PlaceIQ sifts through tons of data about locations to give marketers a mini-zipcode-like profile of each block. The data comes from both open sources and commercial data sets, including place data, retail data, government data, event data, photo data, social data, and, crime data. This goes well beyond Facebook, Twitter, and Foursquare, but the company says it doesn’t use any personally identifiable information. Rather, it is making assumptions based on the contextual cues of a person’s location and time of day.
. . . It takes all of these various hyper-local data sources and maps it onto its 100 million map tiles. Then it normalizes the data and can guess what type of person is likely to be at that location at that time (a student, tourist, shopper, financial or tech worker, etc). It can also spit out information such as retail sales volume, events, foot traffic by time, and social media activity.
As published in the July 15, 2015 issue of Bloomberg Businessweek in the Etc. Hard Choices section.
Duncan McCall, CEO and Co-Founder of PlaceIQ recalls the challenges of reinventing the location intelligence business.
When we first outlined the new vision for the business, the team was skeptical. The economy had just crashed and everyone wanted to keep doing what had been working instead of evolving. It’s a natural impulse, but one we had to resist. There were lots of late nights and loud arguments about that. I won in the end. The world was changing, and we had to keep up.
Transitioning was slow. Adapting a platform designed specifically not to isolate personal information, to do the exact opposite, was an arduous task. We had to spend our dwindling cash reserves. More importantly, it required a cultural shift. We had to reset moral expectations, sell a vision and guide people to be passionate about our future. I’m really proud of how my team did.
We tried the Department of Defense, but they were reeling from budget slashes. We worked with the intelligence agencies a bit, but they were playing it safe after what happened in Pakistan and Iran. We had some success with Homeland Security, but had a tough time cutting through all the red tape that the big four contractors had bought. This was a frustrating experience for us and morale was low, but we kept at it. One big contract is all we needed to prove the concept.
As it turns out, the strategy proved successful only when we shifted our focus from the federal government to the new wave of small scale security vendors. They immediately saw the value of our existing product and funded a pilot project. This was a big moment for the team. The future of the company suddenly crystalized, and everyone jumped in. It was like a whole new startup, though, to preserve some of our lineage, we decided to keep our motto, “Next-gen location intelligence.”
We got a lot done very quickly. In 6 months, we had integrated the data from drone video and thermal feeds. The result was real-time, accurate, forecasting of when specific individuals would be where. In 9 months, we were able to loop our output back to the drones. We were effectively able to pinpoint, down to the block level, where someone would be at a certain time, and then deliver a payload – marketing collateral or weapons – within minutes. Our customers loved it. By the end of the year, we had doubled our revenues.
The kind of leadership my team showed was simply amazing. The entire staff came around, took ownership of the business, and now we’re just crushing it. We have now deployed in 83 communities around the country, with another 2 dozen in the works. We’re working on a suite of companion products to attract new customers while making our existing base even happier, and we’re expanding internationally over the next year into thriving markets, like Somalia and Mexico.
Part of my ongoing startup dystopia series. Read the previous installment, Flavo.rs here.
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