Automated Market Segment Discovery via Visitor Record Tree Analysis
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Solution Overview
Problem
Current methods for analyzing and optimizing retail information distribution struggle to efficiently identify market segments due to the complexity of dynamic web content and large data sets, making it difficult to manually or automatically discover targeted marketing segments.
Innovation Solution
An automated market-segment-discovery system processes visitor records to generate a segment-discovery tree, producing candidate market-segment-defining rules that can be used for targeted marketing and information distribution by applying various techniques and metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to identify market segments, then analysts can examine and understand data in detail, but it is practically impossible to propose and test market-segment definitions due to the large number of possible segments and attributes
Solution Approach 1:
The patent segments the vast market data into hierarchical groups and clusters using automated clustering algorithms. The system divides the market space into manageable segments based on multiple attributes (purchaser demographics, web interaction behavior, information distribution responses), allowing efficient exploration of numerous market segments without manual analysis of each individual segment definition.
Solution Approach 2:
The patent introduces automated statistical analysis software and clustering algorithms as intermediaries between the raw market data and the analysts. These tools automatically process visitor records, generate candidate market segments, and identify patterns, enabling analysts to work with pre-processed insights rather than raw data, thus dramatically improving productivity while maintaining analytical precision.
2Productivity
If automated cluster-detection methodologies are employed to discover market segments, then productivity increases, but it becomes difficult to discover meaningful segments due to wide variation in frequency of occurrence of attribute values in large data sets
Solution Approach 1:
The patent dynamically adjusts analysis parameters and weighting factors based on the characteristics of the data being analyzed. The system modifies clustering parameters, attribute weightings, and segment criteria according to the frequency distributions and variations observed in different data sets, allowing automated methodologies to adapt to varying data conditions and maintain identification accuracy across diverse market scenarios.
Solution Approach 2:
The patent implements dynamic, adaptive clustering algorithms that adjust their behavior based on the data characteristics. The system dynamically determines segment granularity, attribute importance, and clustering thresholds based on the specific data set being analyzed, enabling the automated methodology to handle wide variations in attribute frequency while maintaining meaningful segment discovery.
3Adaptability or versatility
If web-content information is made highly dynamic to respond to user behavior, then adaptability improves, but analysts cannot determine which particular content a visitor may have seen, complicating market-segment discovery
Solution Approach 1:
The patent implements preliminary tracking and logging mechanisms that record visitor interactions with web content before the content dynamically changes. The system pre-captures information about which content visitors view, for how long, and in what sequence, storing this data in visitor records before the highly dynamic web environment alters or removes the content, thus preserving the information needed for market-segment discovery.
Solution Approach 2:
The patent creates copies or representations of the dynamic web content and visitor interactions in a stable, analyzable format. The system generates detailed logs and data structures that replicate the essential information about content viewing, preserving this information in a form that can be analyzed even as the actual web content continues to change dynamically in real-time.
Data Source
AI summary
The current document is directed to automated market-segment-discovery methods and systems that may be incorporated within, or used in combination with, various types of analysis and optimization automated systems for automated discovery of market segments for subsequent use in targeted marketing and information distribution. In one implementation, a log of visitor records collected by an analysis and/or optimization system is processed to generate a segment-discovery tree. Construction of the segment-discovery tree produces a set of candidate market-segment-defining rules. Various different techniques and metrics can be applied to produce a set of market-segment-defining rules from these candidate rules. The market-segment-defining rules can then be exported to marketing systems or subsystems to facilitate targeted marketing and information distribution.