Vehicle Pricing Data Aggregation and Dynamic Model Adaptation
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Solution Overview
Problem
Consumers face difficulties in understanding complex vehicle pricing due to lack of transparent pricing information, with existing solutions providing single-dimensional recommended prices that do not account for various factors influencing negotiations, leading to confusion and inaccurate price interpretations.
Innovation Solution
A system and method for aggregating, analyzing, and presenting pricing data for vehicles, utilizing historical transaction data to determine pricing models that include average, good, and great price ranges, and applying data scarcity models when necessary to enhance accuracy, especially for new or exotic vehicle models with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single recommended price is provided to consumers, then the pricing information is simple and easy to understand, but it does not account for various factors influencing negotiations and leads to inaccurate price interpretations
Solution Approach 1:
The patent segments the single recommended price into multiple price categories (average price, good price, great price) based on different transaction conditions. This segmentation allows consumers to understand pricing better while maintaining accuracy by showing how prices vary under different negotiation scenarios and market conditions.
Solution Approach 2:
The patent adds dimensional complexity to pricing information by incorporating multiple factors such as transaction timing, vehicle characteristics, and market conditions. Instead of a single one-dimensional price point, the system provides a multi-dimensional price distribution that reflects real-world negotiation variability while remaining comprehensible to consumers.
2Measurement precision
If pricing data is aggregated with high specificity for particular vehicle configurations, then the pricing information becomes more accurate, but the amount of data required increases and accuracy decreases for vehicles with limited transaction history
Solution Approach 1:
The patent implements dynamic pricing models that adapt based on the availability of transaction data. For vehicles with sufficient transaction history, the system uses highly specific configuration-based pricing. For vehicles with limited data, the model dynamically adjusts to use broader categorizations and incorporates data from similar vehicles, ensuring reliable pricing estimates across all vehicle types.
Solution Approach 2:
The system changes parameters such as the level of configuration specificity and the scope of comparable vehicles based on data availability. When transaction data is scarce, the model adjusts by expanding the comparison group to include similar vehicles with different configurations, thereby maintaining pricing reliability without requiring excessive data for each specific vehicle type.
3Measurement precision
If multiple pricing models are applied to account for various negotiation factors, then the pricing analysis becomes more comprehensive and accurate, but the complexity of the system increases
Solution Approach 1:
The patent creates a universal pricing framework that integrates multiple pricing models and factors into a single cohesive system. This multi-functional approach allows the same system to handle various negotiation scenarios, vehicle types, and market conditions without requiring separate analysis tools, thereby maintaining comprehensiveness while managing system complexity through unified architecture.
Data Source
AI summary
Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.