Authors: Gavin Steininger
This paper presents an approach to modelling passive forever churn (i.e., the probability that a user never returns to a game that does not require them to cancel it). The approach is based on parametric mixture models (Weibull, Gamma, and Log-normal) for return times. The model and data are inverted using Bayesian methods (MCMC and DIC) to get parameter estimates, uncertainties, as well as determine the return time distribution for retained users. The inversion scheme is tested on three groups of simulated data sets and one observed data set. The simulated data are generated with each of the parametric models. Each data set is censored to six time horizons, creating 18 data sets. All data sets are inverted with all three parametric models and the DIC is used to select the return time distribution. For all data sets the true return time distribution (i.e., the one that is used to simulate the data) has the best DIC value; for 16 inversions the true return time distribution is found to be significantly better than the other options. For the observed data set inversion, the scheme is able to accurately estimate the \% of users that did return (before the game transitioned into open beta) to given 14 days of observations.
Comments: 8 Pages.
[v1] 2019-05-14 14:10:00
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