Location data firm Placer.ai hits unicorn status with $100M Series C

The first proptech unicorn of the new year has landed.

Location analytics startup Placer.ai raised a $100 million Series C round, achieving a $1 billion valuation a little more than three years after launch.

Since 2018, the California-based firm has focused primarily on tracking foot traffic using cell phone data, offering commercial landlords and tenants, particularly in the retail space, insight into how individual buildings and stores perform.

The startup expanded into new verticals last year and more than doubled its headcount to 300. Now, it has a grander vision: By adding a slew of new data sets such as vehicle traffic, web traffic and purchase data to its platform, it aims to provide investment insights for “any professional with a stake in the physical world,” co-founder and CEO Noam Ben-Zvi said.

The Series C was led by Josh Buckley, CEO of the website Product Hunt, who also co-led the Placer.ai’s $50 million Series B in April of last year. Fifth Wall Ventures and Array Ventures also invested in the Series C, along with WndrCo, Lachy Groom and MMC Technology Ventures.

The new funds will propel the firm’s research and development and help it double its headcount again this year, Ben-Zvi said, noting that aggregated data around markets and different property types has become increasingly important for owners, investors and others during the pandemic.

Office landlords, for example, are using the firm’s data to make decisions about the future of work and determine where satellite offices should be, and residential companies are using it to track the evolution of neighborhoods. Manufacturers are using the data to manage the flow of goods and materials.

“What the pandemic has done is accelerated migration patterns across the U.S.,” Ben-Zvi said.

Placer.ai now counts more than 1000 companies as clients, including JLL, retail landlords like Taubman and Regency Centers, and others such as Planet Fitness and BJ’s Wholesale Club. Hedge funds and multinational consumer goods companies like Tyson Foods and Reckitt Benckiser have also become clients.

“Our main challenge is working with all these customers, and understanding their questions, and then further iterating the product to bring in the right datasets, the right processing and the right visualizations to answer those questions,” Ben-Zvi said. “That’s an endless task.”

Ben-Zvi described an ongoing network effect with its location data. Early adopters are now entering their fourth year and establishing best practices for the industry.

“Once you know that this data exists, and once you know that you can trust the underlying accuracy, it’s almost like you can’t unknow it anymore,” Ben-Zvi said. “It’s negligent not to use this data in your decision-making.”

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