Bayesian networks of customer satisfaction survey data
AbstractA Bayesian Network is a probabilistic graphical model that represents a set of variables and their probabilistic dependencies. Formally, Bayesian Networks are directed acyclic graphs whose nodes represent variables, and whose arcs encode the conditional dependencies between the variables. Nodes can represent any kind of variable, be it a measured parameter, a latent variable or a hypothesis. They are not restricted to representing random variables, which forms the "Bayesian" aspect of a Bayesian network. Efficient algorithms exist that perform inference and learning in Bayesian Networks. Bayesian Networks that model sequences of variables are called Dynamic Bayesian Networks. Harel et. al (2007) provide a comparison between Markov Chains and Bayesian Networks in the analysis of web usability from e-commerce data. A comparison of regression models, SEMs, and Bayesian networks is presented Anderson et. al (2004). In this paper we apply Bayesian Networks to the analysis of Customer Satisfaction Surveys and demonstrate the potential of the approach. Bayesian Networks offer advantages in implementing models of cause and effect over other statistical techniques designed primarily for testing hypotheses. Other advantages include the ability to conduct probabilistic inference for prediction and diagnostic purposes with an output that can be intuitively understood by managers
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Bibliographic InfoPaper provided by Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano in its series Departmental Working Papers with number 2007-33.
Date of creation: 08 Oct 2007
Date of revision:
Bayesian Networks; Customer Satisfaction; Eurobarometer; Service Quality;
Other versions of this item:
- Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor and Francis Journals, vol. 36(11), pages 1177-1189.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
- C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-06-17 (All new papers)
- NEP-ECM-2009-06-17 (Econometrics)
- NEP-NET-2009-06-17 (Network Economics)
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- Claudia Tarantola & Paola Vicard & Ioannis Ntzoufras, 2012. "Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data," Quaderni di Dipartimento 160, University of Pavia, Department of Economics and Quantitative Methods.
- Pier Ferrari & Silvia Salini, 2011. "Complementary Use of Rasch Models and Nonlinear Principal Components Analysis in the Assessment of the Opinion of Europeans About Utilities," Journal of Classification, Springer, vol. 28(1), pages 53-69, April.
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