Micro-segment Definition Parsing for User Data Matching
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Solution Overview
Problem
Current technologies face challenges in accurately classifying and segmenting large user populations into micro-segments for targeted content delivery, as consumers increasingly filter content and marketing messages, leading to underutilization of collected data due to lack of industry expertise and technological limitations.
Innovation Solution
A computer program product and process that utilizes a sequential evaluation engine to parse and compile micro-segment definitions into executable instructions, applying condition rules to user data to determine matches and assign scores, enabling precise user segmentation and ranking within micro-segments for targeted content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional segmentation methods are used to classify large user populations, then the system can process data with current technology, but the segmentation accuracy and precision are insufficient due to technological limitations and lack of industry expertise
Solution Approach 1:
The patent applies segmentation by dividing the user population into micro-segments based on multiple attributes and behaviors. The system segments users into fine-grained groups using a sequential evaluation engine that processes multiple segment definitions simultaneously, enabling precise classification without overwhelming system complexity through modular processing of segment criteria.
Solution Approach 2:
The system changes parameters by evaluating multiple user attributes (demographics, behaviors, preferences) simultaneously through a sequential engine. The patent transforms raw user data into segmented classifications by applying multiple conditional parameters in sequence, improving segmentation accuracy through multi-dimensional parameter evaluation rather than single-criterion classification.
2Loss of information
If more consumer data and behaviors are collected to improve segmentation, then the potential for precise micro-segmentation increases, but the data remains under-utilized due to lack of industry expertise and technological limitations
Solution Approach 1:
The system applies self-service by automatically processing and segmenting user data without requiring manual analysis. The sequential evaluation engine autonomously evaluates user attributes against multiple segment definitions, automatically assigning users to appropriate micro-segments. This automation enables full utilization of collected consumer data without requiring continuous industry expertise intervention.
Solution Approach 2:
The patent implements feedback through the sequential evaluation engine that continuously processes user data and provides segmentation results. The system evaluates user attributes against segment criteria, provides matching results, and can update segment assignments as new data becomes available, creating a feedback loop that maximizes data utilization and improves segmentation over time.
3Measurement precision
If conventional recommendation systems are used with large user populations, then the system can provide recommendations, but accurate segmentation is difficult achieving due to the scale and diversity of user data
Solution Approach 1:
The system applies preliminary action by pre-defining multiple segment criteria and rules before processing user data. The sequential evaluation engine is pre-configured with segment definitions that specify attribute thresholds and matching criteria. This preliminary preparation enables efficient processing of large user populations, as the system doesn't need to analyze all possible combinations during runtime but rather evaluates against pre-established segment frameworks.
Solution Approach 2:
The patent segments the recommendation problem by creating distinct micro-segments for different user groups. Instead of treating all users uniformly, the system divides the population into fine-grained segments based on multiple attributes, enabling more accurate recommendations for each segment while maintaining processing efficiency through the sequential evaluation approach that handles segments independently.
Data Source
AI summary
User data and a plurality of micro-segment definitions are received. Each micro-segment definition in the plurality of micro-segment definitions corresponds to one or more offers in an offer provider campaign. Further, a each micro-segment definition from the plurality of micro-segment definitions is parsed into a plurality of parsed expression segments that indicate a plurality of micro-segment condition rules. The plurality of parsed expression segments are compiled into an executable object that indicates a plurality of instructions to determine if the user data matches the plurality of micro-segment definitions. Each micro-segment definition is processed to apply the plurality of micro-segment condition rules to the user data to determine a match of a user belonging to a micro-segment. Further, a score is assigned to indicate the strength of each match. In addition, each match is ranked according to the score for each match.