Personalized Priority Policies in Call Centers Using Past Customer Interaction Information

31 Pages Posted: 4 Jun 2020

See all articles by Brett Hathaway

Brett Hathaway

Johns Hopkins University - Carey Business School

Seyed Morteza Emadi

University of North Carolina (UNC) at Chapel Hill - Kenan-Flagler Business School

Vinayak Deshpande

University of North Carolina (UNC) at Chapel Hill - Operations Area

Date Written: May 8, 2020

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\%.

Keywords: Structural Estimation, Priority Policies, Queues

Suggested Citation

Hathaway, Brett and Emadi, Seyed Morteza and Deshpande, Vinayak, Personalized Priority Policies in Call Centers Using Past Customer Interaction Information (May 8, 2020). Available at SSRN: https://ssrn.com/abstract=3596178 or http://dx.doi.org/10.2139/ssrn.3596178

Brett Hathaway (Contact Author)

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

Seyed Morteza Emadi

University of North Carolina (UNC) at Chapel Hill - Kenan-Flagler Business School

McColl Building
Chapel Hill, NC 27599-3490
United States

Vinayak Deshpande

University of North Carolina (UNC) at Chapel Hill - Operations Area ( email )

300 Kenan Center Drive
Chapel Hill, NC 27599
United States

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