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Bayesian Analysis of Abandonment in Call Center Operations

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  • Tevfik Aktekin
  • Refik Soyer

Abstract

In this paper, we consider the modeling and the inference of abandonment behavior in call centers. We present several time to event modeling strategies, develop Bayesian inference for posterior and predictive analyses, and discuss implications on call center staffing. Different family of distributions, piecewise time to abandonment models, and mixture models are introduced, and their posterior analysis with censored abandonment data is carried out using Markov chain Monte Carlo methods. We illustrate the implementation of the proposed models using real call center data, present additional insights that can be obtained from the Bayesian analysis, and discuss implications for different customer profiles. Copyright © 2012 John Wiley & Sons, Ltd.

Suggested Citation

  • Tevfik Aktekin & Refik Soyer, 2014. "Bayesian Analysis of Abandonment in Call Center Operations," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 30(2), pages 141-156, March.
    • Handle: RePEc:wly:apsmbi:v:30:y:2014:i:2:p:141-156
    DOI: 10.1002/asmb.1949
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    Cited by:

    1. Tevfik Aktekin & Nicholas G. Polson & Refik Soyer, 2020. "A family of multivariate non‐gaussian time series models," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 691-721, September.
    2. Ekin, Tahir & Aktekin, Tevfik, 2021. "Decision making under uncertain and dependent system rates in service systems," European Journal of Operational Research, Elsevier, vol. 291(1), pages 335-348.
    3. Kaan Kuzu & Refik Soyer, 2018. "Bayesian modeling of abandonments in ticket queues," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(6-7), pages 499-521, September.

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