Make Lines Move More Quickly

Imagine a bustling retailer’s contact center on Black Friday, the kickoff to the holiday shopping season. With a limited number of customer-service agents and a deluge of incoming requests, the center—which manages phone calls, emails, live chats, social media, and text messages—has a management challenge. Some people might have transaction issues that could take some time to resolve, while others might have a quick question about an order and therefore be more inclined to abandon if made to wait.

This is a classic scheduling problem in operations management: Which task should be prioritized when resources are limited? Adding to the challenge is that contact centers don’t know all the details of incoming requests, and thus operate with incomplete information. London Business School’s Yueyang Zhong (a recent graduate of Chicago Booth’s PhD program) and Booth’s John R. Birge and Amy R. Ward propose a practical, two-phase approach to this problem designed to balance the twin needs of learning about incoming requests and scheduling effectively.

To illustrate the approach, the researchers say to consider call centers, which are contact centers that primarily answer phone calls. Many such centers seek to put queries into categories. For instance, they might have incoming customers select an option for why they’re calling, such as “transaction issue” or “order inquiry.” Even then, much remains unknown. Which callers have quick and simple questions? Which are more likely to hang up if kept waiting, potentially costing the company their business? The callers themselves often don’t know these details, much less the managers.

Even if a business knew the time each call would take, and the patience level and revenue potential of each customer, solving this scheduling problem wouldn’t be straightforward. Continually arriving calls create infinite possibilities.

The researchers’ proposed solution, which they dub learn-then-schedule (LTS), starts with a learning phase, during which managers gather data about three factors: the complexity of incoming queries, the patience level of callers, and the potential cost of losing each customer.

During this period, the center selects calls from various categories at random—and, the researchers acknowledge, makes inevitable mistakes.

“At the start, managers don’t know if a selected caller is patient or impatient, or if the issue will take one minute to resolve or half an hour,” Zhong explains. Over time, by observing actual call durations, customer drop-off rates, and other patterns, the system learns these unknown characteristics.

These data are then used to assign an “importance score” to each call category, reflecting its priority level. For example, if order questions are generally easier and quicker to resolve than transaction issues, they might receive a higher importance score.

In the scheduling phase, these importance scores guide decisions, with agents answering calls from higher-priority categories first. This score-based policy is intuitive, easy to implement, and effective, says Zhong.