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predictive dialers and crm software
computer telephony software predictive dialer

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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 4

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

Jason Fama, Engineering Group, Blue Pumpkin Software Inc.

The call center itself features two queues: an Inbound queue and an Outbound queue. The time period that we are using for the analysis is one week.

The two agent groups and the basic routing logic are illustrated in Figure 3. Calls from the Inbound queue will arrive and be served by an agent from Group #1, the Inbound- Only group. If no agent from this group is available, the calls will wait in queue. If, after some predefined period of time, the Inbound call has not yet been served, it will then also queue for an agent from Group #2, the Cross-Trained Outbound group.

Meanwhile, Group #2 and Group #3 agents will be logged into a Predictive Dialer, which places Outbound calls to prospects from a list. When an answer is detected by the Predictive Dialer, the call is automatically routed to one of the Outbound Specialists or one of the available Cross-Trained Outbound agents. The agent then engages in a collections discussion (if they have reached the Right Party) or leave a message (if they have reached an answering machine or another party on the same phone number). Along with mailings to delinquent customers, these messages will generate some calls to the Inbound queue.

4.3 Key Inputs: Call Forecasts

Call forecasts are typically driven by a combination of historical data, time series model, and expert judgment. There are two major types of call forecasts: call volumes and Average Handling Time. Both are required for any basic call center simulation.

Due to the telecommunication and call center industries’ history of using steady state M/M/n queue formulas to derive the number of agents needed for each time interval, it has been customary to translate call volume forecasts into ? values for Poisson arrivals and AHT forecasts into µ values for Exponential service times.

A great deal of research has been conducted on call volume forecasting models, and the interested reader is referred to Mabert (1985) and Andrews and Cunningham (1995) for valuable discussions on this topic. Forecasts must be created for each queue for each time interval in the simulation period.

The most common call center forecasting approach is to create weighted averages of historical data for specific time intervals over the course of a week. For example, the initial call volume forecast for 8:15 a.m. - 8:30 a.m. next Tuesday might be computed as the average of call volume for the 8:15 a.m. - 8:30 a.m. period on the past several Tuesdays. From here, alterations may be (or more commonly, should be!) made based on additional information (e.g. specific marketing activities for a sales center or emerging product issues for a technical support center) that may cause volume to differ substantially from previous patterns.

4.3.1 Average Handle Time Forecasts

As mentioned earlier, most call center models assume that call handling times are exponentially distributed. We would recommend using more accurate distributional information about call handling times whenever possible.

For example, it is common to find technical support call center for which call handling times are bi-modal (easy cases with a shorter mean, harder cases with a longer one).

However, the primary reason that the call center industry accepts the assumption of exponential handling times is because the ACD and CTI devices (the primary source for historical call volume data) store only average handling times at the interval level. With a dearth of consistent second moment information available, we have thus accepted this assumption far more often than we would like; in particular, we have modeled exponential handling times in the numerical example presented in Section 5.

Note: in this paper, we refer to Average Handling Time, or AHT. However, when obtaining data from ACD reports, it is not uncommon to find two fields that are then summed together to compute AHT: Average Talk Time (“ATT”) and After Call Work (“ACW”).

4.4 Key Inputs: Agent Schedules

Agent schedules can be thought of as a series of activities taking place over the course of a day. For example, an agent who comes to work at 8:00 am for a nine-hour shift may have a 15-minute break at 9:45 am, lunch at 11:30 am, an on-line training course from 1:00 – 2:00 pm, and a break at 3:15 pm before leaving work at 5:00 pm.

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