Consumer Segmentation via Maturation and Exogenous Curves
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Solution Overview
Problem
Existing methods for creating consumer segments focus on point-in-time demographic or behavioral data, failing to account for the dynamic changes in consumer behavior over time, which limits their effectiveness in long-term forecasting and risk assessment.
Innovation Solution
The dual-time dynamics approach decomposes historical data into age-based maturation and time-based exogenous components, using a system that generates segment maturation and exogenous curves, along with scaling parameters, to identify dynamically similar consumer groups throughout their lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard cluster or discriminant analysis is used to create consumer segments based on demographic or behavioral data, then consumer groups can be identified at a point in time, but the segments fail to account for dynamic changes in consumer behavior over time and cannot provide accurate long-term forecasting
Solution Approach 1:
The patent applies dynamics by transitioning from static, point-in-time consumer segmentation to dynamic segmentation that evolves over time. The system uses longitudinal data to create time-varying segment memberships, allowing consumer segments to adapt as consumer behavior changes. This is achieved through repeated cluster analysis on rolling time windows, ensuring segments remain relevant and accurate for long-term forecasting while capturing dynamic behavioral changes.
Solution Approach 2:
The patent adds the time dimension to traditional consumer segmentation. Instead of analyzing consumer data at a single point in time, the system incorporates temporal evolution by analyzing data across multiple time periods. This dimensional expansion allows the model to capture how consumer behavior changes over time, improving prediction accuracy while maintaining adaptability to dynamic changes.
2Productivity
If point-in-time demographic or behavioral data is used for segmentation, then the segmentation process is simple and computationally efficient, but it limits effectiveness in long-term forecasting and risk assessment
Solution Approach 1:
The patent applies partial action by using a rolling time window approach rather than analyzing the entire historical dataset at once. This allows the system to maintain computational efficiency by processing only relevant recent data while still capturing long-term trends. The rolling window method balances the need for reliable long-term forecasting with computational constraints, providing accurate predictions without requiring excessive computational resources.
3Stability of the object's composition
If traditional segmentation methods are used, then the segmentation reflects current consumer similarity, but it cannot identify groups of consumers who will be dynamically similar throughout the maturation process
Solution Approach 1:
The patent applies preliminary action by using longitudinal data to predict future segment memberships before they actually occur. The system analyzes historical behavior patterns to forecast which consumers will belong to which segments in future time periods. This allows the model to maintain stable consumer group compositions over time while preserving information about future behavior patterns, enabling accurate identification of dynamically similar consumer groups throughout the maturation process.
Data Source
AI summary
A system and method for segmenting and clustering consumer segments is disclosed. The system and method disclosed decompose micro-segments into maturation curves, exogenous curves and scaling parameters. The system and method of the present invention use these generated curves to cluster micro-segments into macro-segments for business analysis purposes.