Principal attributes of Game #1.

<p>Principal attributes of Game #1.</p

Two churners and two active users.

<p>Each row describes play log records of a user, and a user with no play log record in the churn prediction period is defined as a churner. When desired, a larger <i>CP</i> can be chosen for defining churn.</p

Game-specific features of Game #2 and Game #3.

<p>Game-specific features of Game #2 and Game #3.</p

Common features of the three games.

<p>Common features of the three games.</p

Three games and their data.

<p>Three games and their data.</p

Overall average rankings for single features.

<p>Overall average rankings for single features.</p

AUC performance as a function of <i>OP</i> and <i>CP</i> selections. The churn prediction performance is improved by increasing number of observation days and by decreasing number of churn prediction days.

<p>(a)(c)(e) AUC vs. (<i>OP</i>,<i>CP</i>) for Game #1, #2, and #3, respectively (b)(d)(f) AUC vs. <i>CP</i> for four fixed <i>OP</i> values for Game #1, #2, and #3, respectively.</p

AUC vs. number of utilized features.

<p>Increasing number of features has only a marginal effect on AUC performance, especially after 2~3 features are included.</p

Time diagram of a player’s play log data.

<p>The player has played <i>n = 8</i> times. The first three plays occurred within two days of the first play. The next two plays occurred in the following two days.</p

Performance (AUC) comparison of three conventional algorithms and two deep learning algorithms (<i>OP</i> = 5 days, <i>CP</i> = 10 days).

<p>Performance (AUC) comparison of three conventional algorithms and two deep learning algorithms (<i>OP</i> = 5 days, <i>CP</i> = 10 days).</p