Bayesian hierarchical models to analyze customer satisfaction data for quality improvement: a case study
Article first published online: 15 NOV 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Applied Stochastic Models in Business and Industry
Volume 28, Issue 6, pages 571–584, November/December 2012
How to Cite
Gasparini, M., Pellerey, F. and Proietti, M. (2012), Bayesian hierarchical models to analyze customer satisfaction data for quality improvement: a case study. Appl. Stochastic Models Bus. Ind., 28: 571–584. doi: 10.1002/asmb.932
- Issue published online: 26 DEC 2012
- Article first published online: 15 NOV 2011
- Manuscript Accepted: 13 SEP 2011
- Manuscript Revised: 26 AUG 2011
- Manuscript Received: 29 JUN 2010
- chain graph;
- impact–evaluation graphs;
Customer satisfaction data collected by a large cellular phone service provider are to be used to evaluate and improve the quality of their service. For this purpose, we propose a Bayesian treatment of a joint-response chain graph relating partial assessments of specific aspects of quality to an overall assessment of the service quality. The resulting Bayesian model can be used to render basic geographical and temporal differentiation, allowing the company to undertake direct corrective actions. Both normal and binary models are considered for our customer satisfaction data and are compared with other currently used methods in the study of customer satisfaction. Copyright © 2011 John Wiley & Sons, Ltd.