Segmented Coefficient Model for Offer Selection Noise Reduction
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Solution Overview
Problem
Traditional response modeling learns base behavior independently for each offer, leading to inaccurate targeting due to small variations being drowned out by statistical noise, making it difficult to distinguish between offers and effectively target 'Persuadables' among respondents.
Innovation Solution
A mathematical model with both offer-specific and common coefficients is used, where common coefficients represent shared behavior across all offers, and a two-stage update function isolates errors to improve prediction accuracy by separating the learning of Sure Things and Persuadables behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional response modeling learns base behavior independently for each offer, then the model can capture offer-specific variations, but statistical noise drowns out small variations making it difficult to distinguish between offers and effectively target Persuadables
Solution Approach 1:
The patent segments the coefficient structure into offer-specific coefficients (capturing offer variations) and common coefficients (capturing base behavior shared across all offers). This segmentation allows the system to separate signal from noise by pooling data across offers for common behavior while maintaining offer-specific adjustments, thereby improving both prediction accuracy and targeting reliability for Persuadables.
Solution Approach 2:
The patent merges the learning of common base behavior across all offers through shared common coefficients. By combining data from multiple offers to learn common patterns, the system overcomes the noise problem inherent in learning each offer independently, while still preserving offer-specific variations through separate offer-specific coefficients.
2Adaptability or versatility
If separate models are used for each offer, then offer-specific behavior can be captured, but the complexity of the system increases and statistical noise reduces measurement precision
Solution Approach 1:
The patent creates a universal model structure where common coefficients serve all offers by capturing base behavior that is shared across the entire offer portfolio. This multi-functional approach allows a single model to handle multiple offers efficiently, reducing overall system complexity while maintaining the ability to capture offer-specific variations through offer-specific coefficients when needed.
Solution Approach 2:
The model segments coefficients into two functional layers: common coefficients that provide universal baseline predictions across all offers, and offer-specific coefficients that provide targeted adjustments. This segmentation reduces complexity by sharing common learning across offers while maintaining adaptability through offer-specific parameters only where necessary.
3Ease of manufacture
If traditional modeling is used, then implementation is simpler, but the ability to distinguish Persuadables from other respondent types deteriorates due to statistical noise
Solution Approach 1:
The patent implements a segmented coefficient model that separates common base behavior from offer-specific variations. This segmentation improves respondent classification precision by reducing statistical noise through pooled learning of common patterns, while maintaining relatively simple implementation through a structured two-stage training process that builds on traditional modeling approaches.
Solution Approach 2:
The common coefficients act as an intermediary layer between the raw data and offer-specific predictions. This intermediary structure filters out statistical noise by aggregating information across all offers before applying offer-specific adjustments, thereby improving classification precision without significantly complicating the implementation through a systematic two-stage training approach.
Data Source
AI summary
Methods and systems predict a reward resulting from serving one of a set of offers to one or more potential respondents, for example via a webpage, using one or more processors to calculate a predicted reward according to a model. The model may include one or more variables characterizing the respondent, a set of offer-specific coefficients comprising at least one coefficient for each of said one or more variables for each offer, and a set of common coefficients comprising a separate coefficient for each of said one or more variables, each coefficient of said set of common coefficients being common to all of said offers. The model may be trained in a two stage update process including a first update operation updating at least one common coefficient and a second update operation updating at least one offer-specific coefficient.