Book description
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.
Table of contents
- Cover
- Statistics in Practice
- Title Page
- Copyright
- Dedication
- Foreword
- Preface
- Contributors
-
Part I: BASIC ASPECTS OF CUSTOMER SATISFACTION SURVEY DATA ANALYSIS
- 1: Standards and classical techniques in data analysis of customer satisfaction surveys
- 2: The ABC annual customer satisfaction survey
- 3: Census and sample surveys
- 4: Measurement scales
- 5: Integrated analysis
- 6: Web surveys
- 7: The concept and assessment of customer satisfaction
-
8: Missing data and imputation methods
- 8.1 Introduction
- 8.2 Missing-data patterns and missing-data mechanisms
- 8.3 Simple approaches to the missing-data problem
- 8.4 Single imputation
- 8.5 Multiple imputation
- 8.6 Model-based approaches to the analysis of missing data
- 8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example
- 8.8 Summary
- Acknowledgements
- 9: Outliers and robustness for ordinal data
-
Part II: MODERN TECHNIQUES IN CUSTOMER SATISFACTION SURVEY DATA ANALYSIS
- 10: Statistical inference for causal effects
- 11: Bayesian networks applied to customer surveys
- 12: Log-linear model methods
-
13: CUB models: Statistical methods and empirical evidence
- 13.1 Introduction
- 13.2 Logical foundations and psychological motivations
- 13.3 A class of models for ordinal data
- 13.4 Main inferential issues
- 13.5 Specification of CUB models with subjects’ covariates
- 13.6 Interpreting the role of covariates
- 13.7 A more general sampling framework
- 13.8 Applications of CUB models
- 13.9 Further generalizations
- 13.10 Concluding remarks
- Appendix
- 14: The Rasch model
- 15: Tree-based methods and decision trees
-
16: PLS models
- 16.1 Introduction
- 16.2 The general formulation of a structural equation model
- 16.3 The PLS algorithm
- 16.4 Statistical interpretation of PLS
- 16.5 Geometrical interpretation of PLS
- 16.6 Comparison of the properties of PLS and LISREL procedures
- 16.7 Available software for PLS estimation
- 16.8 Application to real data: Customer satisfaction analysis
- 17: Nonlinear principal component analysis
- 18: Multidimensional scaling
- 19: Multilevel models for ordinal data
-
20: Quality standards and control charts applied to customer surveys
- 20.1 Quality standards and customer satisfaction
- 20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction
- 20.3 Control Charts and ISO 7870
- 20.4 Control charts and customer surveys: Standard assumptions
- 20.5 Control charts and customer surveys: Non-standard methods
- 20.6 The M-test for assessing sample representation
- 20.7 Summary
- 21: Fuzzy Methods and Satisfaction Indices
-
Appendix: An introduction to R
- A.1 Introduction
- A.2 How to obtain R
- A.3 Type rather than ‘point and click’
- A.4 Objects
- A.5 S4 objects
- A.6 Functions
- A.7 Vectorization
- A.8 Importing data from different sources
- A.9 Interacting with databases
- A.10 Simple graphics manipulation
- A.11 Basic analysis of the ABC data
- A.12 About this document
- A.13 Bibliographical notes
- Index
Product information
- Title: Modern Analysis of Customer Surveys: with applications using R
- Author(s):
- Release date: January 2012
- Publisher(s): Wiley
- ISBN: 9780470971284
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