Babir Sultan, president and CEO of Kansas City, Mo.-based Fav Trip, has long been known for his innovative use of technology in his c-stores. As the keynote speaker at this year’s CStore Connections conference, he shared with attendees how his chain went viral on social media by using YouTube to shine a light on retail theft.
The viral success has led to a loyal customer base and increased brand recognition, with the Fav Trip YouTube channel amassing more than 100 thousand subscribers.
Now, Sultan is leveraging his marketing and technology expertise in a new AI venture, with his new BRICKandMortar.AI program.
The recently-launched solution offers retailers of any size the ability to harness AI in ways that were previously unheard of, namely heat mapping. Through heat mapping, retailers can identify areas of their c-store that are hot spots for traffic, and conversely they can identify areas that are not performing as well.
“The retail landscape is changing. There are a lot of things that are happening within the retail industry that operators, small or big, can take advantage of,” Sultan told CStore Decisions. “(That’s why) we started looking into it more and more and started investing into AI early on.”
Sultan noted that while the offering is available to retailers of all shapes and sizes, Fav Trip will serve as the “playground” for the technology going forward.
“It’s not limited to Fav Trip — Fav Trip has just been our testing ground,” he continued, noting that there is demand from sectors ranging from coffee shops to car dealerships.
How It Works
The technology uses in-store cameras and AI to gain deeper insights when it comes to customer behavior and effectiveness of employee outreach. The majority of the time, retailers do not need to install any additional technology to take advantage of the offering.
“We can tap into your existing cameras and download this data,” said Sultan. “So, we can use whatever cameras you already have — even something as crazy as Google Nest. We’re using that right now to be able to leverage that data.”
The technology uses these camera feeds to categorize different operational points of emphasis, from employee engagement to shoplifting, waste, customer interaction and much more. Sultan is even working on a solution that deters previous shoplifting offenders via facial recognition software.
“We had an early demo that we’re going to put in motion, where if that same (shoplifter) walks in, we get alerted by that,” he continued. “We do have humans camera-watching, but we have a much better bet with AI.”
Sultan has been experimenting with the technology in his stores for roughly six months, and he has already seen significant improvements in-stores. He noted that the software can identify instances where an employee is present for a customer, versus instances when they are not — in December 2024, the number was 1,600 for times an employee was not present. After addressing the issue over the months, that number went down to about 700, according to Sultan.
“We’re a very ‘playground’ example where we have case study after case study of our usage, but now we’re to the point, finally, where we feel comfortable applying it,” said Sultan. “… I think that it’s just the starting point of what the future holds for AI and leveraging our resources. So, I’m a proponent of ‘we shouldn’t be left behind on the exciting thing that’s going on in AI.'”
