Business Analytics Model Generation via Indicator Segmentation
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Solution Overview
Problem
Current business analytics methods are inadequate for accurately modeling and forecasting industry health due to the difficulty in incorporating external factors, resulting in incomplete, qualitative, and labor-intensive processes that fail to provide meaningful forecasts in a competitive and complex business landscape.
Innovation Solution
A system and method for generating business analytics models by selecting relevant indicators, determining strength scores, ranking, bucketizing, and comparing permutations to select the most accurate model, which includes macroeconomic, target industry, and demand industry datasets, enabling better understanding and forecasting of industry performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional expert opinion methods are used for industry outlook, then the process is simple and qualitative, but the accuracy and quantitative measurement are insufficient
Solution Approach 1:
The patent segments the industry outlook assessment into multiple quantifiable indicators across different categories (macroeconomic, industry-specific, operational). Each indicator is measured separately using statistical methods, allowing precise quantitative assessment while breaking down the complex evaluation into manageable components that can be systematically analyzed and combined.
Solution Approach 2:
The patent transforms qualitative expert opinions into quantitative parameters by establishing measurable indicators with specific data sources and calculation methods. This parameter transformation enables statistical analysis and comparison, converting subjective assessments into objective, measurable data that can be processed systematically while maintaining assessment comprehensiveness.
2Reliability
If multiple external factors are incorporated into business planning, then the forecast accuracy improves, but the process becomes more time-consuming and labor-intensive
Solution Approach 1:
The patent performs preliminary actions by pre-identifying and categorizing relevant external factors into standardized indicator groups before the actual forecasting process. Data collection templates and analysis frameworks are prepared in advance, allowing multiple factors to be incorporated systematically without requiring extensive ad-hoc analysis for each forecasting exercise, thus improving efficiency while maintaining comprehensive factor coverage.
Solution Approach 2:
The patent changes parameters by establishing standardized measurement approaches and data sources for each external factor category. This standardization transforms the incorporation of multiple factors from a custom, labor-intensive process into a repeatable systematic procedure where factors can be efficiently measured and integrated using predefined methodologies.
3Measurement precision
If comprehensive indicator analysis with strength scoring is performed, then the model selection accuracy improves, but the computational complexity and time required increase
Solution Approach 1:
The patent segments the indicator analysis into distinct evaluation dimensions (relevance, strength, reliability) with specific scoring criteria for each. This segmentation allows parallel processing of different indicator attributes and enables systematic comparison across multiple models without requiring exhaustive analysis of every possible indicator combination, thus improving selection accuracy while managing computational time.
Solution Approach 2:
The patent changes parameters by establishing quantitative scoring rules and threshold criteria for indicator evaluation. This parameter transformation converts complex qualitative assessments into standardized numerical scores that can be efficiently calculated and compared, enabling accurate model selection through systematic parameter evaluation rather than exhaustive analysis.
Data Source
AI summary
The present invention relates to systems and methods for model generation. The model is generated by selecting indicators that are relevant to the model, determining a strength score for each of the indicators, ranking the indicators by their strength scores, and bucketizing the indicators. Different permutations of the indicators are then selected for modeling in parallel. The model results are compared, and the ‘best’ model (most historically accurate) is selected for display within a report.