When customers contact a call center, they expect to get answers to their questions or receive guidance on resolving their issues. However, if all agents are busy, customers are placed in a waiting queue – a stage that can affect their experience just as much as the conversation itself.
Some customers remain on hold until an agent becomes available, while others hang up before being connected. In call center management, this behavior is known as call abandonment. It is not merely an operational metric; it can also indicate a gap between customer expectations and their actual experience.
Understanding this behavior requires more than simply knowing the average waiting time. It is important to examine how long customers are willing to wait, how much time is required to deliver the service, and how the information provided during the waiting period shapes customers’ perceptions of the wait.
What Is Call Abandonment, and Why Does It Matter?
Call abandonment occurs when a customer enters a queue after requesting to speak with an agent but disconnects before receiving service. This differs from a call that is disconnected before entering the queue or one that ends after the customer has received service. Therefore, these behaviors must be distinguished in call center data to ensure accurate analysis.
A high abandonment rate can have several causes, including long waiting times, an urgent need for an answer, a lack of clarity about how long the wait will be, previous negative experiences, or even a change of mind. For this reason, not all abandoned calls should be attributed to the same cause.
The importance of this metric becomes clearer when we recognize that hanging up does not necessarily mean the customer’s need has been met. The issue may remain unresolved, prompting the customer to call again or seek assistance through another channel. As a result, evaluating call center performance solely on the basis of answered calls does not provide a complete picture of the customer experience.
Customers Have Different Tolerance Levels for Waiting
One important aspect of queue analysis is the distinction between two concepts:
– Required waiting time: The amount of time a customer must wait, given the current queue conditions and available service capacity, before their turn arrives.
– Waiting tolerance: The amount of time a customer is willing to wait before potentially abandoning the service.
These two time periods are not necessarily equal. If the required waiting time exceeds a customer’s tolerance, the likelihood of abandonment may increase. However, customer behavior also depends on the circumstances of the call, the type of service, and individual characteristics.
In a study conducted by Lawrence Brown and his colleagues, waiting behavior and abandonment were examined alongside other components of the service process. The researchers used operational records from a bank call center for their analysis. In this dataset, approximately 20% of calls in which customers had requested to speak with an agent were abandoned before service was provided.
This figure should not be interpreted as a typical abandonment rate or a standard benchmark for all call centers. Rather, it represents a finding specific to that call center and the period under study. Its significance lies in demonstrating that a portion of customer demand may be lost before it reaches the service stage.
Why Is Average Waiting Time Not Enough?
Suppose the average customer waiting time at a call center is three minutes. This figure alone does not indicate that all customers have a similar experience. Some customers may receive an answer almost immediately, while others may wait significantly longer than the average.
Furthermore, the average does not reveal how long customers who abandoned their calls had been waiting before disconnecting. If only the waiting times of answered calls are examined, the experiences of customers who left the queue before receiving service are overlooked.
To better understand customer behavior, a call center should examine the following metrics in addition to average waiting time:
– Waiting time distribution: The distribution of waiting times and the proportion of calls that exceed a specified threshold.
– Time to abandonment: The amount of time customers spend waiting before abandoning the queue.
– Abandonment rates across different time periods: How abandonment rates vary throughout the day or across other time intervals.
– Differences across service categories: How customer behavior varies among different service groups.
– The relationship between queue conditions, waiting time, and abandonment probability: How these factors interact and influence the likelihood of a customer disconnecting.
These analyses help determine whether abandonment occurs primarily during prolonged waits or whether other factors also influence customer behavior.
What Role Do Queue Messages Play in the Customer Experience?
While waiting, customers generally have no direct information about the internal operations of the call center. They do not know how many agents are handling calls, how many callers are ahead of them, or when their turn will come. Queue messages can help reduce some of this uncertainty.
In the study by Brown and his colleagues, customers were periodically provided with information about the expected waiting time for the person at the front of the queue and their approximate position in the queue. These messages were repeated approximately every 60 seconds, accompanied by music, news, or advertisements.
This description illustrates how queue messages can form part of the waiting experience. However, it does not, by itself, establish that repeating these messages increases or decreases abandonment. To measure such an effect, call center behavioral data must be analyzed carefully, or different versions of the messages must be tested under comparable conditions.
From a customer experience management perspective, queue messages are useful when they help customers better understand their situation. Vague, repetitive, or unrealistic messages may create a greater sense of uncertainty rather than reduce it. In contrast, clear and reliable information can help customers make informed decisions, although no message can replace sufficient capacity to handle incoming calls.
How Can Queue Messages Be Improved?
To design more effective messages, call centers can consider the following measures:
- Provide Clear and Reliable Information
If the system can reliably estimate waiting times, providing this information can help customers make informed decisions. If an accurate estimate is unavailable, it is better to avoid communicating definite but unreliable waiting times.
- Tailor Messages to Customers’ Situations
A single message may not be appropriate for every call. A customer calling with a general question may have different needs from someone seeking an urgent resolution to a problem. Where possible, the type of service and the customer’s needs should be taken into account when designing the waiting experience.
- Evaluate Message Frequency
Repeating similar messages too frequently may become irritating for some customers. Conversely, long intervals between messages may increase uncertainty. Message timing should be evaluated based on customer feedback and actual call center data.
- Keep Messages Consistent with Actual Performance
If a message indicates that the wait will be short but the customer remains in the queue for an extended period, their trust may be undermined. Queue messages should be aligned with the system’s capabilities and the actual state of service delivery.
How Can the Impact of Queue Messages Be Measured?
A simple comparison of abandonment rates before and after changing queue messages is not sufficient to evaluate their effectiveness, as other factors may also have changed, including call volume, the number of agents, the types of requests, or operating conditions.
A suitable approach is to analyze data across comparable time periods and segment it by service type, waiting time, and queue conditions. Where possible, two versions of a message can be tested under similar conditions, with the results compared in terms of abandonment rates, waiting times, and customer feedback.
Call centers should also assess whether a reduction in abandonment actually corresponds to an improved customer experience. For example, if customers remain on hold longer instead of hanging up but are still dissatisfied, a lower abandonment rate alone does not indicate complete success.
Practical Strategies for Reducing Customer Abandonment
To manage call abandonment effectively, call centers should consider implementing the following measures together:
– Analyze time to abandonment: Identify the points during the waiting process at which customers typically disconnect.
– Examine differences between customer groups: Compare customer behavior by service type, time of contact, and queue priorities.
– Evaluate the waiting experience: Assess queue messages, hold music, and the clarity of information from the customer’s perspective.
– Manage service capacity: During periods of longer waits, review the number of active agents and how incoming calls are distributed.
– Monitor multiple metrics simultaneously: Evaluate abandonment rates alongside waiting times, service levels, and customer feedback.
Conclusion
Call abandonment while waiting in a queue should not be viewed merely as a technical incident or a problem related to the number of agents available. It occurs at the intersection of call center capacity, waiting time, and customer tolerance. Understanding this behavior requires analyzing actual operational data and paying attention to the experiences of customers who leave the queue before receiving service.
Queue messages can improve part of the customer experience by reducing uncertainty. However, their effectiveness must be measured, and they should not replace proper management of service capacity and waiting times. Call centers that continuously analyze waiting behavior and abandonment can better identify weaknesses in the service process and make more informed decisions to improve the customer experience.
Author: Zahra Shirband – International Relations Expert ISQI
Reference:
Brown, L., Gans, N., Mandelbaum, A., et al. (2005). *Statistical Analysis of a Telephone Call Center: A Queueing-Science Perspective.* Journal of the American Statistical Association, 100(469), 36–50.
