Home > Archive > 2014 > Volume 4 Number 3 (June 2014) >
IJMLC 2014 Vol.4(3): 286-291 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.426

Predictive Dialer Intensity Optimization Using Genetic Algorithms

Pedro M. T. Amaral and Miguel M. Vital

Abstract—Companies rely on contact centers to act as communication links with their clients. Outbound dialing is often used to reach existing or new customers. This task is generally performed by automatic dialers, which initiate new calls depending on the amount of working agents. The probability of a customer answering a call, however, depends on a set of conditions, such as the time schedule or the type of day. This fact presents itself as a challenge to automatic dialers, since contact lists with low answer probability can make the contact center’s agent occupation rate very low. Predictive dialers tackle this problem in an automated way by generating more calls than the number of available agents. The majority of predictive dialer algorithms use statistical approaches to adjust the automatic dialer intensity, which is used to decide on the amount of calls that should be initiated at each time. In this paper, we propose a method of optimizing the automatic dialer intensity using genetic algorithms – evolutionary methods based on natural selection and genetics. We implement the proposed algorithm by modifying the current proprietary Altitude Software predictive dialer and perform a comparative evaluation between both versions. Our method obtained superior results to those achieved by the original algorithm, with a slightly higher agent utilization rate.

Index Terms—Contact centers, dialer intensity optimization, genetic algorithms, predictive dialers.

Pedro M. T. Amaral and Miguel M. Vital are with Altitude Software, Oeiras, Lisbon, Portugal (email: Pedro.Amaral@altitude.com, Miguel.Vital@altitude.com).


Cite: Pedro M. T. Amaral and Miguel M. Vital, "Predictive Dialer Intensity Optimization Using Genetic Algorithms," International Journal of Machine Learning and Computing vol.4, no. 3, pp. 286-291, 2014.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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