Market Analysis System Using Geolocation Aggregates
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Solution Overview
Problem
Existing methods struggle to accurately predict business operation outcomes due to the influence of external factors such as economic conditions, weather, and competitor activities, which can overshadow the impact of business operations on metrics.
Innovation Solution
A computer system that identifies human resource data with geolocation information, generates location aggregates across various geolocations, and matches markets based on similarities to predict changes in market dynamics, allowing for proactive business decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical models are used to correlate business operations to changes in business metrics, then prediction capability is improved, but accuracy deteriorates due to unaccounted external factors
Solution Approach 1:
The patent introduces an intermediary layer of location aggregates and matched markets that mediate between business operations and business metrics. By inserting this intermediate representation that captures geographic and contextual information, the system can account for external factors like economic conditions and weather that affect metrics but are not directly caused by business operations, thereby improving prediction accuracy.
Solution Approach 2:
The patent adds new dimensions to the traditional business operation-metric correlation by incorporating geographic location data and creating location aggregates. This dimensional expansion allows the system to consider spatial and contextual factors that traditional statistical models miss, enabling more accurate predictions that account for external influences while maintaining the correlation between business operations and metrics.
2Adaptability or versatility
If traditional statistical models are used to analyze business metrics, then analysis simplicity is maintained, but comprehensiveness deteriorates due to failure to account for multiple influencing factors
Solution Approach 1:
The patent segments the analysis into distinct components: business operations data, location data, location aggregates, and matched markets. By dividing the complex analysis task into these segmented parts, the system can comprehensively consider multiple influencing factors (economic conditions, weather, competitor activities) while managing complexity through structured organization of data and processing steps.
Solution Approach 2:
The location aggregates and matched markets framework provides universal applicability across different business contexts and geographic regions. The same underlying mechanism can analyze various business operations, metrics, and external factors across different locations, making the system versatile while maintaining a consistent analytical structure that prevents overwhelming complexity.
3Measurement precision
If business operations are analyzed in isolation, then analysis focus is maintained, but accuracy deteriorates due to omission of contextual and environmental factors
Solution Approach 1:
The patent merges business operations data with location data and contextual information to create location aggregates. By combining these data types, the system can accurately attribute changes in metrics to business operations while accounting for contextual factors like economic conditions and weather, improving measurement precision without requiring completely new analytical operations.
Solution Approach 2:
The system creates matched markets that are copies or representations of actual markets, derived from location aggregates. These matched markets serve as simplified models that capture the essential characteristics of real markets, allowing accurate analysis of metric changes while maintaining operational simplicity through the use of these representative models rather than analyzing every individual detail.
Data Source
AI summary
A method, a computer system, and a computer program product for predicting changes in market dynamics for a geographic region. A computer system identifies human resource data regarding employees of organizations. The human resource data comprises geolocation data based at least partially on a geolocation of the organization and geolocations of the employees. The computer system generates a plurality of location aggregates for different combinations of dimensions of the human resource data across a plurality of different geolocations. The computer system identifies a set of matched markets for a particular geographic region based on similarities among facts for the different combinations of dimensions among the plurality of location aggregates. The computer system identifies a predicted change in market dynamics for the particular geographic region based on a change in market dynamics for the set of matched markets. The computer system digitally presents the predicted change in market dynamics for the particular geographic region.