There’s a growing battle in the location-based services business between Foursquare and Facebook. Foursquare, with its past emphasis on gaming and status building (who wants to be the mayor of the local laundromat?) is now focusing on a more functional aspect: helping people decide where they should go next. According to a report in Brandweek (backed up by this article on a recent job ad), Foursquare sees offering recommendations as its chance to avoid being squeezed out of existence by Facebook, who, with over 500 million users, is the ostensible gorilla in the room.
How does it plan to do this? Brandweek suggests it will adopt predictive services which are common on sites like Amazon and Netflix:
"Those services crunch behavior data—what movies you watch and books you read—to suggest new products. Foursquare wants to do the same, only with recommendations of real-world activities."
For instance, let’s say you are a sushi freak living in Chicago who’s been active on Foursquare for the last year. You’ve been using Foursquare to capture badges for most of the top local Japanese eateries. Foursquare can see your penchant for fine sushi in the windy city and look across its network for others in your area who share the same passion. It realizes that there is a new joint downtown and can suggest you check this out.
How does this crunching work? The data is mined along a process which runs something like this for each individual visitor:
- What are the past actions you have recorded
- What patterns can be determined from your actions
- Who else in the network is like you
- Where are the gaps between your actions and their actions?
- Offer as predictions these actions that people like you have performed
Note, this obviates the need for a user to fill in a vast registration form listing all their likes and interests. The system can figure this out by looking at past behavior.
In terms of making predictions, systems need to be smart enough to factor in elements that can cause shifts in our patterns of behavior:
- Seasonality (no taste for raw fish when snowing)
- Change in tastes (eg. pregnancy pushes sushi off the menu)
- Removing system bias (eg. not only favoring well-established popular places, but allowing new entrants a chance to prove themselves)
Whether Foursquare makes a concerted move in this direction remains to be seen, but as web and mobile applications creep further into every aspect of our existence (with their inherent ability to track behavior), expect to see an increasing use of business intelligence and predictive analytics to create smarter systems offering us more relevant information.