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Modern Analysis of Customer Surveys: with applications using R

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

  1. Cover
  2. Statistics in Practice
  3. Title Page
  4. Copyright
  5. Dedication
  6. Foreword
  7. Preface
  8. Contributors
  9. Part I: BASIC ASPECTS OF CUSTOMER SATISFACTION SURVEY DATA ANALYSIS
    1. 1: Standards and classical techniques in data analysis of customer satisfaction surveys
      1. 1.1 Literature on customer satisfaction surveys
      2. 1.2 Customer satisfaction surveys and the business cycle
      3. 1.3 Standards used in the analysis of survey data
      4. 1.4 Measures and models of customer satisfaction
      5. 1.5 Organization of the book
      6. 1.6 Summary
    2. 2: The ABC annual customer satisfaction survey
      1. 2.1 The ABC company
      2. 2.2 ABC 2010 ACSS: Demographics of respondents
      3. 2.3 ABC 2010 ACSS: Overall satisfaction
      4. 2.4 ABC 2010 ACSS: Analysis of topics
      5. 2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers
      6. 2.6 Summary
      7. Appendix
    3. 3: Census and sample surveys
      1. 3.1 Introduction
      2. 3.2 Types of surveys
      3. 3.3 Non-sampling errors
      4. 3.4 Data collection methods
      5. 3.5 Methods to correct non-sampling errors
      6. 3.6 Summary
    4. 4: Measurement scales
      1. 4.1 Scale construction
      2. 4.2 Scale transformations
      3. Acknowledgements
    5. 5: Integrated analysis
      1. 5.1 Introduction
      2. 5.2 Information sources and related problems
      3. 5.3 Root cause analysis
      4. 5.4 Summary
      5. Acknowledgement
    6. 6: Web surveys
      1. 6.1 Introduction
      2. 6.2 Main types of web surveys
      3. 6.3 Economic benefits of web survey research
      4. 6.4 Non-economic benefits of web survey research
      5. 6.5 Main drawbacks of web survey research
      6. 6.6 Web surveys for customer and employee satisfaction projects
      7. 6.7 Summary
    7. 7: The concept and assessment of customer satisfaction
      1. 7.1 Introduction
      2. 7.2 The quality–satisfaction–loyalty chain
      3. 7.3 Customer satisfaction assessment: Some methodological considerations
      4. 7.4 The ABC ACSS questionnaire: An evaluation
      5. 7.5 Summary
      6. Appendix: SERVQUAL dimensions and items
    8. 8: Missing data and imputation methods
      1. 8.1 Introduction
      2. 8.2 Missing-data patterns and missing-data mechanisms
      3. 8.3 Simple approaches to the missing-data problem
      4. 8.4 Single imputation
      5. 8.5 Multiple imputation
      6. 8.6 Model-based approaches to the analysis of missing data
      7. 8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example
      8. 8.8 Summary
      9. Acknowledgements
    9. 9: Outliers and robustness for ordinal data
      1. 9.1 An overview of outlier detection methods
      2. 9.2 An example of masking
      3. 9.3 Detection of outliers in ordinal variables
      4. 9.4 Detection of bivariate ordinal outliers
      5. 9.5 Detection of multivariate outliers in ordinal regression
      6. 9.6 Summary
  10. Part II: MODERN TECHNIQUES IN CUSTOMER SATISFACTION SURVEY DATA ANALYSIS
    1. 10: Statistical inference for causal effects
      1. 10.1 Introduction to the potential outcome approach to causal inference
      2. 10.2 Assignment mechanisms
      3. 10.3 Inference in classical randomized experiments
      4. 10.4 Inference in observational studies
    2. 11: Bayesian networks applied to customer surveys
      1. 11.1 Introduction to Bayesian networks
      2. 11.2 The Bayesian network model in practice
      3. 11.3 Prediction and explanation
      4. 11.4 Summary
    3. 12: Log-linear model methods
      1. 12.1 Introduction
      2. 12.2 Overview of log-linear models and methods
      3. 12.3 Application to ABC survey data
      4. 12.4 Summary
    4. 13: CUB models: Statistical methods and empirical evidence
      1. 13.1 Introduction
      2. 13.2 Logical foundations and psychological motivations
      3. 13.3 A class of models for ordinal data
      4. 13.4 Main inferential issues
      5. 13.5 Specification of CUB models with subjects’ covariates
      6. 13.6 Interpreting the role of covariates
      7. 13.7 A more general sampling framework
      8. 13.8 Applications of CUB models
      9. 13.9 Further generalizations
      10. 13.10 Concluding remarks
      11. Appendix
    5. 14: The Rasch model
      1. 14.1 An overview of the Rasch model
      2. 14.2 The Rasch model in practice
      3. 14.3 Rasch model software
      4. 14.4 Summary
    6. 15: Tree-based methods and decision trees
      1. 15.1 An overview of tree-based methods and decision trees
      2. 15.2 Tree-based methods and decision trees in practice
      3. 15.3 Further developments
    7. 16: PLS models
      1. 16.1 Introduction
      2. 16.2 The general formulation of a structural equation model
      3. 16.3 The PLS algorithm
      4. 16.4 Statistical interpretation of PLS
      5. 16.5 Geometrical interpretation of PLS
      6. 16.6 Comparison of the properties of PLS and LISREL procedures
      7. 16.7 Available software for PLS estimation
      8. 16.8 Application to real data: Customer satisfaction analysis
    8. 17: Nonlinear principal component analysis
      1. 17.1 Introduction
      2. 17.2 Homogeneity analysis and nonlinear principal component analysis
      3. 17.3 Analysis of customer satisfaction
      4. 17.4 Dealing with missing data
      5. 17.5 Nonlinear principal component analysis versus two competitors
      6. 17.6 Application to the ABC ACSS data
      7. 17.7 Summary
    9. 18: Multidimensional scaling
      1. 18.1 An overview of multidimensional scaling techniques
      2. 18.2 Multidimensional scaling in practice
      3. 18.3 Multidimensional scaling in a future perspective
      4. 18.4 Summary
    10. 19: Multilevel models for ordinal data
      1. 19.1 Ordinal variables
      2. 19.2 Standard models for ordinal data
      3. 19.3 Multilevel models for ordinal data
      4. 19.4 Multilevel models for ordinal data in practice: An application to student ratings
    11. 20: Quality standards and control charts applied to customer surveys
      1. 20.1 Quality standards and customer satisfaction
      2. 20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction
      3. 20.3 Control Charts and ISO 7870
      4. 20.4 Control charts and customer surveys: Standard assumptions
      5. 20.5 Control charts and customer surveys: Non-standard methods
      6. 20.6 The M-test for assessing sample representation
      7. 20.7 Summary
    12. 21: Fuzzy Methods and Satisfaction Indices
      1. 21.1 Introduction
      2. 21.2 Basic definitions and operations
      3. 21.3 Fuzzy numbers
      4. 21.4 A criterion for fuzzy transformation of variables
      5. 21.5 Aggregation and weighting of variables
      6. 21.6 Application to the ABC customer satisfaction survey data
      7. 21.7 Summary
  11. Appendix: An introduction to R
    1. A.1 Introduction
    2. A.2 How to obtain R
    3. A.3 Type rather than ‘point and click’
    4. A.4 Objects
    5. A.5 S4 objects
    6. A.6 Functions
    7. A.7 Vectorization
    8. A.8 Importing data from different sources
    9. A.9 Interacting with databases
    10. A.10 Simple graphics manipulation
    11. A.11 Basic analysis of the ABC data
    12. A.12 About this document
    13. A.13 Bibliographical notes
  12. Index