Increasing use of mobile apps which capture location information has led to wide availability of spatio-temporal data. This paper details our recent efforts on using such data to understand mobile user behaviors in terms of their interaction with apps. Specifically, we aim to mine the association between users' app open (AO) behaviors and their waiting times associated with some transport modes. Here, the transport mode is derived based on speed information measured from a user's location update data, without using any additional map data. One particular case study that we conducted and report here is to understand if users tend to access the app more often while they are waiting at airports. Using a two-week period of a particular iPhone app data from a major U.S. Retailer, the study shows that the app open rate (AR) of air travelers measured during their airport-dwelling time is 8x higher than their AR at other locations. Moreover, for the same group of travelers who have AOs at both airports and other locations, their AR at airports is 45x higher than that at other locations and times. Findings drawn from this study can be applied to assist the definition of geofences by retailers to improve targeting of customers at the right locations, and consequently improve the success of marketing campaigns.
['Ying Li', 'Wesley M. Gifford', 'Anshul Sheopur']
A Case Study of Mobile User Behaviors Using Spatio-temporal Data