Breakpoint Identification in Entity Group Data Analysis
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Solution Overview
Problem
It is challenging to discern the relationship between variables and results for groups of entities, such as sales representatives, making it difficult to determine how to optimize activities to achieve desired outcomes like increased sales revenue.
Innovation Solution
A method involving a breakpoint identification device that generates an enhanced entity group dataset, performs clustering analysis, trains a machine learning model, and uses simulated entities to identify breakpoints in variables that impact results, thereby providing recommendations for improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analysis methods are used to discern relationships between variables and results, then the process is simple, but the ability to identify critical breakpoints and optimize entity performance is insufficient
Solution Approach 1:
The patent segments the analysis process into distinct components: data collection module, clustering analysis module, breakpoint identification module, and recommendation generation module. Each module handles a specific aspect of the complex analysis, making the overall system more manageable and effective at identifying breakpoints with high precision.
Solution Approach 2:
The patent introduces simulated entities as an intermediary between the training data and breakpoint analysis. These simulated entities allow the system to explore variable relationships and identify breakpoints without directly manipulating real entity data, thereby improving measurement precision while managing complexity through a virtual testing layer.
2Loss of information
If comprehensive data analysis is performed to identify all variable relationships, then the completeness of insights is improved, but the computational resources and time required increase
Solution Approach 1:
The patent performs clustering analysis and identifies important variables in advance before conducting the full breakpoint analysis. This preliminary action filters the data to focus only on the most relevant variables and entity groups, ensuring comprehensive insight into variable relationships while significantly reducing the computational time and resources needed for the complete analysis.
Solution Approach 2:
The patent applies breakpoint analysis selectively to identified important variables and specific entity clusters rather than performing exhaustive analysis on all variables. This partial action approach maintains information completeness for critical relationships while avoiding the excessive time cost of analyzing every possible variable combination.
3Measurement precision
If detailed breakpoint analysis is conducted for all entities, then the precision of performance optimization recommendations is improved, but the complexity of implementation increases
Solution Approach 1:
The patent identifies and analyzes breakpoints for specific important variables that have the greatest impact on entity performance, rather than conducting detailed analysis on all variables uniformly. This local quality approach focuses computational resources on the most critical variables, achieving high precision in performance optimization recommendations while keeping the implementation straightforward by concentrating on key leverage points.
Data Source
AI summary
Techniques described herein relate to a method for identifying breakpoints. The method may include obtaining, by a breakpoint identification device, an entity group data set corresponding to an entity group; generating, using the entity group data set, an enhanced entity group data set comprising the entity group data set and a derived data item; performing a clustering analysis using at least a portion of the enhanced entity group data set to obtain a plurality of entity clusters; training a machine learning (ML) model using the enhanced entity group data set to obtain a trained ML model; using the trained ML model and a set of simulated entities to perform a breakpoint analysis; generating a breakpoint graph based on the breakpoint analysis; and providing recommendations to an interested entity based on a breakpoint identified using the breakpoint graph.