Delay announcements for call centers with hyperexponential patience modeling

Abstract

Purpose

The purpose of this paper is to provide delay announcements for call centers with hyperexponential patience modeling. The paper aims to employ a state-dependent Markovian approximation for informing arriving customers about anticipated delay in a real call center.

Design/methodology/approach

Motivated by real call center data, the patience distribution is modeled by the hyperexponential distribution and is analyzed by its realistic significance, with and without delay information. Appropriate M/M/s/r+H₂ queueing model is structured, including a voice response system that is employed in practice, and a state-dependent Markovian approximation is applied for computing abandonment. Based on this approximation, a method is proposed for estimating virtual delays, and it is investigated about the problem of announcing virtual delays to customers upon their arrival.

Findings

There are two parts of findings from the results obtained from the case study and a numerical study of simulation comparisons. First, using an H₂ distribution for the abandonment distribution is driven by an empirical study which shows its good fit to real-life call center data. Second, simulation experiments indicate that the model and approximation are reasonable, and the state-dependent Markovian approximation works very well for call centers with larger pooling. It is concluded that our approach can be applied in a voice response system of real call centers.

Originality/value

Many results pertain to announcing delay information, customer reactions and links to estimating hyperexponential distribution based on real data that have not been established in previous studies; however, this paper analytically characterizes these performance measures for delay announcements.

Citation

Yu, M., Gong, J., Tang, J. and Kong, F. (2017), "Delay announcements for call centers with hyperexponential patience modeling", Industrial Management & Data Systems, Vol. 117 No. 6, pp. 1037-1057. https://doi.org/10.1108/IMDS-07-2016-0254