Modern Analysis of Customer Surveys: with Applications using R

Modern Analysis of Customer Surveys: with Applications using R (Statistics in Practice)
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

PART I BASIC ASPECTS OF CUSTOMER SATISFACTION SURVEY DATA ANALYSIS
1 Standards and classical techniques in data analysis of customer satisfaction surveys 3
2 The ABC annual customer satisfaction survey 19
3 Census and sample surveys 37
4 Measurement scales 55
5 Integrated analysis 71
6 Web surveys 89
7 The concept and assessment of customer satisfaction 107
8 Missing data and imputation methods 129
9 Outliers and robustness for ordinal data 155

PART II MODERN TECHNIQUES IN CUSTOMER SATISFACTION SURVEY DATA ANALYSIS
10 Statistical inference for causal effects 173
11 Bayesian networks applied to customer surveys 193
12 Log-linear model methods 217
13 CUB models: Statistical methods and empirical evidence 231
14 The Rasch model 259
15 Tree-based methods and decision trees 283
16 PLS models 309
17 Nonlinear principal component analysis 333
18 Multidimensional scaling 357
19 Multilevel models for ordinal data 391
20 Quality standards and control charts applied to customer surveys 413
21 Fuzzy Methods and Satisfaction Indices 439

Appendix An introduction to R 457