How the Internet of Things Could Spawn a New Kind of Analytics
Often, it’s what we do in the real world — the restaurants we frequent, the neighborhoods where we live, and even the aisle of the grocery store we visit first — that says the most about who we are as consumers.
That’s why a new set of technology companies are working to help transform the way businesses understand consumer behavior in the real world. Powering these new technologies is a burgeoning web of connected devices — from smartphones and thermostats to wireless routers and traffic cameras – that constantly collect a river of information about their surroundings. Cisco estimates that the so-called “Internet of things” will balloon over the next few years, with the number of connected devices growing from 9 billion today to over 50 billion in 2020.
What’s more, the growth of relatively cheap remote data storage paired with improvements in processing has put the big data opportunity within reach of startups. Today, new companies often focus on analyzing a single sensor, building systems to say, measure store traffic via wireless routers or use computer vision to turn security footage into real-time heat maps. But over the next decade, we will likely see convergence in the sector as startups grow and look to provide a broader, more comprehensive look into real-world consumer behavior.
In many ways, the online analytics industry offers a helpful roadmap for the physical world. We can often break down local consumer data into two buckets: systems that measure places (what’s going on here) and systems that measure people as the move between places (where does she go?).
The key difference between here is the depth of data. In the former, the goal is to measure as much activity within a given place in the same way the Google Analytics measures activity on a website. In the latter, the goal is to measure the activity (places) of a person in the way that cookies help online analytics companies like Omniture track users across sites.
There are two technologies that startups in the market are using to track and measure places in the real-world. The first, used by companies like Euclid and RetailNext, analyzes the data generated when your smartphone interacts with a wifi router to provide Google Analytics-like statistics on the foot traffic into and around a store. Thanks to other attributes passed along, like the phone’s unique MAC address and the strength of signal, these services can also glean the number of returning customers and figure out how many are simply passing by the window without entering.
While wifi technologies can tell a retailer, for instance, whether a customer has visited earlier, they’re less effective at capturing what happens once the person walks through the door. But thanks to rapid developments in “computer vision,” a technology that uses machine learning to identify patterns in video streams, a number of startups have built systems to turn existing video feeds into real-time heatmaps of a location.
One Brooklyn-based startup, Placemeter, can tell you whether Shake Shack is worth the trip or if the Macy’s at Times Square is busy — all by analyzing hundreds of traffic cameras and publicly available video streams of New York City. Meanwhile, Prism Skylabs, a three year-old Silicon Valley startup, uses similar technology to work with retailers directly, turning their existing security footage into live data about how customers move throughout a store.
The big drawback here is that once a person leaves a location — when they exit the store or move beyond the view of the camera — that person is folded into a much larger dataset, and rendered anonymous. They may visit that place again, but those technologies will not be able to connect the dots.
Measuring People, and the Real-World Cookie
With 145 million people in the U.S. owning smartphones, mobile devices have become revolutionary tools in quantifying the real world. Consider Waze, the Israeli mapping startup that sold to Google for $1 billion last year: the company has been able to to build one of the most complex roadway and transportation datsets at a fraction of the cost by tapping the collective data streaming from user’s devices.
Meanwhile, Seattle startup Placed has spent the past three years developing a Nielsen-like approach to location analytics where the company incentivizes a panel of users to share detailed, location data from their mobile devices with the company either by downloading a proprietary app or opting-in with an affiliate developer. With participants opted-in, the company can access extremely rich behavioral data for each user and start to understand where people are going, where they’ve gone in the past, and for how long. The company is monetizing the data through a number of products, including a recently released attribution service for mobile advertising.
The key here is that the person — not the place — is the organizing unit. The company might only capture a fraction of a percent of people who visit a Target, for instance, but its technology can tell you not only where that person has gone, but how often, and what they’ve done in their digital lives as well.
Behavioral data is particularly valuable for the mobile marketing industry, which has struggled in the absence of cookies — the bits of code that allow marketers to target users based on behavior on other sites. Over the past year, a number of mobile-local advertising networks have pushed to build new advertising products, which allow marketers to target messaging based on a user past, as well as present behavioral. Startups like xAd, Verve, and JiWire have built systems that use privacy-friendly device identifiers, recently released by Google and Apple, to compile location profiles of users, which can be used to target messaging in the future.
Open an app at the airport, and then again at a rental car agency, and don’t be surprised to see an ad for an airline a few days later. You’re a business traveler — and they know it.
Steven Jacobs is Street Fight’s deputy editor.
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