The collection, parsing, and analyzing of data is today more prevalent than ever in higher education, and some colleges are using that data to eliminate waiting lines on campus.

lines-qless-studentsIt’s something close to old hat for colleges and universities to spend resources analyzing data that will give the school a better idea of how to optimize lesson plans and alert educators of students who are on the brink of dropping out. Using massive amounts of information to save time for students and faculty, however, is something new on college campuses.

QLess, the company behind the innovative mobile technology that eliminates physical lines, leverages data to save thousands of hours for college students and their teachers every year.

QLess was recently integrated into Blackboard, Inc.’s Mosaic mobile platform — a move that means many more institutions could soon be eliminating time-wasting lines with the cutting-edge mobile technology that banks, supermarkets, theme parks, restaurants, and hospitals have used for years.

By adding QLess to Blackboard’s Mosaic, students are able to schedule, edit, or cancel appointments and receive real-time wait forecasts and text messages alerting them when their turn in line is approaching.

QLess founder and CEO Alex Backer said the mobile app has long enjoyed a high participation rate, and he expects that to translate to higher education.

A college that uses QLess can use its analytics to measure wait time by hour, day, or even month, along with determining the maximum amount of a time a student waits on a service such as cafeteria food. QLess can also determine which campus employees are most proficient in serving students the fastest.

“You can’t improve what you don’t measure,” Backer said, pointing out that colleges and universities don’t typically measure how long it takes for a student to get through a campus book store line, for example. “College students have better uses for their time besides just standing in line, wasting time.”

(Next page: How much time can QLess save, and what are other companies doing to optimize data analysis?)


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