From Gut Feeling to Data-Driven Precision: How kaisin. Accelerates Site Selection with Targomo

With a focus on targeting office workers, kaisin. leverages TargomoLOOP platform to assess key site indicators like demographics and footfall, speeding up their site evaluation process and reducing risks tied to wrong location choices.
About kaisin.
kaisin. is a Swiss quick-service restaurant brand founded in 2018, known for its healthy bowls with a Japanese twist. With 11 locations across Switzerland, kaisin. has built its business around a focused offering of customised, healthy meals catering to various dietary preferences. The brand is committed to promoting a healthy lifestyle, partnering with sports and wellness brands like Adidas. Originally launched as a store-in-store concept, kaisin. is now driving its steady growth through a data-driven approach to expanding new locations.
kaisin.’s Challenge
For QSR businesses, selecting the right location is crucial to success. Before Targomo, kaisin. had a challenge in selecting the right locations for expansion into new cities. The risk of investing in the wrong spot was high, with poor location choices leading to wasted time and money.
Delano Fischer , CEO & Co-Founder of kaisin., emphasised the stakes: “If you choose the wrong location, you’re wasting resources on store development, and it can result in a major loss.”
As a self-funded business with limited resources for trial and error, kaisin. needed a more efficient, data-driven approach to making smarter, faster location decisions.
How Targomo Helped kaisin. Elevate Site Analysis
kaisin. started using TargomoLOOP to enhance its location evaluation process. “Before Targomo, we didn’t really go beyond Zurich. We just stayed where we felt comfortable,” said Delano. The platform allowed kaisin. to assess new areas based on key factors such as foot traffic, population density, and the number of office workers in the area, which were crucial metrics for their lunch-time customer base.
“What convinced me was the ability to quickly assess with data whether a location is worth further investigation,” Delano explained. “With TargomoLOOP, I can estimate how many of my target customers are within 500 meters or a kilometre, and that’s the first step in evaluating a site.”
By combining Targomo’s insights with his own experience, Delano can now filter out poor locations faster, saving valuable time. “Instead of driving two hours to check a location myself, I can analyse it from my office first,” he added.
A standout benefit for kaisin. is Targomo’s ability to prioritise data essential for their business specifically. “For us, it’s not about foot traffic alone—it’s about office workers,” Delano explained. “We need to find areas with a strong white-collar presence because that’s our target market.”
The platform has also allowed kaisin. to avoid costly mistakes by flagging underperforming areas early in the process.
“There are many locations that we thought could work, but the data showed us the opportunity was too low. We ruled them out after a quick check with TargomoLOOP,” said Delano, emphasising that this early-stage rejection is just as valuable as finding the right locations.
Results & Future Plans
kaisin. has already seen clear benefits from using TargomoLOOP, particularly in saving time during the site evaluation process. The platform allows the team to make quicker, more informed decisions without the need for initial site visits.
In just three months of use, TargomoLOOP has proven crucial for streamlining the search for new expansion areas.
“We collaborate with all departments to quickly assess if a location is viable, and Targomo has sped up that process,” said Delano. The collaboration with Targomo’s team has been smooth, with quick feedback and improvements. “Although some questions couldn’t be answered right away, the team was fast and proactive in getting back to us.”
By using TargomoLOOP, kaisin. has refined its location selection, reducing risks and accelerating growth. As the company expands, Targomo’s data-driven insights will continue to play a key role in making fast, accurate location decisions.