  In this work, we predict the user lifetime within the anonymous and
location-based social network Jodel in the Kingdom of Saudi Arabia. Jodel's
location-based nature yields to the establishment of disjoint communities
country-wide and enables for the first time the study of user lifetime in the
case of a large set of disjoint communities. A user's lifetime is an important
measurement for evaluating and steering customer bases as it can be leveraged
to predict churn and possibly apply suitable methods to circumvent potential
user losses. We train and test off the shelf machine learning techniques with
5-fold crossvalidation to predict user lifetime as a regression and
classification problem; identifying the Random Forest to provide very strong
results. Discussing model complexity and quality trade-offs, we also dive deep
into a time-dependent feature subset analysis, which does not work very well;
Easing up the classification problem into a binary decision (lifetime longer
than timespan $x$) enables a practical lifetime predictor with very good
performance. We identify implicit similarities across community models
according to strong correlations in feature importance. A single countrywide
model generalizes the problem and works equally well for any tested community;
the overall model internally works similar to others also indicated by its
feature importances.
