Gaming Device Risk Tracking for Personalized Retention Actions
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Solution Overview
Problem
Existing gaming establishment systems struggle to efficiently retain users by anticipating and mitigating disengagement risks based on real-time user interactions, often relying on manual guesses that waste resources and fail to provide personalized interventions.
Innovation Solution
A system that monitors user interactions with gaming establishment devices, tracks various events and activities, and employs tailored intervention actions, such as operational changes or benefits, to mitigate disengagement risks by analyzing user behavior and historical data, using AI models to customize responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and guessing are used to retain users, then resource consumption increases, but user retention effectiveness remains low
Solution Approach 1:
The system enables self-service by automatically monitoring user interactions, calculating disengagement risk scores, and implementing retention interventions without requiring manual human analysis. The gaming establishment device autonomously tracks user behavior patterns, identifies at-risk users, and executes personalized retention offers, eliminating the need for manual monitoring while improving retention effectiveness.
Solution Approach 2:
The system implements continuous feedback loops by monitoring user interactions in real-time, calculating disengagement risk based on tracked metrics, and adjusting retention strategies dynamically. The system provides feedback to both the operational system (to modify game parameters or offers) and to management (through reports on user engagement trends), enabling data-driven decisions that improve retention while optimizing resource allocation.
2Reliability
If generic retention strategies are applied to all users, then implementation is simple, but personalization and effectiveness are reduced
Solution Approach 1:
The system applies local quality by tailoring retention interventions to individual user characteristics and behavior patterns. Instead of uniform strategies, the system analyzes each user's specific interaction history, risk factors, and preferences to generate personalized retention offers. This enables differentiated treatment where each user receives customized interventions based on their unique engagement profile, significantly improving personalization effectiveness.
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting retention strategy parameters based on user-specific data. The disengagement risk calculation incorporates multiple variable parameters (interaction frequency, wager patterns, session duration, etc.), and the system modifies offer parameters, game parameter adjustments, and intervention timing based on real-time analysis of these parameters, enabling sophisticated personalization without requiring overly complex system architecture.
3Measurement precision
If real-time monitoring of user interactions is implemented, then disengagement risk detection improves, but data processing requirements increase
Solution Approach 1:
The system extracts and focuses on the most critical interaction metrics that directly correlate with disengagement risk, rather than processing all possible user data. By identifying and monitoring key indicators (such as interaction frequency, time between actions, wager patterns), the system achieves high detection accuracy while minimizing data processing requirements. Irrelevant or redundant data points are excluded from the analysis pipeline.
Solution Approach 2:
The system applies partial action by implementing monitoring at strategic intervals and focusing computational resources on users showing early signs of disengagement rather than continuously analyzing all user data at maximum depth. The system processes data at varying levels of intensity based on user risk profiles, applying more rigorous analysis only where needed, thereby reducing overall processing load while maintaining detection accuracy for at-risk users.
Data Source
AI summary
Systems and methods that determine a level of risk associated with a user's interactions with a gaming establishment device and, in certain instances based on the determined level of risk, modify an operation of the gaming establishment device and/or offer the user zero, one or more benefits.