is clear. This is not a technical book and it is not meant to be. This makes the book widely accessible to marketing researchers with different proficiencies in mathematics. I also liked the fact that Chapman and Feit point out the limitations of traditional techniques like confidence intervals.
Finally, the authors do a good job of teaching the R language and graphics to beginners. The book is not unique in that respect because many other books do an equally good job when it comes to teaching R and graphics.
Some standard techniques (neither numerous, nor serious) are missing from this book. A case in point is linear discriminant analysis. While logistic regression (which is included in the book) can be seen as an alternative to LDA, there are several instances where LDA is a better alternative. Other missing topics include correspondence analysis and maxdiff. But it is the authors’ prerogative to choose what goes into their book and Chapman and Feit’s coverage is comprehensive enough for most purposes.
While the authors do indirectly bring up validation issues and deal with them, they do not treat validation as a systematic and explicit part of using any technique. They devote less than a single page to the widespread problem of overfitting and touch upon bootstrapping only minimally while discussing PLS/SEM. I am not sure if they discuss bias-variance tradeoff and cross-validation seriously at all. I believe, as we move into the era of big data, samples drawn from an unknown population, do-it-yourself research and the like, validation issues become critical and they should be a part of any analyst’s thinking. Most users of the techniques know much more about “R-squared” and “number of hits” etc., than about the perils of overfitting, about model bias or about the reproducibility of the results. For many decades we had no alternative. Programming was complicated, datasets were small and computer time was expensive. Now we don’t have any of these limitations and I believe validating results should not be optional or an afterthought but an integral part of data analysis.
Despite the title, which emphasizes R, the book is more about data analysis and analytics. "Data Analysis and Analytics for Marketing Research With R" would have been a more appropriate title for this book. The book has a lot to teach about analysis whether you are interested in R or not.
While I wish the book had dealt more systematically with validation issues, what it does it does well. Beginning and intermediate researchers who need to analyze data will be hard put to find a better source than this book; learning R in the process is a big bonus. I highly recommend this book to beginning and intermediate researchers seriously interested in data analysis and analytic techniques.
9 of 9 people found the following review helpful.
Extremely practical & straightforward
By Preet
If you’re a marketing student or a marketing/analytics professional and you want to learn R, then this book is perfect for you because it covers R specifically for marketing research & analytics. While many how to code books may be difficult to read or comprehend, this particular book is engaging and comprehensible.
The book is easy to comprehend because it uses detailed graphics and clearly designed blocks of code to help you learn R. You will learn hundreds of commands and their specific applications. You’re not just reading code from the book and typing it out, instead the book helps you understand every line of code you write by teaching you the functions/outputs of every command. This style of teaching will help R become more intuitive to you as you progress through the book.
The modern take on R makes this text highly engaging because it uses examples of modern day research techniques like
• Analyzing data from social media platforms
• Analyzing customer survey responses
The book will teach you how to perform data analysis with R as well. You’ll run dozens of statistical models that will help you derive information from large datasets. Not only that, but you will visualize your data by creating interactive graphs such as
• Quantile plots
• Scatter-plot matrices
• Correlation plots
Learning R will take time for you if you’re completely new to coding. This is why the first three chapters will teach you the fundamentals of R. It may seem tedious to you, however it’s essential to learn these basics in order to understand the following chapters. Fortunately, the first few chapters are short and engaging, so you will breeze through it in no time and perform the codes in later chapters.
So if you’re interested in R for marketing research & analytics, then I highly recommend reading this book. It’s straightforward, engaging, comprehensible, and you’ll distinguish yourself from your peers in the job market. Investing in this book is a great investment in your future careers as marketing researchers & analysts.
9 of 10 people found the following review helpful.
Plugs a big gap
By Kevin S. Gray
The authors, two leading-edge marketing scientists, spotted a gap: while there are scores of books on R, until now none have been aimed specifically at marketing research. This is an excellent R book - one of the best of the many I've read - and will be valuable both to marketing scientists new to R and data scientists who do not work in marketing but would like to know more about marketing science. The book is very well written, well organized and comprehensive (with the exception of time-series analysis, which the authors correctly note would be too big a topic to squeeze in). It's not just page after page of R code and geekspeak and the authors demonstrate that data analysis is not the same thing as data wrangling.
See all 13 customer reviews...
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