Why data science matters to Foursquare

For those of you unfamiliar with the app, Foursquare is a social network that allows users to check in to specific locations, such as coffee shops, restaurants, bars and offices. Once users check in, they traditionally receive a pop-up notification containing a useful tip from someone.
When a user checks in to Foursquare, the location-sharing app does more than just check them in. It also notes the location using the phone's GPS tracker, the strength of any surrounding Wi-Fi networks and also collects data on the distance to the closest mobile towers. And when this data is collected from a community of 40 million (and more than five billion check-ins), it gives the company an incredibly valuable data set.
Breaking it down further, this means that when a user attempts to check in, Foursquare can predict where they are, even if they're in the underground basement of a coffee shop without any reception. How? Because after four years of checking in various users, another Foursquare user has probably faced a similar lack of signal in the same venue and connected to a wifi network to check in.
If a user connects to the free wifi in a coffee shop without checking in, for example, it's likely that he or she will receive a notification because the app instantly knows where they are.
This update just brings about something the chief executive Dennis Crowley has wanted from the very beginning. He didn't want users to have to work hard to receive tips but wanted them to receive them automatically, in the form of contextual notifications. Why did it take so long? Previous test iterations of the app were draining battery and often incorrectly predicting where a user was.
Black Shaw, the data scientist said, "It's obviously a big feat of engineering, but a variety of data problems had to be tackled and having someone on the team with a strong scientific background was a key component."
It's these types of tips that make the app worthwhile. If you're looking for a good coffee shop or hidden gem in a new city, for example, chances are that Foursquare users have recommended a nearby place for you to visit.


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