A Time Series Approach To Player Churn and Conversion in Videogames
This work addresses player retention and monetization for game developers, but it is incremental as it compares existing time series methods without introducing a new paradigm.
The authors tackled the problem of modeling player churn and conversion in free-to-play games using a State Space time series approach to predict transition probabilities between user groups, finding that while both Autoregressive Integrated Moving Average and Unobserved Components methods performed similarly for most rates, the latter failed in detecting marketing campaigns and forecasting non-paying user abandonment.
Players of a free-to-play game are divided into three main groups: non-paying active users, paying active users and inactive users. A State Space time series approach is then used to model the daily conversion rates between the different groups, i.e., the probability of transitioning from one group to another. This allows, not only for predictions on how these rates are to evolve, but also for a deeper understanding of the impact that in-game planning and calendar effects have. It is also used in this work for the detection of marketing and promotion campaigns about which no information is available. In particular, two different State Space formulations are considered and compared: an Autoregressive Integrated Moving Average process and an Unobserved Components approach, in both cases with a linear regression to explanatory variables. Both yield very close estimations for covariate parameters, producing forecasts with similar performances for most transition rates. While the Unobserved Components approach is more robust and needs less human intervention in regards to model definition, it produces significantly worse forecasts for non-paying user abandonment probability. More critically, it also fails to detect a plausible marketing and promotion campaign scenario.