Service Quality Variability and Termination Behavior
University of Michigan at Ann Arbor - Marketing
Pradeep K. Chintagunta
University of Chicago
The Stephen M. Ross School of Business at the University of Michigan
While researchers have documented a positive relationship between the average quality of a service and customer retention, the effect of variability on customer retention has been viewed more ambiguously in the literature. We investigate the roles of the level and variability in quality in the context of a new video on demand service in driving customer retention. We find that while high average quality helps in retaining customers, high variability leads to higher termination rates. Apart from these main effects, we empirically document the presence of an interaction effect between average service quality and its variability on termination rates; customers who experience low variability are more responsive to mean quality compared to those experiencing high variability. As an extreme outcome, at the lower end of the quality spectrum, customers experiencing high variability have a higher retention rate than those experiencing low variability; this is contrary to what the main effect of variability would imply. We postulate a mechanism involving risk aversion and learning, which can induce this interaction effect and test this against several alternative explanations. Our results reinforce the notion that high service quality is associated with lower termination rates. Moreover, our estimates suggest that households exhibit risk aversion, implying that, on average, variability increases termination. Based on the model and estimation results, we document that in the context of new services where customers are likely to learn about their quality, households that experience low variability in service are likely to be more responsive to the quality level. This differential responsiveness results in an interaction effect between service quality level and its variability. In terms of managerial implications, we show that while increasing the average quality might be effective in retaining customers at low quality levels, lowering variability is likely to be more appropriate at high quality levels.
Number of Pages in PDF File: 51
Keywords: Service quality, customer retention, bayesian learning, dynamic models
JEL Classification: M31, C10, C50, D81, D83, D90working papers series
Date posted: May 22, 2013 ; Last revised: March 4, 2014
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