  Nowadays, video game developers record every virtual action performed by
their players. As each player can remain in the game for years, this results in
an exceptionally rich dataset that can be used to understand and predict player
behavior. In particular, this information may serve to identify the most
valuable players and foresee the amount of money they will spend in in-app
purchases during their lifetime. This is crucial in free-to-play games, where
up to 50% of the revenue is generated by just around 2% of the players, the
so-called whales.
  To address this challenge, we explore how deep neural networks can be used to
predict customer lifetime value in video games, and compare their performance
to parametric models such as Pareto/NBD. Our results suggest that convolutional
neural network structures are the most efficient in predicting the economic
value of individual players. They not only perform better in terms of accuracy,
but also scale to big data and significantly reduce computational time, as they
can work directly with raw sequential data and thus do not require any feature
engineering process. This becomes important when datasets are very large, as is
often the case with video game logs.
  Moreover, convolutional neural networks are particularly well suited to
identify potential whales. Such an early identification is of paramount
importance for business purposes, as it would allow developers to implement
in-game actions aimed at retaining big spenders and maximizing their lifetime,
which would ultimately translate into increased revenue.
