Dealer Network Optimization Using Spatial Normalization Metrics
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Solution Overview
Problem
Current methods for analyzing the vehicle marketplace fail to accurately predict market indicators due to variability in consumer behavioral patterns across geography, population density, and dealership distribution, leading to poor prediction accuracy and inadequate analysis of vehicle sales data.
Innovation Solution
The development of systems that determine normalization metrics accounting for geography and population density, allowing for the comparison and understanding of spatial behavioral patterns of car buyers and dealers, and providing real-time data visualization to optimize dealer network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distance brackets (e.g., 15, 30 and 60 miles radii) are used to define marketplace boundaries, then analysis can be performed at a national level, but prediction accuracy deteriorates due to variability in consumer behavioral patterns across different geographies
Solution Approach 1:
The patent segments the national marketplace into multiple geographic regions (e.g., by state, county, or custom geographic boundaries) to capture local variations in consumer behavior. Instead of using uniform distance brackets across the entire nation, the system divides the market into distinct geographic segments, each with its own characteristics and behavioral patterns, thereby improving prediction accuracy while maintaining national-level analysis capability
Solution Approach 2:
The patent applies local quality by allowing different geographic regions to have different marketplace boundary definitions based on their specific characteristics. Each region can be assigned custom boundaries and parameters that reflect local consumer behavior, population density, and dealership distribution patterns, rather than applying a one-size-fits-all approach
2Device complexity
If uniform distance brackets are applied across the entire nation, then analysis is simplified, but the variability in consumer behavioral patterns due to population density and dealership distribution is not accounted for
Solution Approach 1:
The patent introduces dynamics by making marketplace boundary definitions adaptive rather than static. The system automatically adjusts geographic boundaries and analysis parameters based on real-time data about population density, dealership locations, and consumer behavior patterns in each region, allowing the analysis methodology to evolve and adapt to changing market conditions
Solution Approach 2:
The patent changes key parameters such as distance brackets, marketplace boundaries, and analysis thresholds based on geographic region characteristics. Instead of using fixed parameters across the nation, the system modifies parameters like search radius, competition zone boundaries, and performance thresholds to match local market conditions, thereby improving prediction reliability
3Measurement precision
If geographic and population density factors are incorporated into analysis, then prediction accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and pre-calculating geographic parameters, marketplace boundaries, and normalization metrics before actual analysis runs. The system pre-computes geographic weights, population density factors, and dealership distribution models, storing them in databases for quick retrieval during analysis, thereby reducing real-time computational complexity while maintaining high prediction accuracy
Solution Approach 2:
The patent introduces intermediary components such as normalization metrics and geographic weights that mediate between raw data and final predictions. These intermediaries simplify the relationship between complex geographic factors and analysis outcomes, making the data processing more manageable while preserving the nuanced information needed for accurate predictions
Data Source
AI summary
Embodiments of vehicle data systems for use in distributed computer network are disclosed. Particular embodiments may determine and enhance vehicle data from various data sources distributed across the computer network and utilize the enhanced vehicle data in the determination of normalization metrics that account for geography and population density or spatial behavioral patterns. Embodiments may utilize these normalization metrics to determine or predict one or more metrics about participants in a network.