In the fast-food business, the old saw that time is money has particular resonance. An industry maxim suggests that a seven-second reduction in customers’ waiting time increases a chain’s market share by 1 percent. A study by Gad Allon, an associate professor of managerial economics and decision sciences at the Kellogg School of Management, and two colleagues has fleshed out that rule of thumb. Not only does their analysis confirm the maxim, it also identifies the extraordinary value that fast-food customers assign to their time. Each extra second of waiting time at the drive-thru window reduces the amount that customers are prepared to pay for their meals by at least four cents.

“This validates our belief that overlooking service as a competitive instrument in the business model results in distorted managerial insights,” explains Allon, who performed the study with Awi Federgruen, a professor at Columbia University, and Margaret Pierson, an assistant professor at Dartmouth University. The project originated when Allon and Federgruen independently read a CNN.com study on the nation’s best drive-thrus. “We contacted the firm that ran the study, obtained the data, and then started working on the required tools to estimate what we wanted,” Allon recalls.

Burgers in Cook County
To simplify its analysis, the team focused on hamburger drive-thrus in Cook County, Illinois. They chose burgers because of the general uniformity of hamburger chains’ products and Cook County because of both the number of outlets there—more than 200—and their proximity to the Kellogg School, which enabled personal interviews. Those interviews, combined with phone calls, provided price information for all the outlets.

The researchers also collected detailed demographic and geographic data for the outlets’ customers. “We distinguished between commuters and residents, two genders and two racial groups, as well as among five age brackets, dividing the population into 40 different demographic groups,” Allon explains. “We also calculated the distance from the consumer to each outlet, using the centroid of the tract in which the consumer is located.”

But to obtain a complete picture of the factors relating wait times to price sensitivity, the team also needed data on specific sales volumes or market shares. Obtaining that information from corporations proved impossible because, as Allon notes, “firms are reluctant to provide it, considering it of the highest strategic value.” So he and his colleagues used game theory to infer the numbers.

The process involved combining two sub-models. A consumer choice sub-model focuses on the utility value that customers assign to all possible outcomes, such as the type of meal they purchase along with the purchase price, waiting time, and distance once they set out on a fast-food run. The model also includes data on an individual customer’s race, gender, age group, and occupation. The other sub-model, called variable cost, expresses fast-food outlets’ costs as a function of expected sales volume. “Combining the two sub-models permits us to derive the outlets’ profit functions,” the researchers explain in a paper.

One extra factor complicated the study. While the companies that offer franchises set the standard for waiting times, the individual outlets determine their prices to avoid illegal price-fixing. The model accounts for that.

Each extra second of waiting time at the drive-thru window reduces the amount that customers are prepared to pay.

A Firm Conclusion
The analysis by Allon and his colleagues resulted in a firm conclusion. “Both the price and waiting time parameters have a significant impact on the consumer’s decision,” they state in their paper. “These results confirm … that in the fast-food drive-thru industry customers trade off price and waiting time. In particular, to overcome an additional second of waiting time, an outlet will need to compensate an average customer by as much as $0.05 in a meal whose typical price ranges from $2.25 to $6. This corresponds with an hourly cost rate of approximately ten times the (pre-tax) average wage of $18/hour and nearly 30 times the (pre-tax) minimum wage in Illinois in 2005.”

Allon and his colleagues had not expected that customers would value their waiting time so highly. “The directionality of the result did not surprise us, and confirmed what many people in the industry believe,” Allon says. “Yet the extent and the robustness of the results were definitely a positive surprise.”

Customers assigned only one-third as much value to the time they spent traveling to their fast-food outlets as they did to the wait once there. “One possible explanation is that people purchase their fast-food meals on the way home or from home to other activities, and thus do not associate any disutility with the travel time,” Allon suggests. “However, the waiting time once in line is considered pure waste.”

Fast-food franchises do not lack tools to mitigate the problem. “There are several strategies that can be used,” Allon says, “from ‘outsourcing’ the order-taking process to a call center to employing more people and using technology to speed up the food preparation.”

Second Helpings
Allon admits their study represents just a beginning. “Several important extensions of our study and underlying model would be valuable,” he explains. “First, it is not clear whether the waiting time experience is best characterized by the average alone or by other measures, such as the standard deviation and/or a percentile of the waiting time distribution. Even if the average waiting time is the best proxy, it is conceivable that the consumer’s utility level diminishes in a nonlinear way with it. A similar nonlinear dependence on the distance variable may be explored as well.”
At the same time, Allon and his colleagues emphasize that their work has value beyond the fast-food business. “Based on this market analysis, we show that the trend to continuously improve waiting times and service levels can be explained on game theoretical grounds, creating a valuable framework for future market dynamics studies in various industries,” they state in their paper.

“I expect the results to vary quite a bit among industries, yet we now have a benchmark against which we can compare these numbers,” Allon adds. “We have also outlined one possible method to overcome the scarcity of sales data.”


Related reading on Kellogg Insight

Firm Size and Service Level: When is it advantageous for a service-oriented firm to differentiate itself along service quality dimensions?

We Will Be Right with You: Managing customers with vague promises

Cutting in Line: Flexible queuing systems may improve customer service