BIS 08 - Modeling and Simulation of a Pacing Engine for Proactive Campaigns in Contact Center Environment

Modeling and Simulation of a Pacing Engine for Proactive Campaigns in Contact Center Environment

Nikolay Korolev, Nikolay Anisimov and Herbert Ristock

Business and Industry Symposium (BIS 08)
Crowne Plaza Ottawa Hotel, Ottawa, Canada, April 14-17, 2008

Summary

Proactive contact campaigns play a growing role in modern contact centers. Their traditional usage as a telemarketing tool now is widely extended to different types of service notifications. In this paper we describe the most common model of a proactive telephone contact campaign, suggest several methods of its pacing algorithm and represent results of their simulation.

The model comprises the following components:

- List of telephone numbers of the customers to be dialed.

- Dialer which originates telephone calls from a contact center to the customers with capability of automatic call progress detection. Pacing algorithm provides the dialing rate for the dialer.

- Queue where all answered by customer calls are placed before transferring to the agents. If a call placed to the queue cannot be routed to an agent due to all agents are busy on other calls, is considered as abandoned. The number of such calls should not exceed a specified limit usually named as abandonment rate.

- Agent group where calls are processed after routing from the queue. The average time the agents process the calls is also one of the campaign parameters named as an average handling time.

The following pacing algorithms are considered in this paper:

- Progressive where calls are generated only when agents become available and the number of calls is equal to the number of available agents.

- Predictive where calls are generated with a dialing rate estimated according to the classical queuing theory.

- Super-progressive is an enhanced progressive method which provides higher agents occupancy than the regular progressive one without significant increase in the abandonment rate.

The paper also includes some recommendations of choosing a proper pacing method for different sets of the campaign parameters. These recommendations are supported by simulation results.

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