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DSC Tech Library
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 EXAMPLE: ROUTING STRATEGIES FOR
A COLLECTIONS CALL CENTER
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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