  The value of mobile apps is traditionally measured by metrics such as the
number of downloads, installations, or user ratings. A problem with these
measures is that they reflect actual usage at most indirectly. Indeed, analytic
companies have suggested that retention rates, i.e., the number of days users
continue to interact with an installed app are low. We conduct the first
independent and large-scale study of retention rates and usage behavior trends
in the wild. We study their impact on a large-scale database of app-usage data
from a community of 339,842 users and more than 213,667 apps. Our analysis
shows that, on average, applications lose 70% of their users in the first week,
while very popular applications (top 100) lose only 45%. It also reveals,
however, that many applications have more complex usage behavior patterns due
to seasonality, marketing, or other factors. To capture such effects, we
develop a novel app-usage behavior trend measure which provides instantaneous
information about the "hotness" of an application. We identify typical trends
in app popularity and classify applications into archetypes. From these, we can
distinguish, for instance, trendsetters from copycat apps. In our results,
roughly 40% of all apps never gain more than a handful of users. Less than 0.4%
of the remaining 60% are constantly popular, 1% flop after an initial steep
rise, and 7% continuously rise in popularity. We conclude by demonstrating that
usage behavior trend information can be used to develop better mobile app
recommendations. With the proposed usage-based measures (retention and trend),
we are able to shift sovereignty in app recommendations back to where it really
matters: actual usage statistics, in contrast to download count and user
ratings which are prone to manipulation by people.
