Unraveling the mystery of service level discrepancies.

You've covered all the bases in creating your call forecast--evaluated past trends and calling patterns, incorporated marketing's new promotional campaign--and you are convinced you've got an accurate forecast. You've fine-tuned handle time predictions by a half hour to be even more accurate. Your "bodies in chairs" count today is just what you scheduled. Ah ..... life is good.

Then, your call center manager drops by with reports from the ACD that show service levels to be significantly different than what you had predicted.

Let's compare forecast and actual numbers for the 9:00 am period below. You've forecasted 173 calls, and 180 calls arrived--a little more than expected--while handle time and staff numbers are perfectly on target. More calls arrived than expected, yet the service level is better than the forecast. What's going on?

Your Prediction

Whether you've done your forecasts and schedules with workforce management software, spreadsheets or the old-fashioned way with a pencil and a calculator, you've likely arrived at your numbers and predictions of service with an Erlang C model. This is the most widely used mathematical model for calculating staff requirements and predicting service delays in a simple call center scenario. It assumes a random arrival of calls within the half-hour and indicates staffing levels needed for various levels of service.

Within any half-hour period, there will be times when only some of the agents are occupied with a call and the others will be idle and available for the next call. A few minutes later, all agents will be busy with perhaps several calls in queue. The Erlang C model takes this randomness of call arrivals into account when determining staff requirements and predicting service levels.

Call Abandonment

It's a fact that during many periods of the day, the workload will exceed the number of available agents. As calls go into queue and the delays get longer, some callers will abandon. Sometimes, callers will abandon as soon as the delay announcement plays, and others will abandon further into the delay time, depending on their tolerance levels for queuing.

One of the assumptions of the Erlang C model is that callers will wait an infinite amount of time for an agent. In real life, however, we know that some will abandon. This is the reason your forecast (based on Erlang C) may be different than the actual service levels reported by the ACD. As the queue builds and callers abandon, there are fewer calls left in the queue, so those remaining are handled more promptly. Actual service levels are therefore better than the predictions.

Erlang C Alternatives

So should we continue to use Erlang C even though it's not 100 percent accurate in predicting service levels? The answer is probably "Yes." Erlang C is still the most reliable model to use in a simple queuing scenario. It shows what would happen if all callers did indeed choose to wait.

Some might argue that abandons should be taken into account in predicting staff needs. Be careful about doing this. Let's say 10 percent of calls are abandoning ... so you only staff for 90 percent of the workload and base your service prediction on the reduced number of callers. The 10 percent of callers who are abandoning are probably doing so based on poor service, and reducing the staff to handle the calls will only result in poorer service levels and more abandons. It becomes a vicious cycle at some point, so beware of modifying the model and your staff requirements without a careful analysis of your abandoned calls.

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Explaining The Differences

In explaining the differences between your service level predictions by half-hour and what the ACD shows as actual numbers, look first to your abandonment levels. If they're high, you will see a sizable variation between forecast and actual numbers. Reducing the number of abandons through shorter delays, better placement of delay announcements or more effective content in the recordings will bring your numbers closer to a perfect match.

Your Forecast:                         ACD Report:

      Number  Handle  Number  Service  Number  Handle  Number  Service
      of      Time    of      Level    of      Time    of      Level
      Calls           Staff            Calls           Staff

8:00  125     240     20      78%      128     240     20      82%
8:30  148     240     23      73%      154     240     23      75%
9:00  173     240     27      80%      180     240     27      82%

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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. Penny can be reached at 615-812-8410 or [e-mail].