Telephone call centers are data-rich environments that, until re- cently, have not received sustained attention from academics. For about a decade now, we have been fortunate to work with our colleague, mentor and friend, Larry Brown, on the collection and analysis of large call-center datasets. This work has provided many fascinating windows into the world of call-center operations, stimulating further research and affecting management practice. Larry’s inexhaustible curiosity and creativity, sharp insight and unique tech- nical power, have continuously been an inspiration to us. We look forward to collaborating with and learning from him on many occasions to come. In this paper, we study operational heterogeneity of call center agents. Our proxy for heterogeneity is agents’ service times (call durations), a performance measure that prevalently “enjoys” tight management control. Indeed, man- agers of large call centers argue that a 1-second increase/decrease in average service time can translate into additional/reduced operating costs on the order of millions of dollars per year. We are motivated by an empirical analysis of call-center data, which identi- fies both short-term and long-term factors associated with agent heterogeneity. Operational consequences of such heterogeneity are then illustrated via discrete event simulation. This highlights the potential benefits of analyzing individual agents’ operational histories. We are thus naturally led to a detailed analysis of agents’ learning-curves, which reveals various learning patterns and opens up new research opportunities.
Gans, N., Liu, N., Mandelbaum, A., Shen, H., & Ye, H. (2010). Service times in call centers: Agent heterogeneity and learning with some operational consequences (pp. 99–123). https://doi.org/10.1214/10-imscoll608