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Bayesian hierarchical models to analyze customer satisfaction data for quality improvement: a case study
Author(s) -
Gasparini Mauro,
Pellerey Franco,
Proietti Mauro
Publication year - 2011
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.932
Subject(s) - customer satisfaction , service quality , computer science , bayesian probability , quality (philosophy) , phone , service (business) , data mining , marketing , artificial intelligence , business , philosophy , linguistics , epistemology
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.