Modern Analysis Of Customer Surveys With Applications Using R Ron S Kenett by Ron S. Kenett, Silvia Salini 9780470971284, 9781119961154, 0470971282, 1119961157 instant download after payment.

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.Content:
  Chapter 1 Standards and Classical Techniques in Data Analysis of Customer Satisfaction Surveys (pages 1–18): Silvia Salini and Ron S. Kenett
  Chapter 2 The ABC Annual Customer Satisfaction Survey (pages 19–36): Ron S. Kenett and Silvia Salini
  Chapter 3 Census and Sample Surveys (pages 37–53): Giovanna Nicolini and Luciana Dalla Valle
  Chapter 4 Measurement Scales (pages 55–70): Andrea Bonanomi and Gabriele Cantaluppi
  Chapter 5 Integrated Analysis (pages 71–88): Silvia Biffignandi
  Chapter 6 Web Surveys (pages 89–105): Roberto Furlan and Diego Martone
  Chapter 7 The Concept and Assessment of Customer Satisfaction (pages 107–127): Irena Ograjensek and Iddo Gal
  Chapter 8 Missing Data and Imputation Methods (pages 129–154): Alessandra Mattei, Fabrizia Mealli and Donald B. Rubin
  Chapter 9 Outliers and Robustness for Ordinal Data (pages 155–169): Marco Riani, Francesca Torti and Sergio Zani
  Chapter 10 Statistical Inference for Causal Effects (pages 171–192): Fabrizia Mealli, Barbara Pacini and Donald B. Rubin
  Chapter 11 Bayesian Networks Applied to Customer Surveys (pages 193–215): Ron S. Kenett, Giovanni Perruca and Silvia Salini
  Chapter 12 Log?Linear Model Methods (pages 217–229): Stephen E. Fienberg and Daniel Manrique?Vallier
  Chapter 13 CUB Models: Statistical Methods and Empirical Evidence (pages 231–258): Maria Iannario and Domenico Piccolo
  Chapter 14 The Rasch Model (pages 259–281): Francesca De Battisti, Giovanna Nicolini and Silvia Salini
  Chapter 15 Tree?Based Methods and Decision Trees (pages 283–307): Giuliano Galimberti and Gabriele Soffritti
  Chapter 16 PLS Models (pages 309–331): Giuseppe Boari and Gabriele Cantaluppi
  Chapter 17 Nonlinear Principal Component Analysis (pages 333–356): Pier Alda Ferrari and Alessandro Barbiero
  Chapter 18 Multidimensional Scaling (pages 357–390): Nadia Solaro
  Chapter 19 Multilevel Models for Ordinal Data (pages 391–411): Leonardo Grilli and Carla Rampichini
  Chapter 20 Quality Standards and Control Charts Applied to Customer Surveys (pages 413–438): Ron S. Kenett, Laura Deldossi and Diego Zappa
  Chapter 21 Fuzzy Methods and Satisfaction Indices (pages 439–456): Sergio Zani, Maria Adele Milioli and Isabella Morlini