Keywords
Structural Estimation, Priority Policies, Queues
Abstract
Companies are increasingly personalizing their product or service offerings based on their customers' history of interactions to increase revenue or improve customer service. In this paper we show how call centers can improve customer service by implementing personalized priority policies. Under personalized priority policies, managers use customer contact history to predict individual-level caller abandonment and redialing behavior and prioritize them based on these predictions to improve operational performance. We provide a framework for how companies can use individual-level customer history data to capture the idiosyncratic preferences and beliefs that impact caller abandonment and redialing behavior, and quantify the improvements to operational performance of these policies by applying our framework using caller history data from a real-world call center. We achieve this by formulating a structural model that uses a Bayesian learning framework to capture how callers' past waiting times and abandonment/redialing decisions affect their current abandonment and redialing behavior, and use our data to impute the callers' underlying primitives such as their rewards for service, waiting costs, and redialing costs. These primitives allow us to simulate caller behavior under a variety of personalized priority policies, and hence collect relevant operational performance measures. We find that, relative to the first-come, first-served policy, our proposed personalized priority policies have the potential to decrease average waiting times by up to 29\% or increase system throughput by reducing the percentage of service requests lost to abandonment by up to 6.3\%.
Original Publication Citation
B. Hathaway, S. Emadi, and V. Deshpande. Personalized Priority Policies in Call Centers Using Past Customer Interaction Information. Management Science 68(4) (2806-2823), 2022
BYU ScholarsArchive Citation
Hathaway, Brett A.; Emadi, Seyed Morteza; and Deshpande, Vinayak, "Personalized Priority Policies in Call Centers Using Past Customer Interaction Information" (2022). Faculty Publications. 8349.
https://scholarsarchive.byu.edu/facpub/8349
Document Type
Peer-Reviewed Article
Publication Date
2022
Publisher
Management Science
Language
English
College
Marriott School of Business
Department
Marketing
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