User LTV Cohort Modeling for Early Wagering Network Prediction
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Solution Overview
Problem
Current wagering platforms struggle to determine a user's long-term value and engagement, group valuable users into cohorts, and assess new users' value accurately due to insufficient data.
Innovation Solution
A method and system for evaluating users on a wagering network by rating long-term value and engagement, performing correlations, and finding similar new users based on wagering data, using modules like LTV, user engagement, and user correlation to analyze user parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wagering platforms use traditional data collection methods, then they can gather user wagering history, but they struggle to determine user long-term value and engagement accurately
Solution Approach 1:
The system performs preliminary actions by establishing correlation databases during the user's initial wagers (training phase). These databases capture the relationship between wagering parameters and user behavior patterns before the user reaches sufficient historical data volume, enabling accurate LTV prediction even with limited data.
Solution Approach 2:
The patent introduces correlation databases as intermediary structures that mediate between raw user wagering data and LTV calculations. These databases pre-process and organize user behavior patterns into correlated parameter sets, transforming unstructured wagering history into actionable insights for value assessment.
2Measurement precision
If the system waits for users to accumulate sufficient wagering data, then predictions become more accurate, but new users with limited data are difficult to evaluate
Solution Approach 1:
The system performs preliminary correlation analysis during the user's early wagering phase and stores these patterns in correlation databases. This preliminary action enables the system to make accurate LTV predictions for new users almost immediately, without waiting for extensive historical data accumulation.
Solution Approach 2:
The system creates virtual copies of user behavior patterns through correlation databases, which replicate the statistical relationships observed in experienced users. These copied patterns allow the system to infer LTV for new users based on their wagering behavior matching established patterns, eliminating the need for lengthy data collection periods.
3Adaptability or versatility
If the system analyzes detailed user wagering parameters, then user segmentation and cohort grouping improve, but the complexity of data processing increases
Solution Approach 1:
The system segments user behavior into distinct correlation categories (e.g., wagering frequency, bet size patterns, timing patterns) and stores these in separate correlation databases. This segmentation allows the system to process and analyze user parameters in manageable chunks, reducing overall processing complexity while enabling sophisticated cohort grouping.
Solution Approach 2:
The system performs preliminary organization of user parameters into correlated groups during data collection and stores these pre-processed relationships in correlation databases. This preliminary action eliminates the need for complex real-time analysis, reducing processing complexity while maintaining advanced user segmentation capabilities.
Data Source
AI summary
The present disclosure provides a method of determining a user's long-term value to a wagering network and identifying new users similar to the users that provide long-term value to the wagering network. This method determines a user's long-term value and the user's engagement with a wagering network and places the users into cohorts. The method also provides finding correlations with the users' data and then correlating the data of new users and comparing the correlation coefficients of the new users with the older users to group the new users into the cohorts similar to the older users to predict their long-term value to the wagering network.