Micro-segmentation System Parallel Processing User Data
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Solution Overview
Problem
Current methods struggle to accurately classify and segment large user populations into micro-segments for targeted content delivery, as existing technologies lack precision and industry expertise, leading to underutilization of consumer data and reduced marketer efficacy.
Innovation Solution
A computer program product and process that dispatches user data and micro-segment definitions across a network, utilizing parsers and compilers to parse and compile micro-segment condition rules, and scalable evaluation engines to determine user matches and assign scores, enabling precise micro-segmentation and 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 process is simpler to implement, but the precision and accuracy of micro-segment identification deteriorates
Solution Approach 1:
The system segments the large user population into micro-segments based on multiple attributes including demographics, psychographics, and behavioral data. The segmentation process divides consumers into fine-grained groups by recognizing and predicting minute consumer spending and behavioral patterns, enabling precise identification of specific market segments within the larger population.
Solution Approach 2:
The system changes multiple parameters simultaneously to achieve accurate micro-segmentation: it analyzes demographic parameters (age, gender, location), psychographic parameters (interests, values, lifestyle), and behavioral parameters (purchase history, browsing patterns, engagement metrics). By evaluating multiple parameters together, the system achieves high precision in micro-segment identification.
2Measurement precision
If more consumer data is collected to improve segmentation accuracy, then the precision of micro-segments improves, but the difficulty of processing and analyzing the data increases
Solution Approach 1:
The system introduces an intermediary processing layer that includes data cleaning, normalization, and feature extraction components. This intermediary layer transforms raw consumer data from multiple sources into structured, standardized formats that can be efficiently analyzed. The intermediary processing resolves the complexity of handling diverse data sources while maintaining high segmentation accuracy.
Solution Approach 2:
The data processing pipeline is segmented into distinct stages: data collection from multiple sources, data cleaning and validation, feature extraction and transformation, segmentation model application, and result aggregation. This segmented approach to data processing makes the complex task of analyzing large volumes of consumer data more manageable and efficient.
3Measurement precision
If advanced algorithms and AI techniques are used to create micro-segments, then the precision of consumer classification improves, but the computational resources and time required increase
Solution Approach 1:
The system performs preliminary actions by pre-processing consumer data, pre-computing feature vectors, and pre-segmenting the population into broader groups before applying detailed micro-segmentation algorithms. This preliminary segmentation reduces the computational burden of advanced AI techniques while maintaining classification accuracy, as the algorithms work with pre-organized data rather than raw information.
Solution Approach 2:
The system implements periodic action by updating and re-training segmentation models at scheduled intervals rather than continuously processing all data. The model is re-trained periodically with new consumer data to maintain accuracy, while between updates it efficiently classifies new consumers using the established model, reducing overall computational time and resource consumption.
Data Source
AI summary
User data and a plurality of micro-segment definitions such that each micro-segment definition in the plurality of micro-segment definitions corresponds to one or more offers in an offer provider campaign are received. Further, a dispatcher dispatches a first subset of the user data and a first subset of the plurality of the micro-segment definitions to a first node in a network. In addition, the dispatcher dispatches a second subset of the user data and a second subset of the plurality of the micro-segment definitions to a second node in the network. Parsing and compiling are performed at each node. Further, parallel processing is performed at a scalable evaluation engine at each node to apply micro-segment condition rules to user data to determine matches to micro-segments. Computation of micro-segments occurs in parallel and resulting micro-segment assignments are collected, filtered to remove duplicates, then ranked to produce a final set of micro-segments that can be used to find offers.