Predictive Dialer Intensity Optimization Using Genetic Algorithms

Abstract

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.

Analysis of Pacing Algorithms for Proactive Chat Campaign

“…CX centers use various pacing algorithms primarily for voice outbound campaigns, as described and analyzed in (Anisimov, Korolev and Ristock 2008;Amaral and Vital 2014). This paper expands those solutions to proactive chat campaigns, which bear many similarities but also some differences.…”