Modern Analysis of Customer Surveys
with Applications using R
Statistics in Practice
1. Edition January 2012
524 Pages, Hardcover
Wiley & Sons Ltd
ISBN:
978-0-470-97128-4
John Wiley & Sons
Short Description
This book introduces customer satisfaction surveys, with a focus on the classical problems of analyzing them. Each chapter describes, in detail, a different technique applied to the standard dataset along with R scripts on a supporting website. Most of the techniques featured are applied to a standard set of data collected from 266 companies (customers) participating in the ABC Annual Customer Satisfaction Survey conducted by KPA in 2004 for an international electronics company. The data refers to a questionnaire that covered a wide range of service and product perspectives.
Customer survey studies deals with customers, consumers and user satisfaction from a product or service. In practice, many of the customer surveys conducted by business and industry are analyzed in a very simple way, without using models or statistical methods. Typical reports include descriptive statistics and basic graphical displays. As demonstrated in this book, integrating such basic analysis with more advanced tools, provides insights on non-obvious patterns and important relationships between the survey variables. This knowledge can significantly affect the conclusions derived from a survey.
Key features:
* Provides an integrated, case-studies based approach to analysing customer survey data.
* Presents a general introduction to customer surveys, within an organization's business cycle.
* Contains classical techniques with modern and non standard tools.
* Focuses on probabilistic techniques from the area of statistics/data analysis and covers all major recent developments.
* Accompanied by a supporting website containing datasets and R scripts.
Customer survey specialists, quality managers and market researchers will benefit from this book as well as specialists in marketing, data mining and business intelligence fields.
Foreword xvii
Preface xix
Contributors xxiii
PART I BASIC ASPECTS OF CUSTOMER SATISFACTION
SURVEY DATA ANALYSIS
1 Standards and classical techniques in data analysis of customer satisfaction surveys 3
Silvia Salini and Ron S. Kenett
1.1 Literature on customer satisfaction surveys 4
1.2 Customer satisfaction surveys and the business cycle 4
1.3 Standards used in the analysis of survey data 7
1.4 Measures and models of customer satisfaction 12
1.5 Organization of the book 15
1.6 Summary 17
References 17
2 The ABC annual customer satisfaction survey 19
Ron S. Kenett and Silvia Salini
2.1 The ABC company 19
2.2 ABC 2010 ACSS: Demographics of respondents 20
2.3 ABC 2010 ACSS: Overall satisfaction 22
2.4 ABC 2010 ACSS: Analysis of topics 24
2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers 27
2.6 Summary 28
References 28
Appendix 29
3 Census and sample surveys 37
Giovanna Nicolini and Luciana Dalla Valle
3.1 Introduction 37
3.2 Types of surveys 39
3.3 Non-sampling errors 41
3.4 Data collection methods 44
3.5 Methods to correct non-sampling errors 46
3.6 Summary 51
References 52
4 Measurement scales 55
Andrea Bonanomi and Gabriele Cantaluppi
4.1 Scale construction 55
4.2 Scale transformations 60
Acknowledgements 69
References 69
5 Integrated analysis 71
Silvia Biffignandi
5.1 Introduction 71
5.2 Information sources and related problems 73
5.3 Root cause analysis 78
5.4 Summary 87
Acknowledgement 87
References 87
6 Web surveys 89
Roberto Furlan and Diego Martone
6.1 Introduction 89
6.2 Main types of web surveys 90
6.3 Economic benefits of web survey research 91
6.4 Non-economic benefits of web survey research 94
6.5 Main drawbacks of web survey research 96
6.6 Web surveys for customer and employee satisfaction projects 100
6.7 Summary 102
References 102
7 The concept and assessment of customer satisfaction 107
Irena OgrajenÇsek and Iddo Gal
7.1 Introduction 107
7.2 The quality-satisfaction-loyalty chain 108
7.3 Customer satisfaction assessment: Some methodological considerations 115
7.4 The ABC ACSS questionnaire: An evaluation 119
7.5 Summary 121
References 122
Appendix 126
8 Missing data and imputation methods 129
Alessandra Mattei, Fabrizia Mealli and Donald B. Rubin
8.1 Introduction 129
8.2 Missing-data patterns and missing-data mechanisms 131
8.3 Simple approaches to the missing-data problem 134
8.4 Single imputation 136
8.5 Multiple imputation 138
8.6 Model-based approaches to the analysis of missing data 144
8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example 145
8.8 Summary 149
Acknowledgements 150
References 150
9 Outliers and robustness for ordinal data 155
Marco Riani, Francesca Torti and Sergio Zani
9.1 An overview of outlier detection methods 155
9.2 An example of masking 157
9.3 Detection of outliers in ordinal variables 159
9.4 Detection of bivariate ordinal outliers 160
9.5 Detection of multivariate outliers in ordinal regression 161
9.6 Summary 168
References 168
PART II MODERN TECHNIQUES IN CUSTOMER SATISFACTION SURVEY DATA ANALYSIS
10 Statistical inference for causal effects 173
Fabrizia Mealli, Barbara Pacini and Donald B. Rubin
10.1 Introduction to the potential outcome approach to causal inference 173
10.2 Assignment mechanisms 179
10.3 Inference in classical randomized experiments 182
10.4 Inference in observational studies 185
References 190
11 Bayesian networks applied to customer surveys 193
Ron S. Kenett, Giovanni Perruca and Silvia Salini
11.1 Introduction to Bayesian networks 193
11.2 The Bayesian network model in practice 197
11.3 Prediction and explanation 211
11.4 Summary 213
References 213
12 Log-linear model methods 217
Stephen E. Fienberg and Daniel Manrique-Vallier
12.1 Introduction 217
12.2 Overview of log-linear models and methods 218
12.3 Application to ABC survey data 224
12.4 Summary 227
References 228
13 CUB models: Statistical methods and empirical evidence 231
Maria Iannario and Domenico Piccolo
13.1 Introduction 231
13.2 Logical foundations and psychological motivations 233
13.3 A class of models for ordinal data 233
13.4 Main inferential issues 236
13.5 Specification of CUB models with subjects' covariates 238
13.6 Interpreting the role of covariates 240
13.7 A more general sampling framework 241
13.8 Applications of CUB models 244
13.9 Further generalizations 248
13.10 Concluding remarks 251
Acknowledgements 251
References 251
Appendix 255
A program in R for CUB models 255
A.1 Main structure of the program 255
A.2 Inference on CUB models 255
A.3 Output of CUB models estimation program 256
A.4 Visualization of several CUB models in the parameter space 257
A.5 Inference on CUB models in a multi-object framework 257
A.6 Advanced software support for CUB models 258
14 The Rasch model 259
Francesca De Battisti, Giovanna Nicolini and Silvia Salini
14.1 An overview of the Rasch model 259
14.2 The Rasch model in practice 267
14.3 Rasch model software 277
14.4 Summary 278
References 279
15 Tree-based methods and decision trees 283
Giuliano Galimberti and Gabriele Soffritti
15.1 An overview of tree-based methods and decision trees 283
15.2 Tree-based methods and decision trees in practice 300
15.3 Further developments 304
References 304
16 PLS models 309
Giuseppe Boari and Gabriele Cantaluppi
16.1 Introduction 309
16.2 The general formulation of a structural equation model 310
16.3 The PLS algorithm 313
16.4 Statistical interpretation of PLS 319
16.5 Geometrical interpretation of PLS 320
16.6 Comparison of the properties of PLS and LISREL procedures 321
16.7 Available software for PLS estimation 323
16.8 Application to real data: Customer satisfaction analysis 323
References 329
17 Nonlinear principal component analysis 333
Pier Alda Ferrari and Alessandro Barbiero
17.1 Introduction 333
17.2 Homogeneity analysis and nonlinear principal component analysis 334
17.3 Analysis of customer satisfaction 338
17.4 Dealing with missing data 340
17.5 Nonlinear principal component analysis versus two competitors 343
17.6 Application to the ABC ACSS data 344
17.7 Summary 355
References 355
18 Multidimensional scaling 357
Nadia Solaro
18.1 An overview of multidimensional scaling techniques 357
18.2 Multidimensional scaling in practice 374
features: The incomplete data set 383
18.3 Multidimensional scaling in a future perspective 386
18.4 Summary 386
References 387
19 Multilevel models for ordinal data 391
Leonardo Grilli and Carla Rampichini
19.1 Ordinal variables 391
19.2 Standard models for ordinal data 393
19.3 Multilevel models for ordinal data 395
19.4 Multilevel models for ordinal data in practice: An application to student ratings 404
References 408
20 Quality standards and control charts applied to customer surveys 413
Ron S. Kenett, Laura Deldossi and Diego Zappa
20.1 Quality standards and customer satisfaction 413
20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction 414
20.3 Control Charts and ISO 7870 417
20.4 Control charts and customer surveys: Standard assumptions 420
20.5 Control charts and customer surveys: Non-standard methods 426
20.6 The M-test for assessing sample representation 433
20.7 Summary 435
References 436
21 Fuzzy Methods and Satisfaction Indices 439
Sergio Zani, Maria Adele Milioli and Isabella Morlini
21.1 Introduction 439
21.2 Basic definitions and operations 440
21.3 Fuzzy numbers 441
21.4 A criterion for fuzzy transformation of variables 443
21.5 Aggregation and weighting of variables 445
21.6 Application to the ABC customer satisfaction survey data 446
21.7 Summary 453
References 455
Appendix An introduction to R 457
Stefano Maria Iacus
A.1 Introduction 457
A.2 How to obtain R 457
A.3 Type rather than 'point and click' 458
A.4 Objects 460
A.5 S4 objects 470
A.6 Functions 472
A.7 Vectorization 473
A.8 Importing data from different sources 475
A.9 Interacting with databases 476
A.10 Simple graphics manipulation 477
A.11 Basic analysis of the ABC data 481
A.12 About this document 496
A.13 Bibliographical notes 496
References 496
Index 499
Preface xix
Contributors xxiii
PART I BASIC ASPECTS OF CUSTOMER SATISFACTION
SURVEY DATA ANALYSIS
1 Standards and classical techniques in data analysis of customer satisfaction surveys 3
Silvia Salini and Ron S. Kenett
1.1 Literature on customer satisfaction surveys 4
1.2 Customer satisfaction surveys and the business cycle 4
1.3 Standards used in the analysis of survey data 7
1.4 Measures and models of customer satisfaction 12
1.5 Organization of the book 15
1.6 Summary 17
References 17
2 The ABC annual customer satisfaction survey 19
Ron S. Kenett and Silvia Salini
2.1 The ABC company 19
2.2 ABC 2010 ACSS: Demographics of respondents 20
2.3 ABC 2010 ACSS: Overall satisfaction 22
2.4 ABC 2010 ACSS: Analysis of topics 24
2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers 27
2.6 Summary 28
References 28
Appendix 29
3 Census and sample surveys 37
Giovanna Nicolini and Luciana Dalla Valle
3.1 Introduction 37
3.2 Types of surveys 39
3.3 Non-sampling errors 41
3.4 Data collection methods 44
3.5 Methods to correct non-sampling errors 46
3.6 Summary 51
References 52
4 Measurement scales 55
Andrea Bonanomi and Gabriele Cantaluppi
4.1 Scale construction 55
4.2 Scale transformations 60
Acknowledgements 69
References 69
5 Integrated analysis 71
Silvia Biffignandi
5.1 Introduction 71
5.2 Information sources and related problems 73
5.3 Root cause analysis 78
5.4 Summary 87
Acknowledgement 87
References 87
6 Web surveys 89
Roberto Furlan and Diego Martone
6.1 Introduction 89
6.2 Main types of web surveys 90
6.3 Economic benefits of web survey research 91
6.4 Non-economic benefits of web survey research 94
6.5 Main drawbacks of web survey research 96
6.6 Web surveys for customer and employee satisfaction projects 100
6.7 Summary 102
References 102
7 The concept and assessment of customer satisfaction 107
Irena OgrajenÇsek and Iddo Gal
7.1 Introduction 107
7.2 The quality-satisfaction-loyalty chain 108
7.3 Customer satisfaction assessment: Some methodological considerations 115
7.4 The ABC ACSS questionnaire: An evaluation 119
7.5 Summary 121
References 122
Appendix 126
8 Missing data and imputation methods 129
Alessandra Mattei, Fabrizia Mealli and Donald B. Rubin
8.1 Introduction 129
8.2 Missing-data patterns and missing-data mechanisms 131
8.3 Simple approaches to the missing-data problem 134
8.4 Single imputation 136
8.5 Multiple imputation 138
8.6 Model-based approaches to the analysis of missing data 144
8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example 145
8.8 Summary 149
Acknowledgements 150
References 150
9 Outliers and robustness for ordinal data 155
Marco Riani, Francesca Torti and Sergio Zani
9.1 An overview of outlier detection methods 155
9.2 An example of masking 157
9.3 Detection of outliers in ordinal variables 159
9.4 Detection of bivariate ordinal outliers 160
9.5 Detection of multivariate outliers in ordinal regression 161
9.6 Summary 168
References 168
PART II MODERN TECHNIQUES IN CUSTOMER SATISFACTION SURVEY DATA ANALYSIS
10 Statistical inference for causal effects 173
Fabrizia Mealli, Barbara Pacini and Donald B. Rubin
10.1 Introduction to the potential outcome approach to causal inference 173
10.2 Assignment mechanisms 179
10.3 Inference in classical randomized experiments 182
10.4 Inference in observational studies 185
References 190
11 Bayesian networks applied to customer surveys 193
Ron S. Kenett, Giovanni Perruca and Silvia Salini
11.1 Introduction to Bayesian networks 193
11.2 The Bayesian network model in practice 197
11.3 Prediction and explanation 211
11.4 Summary 213
References 213
12 Log-linear model methods 217
Stephen E. Fienberg and Daniel Manrique-Vallier
12.1 Introduction 217
12.2 Overview of log-linear models and methods 218
12.3 Application to ABC survey data 224
12.4 Summary 227
References 228
13 CUB models: Statistical methods and empirical evidence 231
Maria Iannario and Domenico Piccolo
13.1 Introduction 231
13.2 Logical foundations and psychological motivations 233
13.3 A class of models for ordinal data 233
13.4 Main inferential issues 236
13.5 Specification of CUB models with subjects' covariates 238
13.6 Interpreting the role of covariates 240
13.7 A more general sampling framework 241
13.8 Applications of CUB models 244
13.9 Further generalizations 248
13.10 Concluding remarks 251
Acknowledgements 251
References 251
Appendix 255
A program in R for CUB models 255
A.1 Main structure of the program 255
A.2 Inference on CUB models 255
A.3 Output of CUB models estimation program 256
A.4 Visualization of several CUB models in the parameter space 257
A.5 Inference on CUB models in a multi-object framework 257
A.6 Advanced software support for CUB models 258
14 The Rasch model 259
Francesca De Battisti, Giovanna Nicolini and Silvia Salini
14.1 An overview of the Rasch model 259
14.2 The Rasch model in practice 267
14.3 Rasch model software 277
14.4 Summary 278
References 279
15 Tree-based methods and decision trees 283
Giuliano Galimberti and Gabriele Soffritti
15.1 An overview of tree-based methods and decision trees 283
15.2 Tree-based methods and decision trees in practice 300
15.3 Further developments 304
References 304
16 PLS models 309
Giuseppe Boari and Gabriele Cantaluppi
16.1 Introduction 309
16.2 The general formulation of a structural equation model 310
16.3 The PLS algorithm 313
16.4 Statistical interpretation of PLS 319
16.5 Geometrical interpretation of PLS 320
16.6 Comparison of the properties of PLS and LISREL procedures 321
16.7 Available software for PLS estimation 323
16.8 Application to real data: Customer satisfaction analysis 323
References 329
17 Nonlinear principal component analysis 333
Pier Alda Ferrari and Alessandro Barbiero
17.1 Introduction 333
17.2 Homogeneity analysis and nonlinear principal component analysis 334
17.3 Analysis of customer satisfaction 338
17.4 Dealing with missing data 340
17.5 Nonlinear principal component analysis versus two competitors 343
17.6 Application to the ABC ACSS data 344
17.7 Summary 355
References 355
18 Multidimensional scaling 357
Nadia Solaro
18.1 An overview of multidimensional scaling techniques 357
18.2 Multidimensional scaling in practice 374
features: The incomplete data set 383
18.3 Multidimensional scaling in a future perspective 386
18.4 Summary 386
References 387
19 Multilevel models for ordinal data 391
Leonardo Grilli and Carla Rampichini
19.1 Ordinal variables 391
19.2 Standard models for ordinal data 393
19.3 Multilevel models for ordinal data 395
19.4 Multilevel models for ordinal data in practice: An application to student ratings 404
References 408
20 Quality standards and control charts applied to customer surveys 413
Ron S. Kenett, Laura Deldossi and Diego Zappa
20.1 Quality standards and customer satisfaction 413
20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction 414
20.3 Control Charts and ISO 7870 417
20.4 Control charts and customer surveys: Standard assumptions 420
20.5 Control charts and customer surveys: Non-standard methods 426
20.6 The M-test for assessing sample representation 433
20.7 Summary 435
References 436
21 Fuzzy Methods and Satisfaction Indices 439
Sergio Zani, Maria Adele Milioli and Isabella Morlini
21.1 Introduction 439
21.2 Basic definitions and operations 440
21.3 Fuzzy numbers 441
21.4 A criterion for fuzzy transformation of variables 443
21.5 Aggregation and weighting of variables 445
21.6 Application to the ABC customer satisfaction survey data 446
21.7 Summary 453
References 455
Appendix An introduction to R 457
Stefano Maria Iacus
A.1 Introduction 457
A.2 How to obtain R 457
A.3 Type rather than 'point and click' 458
A.4 Objects 460
A.5 S4 objects 470
A.6 Functions 472
A.7 Vectorization 473
A.8 Importing data from different sources 475
A.9 Interacting with databases 476
A.10 Simple graphics manipulation 477
A.11 Basic analysis of the ABC data 481
A.12 About this document 496
A.13 Bibliographical notes 496
References 496
Index 499
Ron S. Kenett, KPA Ltd., Raanana, Israel, University of Turin, Italy, and NYU-Poly, Center for Risk Engineering, New York, USA
Silvia Salini, Department of Economics, Business and Statistics ,University of Milan, Italy
Silvia Salini, Department of Economics, Business and Statistics ,University of Milan, Italy
