Title: Measuring Player Retention and Monetization using the Mean Cumulative Function,
Abstract: Game analytics supports game development by providing direct quantitative
feedback about player experience. Player retention and monetization in
particular have become central business statistics in free-to-play game
development. Many metrics have been used for this purpose. However, game
developers often want to perform analytics in a timely manner before all users
have churned from the game. This causes data censoring which makes many metrics
biased. In this work, we introduce how the Mean Cumulative Function (MCF) can
be used to generalize many academic metrics to censored data. The MCF allows us
to estimate the expected value of a metric over time, which for example may be
the number of game sessions, number of purchases, total playtime and lifetime
value. Furthermore, the popular retention rate metric is the derivative of this
estimate applied to the expected number of distinct days played. Statistical
tools based on the MCF allow game developers to determine whether a given
change improves a game, or whether a game is yet good enough for public
release. The advantages of this approach are demonstrated on a real
in-development free-to-play mobile game, the Hipster Sheep.