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R for Marketing Research and Analytics [Pehme köide]

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  • Formaat: Paperback / softback, 454 pages, kõrgus x laius: 235x155 mm, kaal: 7939 g, 54 Illustrations, color; 54 Illustrations, black and white; XVIII, 454 p. 108 illus., 54 illus. in color., 1 Paperback / softback
  • Sari: Use R!
  • Ilmumisaeg: 25-Mar-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319144359
  • ISBN-13: 9783319144351
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R for Marketing Research and AnalyticsSuurem pilt
  • Formaat: Paperback / softback, 454 pages, kõrgus x laius: 235x155 mm, kaal: 7939 g, 54 Illustrations, color; 54 Illustrations, black and white; XVIII, 454 p. 108 illus., 54 illus. in color., 1 Paperback / softback
  • Sari: Use R!
  • Ilmumisaeg: 25-Mar-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319144359
  • ISBN-13: 9783319144351
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319144351.html
Märksõnad:

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.

Arvustused

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 readerseven for those who know R and have marketing research and analytics experiencethis 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)

Muu info

"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
Preface vii
Part I Basics of R
1 Welcome to R
3(8)
1.1 What Is R?
3(1)
1.2 Why R?
4(1)
1.3 Why Not R?
5(1)
1.4 When R?
6(1)
1.5 Using This Book
6(4)
1.5.1 About the Text
6(1)
1.5.2 About the Data
7(1)
1.5.3 Online Material
8(1)
1.5.4 When Things Go Wrong
9(1)
1.6 Key Points
10(1)
2 An Overview of the R Language
11(36)
2.1 Getting Started
11(2)
2.1.1 Initial Steps
11(1)
2.1.2 Starting R
12(1)
2.2 A Quick Tour of R's Capabilities
13(4)
2.3 Basics of Working with R Commands
17(1)
2.4 Basic Objects
18(12)
2.4.1 Vectors
19(2)
2.4.2 Help! A Brief Detour
21(3)
2.4.3 More on Vectors and Indexing
24(2)
2.4.4 aaRgh! A Digression for New Programmers
26(1)
2.4.5 Missing and Interesting Values
26(2)
2.4.6 Using R for Mathematical Computation
28(1)
2.4.7 Lists
28(2)
2.5 Data Frames
30(4)
2.6 Loading and Saving Data
34(4)
2.6.1 Image Files
36(1)
2.6.2 CSV Files
36(2)
2.7 Writing Your Own Functions*
38(4)
2.7.1 Language Structures*
40(1)
2.7.2 Anonymous Functions*
41(1)
2.8 Clean Up!
42(1)
2.9 Learning More*
43(1)
2.10 Key Points
44(3)
Part II Fundamentals of Data Analysis
3 Describing Data
47(30)
3.1 Simulating Data
47(5)
3.1.1 Store Data: Setting the Structure
48(2)
3.1.2 Store Data: Simulating Data Points
50(2)
3.2 Functions to Summarize a Variable
52(4)
3.2.1 Discrete Variables
52(2)
3.2.2 Continuous Variables
54(2)
3.3 Summarizing Data Frames
56(5)
3.3.1 summary()
57(1)
3.3.2 describe()
58(1)
3.3.3 Recommended Approach to Inspecting Data
59(1)
3.3.4 apply()*
59(2)
3.4 Single Variable Visualization
61(13)
3.4.1 Histograms
61(5)
3.4.2 Boxplots
66(2)
3.4.3 QQ Plot to Check Normality*
68(1)
3.4.4 Cumulative Distribution*
69(1)
3.4.5 Language Brief: by () and aggregate ()
70(2)
3.4.6 Maps
72(2)
3.5 Learning More*
74(1)
3.6 Key Points
75(2)
4 Relationships Between Continuous Variables
77(34)
4.1 Retailer Data
77(6)
4.1.1 Simulating Customer Data
78(1)
4.1.2 Simulating Online and In-Store Sales Data
79(1)
4.1.3 Simulating Satisfaction Survey Responses
80(2)
4.1.4 Simulating Non-Response Data
82(1)
4.2 Exploring Associations Between Variables with Scatterplots
83(7)
4.2.1 Creating a Basic Scatterplot with plot()
83(3)
4.2.2 Color-Coding Points on a Scatterplot
86(2)
4.2.3 Adding a Legend to a Plot
88(1)
4.2.4 Plotting on a Log Scale
89(1)
4.3 Combining Plots in a Single Graphics Object
90(2)
4.4 Scatterplot Matrices
92(3)
4.4.1 pairs()
92(1)
4.4.2 scatterplotmatrix()
93(2)
4.5 Correlation Coefficients
95(9)
4.5.1 Correlation Tests
97(1)
4.5.2 Correlation Matrices
98(2)
4.5.3 Transforming Variables before Computing Correlations
100(2)
4.5.4 Typical Marketing Data Transformations
102(1)
4.5.5 Box—Cox Transformations*
102(2)
4.6 Exploring Associations in Survey Responses*
104(3)
4.6.1 jitter()*
105(1)
4.6.2 polychoric()*
106(1)
4.7 Learning More*
107(1)
4.8 Key Points
108(3)
5 Comparing Groups: Tables and Visualizations
111(24)
5.1 Simulating Consumer Segment Data
111(9)
5.1.1 Segment Data Definition
112(2)
5.1.2 Language Brief: for() Loops
114(2)
5.1.3 Language Brief: if() Blocks
116(2)
5.1.4 Final Segment Data Generation
118(2)
5.2 Finding Descriptives by Group
120(12)
5.2.1 Language Brief: Basic Formula Syntax
123(1)
5.2.2 Descriptives for Two-Way Groups
124(2)
5.2.3 Visualization by Group: Frequencies and Proportions
126(3)
5.2.4 Visualization by Group: Continuous Data
129(3)
5.3 Learning More*
132(1)
5.4 Key Points
133(2)
6 Comparing Groups: Statistical Tests
135(24)
6.1 Data for Comparing Groups
135(1)
6.2 Testing Group Frequencies: chisq.test()
136(3)
6.3 Testing Observed Proportions: binom.test()
139(3)
6.3.1 About Confidence Intervals
140(1)
6.3.2 More About binom. test() and Binomial Distributions
141(1)
6.4 Testing Group Means: t.test()
142(2)
6.5 Testing Multiple Group Means: ANOVA
144(5)
6.5.1 Model Comparison in ANOVA*
146(1)
6.5.2 Visualizing Group Confidence Intervals
147(1)
6.5.3 Variable Selection in ANOVA: Stepwise Modeling*
148(1)
6.6 Bayesian ANOVA: Getting Started*
149(7)
6.6.1 Why Bayes?
150(1)
6.6.2 Basics of Bayesian ANOVA*
150(2)
6.6.3 Inspecting the Posterior Draws*
152(3)
6.6.4 Plotting the Bayesian Credible Intervals*
155(1)
6.7 Learning More*
156(1)
6.8 Key Points
157(2)
7 Identifying Drivers of Outcomes: Linear Models
159(36)
7.1 Amusement Park Data
160(2)
7.1.1 Simulating the Amusement Park Data
160(2)
7.2 Fitting Linear Models with 1m()
162(11)
7.2.1 Preliminary Data Inspection
163(2)
7.2.2 Recap: Bivariate Association
165(1)
7.2.3 Linear Model with a Single Predictor
165(1)
7.2.4 1m Objects
166(3)
7.2.5 Checking Model Fit
169(4)
7.3 Fitting Linear Models with Multiple Predictors
173(6)
7.3.1 Comparing Models
175(1)
7.3.2 Using a Model to Make Predictions
176(1)
7.3.3 Standardizing the Predictors
177(2)
7.4 Using Factors as Predictors
179(3)
7.5 Interaction Terms
182(3)
7.5.1 Language Brief: Advanced Formula Syntax*
183(2)
7.6 Caution! Overfitting
185(1)
7.7 Recommended Procedure for Linear Model Fitting
186(1)
7.8 Bayesian Linear Models with MCMCregress()*
186(2)
7.9 Learning More*
188(2)
7.10 Key Points
190(5)
Part III Advanced Marketing Applications
8 Reducing Data Complexity
195(30)
8.1 Consumer Brand Rating Data
195(5)
8.1.1 Resealing the Data
197(1)
8.1.2 Aggregate Mean Ratings by Brand
198(2)
8.2 Principal Component Analysis and Perceptual Maps
200(9)
8.2.1 PCA Example
200(3)
8.2.2 Visualizing PCA
203(1)
8.2.3 PCA for Brand Ratings
204(2)
8.2.4 Perceptual Map of the Brands
206(2)
8.2.5 Cautions with Perceptual Maps
208(1)
8.3 Exploratory Factor Analysis
209(9)
8.3.1 Basic EFA Concepts
210(1)
8.3.2 Finding an EFA Solution
211(2)
8.3.3 EFA Rotations
213(3)
8.3.4 Using Factor Scores for Brands
216(2)
8.4 Multidimensional Scaling
218(3)
8.4.1 Non-metric MDS
219(2)
8.5 Learning More*
221(1)
8.5.1 Principal Component Analysis
221(1)
8.5.2 Factor Analysis
221(1)
8.5.3 Multidimensional Scaling
222(1)
8.6 Key Points
222(3)
8.6.1 Principal Component Analysis
222(1)
8.6.2 Exploratory Factor Analysis
222(1)
8.6.3 Multidimensional Scaling
223(2)
9 Additional Linear Modeling Topics
225(42)
9.1 Handling Highly Correlated Variables
226(5)
9.1.1 An Initial Linear Model of Online Spend
226(3)
9.1.2 Remediating Collinearity
229(2)
9.2 Linear Models for Binary Outcomes: Logistic Regression
231(11)
9.2.1 Basics of the Logistic Regression Model
231(1)
9.2.2 Data for Logistic Regression of Season Passes
232(1)
9.2.3 Sales Table Data
233(1)
9.2.4 Language Brief: Classes and Attributes of Objects*
234(2)
9.2.5 Finalizing the Data
236(1)
9.2.6 Fitting a Logistic Regression Model
237(2)
9.2.7 Reconsidering the Model
239(3)
9.2.8 Additional Discussion
242(1)
9.3 Hierarchical Linear Models
242(10)
9.3.1 Some HLM Concepts
243(1)
9.3.2 Ratings-Based Conjoint Analysis for the Amusement Park
244(1)
9.3.3 Simulating Ratings-Based Conjoint Data
245(1)
9.3.4 An Initial Linear Model
246(2)
9.3.5 Hierarchical Linear Model with lme4
248(1)
9.3.6 The Complete Hierarchical Linear Model
249(2)
9.3.7 Summary of HLM with lme4
251(1)
9.4 Bayesian Hierarchical Linear Models*
252(7)
9.4.1 Initial Linear Model with MCMCregress()*
253(1)
9.4.2 Hierarchical Linear Model with MCMChregress()*
253(3)
9.4.3 Inspecting Distribution of Preference*
256(3)
9.5 A Quick Comparison of Frequentist & Bayesian HLMs*
259(4)
9.6 Learning More*
263(1)
9.6.1 Collinearity
263(1)
9.6.2 Logistic Regression
263(1)
9.6.3 Hierarchical Models
263(1)
9.6.4 Bayesian Hierarchical Models
263(1)
9.7 Key Points
264(3)
9.7.1 Collinearity
264(1)
9.7.2 Logistic Regression
264(1)
9.7.3 Hierarchical Linear Models
265(1)
9.7.4 Bayesian Methods for Hierarchical Linear Models
266(1)
10 Confirmatory Factor Analysis and Structural Equation Modeling
267(32)
10.1 The Motivation for Structural Models
268(2)
10.1.1 Structural Models in This
Chapter
269(1)
10.2 Scale Assessment: CFA
270(13)
10.2.1 Simulating PIES CFA Data
272(5)
10.2.2 Estimating the PIES CFA Model
277(1)
10.2.3 Assessing the PIES CFA Model
278(5)
10.3 General Models: Structural Equation Models
283(5)
10.3.1 The Repeat Purchase Model in R
284(2)
10.3.2 Assessing the Repeat Purchase Model
286(2)
10.4 The Partial Least Squares (PLS) Alternative
288(9)
10.4.1 PLS-SEM for Repeat Purchase
289(3)
10.4.2 Visualizing the Fitted PLS Model*
292(1)
10.4.3 Assessing the PLS-SEM Model
293(2)
10.4.4 PLS-SEM with the Larger Sample
295(2)
10.5 Learning More*
297(1)
10.6 Key Points
297(2)
11 Segmentation: Clustering and Classification
299(40)
11.1 Segmentation Philosophy
299(3)
11.1.1 The Difficulty of Segmentation
299(1)
11.1.2 Segmentation as Clustering and Classification
300(2)
11.2 Segmentation Data
302(1)
11.3 Clustering
302(20)
11.3.1 The Steps of Clustering
303(2)
11.3.2 Hierarchical Clustering: hclust() Basics
305(4)
11.3.3 Hierarchical Clustering Continued: Groups from hclust()
309(2)
11.3.4 Mean-Based Clustering: kmeans()
311(3)
11.3.5 Model-Based Clustering: Mc lust()
314(1)
11.3.6 Comparing Models with BIC()
315(2)
11.3.7 Latent Class Analysis: poLCA()
317(3)
11.3.8 Comparing Cluster Solutions
320(2)
11.3.9 Recap of Clustering
322(1)
11.4 Classification
322(11)
11.4.1 Naive Bayes Classification: naiveBayes()
323(4)
11.4.2 Random Forest Classification: randomForest()
327(3)
11.4.3 Random Forest Variable Importance
330(3)
11.5 Prediction: Identifying Potential Customers*
333(3)
11.6 Learning More*
336(1)
11.7 Key Points
337(2)
12 Association Rules for Market Basket Analysis
339(24)
12.1 The Basics of Association Rules
340(1)
12.1.1 Metrics
340(1)
12.2 Retail Transaction Data: Market Baskets
341(5)
12.2.1 Example Data: Groceries
342(2)
12.2.2 Supermarket Data
344(2)
12.3 Finding and Visualizing Association Rules
346(10)
12.3.1 Finding and Plotting Subsets of Rules
348(1)
12.3.2 Using Profit Margin Data with Transactions: An Initial Start
349(2)
12.3.3 Language Brief: A Function for Margin Using an Object's class*
351(5)
12.4 Rules in Non-Transactional Data: Exploring Segments Again
356(4)
12.4.1 Language Brief: Slicing Continuous Data with cut()
356(1)
12.4.2 Exploring Segment Associations
357(3)
12.5 Learning More*
360(1)
12.6 Key Points
360(3)
13 Choice Modeling
363(40)
13.1 Choice-Based Conjoint Analysis Surveys
364(1)
13.2 Simulating Choice Data*
365(5)
13.3 Fitting a Choice Model
370(13)
13.3.1 Inspecting Choice Data
371(1)
13.3.2 Fitting Choice Models with mlogit()
372(3)
13.3.3 Reporting Choice Model Findings
375(5)
13.3.4 Share Predictions for Identical Alternatives
380(1)
13.3.5 Planning the Sample Size for a Conjoint Study
381(2)
13.4 Adding Consumer Heterogeneity to Choice Models
383(5)
13.4.1 Estimating Mixed Logit Models with mlogit()
383(3)
13.4.2 Share Prediction for Heterogeneous Choice Models
386(2)
13.5 Hierarchical Bayes Choice Models
388(9)
13.5.1 Estimating Hierarchical Bayes Choice Models with ChoiceModelR
388(7)
13.5.2 Share Prediction for Hierarchical Bayes Choice Models
395(2)
13.6 Design of Choice-Based Conjoint Surveys*
397(1)
13.7 Learning More*
398(1)
13.8 Key Points
399(2)
Conclusion 401(2)
A Appendix: R Versions and Related Software 403(8)
A.1 R Base
403(1)
A.2 RStudio
404(1)
A.3 Emacs Speaks Statistics
405(1)
A.4 Eclipse + StatET
406(1)
A.5 Revolution R
407(1)
A.6 Other Options
408(3)
A.6.1 Text Editors
408(1)
A.6.2 R Commander
408(1)
A.6.3 Rattle
409(1)
A.6.4 Deducer
409(1)
A.6.5 TIBCO Enterprise Runtime for R
409(2)
B Appendix: Scaling Up 411(12)
B.1 Handling Data
411(4)
B.1.1 Data Wrangling
411(1)
B.1.2 Microsoft Excel: gdata
412(1)
B.1.3 SAS, SPSS, and Other Statistics Packages: foreign
412(1)
B.1.4 SQL: RSQLite, sqldf and RODBC
413(2)
B.2 Handling Large Data Sets
415(1)
B.3 Speeding Up Computation
416(2)
B.3.1 Efficient Coding and Data Storage
416(1)
B.3.2 Enhancing the R Engine
417(1)
B.4 Time Series Analysis, Repeated Measures, and Longitudinal Analysis
418(1)
B.5 Automated and Interactive Reporting
419(4)
C Appendix: Packages Used 423(8)
C.1 Core and Frequentist Statistics
424(1)
C.2 Graphics
424(1)
C.3 Bayesian Methods
425(1)
C.4 Advanced Statistics
426(1)
C.5 Machine Learning
426(1)
C.6 Data Handling
427(1)
C.7 Other Packages
428(3)
D Appendix: Online Materials and Data Files 431(4)
D.1 Data File Structure
431(1)
D.2 Data File URL Cross-Reference
432(3)
D.2.1 Update on Data Locations
432(3)
References 435(12)
Index 447
Chris Chapman is a Senior Quantitative Researcher at Google. He is also a member of the editorial board of Marketing Insights magazine and the Marketing Insights Council of the American Marketing Association, and has served as chair of the AMA Advanced Research Techniques Forum and AMA Analytics with Purpose conferences. He is an enthusiastic contributor to the quantitative marketing community, where he regularly presents innovations in strategic research and teaches workshops on R and analytic methods.





Elea McDonnell Feit is an Assistant Professor at the LeBow College of Business at Drexel University. Her research focuses on leveraging customer data to make better product design and advertising decisions, particularly when data is incomplete, unmatched or aggregated. Much of her career has focused on building bridges between academia and practice, most recently as a Fellow of the Wharton Customer Analytics Initiative. She enjoys making quantitative methods accessible to a broad audience and regularly gives popular practitioner tutorials on marketing analytics, in addition to teaching courses at LeBow in data-driven digital marketing and design of marketing experiments.