Behavior Isolating Prediction Model for Attrition
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Solution Overview
Problem
Traditional attrition models in the financial industry are dominated by demographic variables, leading to inaccurate predictions and misclassification, as they fail to account for other influential factors like network strength and product relationships, and often rely on arbitrary segmentation cutoffs, resulting in lost information within segments.
Innovation Solution
A method and system that isolate the effect of dominant demographic variables to predict behavior based on network strength and product relationship variables, allowing for more accurate attrition modeling by neutralizing the influence of demographics, enabling targeted marketing efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional attrition models use demographic variables as primary predictors, then the model structure is simple and easy to implement, but the prediction accuracy deteriorates due to dominant demographic effects masking other important behavioral factors
Solution Approach 1:
The patent segments the customer base into homogeneous demographic groups first, then builds separate prediction models for each segment. This allows the model to control for demographic effects while still capturing behavioral patterns specific to each demographic group, thereby improving prediction accuracy without completely abandoning the simplicity of demographic-based modeling.
Solution Approach 2:
The patent extracts and removes the dominant demographic effect from the prediction model by using demographic variables as control variables or stratification factors. This isolation technique allows the model to focus on predicting behavior based on non-demographic factors (such as transaction patterns and product relationships) while still accounting for demographic heterogeneity, thus improving prediction accuracy.
2Reliability
If traditional models apply arbitrary segmentation cutoffs to control for affluence, then the model can account for demographic effects, but information is lost within segments due to variability
Solution Approach 1:
Instead of using arbitrary cutoffs, the patent employs data-driven segmentation techniques that create homogeneous groups based on actual customer characteristics and behavior patterns. This ensures that customers within each segment are truly similar, reducing intra-segment variability and information loss while still effectively controlling for demographic effects.
Solution Approach 2:
The patent transforms the segmentation approach by changing from fixed arbitrary cutoffs to dynamic, data-driven boundaries. By using statistical methods and customer data to determine segment boundaries, the model optimizes the segmentation parameters to minimize information loss while maintaining the ability to control for demographic effects.
3Device complexity
If models focus on affluent households as the dominant demographic group, then the model structure is simplified, but the results become biased and misclassify customers regardless of their actual behavior
Solution Approach 1:
The patent divides the customer population into multiple demographic segments rather than focusing on a single dominant group. This segmentation allows the model to capture behavioral patterns across different demographic groups, reducing bias and improving prediction accuracy for all customer types while maintaining reasonable model complexity through shared modeling frameworks.
Solution Approach 2:
Instead of having demographic groups dominate the prediction and then adjusting for behavior, the patent inverts the approach by first analyzing behavioral patterns within each demographic group and then combining these insights. This ensures that actual behavior, rather than demographic labels, drives the prediction outcomes, reducing misclassification bias.
Data Source
AI summary
According to an embodiment of the present invention, a computer implemented method and system for isolating variables in a behavior prediction model comprises: identifying a plurality of groups comprising a first group of variables and a second group of variables; building a model, using a computer processor, for capturing an effect of the first group of variables in predicting behavior for customers; building a subsequent stage of the model, using a computer processor, on a second group of variables to neutralize the effect of the first group of variables; displaying results of the model wherein the results minimize the effect of the first group of variables in predicting behavior at a user interface; and identifying a response based on the results for a segment of the customers.