About half of the US states raised minimum wage levels in 2024, with some, like California, going as high as $20/hour. For anyone who understands economics, the resulting loss of scores of fast-food restaurant jobs is not just unsurprising, but inevitable.
Again unsurprisingly, the restaurant owners – those who were not forced to shut down their businesses – increasingly look into automation, specifically, robots.
Robots are not a brand-new technology, but they are poised to become a new frontier for the QSRs and c-stores, which are bent on serving as much hot food to their on-the-go customers as they possibly could.
Experts tell us that robotics is one of the most exciting, promising, and quickly evolving areas of retail technology. Robots are taking on an increasing number of roles, including tasks for which many retailers are currently struggling to find enough human employees, including driving and warehouse work.
But the implementation of any new technology takes time, and quite possibly, the human burger-flippers and food servers are not going to all be replaced by robots overnight. It might take a bit.
In the meantime, the retailers have to make sure they manage the increasing flow of customers with a diminishing number of employees. It takes a lot of careful planning, and solid retail demographics analysis – in the trade area as well as in the traffic flow.
Traffic flow demographics and retail demographics analysis provided by Ticon are a set of data describing the drivers in the flow of traffic that passes by your store as well as the demographic by address. Ticon’s new product is available with nationwide coverage and high granularity, at Ticon’s industry-leading accuracy and precision in traffic counts and traffic analysis. Armed with this data you can optimize the staff scheduling with high precision, matching the fluctuations in the number of people entering your store at a different time.
So, while you are shopping for your new robotic employees, give us a call to talk about stope performance optimization with Ticon’s highly granular data.
