  Data-driven approaches which aim to identify and predict player engagement
are becoming increasingly popular in games industry contexts. This is due to
the growing practice of tracking and storing large volumes of in-game
telemetries coupled with a desire to tailor the gaming experience to the
end-user's needs. These approaches are particularly useful not just for
companies adopting Game-as-a-Service (GaaS) models (e.g. for re-engagement
strategies) but also for those working under persistent content-delivery
regimes (e.g. for better audience targeting). A major challenge for the latter
is to build engagement models of the user which are data-efficient, holistic
and can generalize across multiple game titles and genres with minimal
adjustments. This work leverages a theoretical framework rooted in engagement
and behavioural science research for building a model able to estimate
engagement-related behaviours employing only a minimal set of game-agnostic
metrics. Through a series of experiments we show how, by modelling early
user-game interactions, this approach can make joint estimates of long-term
survival time and churn probability across several single-player games in a
range of genres. The model proposed is very suitable for industry applications
since it relies on a minimal set of metrics and observations, scales well with
the number of users and is explicitly designed to work across a diverse range
of titles.
