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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 5

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

Jason Fama, Engineering Group, Blue Pumpkin Software Inc.

From a simulation perspective, each agent is viewed as a resource to perform certain types of work. Note that in the call center context, agents are actually productive only during the interval in which the agents are scheduled to be actually handling phone calls.

In addition, it is conventional to model agents as completing the task that they are engaged, even if it extends past the time at which they are to switch activities. That is, an agent within our simulation will be modeled as completing the phone call that he is working on before leaving for a break or a lunch.

A common step in call center simulation is to translate a set of individual agent schedules into a matrix of resources, where the dimensions of the matrix are defined by the number of Agent Groups and the number of Time Intervals. In our example, we have leveraged the fact that our schedules are at a 15-minute level of granularity, and therefore prior to running the simulation we have converted these schedules into a number of on-phone agents for each group for each 15-minute interval.

4.5 Key Inputs: Abandonment Model and Parameters

Abandonment is one of the most hotly debated topics in call center management and research. There are two basic questions that must be answered in order to effectively model customer abandonment behavior:
    1. What is the customer’s tolerance for waiting, and at what point will this customer hang up and thereby leave the queue?
    2. How likely is the customer to call back, and after how long?
Many researchers (e.g. Hoffman and Harris 1986, Andrews and Parsons 1993) have examined the challenge of modeling these problems from both an empirical point of view and from an analytic perspective.

From our experience, these questions are difficult to answer not only because of the mathematical complexity of the queue dynamics but also because of a lack of observable data about customer abandonment and retrial. While many surveys have been done, we have observed great differences in customer behavior across different industries and different companies’ operations. In addition, information provided to callers about expected waiting time and/or position in queue can have a marked impact on abandonment behavior.

In our example model, simulated customers arrive at the call center and are served by an agent if one is available. If not, they join the queue, at which point they are also assigned a “life span” which is drawn from an exponential distribution. If a customer’s life span expires while they are still waiting in queue, they then abandon the queue.

That is, we represent customers’ tolerance for waiting in queue as an exponential random variable (as suggested by Garnett et al. 2002). We refer to the mean of this distribution as “the patience factor.”

Given this modeling choice, we must still with the challenge of selecting the patience factor, which we estimate from historical data about callers’ time in queue. We do not include caller retrial in the example model.

4.6 Key Inputs: Agent Skills

Our definition of “Agent Skills” is comprised of three major types of inputs for each agent or group of agents:
    1. What calls is the agent capable of handling?
    2. Given a choice of multiple calls waiting, which will the agent handle (“call priority”)
    3. How fast will the agent be able to handle each type of call, and how often will the agent resolve the issue successfully (“call proficiency”)
When combined with routing logic and call forecasts, these attributes fully specify the queueing model to be simulated.

In our example, we have three distinct groups of agents, each with different skills:
  • Agent Group #1 (Inbound Only) handle only Inbound calls on a First-Come-First-Served basis. These agents have a call proficiency of 1.0 for Inbound Calls, so that their AHT is equal to the forecasted AHT for Inbound Calls.
  • Agent Group #3 (Outbound Specialists) handle only Outbound calls. These agents have a call proficiency of 1.0 for Outbound Calls, meaning that their AHT is equal to the forecasted AHT for Outbound Calls.
  • Agent Group #2 (Cross-Trained Outbound) handle both Inbound and Outbound calls. These agents have a call proficiency of 1.0 for Outbound Calls, meaning that their AHT is equal to the forecasted AHT for Outbound Calls. However, these agents will give priority to Inbound Callers if there are any waiting in queue, and have a call proficiency of 2.0 for Inbound calls, reflecting the relative inefficiency of cross-training (see Pinker and Shumsky 2000 for more discussion of this phenomenon both in and out of the contact center).
4.7 Other Modeling Considerations

4.7.1 Shrinkage
It is well known that a certain amount of agent time will be lost, either in large blocks (unanticipated shift cancellations, partial day absences for personal reasons) or in small blocks (late arrivals to the call center, extra-long breaks, trips to the bathroom).

There is an important distinction between two different kinds of lost agent time. On one hand, agent time that is known to be lost prior to the creation and publication of a schedule has essentially no additional impact on the simulation model beyond the fact that this particular agent is not included in the schedule.

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