Foursquare, like any game, allows a mix of competition and connection among players; even if — as in the case of solitaire — you’re only competing with yourself. With this in mind, I tracked each of the quantifiable measures from my profile and for each of the “friends” in my Foursquare network. Specifically, I tracked and analyzed the following indicators: friends, tips, photos, lists, badges, maryorships (current data), total check-ins, total top places check-ins (for the past six months), and total most explored categories check-ins (for the past six months). I computed sums, averages, and ratios to see if correlations or patterns emerged. In reconsidering the game element of Foursquare, I experimented with grouping indicators according to the effort a person makes and the rewards they earn. In a game, this might equate to strategy, practice, focus, and improving or winning. In this case I identified effort as the sum of user-generated tips, photos, and lists (but not check-ins). I identified the earnings as current mayorships and accumulated badges. Obviously, names have been changed to protect the innocent.
In looking at “earn” and “effort” I could not immediately discern a correlation but if I made them a “closed value;” i.e., making them account for total participation then I could create a spectrum in which a person’s earn-effort activity would place them on some point of the scale. I looked at total check-ins for a correlation and could not identify anything, try as I may, to account for the large variation in user activity. Total check-ins ranged from 61 to 6,305. I wasn’t able to incorporate friend scores because friends ranged from 7 to 309 (note: all friends are also Foursquare users). I needed a way to even these numbers. I decided to look at check-ins for “top places” and “most explored categories” because they are limited to the past six months — for everyone. With this new data set of recent activity, I found myself getting closer to an understanding but not a correlation. I then looked to see what patterns did emerge. I then coded/quantified both effort and recent activity data sets for high, medium, and low. The result was a matrix of user profiles.
|High Effort||Medium Effort||Low Effort|
|High Activity||Scooby Doo
|Medium Activity||Bart Simpson|
|Low Activity||Sponge Bob
|Elmer Fudd||Pink Panther
If user profiles were formalized or identified by Foursquare, in an automated way, and fed back to users this may open up opportunities for Foursquare, its developers, venues and users themselves, in terms of encouraging more “active” participation in the app; specifically, in leaving tips, submitting photographs, and curating lists.