Customer Learning in Call Centers from Previous Waiting Experiences

Seyed Morteza Emadi · Jayashankar M. Swaminathan

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Advanced Queuing Theory Analysis Customer Service Quality and Loyalty Healthcare Operations and Scheduling Optimization

Abstract

Designing modern call centers requires an understanding of callers’ patience and abandonment behavior. Using a Cox regression analysis, we show that callers’ abandonment behavior may differ based on their contact history, and changes across their different contacts. We control for caller heterogeneity using a two-step grouped-fixed effect method. This analysis shows that differences in callers’ abandonment behavior are not only driven by their heterogeneity but also by differences in their beliefs about their delays affected by their contact history. As a result, callers’ beliefs about the waiting time distribution may not match the actual distribution in the call center, and the equilibrium condition in the rational expectation equilibrium assumption may not hold. To understand callers’ prior belief about the waiting time distribution, and to disentangle the impact of changes in their beliefs driven by their contact history from the impact of their intrinsic parameters, we use a structural estimation approach in a Bayesian learning framework. We estimate the parameters of this model from a call center data set with multiple priority classes. We show that in this call center, new callers who do not have any prior experience with the call center are optimistic about their delay in the system and underestimate its length irrespective of their priority classes. We also show that our Bayesian learning model not only has a better fit to the data set compared to the rational expectation equilibrium model but also outperforms the rational expectation equilibrium model in out-of-sample tests. Our Bayesian framework not only sheds light on callers’ learning process and their beliefs about their delays, but also could leverage callers’ contact history to provide personalized patience level for callers. This personalized information enables implementation of patience-based scheduling policies studied in the queueing literature. The online appendix is available at https://doi.org/10.1287/opre.2018.1738 .

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References

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