Foursquare has come a long way since its launch in 2009 as a pioneer of the check-in. I remember it well.
Along with competitor Gowalla, which got absorbed into Facebook in 2012, Foursquare made quite a splash with the novel concept that users could click a button to share their location with friends and compete for badges and the much-coveted position of “mayor” at a local venue. The gamification of search and the still-novel location layer inherent in smartphones were fresh ideas at the time, and Foursquare entered the market at just the right moment to take advantage of the buzz.
Fast forward to a few years later. By 2014, the novelty of sharing location had worn off a bit, and other companies like Yelp and Facebook were also offering a check-in button. Foursquare saw the need to evolve, and the company made a radical move. They spun off the check-in concept by creating a new app called Swarm, and rebuilt Foursquare as Foursquare City Guide, a tool for local recommendations and discovery.
What about Foursquare today? Interestingly enough, the company has remade itself again, though this time with less fanfare, as a leader in natively digital location intelligence, integrating with some of the most cutting-edge startups in the local space such as Uber, Microsoft, and Snapchat, and providing a local data layer on sites like Twitter and in an extensive partnership with Samsung, both of which I’ll discuss below.
The company has amassed a huge global dataset of 105 million places over many years of check-ins and passive location monitoring. Users have added 630 million photos to Foursquare venues, and they’ve shared 91 million tips, Foursquare’s version of a local review. Through its own apps and its extensive network of partnerships with companies like SnipSnap, Retale, Wallaby, and TouchTunes, Foursquare gathers data on a remarkable 1 billion place visits every month.
The broad syndication of Foursquare data and the way partners are using it marks a significant shift in the company’s position in the local ecosystem. In its own messaging, Foursquare now calls itself a data amplifier, a term that I believe was coined by Gib Olander, who discussed it last year in these pages.
Olander includes traditional aggregators like Infogroup and Localeze in the data amplifier category along with Foursquare, but they are of course not precisely the same. Aggregators get their name because they pull together data from multiple online and offline sources in order to produce a unified data file. In Foursquare’s case, the core dataset is produced and maintained through crowdsourcing, and thus it may well be accurate to say that Foursquare is the first company to provide a ground-up digital source of global data on locations around the globe.
“Beyond the data that feeds Foursquare’s consumer apps, Foursquare helps local business owners reach their customers in the growing ever-changing digital landscape,” says Elliot Danforth, Senior Manager of Strategic Partnerships at Foursquare. “More than 100,000 app developers, including marquee companies, use our data to power rich, location-aware features that improve the consumer journey.”
Since establishing the crowdsource model, Foursquare has opened its doors to data contributions from small businesses, who can claim and manage their venue listings, and multi-location brands, who reach Foursquare through carefully vetted data syndication partners. (Full disclosure: Brandify is one of them.) In its essence though, Foursquare data is sourced directly by Foursquare from its users. Because of the company’s sophisticated location technology, the presence of those users at a venue goes beyond a simple latitude and longitude signal; the technology is trained on over 12 billion human-verified visits in the form of check-ins, and incorporates several other data points such as duration of a visit, or the altitude of the user, which can help to determine which floor of a multistory building a business is on. Not only is Foursquare data natively digital, but it takes advantage of a multidimensionality that is not available from traditional data sources.
This may be why leading edge companies like Snap and Uber have chosen to work with Foursquare when they need location data. Foursquare data integrations work in a few different ways. For companies like Snap and Uber, Foursquare adds a POI layer to the map, allowing the company to serve up the appropriate geofilter for a business location or chain, or giving advertisers more flexibility and creativity when they purchase geofilters. For Airbnb, Foursquare helps to promote the “live like a local” concept, providing Airbnb with photos of recommended places.
For Twitter, Foursquare data adds a geographic dimension to the platform, allowing users to tag tweets with their location and creating the opportunity to monitor hyperlocal trends, as demonstrated by tools like Trendsmap.
Perhaps the most comprehensive integration of Foursquare to date is that of Samsung, whose Galaxy phones employ Foursquare data to tag the location of photos and offer personalized recommendations using Bixby, Samsung’s AI engine. Even cooler, Foursquare adds location awareness to the Galaxy’s Bixby Vision tool, which identifies businesses and landmarks automatically when you point the phone’s camera at them.
Foursquare has productized its location data in many ways under the banner of Foursquare Location Intelligence, helping brands reach out to target audiences, measure foot traffic, and link advertising to store visits. Working with brands is yet another way Foursquare has insinuated itself into the local ecosystem through partnership.
But I’m slightly more interested for the time being in the extent to which startups and app developers have seen Foursquare as a go-to source for building location awareness into their offerings. I think that story is less well known, and yet it’s remarkable to see how often Foursquare data is popping up today in the apps that garner the most consumer traffic and press attention. These votes of confidence would seem to solidify Foursquare’s position as the forefather of natively digital location data.
Of course, there are nuances in a crowdsource data model that will make some publishers reluctant to go that route. Maps providers like Google and Apple would not want to take the risk that some places aren’t covered merely due to lack of user interest. But the crowdsource model has proven over and over again that with sufficient volume and variety of inputs, the system self-corrects, and the information people care most about gets a proportional share of attention and is likely to be the most detailed and accurate. And it’s that notion of people caring about data that’s truly transformational.