Hierarchical Bayes Modeling of the Customer Satisfaction Index

Customer Satisfaction Index has been developed in many countries including North America, Europe and Asia last decades, which are based on Americal Customer Satisfaction Index (ACSI) by the University of Michigan, where the latent factor "Customer Satisfaction" related to the customer loyalty is estimated by a covariance structural model with six factors generated from 17 question items and PLS method. They apply the identical structural model to all companies in order to measure the national and industrial indexes that are used to compare the services in different companies as well as industries. In this paper, by using the assumption that the same model must be applied to every company, we link the path coefficients of each company as the hierarchical regression model to estimate the structure for customer satisfaction across companies to show that, representing "communality" inside industry and "heterogeneity" outside industry, the hierarchical Bayes modeling produces more stable significant path coefficients. Moreover, our approach has the additional advantages. (i) The volume of information (number of survey data) can be augmented, (ii) The index can be constructed without additional surveys for new company (forecasting) and not-surveyed company (missing observations), (iii) When aggregating individual index of each company up to the industrial index and national index, the communality assumption could increase the stability of the macro index. [<i>Service Science</i>, ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3.]