Ron Kenett, Silvia Salini

Modern Analysis of Customer Surveys

With Applications Using R

• Gebundenes Buch

Produktbeschreibung

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.

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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.

Produktdetails

• Statistics in Practice .
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 528
• Erscheinungstermin: 30. Januar 2012
• Englisch
• Abmessung: 251mm x 175mm x 30mm
• Gewicht: 909g
• ISBN-13: 9780470971284
• ISBN-10: 0470971282
• Artikelnr.: 34551598

Autorenporträt

Edited by 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

Inhaltsangabe

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