Retention Systems

Disclaimer

• All these models does not require you to add new content; these models can use existing content to optimize your games.

Prediction Models

• Creating Time-To-Leave Prediction Model

  • Minimal implementation takes a minimum of 30 minutes using DataPredict™.

• Creating Probability-To-Leave Prediction Model

  • Minimal implementation takes a minimum of 30 minutes using DataPredict™.

• Creating Probabilistic Time-To-Leave Prediction Model

  • Combines both “Time-To-Leave Prediction Model” and “Probability-To-Leave Prediction Model”.

  • Minimal implementation takes a minimum of 30 minutes using DataPredict™.

• Creating Probability-To-Interact Prediction Model

  • Can be combined with generative and reward-maximization-based models for optimized retention and interaction.

  • Minimal implementation takes a minimum of 30 minutes using DataPredict™.

Control Models

• Creating Deep Play Time Maximization Model

  • The model chooses actions or events that maximizes play time.

  • Have higher play time potential due to its ability to exploit and explore than the other four models, but tend to be risky to use.

  • Minimal implementation takes a minimum of 2 hours using DataPredict™, especially if custom events are associated with the model’s output.

• Creating Simple Play Time Maximization Model

  • Uses discrete input values (e.g. “focus”, “run” and “attack”) to maximize play time.

  • More safer and faster to learn but more limited in expressive power compared to the deep version.

  • Minimal implementation takes a minimum of 1 hour using DataPredict™, especially if custom events are associated with the model’s output.

• Creating Junior-Senior Play Time Maximization Ensemble Model

  • Uses a combination of:

    • Simple Play Time Maximization Model

    • Deep Play Time Maximization Model

  • When the simple model chooses to “consult” the deep model, the deep model will generate actions instead of the simple model.

  • Less risky and learns faster than the original “Deep Play Time Maximization Model”, but takes more time to implement.

  • Minimal implementation takes a minimum of 3 hours using DataPredict™.

• Creating Gated Deep Play Time Maximization Ensemble Model

  • Uses a combination of:

    • Time-To-Leave Prediction Model

    • Probability-To-Leave Prediction Model

    • Deep Play Time Maximization Model

  • Less risky than the original “Deep Play Time Maximization Model”, but takes more time to implement.

  • Minimal implementation takes a minimum of 4 hours using DataPredict™.

• Creating Gated Junior-Senior Play Time Maximization Ensemble Model

  • Uses a combination of:

    • Time-To-Leave Prediction Model

    • Probability-To-Leave Prediction Model

    • Simple Play Time Maximization Model

    • Deep Play Time Maximization Model

  • The least riskiest model out there for play time maximization, but takes the longest time to implement.

  • Minimal implementation takes a minimum of 6 hours using DataPredict™.

Detection Models

• Creating Left-Too-Early Detection Model

  • Inverse of probability-to-leave model by detecting outliers.

  • Highly exploitable if the player accumulates long session times over many sessions before suddenly decrease the session times gradually if rewards are involved.

  • Minimal implementation takes a minimum of 30 minutes using DataPredict™.

• Creating Labelless Left-Too-Early Detection Model

  • Same as “Left-Too-Early Detection Model”, but it does not require manual tracking of label data, which makes it less accurate.

  • Minimal implementation takes a minimum of 30 minutes using DataPredict™.

• Creating Engagement Milestone Detection Ensemble Model

  • Uses a combination of:

    • Time-To-Leave Prediction Model

    • Probability-To-Leave Prediction Model

    • Left-Too-Early Detection Model

  • The model periodically checks if the player is playing much more longer or more engaged than usual.

  • Minimal implementation takes a minimum of 4 hours using DataPredict™.