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The basics of calculating call center staff.

Sharpen your pencils. Dust off the calculator. It's time for a math lesson.

Running a successful call center operation means managing by the numbers. And the most important number of all is the number of bodies in seats each hour to respond to customer contacts. Here's the step-by-step process for calculating call center resource requirements.

Calculating Workload

The first step in calculating call center staff is to determine workload for each hour or half-hour of the day. That's simply the number of forecast calls for an hour multiplied by the average handle time of a call. The average handle time (AHT) is made up of two components: actual conversation time plus any after call wrap-up time.

The part that makes staffing for a call center different than any other kind of staffing situation is that call workload doesn't represent typical work patterns. Let's compare an incoming call center to a group of clerical workers processing mail in the same company. Between 8:00 am and 9:00 am, the clerical staff has 400 pieces of mail to process and each piece takes three minutes to handle. That's 1,200 minutes, or 20 hours, of workload and 20 staff would be needed. The reason for the 1:1 ratio is that the mail tasks represent sequential workload. In other words, the staff can process the work as back-to-back tasks and each person can accomplish one hour of work in an hour timeframe.

Staffing for incoming calls is a little different. If 300 calls arrive and each one takes an average of four minutes to handle, we would again have 1,200 minutes, or 20 hours, of workload. But this time we can't handle the workload with only 20 people. At 8:05, there may be 22 calls arriving, meaning all 20 agents are busy, with another 2 calls in queue. Then at 8:15, there may be only 16 calls in progress, meaning four of our staff are idle. Those four people won't be able to accomplish a full hour's work, simply because of the way the calls have arrived. In an incoming call center, the work doesn't arrive in a back-to-back fashion. Rather, there's an ebb and flow of work based on the timing of customers picking up the phone. Therefore, the work is random workload instead of sequential workload. This brings us to the cardinal rule of call center staffing: You must have more staff hours in place than hours of actual work to do.

The number of "extra" staff needed depends on how fast the center wishes to answer calls. Obviously, the more staff in place, the shorter the delay. The fewer the staff, the longer the caller will wait.

Determining what happens with a given number of resources in place to accomplish a defined amount of workload requires a mathematical model that replicates the situation at hand. There are several telephone traffic engineering models available and one of these in particular is well-suited to the world of incoming call centers. Most call centers use a model called Erlang C that takes into account the randomness of the arriving workload as well as the queuing behavior (holding for the first available agent) of the calls.

An Example Of Erlang C

Let's take a look at Erlang C predictions based on the 20 hours of workload we defined earlier. Table 1 shows what would happen with anywhere from 21 to 28 staff in place to handle the 20 hours of incoming call workload.

Let's take a look at each of the columns and measures of service. The second column shows the portion of calls that would find no agent available and go into queue, and the third column shows how long those delayed callers would wait on average. So, with 24 staff in place, the Erlang C model predicts that 30 percent of callers would be delayed and that they would wait an average of 45 seconds in queue.

The third column represents the average delay of all calls, including the ones that are answered immediately. So, with 24 staff in place, 30 percent of calls would go to the queue and wait there 45 seconds, while the other 70 percent would be answered immediately. The average delay, or average speed of answer (ASA), is the weighted average of both these groups, or 13 seconds. It's important to understand that this ASA number is not the average queue experience for the callers. Either they wait (and do so for an average of 45 seconds), or they don't wait at all. The ASA isn't a "real life" number--it's a statistic to represent the average of the two other numbers.

[ILLUSTRATION OMITTED]

The fourth column represents service level. Service level represents X percent of callers who are handled in a specified Y seconds of delay time. This table shows the percentage that are handled within a specified 20 seconds of wait time. A common call center service goal is 80 percent of the calls handled in 20 seconds or less. To meet this goal, we would need 24 staff in place, yielding a service level of 81 percent in 20 seconds.

Once base staff requirements have been calculated by half-hour, there are adjustments to make to translate the "bodies in chairs" requirement into a schedule number. We'll discuss this in next month's article ... stay tuned!
Number Delayed Delay of Average Service Level
of Staff Portion Delayed Callers Delay (ASA) (in 20 sec)

21 76% 180 sec 137 sec 32%
22 57% 90 sec 51 sec 55%
23 42% 60 sec 25 sec 70%
24 30% 45 sec 13 sec 81%
25 21% 36 sec 8 sec 88%
26 14% 30 sec 4 sec 93%
27 9% 26 sec 2 sec 96%
28 6% 23 sec 1 sec 97%

Table 1


By Penny Reynolds, The Call Center School

Penny Reynolds is a Founding Partner of The Call Center School, a Nashville, Tennessee-based consulting and education company. The company provides a wide range of educational offerings for call center professionals, including traditional classroom courses, Web-based seminars and self-paced e-learning programs at the manager, supervisor and frontline staff level. You can reach Penny at penny.reynolds@thecallcenterschool.com or 615-812-8410.
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Copyright 2004, Gale Group. All rights reserved. Gale Group is a Thomson Corporation Company.

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Title Annotation:Managing by the Numbers
Author:Reynolds, Penny
Publication:Customer Interaction Solutions
Geographic Code:1USA
Date:Jun 1, 2004
Words:1036
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