Slicing the Pie: Simplifying Data For Fast Casual Restaurants
Troy remembers a world before “fast casual.” Sure, the words existed back in the day, but they were not a restaurant segment. All he knew back then was that to keep his restaurant profitable he had to maintain his back of house processes and stay aware of his physical real estate. The fast-casual development of the mid-2000’s was swift though. The economy created a swell of the 18-34 demographic, seeking food alternatives. They wanted a high quality, inexpensive replacement for traditional fast food.
Restaurants offering limited-service, made-to-order food became a hit with fresher, more complex fare than the standard drive-thru joint. In time, these fast-casual spots proliferated, creating an “intermediate” restaurant concept, between quick service and casual dining. Troy’s business was at the forefront.
First, he employed technology to keep his restaurant in ideal working order. A quality kitchen display system could route his orders with precision, eliminating the inaccuracies of paper tickets. Furthermore, he could use the features in his technology to analyze restaurant analytics, a “vitals check” on his operation’s performance.
Through these restaurant data analytics, Troy could target bottlenecked areas in his workflow, as well as activity levels and trends. Then, he could set restaurant metrics specific to his store. He knew that many customers depended on his food for a quick lunch bite on limited lunch break time. With this knowledge and restaurant data on his side, he could staff his restaurant to focus on high-traffic times. He developed a streamlined “lunch only” menu with his high-margin items on the forefront. This helped his efficiency in getting customers in, served and back out again swiftly. As “fast casual” evolved into a legitimate restaurant segment, Troy’s exemplified the name: Food without waitING and a casual atmosphere.
The explosion of internet shopping and subsequent off-premise dining boom solidified Troy’s decision to utilize restaurant data. Once a restaurant starts accepting off-premise orders, they must blend the stream of in-store and off-premise traffic. With customers waiting in the store for their food, Troy didn’t want off-premise orders to disrupt walk-in traffic. His automated technology used capacity management features which could “read” the activity level, the data collected by the technology in Troy’s restaurant. When things were hectic, his KDS would adjust the order quote for an off-premise order to account for this activity. Then, when things died down, so would the off-premise quotes. As a result, Troy’s restaurant operates in real-time, adjusting to its specific, in-the-moment needs.
The fast-casual segment continues to surge in popularity. From 2011 to 2016, fast-casual restaurants saw growth sales of 10 to 11 percent, with quick service only 3 to 4 percent and full-service 1.5 to 2 percent. On that upswell, Troy’s restaurant continues to embody the name. With a data-powered approach, he’s on top of his orders. He maintains a consistent dining experience for on and off-premise diners and can quickly adapt to any changes that come his way.
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About the Author
Brian leads the Implementation, Project Management, Training, and Support Services groups, guiding customers to get the most out of QSR products. He has dual degrees in Information Systems and Operations Management and is a big baseball fan—he’s visited most of the Major League Baseball parks and loves spending summer evenings at Louisville Bats games with his family.