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We model the decision-making process of callers in call centers as an optimal stopping problem. After each waiting period, a caller decides whether to abandon a call or continue to wait. The utility of a caller is modeled as a function of her waiting cost and reward for service. We use a random-coefficients model to capture the heterogeneity of the callers and estimate the cost and reward parameters of the callers using the data from individual calls made to an Israeli call center. We also conduct a series of counterfactual analyses that explore the effects of changes in service discipline on resulting waiting times and abandonment rates. Our analysis reveals that modeling endogenous caller behavior can be important when major changes (such as a change in service discipline) are implemented and that using a model with an exogenously specified abandonment distribution may be misleading. This paper was accepted by Assaf Zeevi, stochastic models and simulation.

Worker attrition is a costly and operationally disruptive challenge throughout the world. Although large bodies of research have documented drivers of attrition and the operational consequences of attrition, managers still lack an integrated approach to understanding attrition and making decisions to address it on a forward-going basis. To fill this need, we build a structural model that both captures the firm's decision to terminate a worker's employment (involuntary attrition) and uses an optimal stopping problem process to model a worker's decision to leave the firm (voluntary attrition). We then estimate the parameters of the model and conduct counterfactual analyses on the population of 1,118 agents serving one client over 3 years for an Indian business process management company. Our model reveals a number of interesting findings. We find that supervisors have a strong impact on whether employees stay because they reshape the way that agents make their decisions. We also find that the impact of supervisors on agent attrition is more significant than the impact of salary. For example, increasing salary by 20% decreases the total attrition level by 5%. However, if agents were managed by the best supervisors, among those that manage similar agents, the attrition rate decreases by 10%. Altogether, our paper contributes to the burgeoning literature on people operations and managerial practice.

We undertake an empirical study of the impact of delay announcements on callers’ abandonment behavior and the performance of a call center with two priority classes. A Cox regression analysis reveals that in this call center, callers’ abandonment behavior is affected by the announcement messages heard. To account for this, we formulate a structural estimation model of callers’ (endogenous) abandonment decisions. In this model, callers are forward-looking utility maximizers and make their abandonment decisions by solving an optimal stopping problem. Each caller receives a reward from service and incurs a linear cost of waiting. The reward and per-period waiting cost constitute the structural parameters that we estimate from the data of callers’ abandonment decisions as well as the announcement messages heard. The call center performance is modeled by a Markovian approximation. The main methodological contribution is the definition of an equilibrium in steady state as one where callers’ expectation of their waiting time, which affects their (rational) abandonment behavior, matches their actual waiting time in the call center, as well as the characterization of such an equilibrium as the solution of a set of nonlinear equations. A counterfactual analysis shows that callers react to longer delay announcements by abandoning earlier, that less patient callers as characterized by their reward and cost parameters react more to delay announcements, and that congestion in the call center at the time of the call affects caller reactions to delay announcements.

Worker attrition is a costly and operationally disruptive challenge throughout the world. Although large bodies of research have documented drivers of attrition and the operational consequences of attrition, managers still lack an integrated approach to understanding attrition and making decisions to address it on a forward-going basis. To fill this need, we build a structural model that both captures the firm's decision to terminate a worker's employment (involuntary attrition) and uses an optimal stopping problem process to model a worker's decision to leave the firm (voluntary attrition). We then estimate the parameters of the model and conduct counterfactual analyses on the population of 1,118 agents serving one client over 3 years for an Indian business process management company. Our model reveals a number of interesting findings. We find that supervisors have a strong impact on whether employees stay because they reshape the way that agents make their decisions. We also find that the impact of supervisors on agent attrition is more significant than the impact of salary. For example, increasing salary by 20% decreases the total attrition level by 5%. However, if agents were managed by the best supervisors, among those that manage similar agents, the attrition rate decreases by 10%. Altogether, our paper contributes to the burgeoning literature on people operations and managerial practice.

Worker attrition is a costly and operationally disruptive challenge throughout the world. Although large bodies of research have documented drivers of attrition and the operational consequences of attrition, managers still lack an integrated approach to understanding attrition and making decisions to address it on a forward-going basis. To fill this need, we build a structural model that both captures the firm's decision to terminate a worker's employment (involuntary attrition) and uses an optimal stopping problem process to model a worker's decision to leave the firm (voluntary attrition). We then estimate the parameters of the model and conduct counterfactual analyses on the population of 1,118 agents serving one client over 3 years for an Indian business process management company. Our model reveals a number of interesting findings. We find that supervisors have a strong impact on whether employees stay because they reshape the way that agents make their decisions. We also find that the impact of supervisors on agent attrition is more significant than the impact of salary. For example, increasing salary by 20% decreases the total attrition level by 5%. However, if agents were managed by the best supervisors, among those that manage similar agents, the attrition rate decreases by 10%. Altogether, our paper contributes to the burgeoning literature on people operations and managerial practice.

Worker attrition is a costly and operationally disruptive challenge throughout the world. Although large bodies of research have documented drivers of attrition and the operational consequences of attrition, managers still lack an integrated approach to understanding attrition and making decisions to address it on a forward-going basis. To fill this need, we build a structural model that both captures the firm’s decision to terminate a worker’s employment (involuntary attrition) and uses an optimal stopping problem process to model a worker’s decision to leave the firm (voluntary attrition). We then estimate the parameters of the model and conduct counterfactual analyses on the population of 1,118 agents serving one client over 3 years for an Indian business process management company. Our model reveals a number of interesting findings. We find that supervisors have a strong impact on whether employees stay because they reshape the way that agents make their decisions. We also find that the impact of supervisors on agent attrition is more significant than the impact of salary. For example, increasing salary by 20% decreases the total attrition level by 5%. However, if agents were managed by the best supervisors, among those that manage similar agents, the attrition rate decreases by 10%. Altogether, our paper contributes to the burgeoning literature on people operations and managerial practice. This paper was accepted by Serguei Netessine, operations management.

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 .