Modern Analysis of Customer Surveys

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.

InhaltsangabeForeword 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 Nonsampling 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 Noneconomic 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 Missingdata patterns and missingdata mechanisms 131 8.3 Simple approaches to the missing-data problem 134 8.4 Single imputation 136 8.5 Multiple imputation 138 8.6 Modelbased 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 Loglinear model methods 217 Stephen E. Fienberg and Daniel Manrique-Vallier