Peer-Group Business Analysis via Market Segmentation
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Solution Overview
Problem
Current systems lack an efficient method to provide users with a comprehensive and relative comparison of business entities operating in specific markets, failing to effectively utilize curated market and performance information for peer-group analysis.
Innovation Solution
A peer-group based business information system that includes a data store for curated market information, a peer-group analyzer for selecting entities with competitive overlaps, and a performance ranker for user comparison, utilizing natural language processing and normalization of data to facilitate apples-to-apples comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If comprehensive market information is collected for all business entities, then the completeness of market analysis is improved, but the complexity of data processing and system structure increases
Solution Approach 1:
The system segments business entities into peer-groups based on market presence and competitive overlaps. Instead of analyzing all entities uniformly, the patent divides them into manageable clusters that share common market characteristics, making comprehensive analysis feasible without overwhelming system complexity
Solution Approach 2:
The patent introduces curated market information as an intermediary layer between raw data and analysis. This curated information serves as a standardized mediator that simplifies comparison across entities while preserving comprehensive market details, resolving the contradiction between information completeness and processing complexity
2Measurement precision
If peer-group clustering is performed based on multiple market parameters, then the accuracy of competitive comparison is improved, but the computational complexity increases
Solution Approach 1:
The system changes parameters by selecting specific market presence indicators (geographic regions, product categories, customer segments) as clustering criteria. This parameter selection approach enables accurate peer-group formation based on meaningful business dimensions while avoiding unnecessary computational complexity from analyzing all possible parameters
3Measurement precision
If detailed performance metrics are tracked for all entities, then the precision of performance evaluation is improved, but the loss of time for data processing increases
Solution Approach 1:
The patent extracts only the relevant performance metrics needed for peer-group comparison rather than tracking all possible metrics for all entities. This extraction approach maintains evaluation precision for the selected metrics while significantly reducing data processing time by eliminating unnecessary data collection and analysis
Data Source
AI summary
Peer-group based business information can include: generating a set of curated market information for each of a set of business entities such that each set of curated market information enables a determination of one or more markets in which the corresponding business entity currently operates; selecting a subset of the business entities for inclusion in a peer-group cluster in response to the curated market information such that the business entities specified in the peer-group cluster share at least one competitive overlap in one or more of the markets; and providing a user with a relative comparison of the business entities specified in the peer-group cluster.