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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 6

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

Jason Fama, Engineering Group, Blue Pumpkin Software Inc.

On the other hand, scheduled time that is not worked, either because of unexpected absences or because of lack of rigorous adherence with agent schedules, is time that should be accounted for in the simulation if this represents a known phenomenon (e.g. higher absenteeism on Mondays).

In the call center industry, this is known as “shrinkage” and it is a major management problem as well as significant modeling challenge.

Most call centers have significant levels of shrinkage – we have seen many sites with over 30% overall. We have included a shrinkage level of 10% in our example model.

4.7.2 Additional Detail for Outbound Queues

As we have discussed earlier, the workflow associated with Outbound calls is very different than the logic for Inbound queues. At heart, this modeling difference stems from the fact that inbound calls are characterized by a random arrival pattern; in contrast, the outbound dialing pattern can be scheduled but each call features a random outcome (right party connect, wrong party connect, no answer).

In addition, as discussed in Section 3 above, the performance metrics associated with Outbound queues are quite different (overall RPCs achieved, rather than the queue and abandonment statistics that are typically used to evaluate Inbound queues). In order to effectively estimate the number and pattern of RPCs, simulation models require information about the probabilities that a given dial achieves an RPC, which typically varies by time of day, as well as the AHT associated with an RPC.

To model one level deeper, one might consider actually representing the detailed logic of the predictive dialer (see Samuelson 1999 for more on predictive dialer logic). However, this level of detail was not necessary for the types of business decisions being addressed by our example model, and so we have not included detailed dialer logic in it.


5.1 Operational Problem and Business Decisions

Throughout Section 4, we have described parts the simulation model associated with this example. The call center of interest is illustrated in Figure 3, and the formulation was motivated by discussions with several blended inboundoutbound centers about optimal system design.

In our example, the call center is open Monday - Friday from 7:00 am to 6:00 pm. There are 50 Inbound agents (Group #1) and a total of 150 Outbound agents (Group #2 and Group #3). We treat agent schedules for each of the three agent groups, as well as call forecasts (a total of about 20,000 calls for the week) for the Inbound calls, as fixed inputs for this simulation model. In addition, we assume that there is an effectively unlimited pool of customers to contact with Outbound calls.

The operational problem facing the management of this call center is focused on call routing and agent skilling. Underlying this problem is the classical tension between specialization and cost.

In terms of specialization, Inbound agents are far more effective in handling Inbound calls than Outbound agents, while Inbound calls disrupt the rhythm and effectiveness of Outbound agents; for both of these reasons, it would be far better to have specialized agents for Inbound and Outbound calls respectively.

In terms of cost, there is a management objective of handling 80% of Inbound calls within 60 seconds for each interval of the day. With dedicated agents, this translates into a larger amount of Inbound agents required than are actually available. Current staffing levels, therefore, will result in longer than desirable waiting times, which in turn is correlated with higher abandonment rates.

Specifically, the business decisions to be addressed are as follows:

  • Of the 150 Outbound-skilled agents, how many of them should be enabled to handle Inbound calls and included in the Cross-Trained Outbound group?
  • If no Inbound agents are available, how quickly should an Inbound call be offered to the Cross- Trained Outbound group?
In an ideal world, there would be an empirical “right answer” to these questions, a mathematically optimal solution that could be determined through sequential simulation runs.

In practice, however, such decisions typically involve substantial trade-offs that are difficult to value in relation to one another, and simulation’s role is to quantify the impact of different possible decisions.

The key output metrics for these simulations are:
    1. Phone Service Levels (% of Inbound calls handled within 60 seconds).
    2. Abandonment Levels (% of Inbound callers hanging up prior to receiving service).
    3. Right Party Connects (total number of Outbound calls completed to the correct individuals).
    4. Number of Overflows (of Inbound calls to Cross- Trained Outbound group).

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