Propensity Prediction Engine for Customer Churn Analysis
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Solution Overview
Problem
The increasing volume of data makes it difficult for entities to quickly identify relevant features and predict future actions, as existing methods lack efficiency in data processing and analysis.
Innovation Solution
A computer system that accesses multiple data sources, integrates information, generates features, and processes them to determine the propensity of an entity to take a specified action, such as churn, using a trained model and user interfaces for outputting propensity scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entities process and analyze large volumes of data to identify patterns and predict future actions, then prediction accuracy improves, but data processing time and computational complexity increase
Solution Approach 1:
The patent segments the large volume of data into structured records with specific fields (entity information, transaction history, behavioral patterns). Each record is processed independently through the trained model, allowing parallel processing and reducing overall computation time while maintaining prediction accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-training the machine learning model offline using historical data. This pre-processing creates a ready-to-use propensity prediction engine that can quickly evaluate new entities without requiring complex real-time computations, thus reducing data processing time while maintaining high prediction accuracy.
2Adaptability or versatility
If entities collect and store vast amounts of data to enable detailed analysis, then analytical capability improves, but data management complexity increases
Solution Approach 1:
The patent structures vast amounts of data into segmented records with standardized fields (entity identifiers, transaction data, behavioral attributes). This segmentation creates a manageable data architecture where each record can be independently processed by the propensity model, reducing data management complexity while preserving comprehensive analytical capability.
Solution Approach 2:
The trained machine learning model acts as an intermediary between the raw data storage system and the prediction output. It receives structured records, performs complex pattern recognition internally, and outputs simplified propensity scores. This intermediary layer shields users from data management complexity while enabling sophisticated analytical capabilities.
3Measurement precision
If entities use complex data analysis methods to predict future actions, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent uses a trained machine learning model that copies successful prediction patterns learned from historical data. Instead of implementing complex analysis algorithms from scratch, the system replicates effective prediction logic through the trained model, maintaining high prediction accuracy while reducing system complexity through proven computational approaches.
Solution Approach 2:
The system transforms complex multi-dimensional data into a simplified propensity score parameter. The trained model processes numerous input features (transaction history, behavioral patterns, demographic data) and converts them into a single predictive output parameter (propensity score). This parameter transformation maintains prediction accuracy while reducing system complexity by focusing on the most influential factors.
Data Source
AI summary
Systems and methods are disclosed for determining a propensity of an entity to take a specified action. In accordance with one implementation, a method is provided for determining the propensity. The method includes, for example, accessing one or more data sources, the one or more data sources including information associated with the entity, forming a record associated with the entity by integrating the information from the one or more data sources, generating, based on the record, one or more features associated with the entity, processing the one or more features to determine the propensity of the entity to take the specified action, and outputting the propensity.