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predictive dialers and crm software
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DSC Tech Library

Contact Center Equipment

telecommunications software solution This section of our technical library presents information and documentation relating to Call Center technology and Best Practices plus software and products. DSC is a leading provider of contact center technology and software solutions as well as predictive dialer phone systems for the modern call center. Customer contact center software includes CRM software and computer telephony integration solutions. These modern products help call center phone agents communicate effectively with your customers and prospects.

The following article presents product or service information relating to call centers and customer service help desks.

Call Center Simulation Modeling:
Methods, Challenges, And Opportunities

Page 3

By Vijay Mehrotra, Department of Decision Sciences
College of Business - San Francisco State University

Jason Fama, Engineering Group, Blue Pumpkin Software Inc.

In each of these cases, the key output statistics typically include the some or all of the following metrics:
  • Queue Statistics: The two dominant queue statistics for inbound queues and call centers are Average Speed of Answer (“ASA”) and Percent of Calls Answered with a queue time of less than some defined value (“PCA” or, more commonly, “Service Level).” Note that for each queue this statistic is interesting at the interval level (typically 15 minutes, 30 minutes, or one hour) and also at the aggregate daily and weekly levels; additionally, management is interested in the overall performance across a collection of queues that draw upon a common pool of agent resources.
  • Abandonment Statistics: For most inbound call centers, particularly those focused on customer service and/or sales, a great deal of attention is paid to the overall number of customers who abandon (that is, hang up and thus leave the queue before being served). This is known to be a significant indicator of customer satisfaction (see Feinberg et al. 2000 for a recent published study on this). Many centers will look at the more restrictive metric of number of customers abandoning beyond the target Service Level parameter, based on the rationale that a certain waiting time in queue (as defined by the Service Level parameter, which ranges from 5 seconds to several minutes across companies and industries) is inevitable.
  • Volume Statistics: For outbound queues and call centers, the real statistic of interest is Right Party Connects (“RPCs”). That is, for all of the attempted calls that were made, what percentage of these calls reached the targeted individual (as opposed to no answer, answering machine, or some other human being). Outbound contact center managers are typically interested in RPC on both an absolute and a percentage basis. For inbound queues, the Calls Handled statistic is of interest, and is easily derived by subtracting Abandoned Calls from the total number of incoming calls (referred to as “Offered Calls”).


4.1 Framework

The biggest challenge of call center simulation modeling is the definition and organization of model inputs. Figure 2 below illustrates our framework for call center simulation model definition and key inputs.

As reflected in Figure 2, call center simulation models feature a diverse range of inputs from multiple data sources, and as with all simulation designs, there are decisions to be made about the level of detail to include in the model.

In the sections below, we discuss these key input areas in more detail. In the process, we will use our example model to illustrate these modeling concepts.

4.2 Key Inputs: Queue Definitions, Time Period, and Routing Logic

The basic building blocks of a call center simulation model are the calls, the agents, and the time period during which the call center is open. In turn, the basic routing logic connects the way that the calls interact with the people during that time period.

Typical call center simulation models contain more than one queue (as single queue models are ordinarily dealt with analytically) and run for a period of one day, one week, or multiple weeks.

Our example model is for a Collections call center. As is typical for call centers, this operation is part of a larger business context, in which creditors’ records are being monitored on a regular basis for potential delinquency. Once a customer falls into delinquency, several things happen: (a) the information on the account is added to a list of prospects for an Outbound collections call; (b) they are notified about the state of their credit by mail; and (c) additional limitations may be placed on the account.

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