Personalization and experimentation in games enhance player retention and promote sustainable long-term engagement. By tailoring experiences to individual play styles, preferences, and activities, developers create unique journeys for each player. This customization improves various game aspects, including matchmaking, onboarding, and store offers. Personalization and Experimentation solutions on AWS use machine learning (ML) models, customer analytics, and game backend technology to rapidly identify player needs, inform strategy, and deploy personalized features. These tools help developers understand and respond to distinct player requirements, ultimately creating more engaging and profitable games.

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  • AI-Driven Player Insights on AWS

    This guidance helps game owners, game admins, and LiveOps admins to automate the process for building an end-to-end machine learning (ML) pipeline, that takes labeled player data and automatically builds, trains, tunes and deploys the best ML model for predicting player behavior, and gaining player insights.
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