Modeling Customer Satisfaction’s Impact on Loyalty: Insights for Customer-Centric Resource Allocation

Yinxing Li (), Aijing Xing () and Nobuhiko Terui ()
Additional contact information
Yinxing Li: Tohoku University, Miyagi 980-8577, Japan
Aijing Xing: Tohoku University, Miyagi 980-8577, Japan
Nobuhiko Terui: Tokyo University of Science, Tokyo 162-8601, Japan

Service Science, 2023, vol. 15, issue 2, 107-128

Abstract: This study explores the nonlinear modeling of customer satisfaction (CS) and loyalty for resource allocation strategies that promote efficient loyalty programs. We first introduce global models to reflect several important nonlinear characteristics, particularly (a) a saturation-attainable limit of effectiveness, (b) nonconstant marginal returns, and (c) asymmetric responses between satisfied and dissatisfied customers. In the proposed models, we put forth the joint use of two types of mixture models to deal with different levels of heterogeneity. Based on the model best supported among alternatives in the literature, we targeted customers using expected incremental loyalty, which is derived from the joint use of an estimated response curve of loyalty to satisfaction and an empirical distribution of CS scores. Then, we evaluate the efficiency of loyalty programs under the assumption of full and limited access to customers and subsequently derive managerial implications. For instance, through a counterfactual simulation of the models for the three industries, we find that improving the perceived quality in the mobile communication industry may have a greater effect on loyalty than in the hotel and convenience store industries because of its higher switching cost.

Keywords: customer satisfaction index model; customer loyalty; finite-mixture modeling; loyalty program; structural equation model; zone of tolerance (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/serv.2022.0313 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orserv:v:15:y:2023:i:2:p:107-128

Access Statistics for this article

More articles in Service Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().