Level-Up Player Retention with No-Code Machine Learning Using Amazon SageMaker Canvas

This article explains how to use Amazon SageMaker Canvas, a no-code machine learning tool, to build a model that predicts player churn for free-to-play (F2P) games. Retaining players is crucial for F2P games to generate revenue, so predicting churn can help game developers take actions to improve retention.

Specifically, the article covers:

• The importance of player retention for F2P games
• Prerequisites and steps to set up SageMaker Canvas
• Preparing a dataset with game event data and relevant features for predicting churn
• Building and training a churn prediction model in SageMaker Canvas
• Evaluating the model's performance using metrics like accuracy and a confusion matrix
• Using the trained model to generate churn predictions for new player data
• Insights on how to act on the predictions to improve retention
• Conclusion on the value of using no-code ML for player retention in games