Understanding Abandonment Rate Goals and Metrics

Understanding Abandonment Rate Goals and Metrics

By Maggie Klenke

A long with service level and average speed of answer, another commonly reported statistic of call center performance is abandonment rate. While it can provide some useful information, it can be misunderstood.

By definition, abandonment rate measures the percentage of callers who select a destination but hang up before the destination is reached. It can be a measure of the percentage of callers  who enter an automated attendant or IVR menu, but who hang up before the IVR delivers the call to an agent queue. But most often it is used to measure the percentage of callers who enter an agent queue but hang up before reaching an agent.

In a center where callers are potential buyers of a product or service, loss of the opportunity to talk to that caller could result in a loss of revenue. Even in a situation where direct revenue  is not at stake, losing customers due to poor service will have a direct impact on the company’s bottom line. This makes managing the level of abandons a focus in many centers.

The key to managing abandon rate is to determine why callers abandon in the first place and the wait in queue is just one of the reasons. Other possibilities include:

  • The caller is interrupted by something more urgent.
  • The delay announcement answered the caller’s question.
  • A predicted wait announcement indicated the wait is longer than the caller wants to experience at that time.
  • An alternative offered in the delay announcement was chosen (e.g., website).
  • A “virtual queue” offering allows the caller to key in a telephone number for a callback but the call remains in queue even though the caller has hung up.

Analysis of the time the caller spent in the queue before abandoning can provide useful data. Most phone systems can provide a report that gives the breakdown of when people abandoned at various intervals such as at 5, 10, 30, 60, and 120 seconds. These times can typically be set for the intervals that would be most helpful in understanding caller behavior. If,  for example, a significant percentage of callers abandon at 5 or 10 seconds into the queue, the length of the wait is probably not the primary driver. It is common to see the number of abandons early in the wait be fairly low but at some point, the curve takes a sharp upward direction. This is the point that indicates caller tolerance for wait has been exceeded and can be a good indicator of what the service level or ASA goal might need to be.

(Remember, of course, that if the tolerance level appears to be about 40 seconds, the service level will be some percentage in the number of seconds so you might want to set it a bit shorter than 40 seconds. ASA is the average, so a lot of callers will wait longer than that goal, so adjust accordingly.)

Setting a goal for abandonment rate is a slippery slope. It is not a mathematically predictable number as it is totally a function of human behavior and will change due to several potential conditions. For example,

  • The urgency of the need/desire
  • The alternative options available to solve the problem
  • Time of day (caller might not be willing to wait when calling on their break from work rather than in the evening at home)

It is not uncommon for abandonment rates to vary by time of day and day of week as well as in response to whatever stimulated the call. The behavior around a surge of calls responding  to a time-limited marketing offer is one example. Another would be the flood of calls that would be received at an electrical service provider the moment the lights go out. In the latter case, if the delay announcement tells callers that the company is aware of the problem in a certain area, some portion of the callers will hang up rather than wait for an agent. They are satisfied and these are “good abandons.”

The typical Erlang-based models are not effective for predicting abandon behaviors. There are some models that accept a prediction that you specify and then plan the agent staffing  model assuming those calls won’t have to be answered. There are also simulation systems that can be used to take abandonment into account, but they too depend on the human prediction of what the abandon behavior will be. There really isn’t an accurate way to predict staffing models based on abandonment rates, so turning your goal into a service level or ASA is commonly required to generate schedules.

If you over-predict abandons and callers stay in the queue, you will see a significant negative impact on the service level due to the extra work. If you under-predict abandons, you may have more staff than you need and deliver a better than planned service level. Determining the right approach to planning for abandons is something that every WFM team should  undertake. Educating the entire team on the implications of the options available is an important part of that project.

Maggie Klenke is a Founding Partner of The Call Center School. She may be reached at Maggie.klenke@mindspring.com.