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R for Marketing Research and Analytics
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.
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.
- GenresBusiness
472 pages, Paperback
First published March 14, 2015
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111 people want to read
About the author
Chris Chapman
8 books1 followerThere is more than one author in the Goodreads catalog with this name. This entry is for Chris ^3 Chapman, professor of management science.
Professor Chris Chapman is Emeritus Professor of Management Science within Southampton Business School at the University of Southampton.
Chris Chapman is a consultant and academic focused on opportunity, risk and uncertainty management. He advises organisations how to design, implement and develop processes which increase effectiveness and efficiency. Chris has extensive experience as a consultant with a wide range of organisations, mainly in the UK, Canada and the USA, but also in other European and South American countries.
Professor Chris Chapman is Emeritus Professor of Management Science within Southampton Business School at the University of Southampton.
Chris Chapman is a consultant and academic focused on opportunity, risk and uncertainty management. He advises organisations how to design, implement and develop processes which increase effectiveness and efficiency. Chris has extensive experience as a consultant with a wide range of organisations, mainly in the UK, Canada and the USA, but also in other European and South American countries.
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Displaying 1 - 9 of 9 reviews
August 1, 2021
So, for a work project, I picked up this book and learned a bit more of the programming language R, and in particular how to use it to analyze data related to marketing research. Most of the statistics involved (ANOVA, PCA, hierarchical bayesian modeling, clustering and classification, etc.) are not especially limited to marketing topics. It's just that, instead of skimming past a bunch of statistical equation gobbledygook to get to the good stuff (for me, the code), you're given examples like e-commerce surveys vs. purchases, a survey of visitors to an amusement park, asking customers if they relate more to the terms "trendy" vs. "bargain", and so forth. In some ways, I think it would be better to teach all math and programming topics this way, diving into a given topic of some interest to the reader in some depth. It helps, of course, if one reads the book thinking "I need to learn this quickly because I'm doing it in my job next week".
That all said, it was an extraordinarily readable book on a technical topic. I found myself occasionally thinking, "this is what all my textbooks in school should have been like". I rarely felt frustrated, it had just the right mix of examples and explanation, it introduced the topics in a sequence which built one on the other in a useful way. I did have to take it a chapter or two a day, because it did give my brain a workout, but then I was learning a lot. I had to resist the temptation to keep plowing through it, since I knew that I would comprehend more if I gave my brain the rest of the day and night to "digest" each chapter before I moved on.
Really, that's about all I have to say!
That all said, it was an extraordinarily readable book on a technical topic. I found myself occasionally thinking, "this is what all my textbooks in school should have been like". I rarely felt frustrated, it had just the right mix of examples and explanation, it introduced the topics in a sequence which built one on the other in a useful way. I did have to take it a chapter or two a day, because it did give my brain a workout, but then I was learning a lot. I had to resist the temptation to keep plowing through it, since I knew that I would comprehend more if I gave my brain the rest of the day and night to "digest" each chapter before I moved on.
Really, that's about all I have to say!
May 4, 2019
It is "An Introduction to Statistical Learning: With Applications in R" but applied to Marketing research. A must-read for data scientists, as it introduces (based on R codes) exploratory data analysis, data selection & transformation, hypothesis testing, things to verify or correct when applying linear regression, data complexity reduction, segmentation, etc & explains those tools and models based on marketing examples - I especially enjoyed the parts describing how synthetic data were simulated. A great complement to an Intro to SL, the present book is lighter on equations but heavier on business case studies.
February 12, 2019
Decent introduction to the R language with a heavy focus on quantitative marketing applications.
April 11, 2019
This book is well-written and I agree with almost all of the authors' advice about how analyses should be carried out.
The problem is that it's a survey: it shows you a bunch of techniques and sketches out how to implement them in R, but it does not teach you enough about any of them to conduct them proficiently (even graphing in the beginning is taught in Base R, which almost nobody currently uses). So you invest in reading a 400 page technical book, and then you're still not actually skilled enough to execute anything at the end. For most people, I would recommend starting with R for Data Science which has less breadth and more depth.
Like Data Science for Business, this book will be useful for managers who need to know about the skills without having the skills. It may also be useful to people who already understand statistics and want to see the R tools which implement them, or to people from very different fields who want to see what marketing problems look like.
Perhaps the best use is for people who already work in the field and would like an easy read refresher with some new tips here and there. I especially liked the authors' use of simulated data to illustrate techniques, a practice I recommend.
The problem is that it's a survey: it shows you a bunch of techniques and sketches out how to implement them in R, but it does not teach you enough about any of them to conduct them proficiently (even graphing in the beginning is taught in Base R, which almost nobody currently uses). So you invest in reading a 400 page technical book, and then you're still not actually skilled enough to execute anything at the end. For most people, I would recommend starting with R for Data Science which has less breadth and more depth.
Like Data Science for Business, this book will be useful for managers who need to know about the skills without having the skills. It may also be useful to people who already understand statistics and want to see the R tools which implement them, or to people from very different fields who want to see what marketing problems look like.
Perhaps the best use is for people who already work in the field and would like an easy read refresher with some new tips here and there. I especially liked the authors' use of simulated data to illustrate techniques, a practice I recommend.
October 3, 2020
If you want to learn statistics with R, but you are not a total beginner, this is the book for you. I have found many valuable example in this book, and I have highlighted many parts of it which have helped me ever since. Sometimes I come back to those highlighted parts and I re-use then or just to refresh my memory and skills. You learn various technique and graphical methods. Highly recommended!
February 1, 2019
Very informative book about marketing analytics. The book starts from beginning so it is good introduction to learn R language. Besides, there are many advanced business analytics approaches with their implementations.
September 27, 2017
Só faltou a parte de "Marketing Research and Analytics". O livro é uma introdução razoável ao R, mas a proposta dele em teoria é outra. Péssimo.
July 7, 2018
A great guide for those who are serious about a career in data analytics as it tackles the very fundamental yet paramount parts of the area.
November 24, 2022
This is one of my favorite books. reading this book in the first step of my career allowed me to be a pioneer. Many topics are covered, including R, Statistics, and Marketing research.
Displaying 1 - 9 of 9 reviews