Sometimes when you “check-in”, the app will tell you the last time you were there (if it’s been awhile), how many consecutive weeks you’ve been there (which I like seeing when I check-in at the gym), and also give you any tips that others may have left about the place you’re at–such as, try the sea urchin ceviche.
But Foursquare has been struggling. Check-ins proved to be a bit of a fad and Yelp solved the what-do-you-want-to-do-tonight problem better. However I’ve always felt that, on a fundamental level, Foursquare had the potential to be so much more powerful than Yelp.
Well, today the big news in the tech world is that Foursquare is unbundling its app. There will be Foursquare and there will be Swarm. Foursquare will be a recommendation engine that helps people find places to eat, drink, shop and so on (just like Yelp), and Swarm will be all about social–seeing where your friends are and which ones are nearby. And along with this unbundling, there will be no more check-ins:
But how can Foursquare personalize its users’ results if they are no longer collecting check-ins, the foundation of Foursquare’s recommendation engine? Crowley smiles and says something a bit shocking. He no longer needs check-ins, the meat and potatoes of Foursquare’s entire business and data collection engine for the last five years.
Not only has Foursquare collected 6 billion check-ins, he says, but it has collected five billion signals to help it map out over 60 million places around the world. Each place is a shape that looks like a hot zone of check-ins — of times when people have said “I’m here.” Foursquare’s “Pilgrim” location-guessing engine factors in everything from your GPS signal, to cell tower triangulation, to the number of bars you have, to the Wi-Fi networks, in order to create these virtual shapes.
Now that it has this data, Foursquare can make a very accurate guess at where you are when you stop moving, even without a check-in, it’s a technology it hopes will allow it to keep its database of places fresh and accurate. Foursquare calls these implicit check-ins “p-check-ins,” or Neighborhood Sharing. Take your phone into four or five different Japanese restaurants over the course of six months and without a single check-in Foursquare will learn that you like Japanese food and start making recommendations for you based on that data.
There will obviously be a number of people who have anxiety about an app that’s passively tracking everywhere they go and then trying to feed them recommendations (come eat here!), but I do think they’re on to something.
The opportunity with Foursquare (and its data) is that the recommendations can be tailored. If I’m looking for a place to eat, Foursquare will already know that I love Mexican and that I just worked out (meaning I’m probably extra hungry). Personally, I’m okay with that.
But then I start to wonder how this might impact cities. If the process of discovery becomes this automated and this tailored, how might it change the way we organize and design our cities?