In this paper, we discuss a novel method of learning compressed temporal and behavioral features to predict players that are likely to churn or to continue engaging with the game. Predicting the behavior of players is an important analytical tool for understanding how a game is being played and understand why players depart (churn). Combined, these factors can make it challenging to develop insights about player behavior to inform design and live operations in open world games. Open world games are typically either persistent, or for single-player versions semi-persistent, meaning that they can be played for long periods of time and generate substantial volumes and variety of user telemetry. Open world video games are designed to offer free-roaming virtual environments and agency to the players, providing a substantial degree of freedom to play the games in the way the individual player prefers. These approaches are validated against a detailed behavioral data set from the AAA open-world game Just Cause 2. Furthermore, we describe two new algorithms for three way DEDICOM including a fast gradient method and a semi-nonnegative constrained method. We show how knowledge as to trajectories can help with predicting player retention. This paper introduces a model for predicting retention of players in AAA games and provides a tensor-based spatio-temporal model for analyzing player trajectories in 3D games. This is because player retention is a key factor in driving revenue in semi-persistent titles, for example via downloadable content.

Given this shift in the design of AAA titles, game analytics needs to address new types of problems, notably the problem of forecasting future player behavior. Major commercial (AAA) games increasingly transit to a semi-persistent or persistent format in order to extend the value of the game to the player, and to add new sources of revenue beyond basic retail sales.

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