New Foursquare App Bets The Company On Personalization

After months of anticipation, the new Foursquare app has finally arrived. It will be available this morning from the three major app stores. The new app is built around two principal concepts: Tips and Tastes.

It attempts to create a better “local search” experience using data (in the background) and personalization. Taking aim at Yelp and similar apps, the company said on its blog this morning:

The new Foursquare frees you from having to read long, random reviews, wondering if those people share your tastes. With Foursquare, find things based on your tastes, the places you like, and the friends and experts you trust most.

I haven’t used it quite enough in the real world to come to a firm conclusion yet about its mainstream viability in a highly-competitive market. But with its emphasis on recommendations and personalization, the app does manage to stand apart from Yelp, Google and other similar apps (e.g., Zagat, TripAdvisor).

Foursquare has taken all the data it has amassed over the past five years to generate individualized recommendations nearby or for other specific locations you select. When you open the new Foursquare you’re presented with what the company calls a “taste game” (above). A set of labels you select, it asks you to identify things you like and are interested in (e.g., fish tacos, mimosas).

These suggestions are extracted from Foursquare Tips and are very quick and easy to select. You can do as much or as little explicit personalization as you like; there are many opportunities later to modify these lists. It’s a pretty simple process and it’s probably one, accordingly, that most people will engage with.

Even if they don’t, there’s considerable personalization going on in the background based on other variables such as those you follow, your check-in history and your movements in the world. Foursquare is creating a profile of your tastes based in part on where you go. However, the company says that none of this data is used for any other purpose or shared with others (in your network or other third parties).

Swarm check-ins are integrated pretty seamlessly into the app; although if you haven’t downloaded Swarm you’ll be prompted to. Essentially from the business profile page, when you check-in, you’re taken to a framed page from Swarm and then sent back to the new Foursquare. (It sounds more awkward than it is.)

As we previously discussed Foursquare hopes to further personalize and differentiate by creating experts whom users follow in specific categories or for specific types of suggestions (e.g., coffee expert, taco expert, city expert). As you follow more people or entities your “follow graph” becomes richer and another variable or signal for Foursquare personalization.

Users become experts by being active and leaving Tips. The degree to which Tips are saved or liked by others determines one’s expertise. This system is intended to encourage participation, content creation and quality.

If you follow Greg Sterling, taco maven (not that I am), for example, my taco-related Tips and recommendations will be among those that are served up to you as part of your personalized experience on Foursquare.

The app’s overall objective is to provide a faster, more credible, reliable and individualized way to find places and things you’ll like. However, there’s more complexity and more moving parts here than on something like Google or Yelp. That may be a deterrent for some.

I do think, however, the app mostly succeeds in creating a unique user experience. It may thus get a second look or new life for those who had used Foursquare in the past but more recently stopped using it. I’m not as certain how Foursquare loyalists will react. Many of them don’t like Swarm or the splitting off of check-ins from the main app (this repairs that to an extent).

Foursquare has been doing interesting things with data and recommendations for quite some time, though it wasn’t entirely obvious to people. The new app takes it all to the next level and makes it more explicit.

I’m sure there are a number of ways to streamline and improve the app, which will come. But this is good start on something that takes the various layers and flavors of data and context to offer up a new and different local discovery experience.