What Placer.ai intends to do with its $100 million cash infusion
At ICSC’s biggest show of 2021 in Las Vegas last December, one booth, crammed to its borders with visitors, shone out as the rock star of the event. That was the booth of Placer.ai.
Started just six years ago by experienced digital entrepreneurs who had absolutely no involvement with and little knowledge of either brick-and-mortar retail or real estate, Placer has since become the digital darling of that world, opening consumer data doors that had steadfastly remained shut. Last month it landed $100 million in financing, its largest cash infusion to date.
“Placer has given our industry an incredible tool to make decisions in real time. They make center and tenant data accessible, and we now can make decisions with real data, not just intuition,” said one of the company's first clients, Newmark Merrill CEO Sandy Sigal. “Until Placer arrived, nobody knew how to put all that information in one place, verify its accuracy, or allow owners to easily access it.”
Chain Store Age learned of the special impact Placer was having on retail real estate a year ago during a visit with Yaromir Steiner, CEO of Steiner + Associates, at Easton Town Center in Columbus.
“When we learned from Placer that 30% of the dollars spent here were from customers living over 50 miles away, that really surprised us,” Steiner told us. “We also found out that our traffic numbers placed us in the top dozen of regional malls in the U.S. We knew we were good, but not that good.”
Stephen Congel, CEO of Pyramid Management Corp., the operator of New York State’s largest enclosed shopping center, Destiny USA in Syracuse, was another early client of Placer.ai. He sees the data provider as playing a key role in the post-pandemic remake of physical retail.
“With Placer.ai, we’re able to validate the dominant positioning of our centers with tenants, learn more about changing consumer preferences, identify new opportunities, and position ourselves confidently for what’s next,” Congel said.
Where will Placer.ai go now with that $100 million? We spoke with company co-founder and CEO Noam Ben-Zvi to find out.
One of the reasons you decided to provide data to companies involved in physical retail was that you did not believe in the retail apocalypse. Why not?
I’m an elder millennial. I grew up with computers and was comfortable buying stuff online. But I still love to go and shop. I love the experience. I love seeing the product. I never underestimated the importance of that.
Previously, I had built a data company called Bluetail that mined sales intelligence from social networks. We entered the physical world from out of nowhere. We were very lucky to meet a few important commercial real estate people early—Sandy Sigal from Newmark Merrill, and a VP from Walmart. This allowed us to see the unique opportunity that existed in the market. Once we saw the potential for disruption and the ability to bring a lot of value to the market, we dove in.
You’ve said that brick-and-mortar retail was data-poor because data moves so slowly in the physical world. How did you speed it up?
The shift in the market started in 2016. Prior to that time, the tower data provided by cell phone companies was not broad enough. The hardware wasn’t where it needed to be in order to gather the data at scale. But it was available in 2018 when we launched. We could obtain the critical mass. Prior to that, all you had was surveys, and those are slow and biased. A lot of retail real estate executives would drive down to their properties to observe and try to learn some of the things we now provide them. That, too, was slow, expensive, and limited. But we could partner with mobile apps that sent us anonymized data. We could observe 30 million devices and see where people shopped and what they did before and after. What did they look at? We could empower executives to analyze hundreds of properties in the time it used to take them to look at one.
Do you think it helped that you knew little or nothing about retail? Did you go in with an open mind?
Absolutely that helped us. Data companies get lured into making recommendations. “You should buy this center and you’ll make a million bucks.” We came into this a lot more humble. Understand the questions. Answer them with simple insights. We made a big leap of faith when we said, “We could apply this to real estate.” We invented very little. We found the information our customers wanted.
In 2017 we raised capital for the first time and built out the first version of the product. No website, nothing. The first year we worked with our first five customers. We started out in Santa Cruz and we worked with Sandy Sigal, who has a lot of centers in California. We’d show him data about one of his properties and he’d come back and say, “This doesn’t make sense.” So we cleared out some true data problems and we showed him some things that he didn’t know. We’re not just providing confirmatory evidence of something you suspect; we allow you to see things you didn’t realize were happening.
What have you been unable to do that the new funding will help get done?
The funding allows us to move forward towards an even bigger vision. Mobile location data is the foundation to understanding the physical world, but it is only one ingredient. There’s purchase data, crime data, and pedestrian data, too. New companies are building new data sets all the time and we want to bring all of this information into our platform. The result is the most comprehensive view of what’s really happening in the offline retail world.
But to do this we’ll have to hire more data engineers to work with it. We had eight employees in 2016. We have 300 now, and we’ll likely be hiring 200 more in the next year. The funding will be focused on building out our ability to deliver even more features and products to our audience.
Will you be helping retailers with site selection?
We have more than 200 retail customers, and site selection is a big part of the equation. Opening locations is expensive and comes with significant risk. We can help maximize the likelihood of success to help retailers optimize their retail footprint.
What more will you be able to do for them?
Some of the cool things we’ll be able to do for them once we’re armed with all these data sets are to help answer some questions like “What are the drivers of success and where can you find them? Why are my best stores the best stores, and can I do some of the same things with my other stores? Is it the people who run the stores? Is it the people who live near the stores?” Once you actually understand what is working in your best stores, you can replicate it.