Foursquare 7.0 Brings Scannable Design And Expands Proactive Recommendations To All Users
Way back in the ancient prehistory of 2009, Foursquare made its first foray into push notifications, at a time when using push at all was still rare. Two years later, it made an attempt to capitalize …
Context & Ripple Effects
Foursquare has spent four years walking away from the mechanic that built it. The 2011 pivot from present tense to future and Dennis Crowley's stated de-emphasis of check-ins in favor of mining user data set up the current move: last month the company began pushing unsolicited tips on iPhone, and version 7.0 now makes those proactive recommendations available to every user rather than a test cohort.
The redesign matters commercially as well as cosmetically. Foursquare confirmed plans earlier in 2013 to turn user life data into targeted advertising, and an app that surfaces places without being opened generates exactly the ambient location signal such an ad business requires. The rollout drew same-day pickups from ReadWrite, The Verge, Engadget, Gigaom, The Next Web and others — unusually broad syndication for an app update.
First-order effects
- Every Foursquare member, not just the October iPhone test group, now receives automatic alerts about nearby places without opening the app, shifting daily usage from deliberate check-ins to passively consumed notifications.
- The scannable 7.0 design lowers the friction of acting on those alerts, pairing each push with an interface built for quick glances rather than browsing.
Second-order effects
- Passive notifications multiply the location and preference data Foursquare collects per user, feeding directly into its confirmed 2013 plan to monetize that life data through targeted advertising.
- Rivals in local discovery face pressure to match push-based recommendations or cede the 'notify me before I ask' moment, where Foursquare's check-in history gives it a head start on intent data.
Third-order effects
- If the pattern holds, the check-in — Foursquare's founding gesture since its 2009 launch — completes its transition from product to training data, with the company positioned as a local-intent layer whose value is the recommendation engine rather than the log itself.
- Push-first discovery normalizes apps contacting users unprompted, raising the bar on notification relevance and setting up the opt-in fatigue and filtering controls that follow any channel used at this scale.
The trend: Local discovery is moving from manual user logging toward proactive, data-driven recommendation engines, with Foursquare converting four years of check-in history into the raw material for an advertising business.