Wagering Game Offer Tailoring via Activity Data Analysis
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Solution Overview
Problem
Wagering game establishments face challenges in tailoring offers to patrons effectively, as existing systems fail to accurately analyze current and past activity data to provide personalized and impactful promotions that enhance the gaming experience and increase engagement.
Innovation Solution
A method and system that analyze past and current wagering game establishment activity data to determine the likelihood of desired effects from various offers, selecting and modifying offers based on confidence values and patterns in user behavior, and presenting them to patrons through a network-based system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wagering game establishments collect and analyze past activity data to tailor offers, then the personalization and effectiveness of offers improve, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the offer selection process into distinct modules: data collection module, pattern recognition module, offer generation module, and confidence value calculation module. Each module handles a specific aspect of the tailoring process, making the overall complex system manageable and maintainable while achieving high personalization
Solution Approach 2:
The system performs preliminary actions by pre-collecting and storing patron activity data, pre-identifying behavioral patterns, and pre-generating potential offers with associated confidence values. This preparation work is done before the actual offer presentation, reducing real-time processing complexity while maintaining high adaptability
2Measurement precision
If the system analyzes current and past activity data to compute likelihood of desired effects, then the accuracy of offer selection improves, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing by pre-analyzing historical activity data to identify patterns and pre-computing confidence values for various offer scenarios. This allows the system to make accurate real-time offer selections without performing complete data analysis from scratch each time
Solution Approach 2:
The system uses self-service mechanisms by automatically collecting activity data, identifying patterns, and generating offers without requiring manual intervention. The confidence value computation is automated based on pre-established criteria, enabling rapid accurate decision-making
3Productivity
If the system presents tailored offers based on patron behavior patterns, then patron engagement and entertainment value improve, but the complexity of tracking and analyzing multiple data types increases
Solution Approach 1:
The system implements a universal data collection framework that handles multiple data types (wagering activity, preferences, demographics) through a single integrated architecture. The same pattern recognition engine processes all data types, reducing the complexity that would arise from separate tracking systems for each data type
Data Source
AI summary
A system determines that current wagering game establishment activity data of a user satisfies wagering game establishment offer evaluation criteria (401). The system accesses, over a network, past activity data of the user at least partially in response to determining that the current wagering game establishment activity data of the user satisfies the wagering game establishment offer evaluation criteria (403). The system analyzes the past activity data based, at least in part, on a desired effect and the current wagering game establishment activity data (405). The system computes likelihood that at least one of a set of offers can achieve the desired effect based on the analysis result (407). The system selects a first of the set of offers based, at least in part, on determining the likelihood that at least one of the set of offers can achieve the desired effect (409). The system presents the selected first offer to the user (413).