There’s only so long that you can keep a customer waiting.
Whether it’s for a food order or a doctor’s appointment, “as customers wait in a queue, there comes a point when they eventually get so bored or frustrated that they leave,” says Achal Bassamboo, a Kellogg professor of operations.
This problem of customers leaving without ever getting served is an issue that the service industry has studied for decades, he says, and it happens in two main ways: “balking,” which is when someone sees a long line and decides not to join it at all, and “reneging” (often called “abandonment”), which is when someone gets in line but leaves before service begins because they’re not willing to tolerate the wait.
Fixing the problem isn’t only about adding staff or speeding things up; customer behavior and expectations also shape whether people join a line in the first place—and whether they stick around.
Imagine, for example, a trendy ice-cream parlor with lines out the door. Every customer who gets in line has a certain amount of time they’ll wait before giving up. Those who do make it in the door, however, may feel entitled to celebrate their successful patience.
“When it’s your turn to get ice cream after standing for a long time, you feel that you have earned the right to taste more flavors,” Bassamboo says. “As I make you wait, you change your behavior.”
A series of studies from Bassamboo with Ohad Perry of Southern Methodist University, Chenguang Wu of the Hong Kong University of Science and Technology, and Deniz Şimşek, a Kellogg doctoral student, sheds light on this phenomenon and how businesses can use it to optimize wait times.
One of their key findings is that how long customers have to wait for a service—their “patience clock”—is connected to how much time they end up spending on that service—their “service clock.”
“What we’ve been after in our research is, ‘Why could this be happening?’” he says. “And the simplest explanation is that these two clocks—your patience clock and your service clock—are not actually independent.”
To wait or not to wait
Customer service systems are highly complex, so researchers like Bassamboo often convert real-world scenarios into simplified models with core assumptions so that they can analyze them and draw insights.
One such common assumption is that the maximum amount of time a customer is willing to wait before they give up and leave is independent of the amount of time they spend with the business when it’s their turn.
But when Bassamboo and his colleagues took a closer look at modeling service systems, they suspected this longstanding assumption failed to capture how customers respond in the real world.
Bassamboo and his colleagues categorized the dependence between a customer’s maximum wait time and the amount of time they spend on a service into two settings.
First, there is “exogenous” dependence, where customers enter a place of service with a predetermined maximum wait time based on their individual needs.
A simple example might involve someone who is on a time crunch and can spend up to 10 minutes on a service call with their internet provider. Whether they have to wait 1 minute or 3 minutes before they are transferred to customer service won’t directly change how much time they spend speaking with an agent, since they have a specific question they want answered as quickly as possible. Still, even though the wait time might not alter the service time, they are connected in the sense that the customer will simply drop the service call if the wait time exceeds 10 minutes.
With “endogenous” dependence, the wait time does alter the amount of time a person is willing to spend receiving a service, as in the ice-cream-parlor example. The more time people spend waiting in line, the more time they might spend sampling and choosing flavors.
When the researchers input data from various service systems into models of “exogenous” and “endogenous” dependence, they turned out to be much more closely linked than the researchers had imagined.
“What we observed is that for every exogenous model, there is an equivalent endogenous model,” Bassamboo says. “So the endogenous model is actually a much bigger family of models, and exogenous is a smaller subset.”
Discovering this connection between the two types of dependence helped them confirm that customers’ patience time and their service time are indeed connected.
Congestion collapse
The researchers then developed a unified model that captured these two types of dependences simultaneously. This unified model was a “vehicle for analysis” that allowed the team to draw insights from service systems by inputting data, such as the rate at which customers enter a system and the time it takes them to get service.
The unified model helped Bassamboo and his colleagues identify critical differences between exogenous and endogenous dependence and how they affect what businesses care about: customer wait times.
In the case of exogenous dependence—in settings where customers with a preset time limit seek a service—there comes a unique point of stability when there are no erratic changes in customer wait times and abandonment. For businesses, these settings would allow for predictable wait times and, in turn, consistent staffing.
But in the case of endogenous dependence—in settings where waiting induces customers to change their service time—wait times can fluctuate dramatically and often. This scenario is particularly relevant to businesses where the customer has discretion on how long a service takes.
“You can go from very short queues and a very well-behaved system to suddenly having very large queues,” Bassamboo explains. When the wait at, say, an ice-cream parlor gets congested in this sudden, erratic way, it’s likely because of “congestion collapse, where, even though my system has enough staff and capacity for the amount of customers that should have been in the queue, because I got unlucky with two or three picky customers who decided to taste 10 flavors because they had to wait, I’m suddenly seeing very long waits.”
One of the practical takeaways from this finding is that, in this latter scenario, a business should be able to address long waiting times with a relatively simple, temporary fix rather than a major change to the waiting system.
“You can actually bring maybe someone from the back of the store to come and help bring the queue down very quickly so that the wait is reset,” Bassamboo says. “And once the wait is reset, everyone goes back to tasting one or two flavors rather than five flavors.”