Geographically Normalized Dealer Performance Metrics
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Solution Overview
Problem
Conventional systems for assessing auto dealer performance inadequately account for geography and population density, leading to inaccurate predictions and noise in market indicators, as they fail to understand consumer demand and behavioral patterns varying across different geographic areas.
Innovation Solution
The development of normalization metrics that account for geography and population density to compare and understand spatial behavioral patterns of car buyers and their links to competitiveness, allowing for the computation of performance metrics like close rate and expected sales, and the use of dealer competition zones (DCZ) and customer competition zones (CCZ) to define a dealer's area of influence based on actual consumer behavior data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional Area of Influence (AOI) methods are used to assess dealer performance, then the assessment process is simplified, but the accuracy and meaningfulness of the performance metrics deteriorate due to failure to account for geographic and demographic variations
Solution Approach 1:
The patent applies local quality by creating geographically-specific normalization metrics tailored to each dealer's local market conditions. Instead of using a uniform assessment approach nationwide, the system calculates distinct normalization factors for each geographic area based on local population density, income levels, and competitive landscapes. This allows performance metrics to be evaluated against locally-relevant benchmarks rather than national averages, significantly improving measurement precision while maintaining manageable complexity through automated calculations.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting normalization metrics based on multiple varying parameters including population density, median income, competitive dealer density, and geographic characteristics. These parameters are continuously updated to reflect current market conditions, allowing the assessment system to adapt to changing local environments. The system transforms static AOI boundaries into dynamic, data-driven normalization factors that automatically adjust as market conditions evolve.
2Ease of manufacture
If arbitrary Area of Influence (AOI) boundaries are declared for each dealer, then the system implementation is straightforward, but the metrics fail to reflect actual consumer demand and behavioral patterns
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing normalization metrics for each geographic area based on historical data and established demographic parameters. These normalization factors are computed in advance using comprehensive datasets including census information, income statistics, and competitive analysis, then stored for rapid retrieval during performance assessments. This preliminary preparation eliminates the need for complex real-time calculations while ensuring that assessments are always based on up-to-date, reliable local market data.
Solution Approach 2:
The patent introduces normalization metrics as an intermediary layer between arbitrary AOI boundaries and actual consumer behavior data. These metrics serve as mediators that translate raw geographic and demographic data into meaningful performance benchmarks. The normalization factors act as a bridge, connecting simplified AOI definitions with complex local market realities, allowing the system to maintain implementation simplicity while achieving metric reliability through data-driven adjustment factors.
3Adaptability or versatility
If distance brackets are used uniformly across the nation for market analysis, then the methodology is consistent and easy to apply, but predictions become inaccurate due to ignoring local market relevance
Solution Approach 1:
The patent implements dynamics by transforming static, uniform distance brackets into dynamic, location-adaptive normalization metrics. Instead of applying the same geographic radii nationwide, the system calculates variable normalization factors that automatically adjust based on local population density, income levels, and competitive conditions. In high-density urban areas with shorter typical travel distances, the effective analysis radius is smaller, while in rural areas with longer travel patterns, the radius expands accordingly. This dynamic adaptation maintains methodological consistency through standardized calculation procedures while achieving prediction accuracy through location-specific parameter adjustment.
Data Source
AI summary
Systems, method and computer program products for presenting to a user a visualization of a vehicle dealer performance assessment based on dealer location and vehicle sales transaction data, where the assessment is based on geographically normalized metrics. Dealer location data and historical vehicle sales transaction data is collected by a in a vehicle data system from external data sources. Distances from dealers to geographical regions of interest are determined, and differences between these distances are normalized to produce competition zone indices for the geographical regions. The competition zone indices are then used to aggregate the geographical regions into different competition zones in which dealers of interest have corresponding levels of competitive advantage or disadvantage, normalized according to typical distances associated with transactions in the respective geographical regions.