There is no question that the rise of app-based technology has altered the expectations of students and that university dining service departments have had to adapt. As this trend developed, Yale University’s dining services created its Fast Track app, which displays real-time dining hall capacity data, allowing students to see which dining hall is less crowded.
“Students can take a quick look at and see which dining halls are available and [shift] traffic to slower dining halls during our peak periods,” says Adam Millman, director of auxiliary operations at the university based in New Haven, Conn.
As students’ habits continue to evolve, data remains at the forefront of Yale Dining’s efforts to embrace customer-facing technology. Here’s what’s currently on its radar.
Digging deeper into classroom data
Though the Fast Track app has been available to students for six years, Yale Dining seeks to aggregate real-time classroom data to continually improve service, according to Michael van Emmenes, director of business intelligence & optimization. “If we know that classes get out at 12:15pm and 300 students are destined to hit one location, we know to make sure food is fresh and hot at 12:05pm,” says van Emmenes.
Integrating inventory data to streamline customer transactions
In addition, Yale Dining is looking to integrate real-time inventory data into Fast Track, allowing students to view what’s currently in stock at campus convenience stores. Students could plan out purchases and find which locations have a preferred item available “before they even enter the location, which helps transaction time,” says Millman.
Building location-based promotions
Yale Dining also leverages Huddlr, an events app created by a Yale alumnus, to foster socialization among students at dining halls. Students can use the location-based app to find the dining hall where their friends are congregating. “What it allows is the sociability of meal periods without the hassle of texting back and fourth,” van Emmenes says. As Huddlr evolves, they plan to eventually tailor digital promotional deals to students based on location data and their proximity to dining facilities. For example, if a student visits a specific coffee shop each day around 3pm but skips a few days, the app would send a promotion to encourage him to return. “The potential of this is very promising,” Millman says. “We’ll continue to integrate it on how we see a place within dining.”