Vehicle Recommendation Model Using Credit and Vehicle Data
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Solution Overview
Problem
Vehicle dealers face challenges in profitability due to rising wholesale costs and declining retail prices, with manual vehicle product or service recommendations often resulting in inefficiencies and human bias, leading to potential sales losses and lengthy processing times.
Innovation Solution
Implementing a machine-learned model that evaluates user data to predict the likelihood of purchasing vehicle products or services, automating the recommendation process by determining weights for credit and vehicle data to identify profitable opportunities, and providing instant deal structuring and real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual vehicle product or service recommendations are used, then human judgment and flexibility are applied, but processing time is lengthy and human bias affects recommendation accuracy
Solution Approach 1:
The patent replaces the manual mechanical evaluation process with an automated machine-learned model that processes credit data and vehicle data to generate product recommendations. This substitution eliminates human bias and significantly reduces processing time from hours to seconds while maintaining or improving recommendation accuracy through systematic data analysis.
Solution Approach 2:
The system enables self-service by automatically generating product recommendations without requiring manual dealer intervention. The machine-learned model independently evaluates customer credit data and vehicle data to produce tailored recommendations, freeing dealers from time-consuming manual assessment while ensuring consistent, bias-free recommendations.
2Productivity
If manual evaluation processes are used, then flexibility in judgment is maintained, but dealership profitability is reduced due to inefficiencies
Solution Approach 1:
The machine-learned model serves multiple functions: it evaluates credit data, analyzes vehicle data, generates product recommendations, and provides pricing estimates all within a single integrated system. This multi-functionality improves dealership productivity across multiple tasks while the standardized approach reduces operational complexity despite the advanced technology involved.
Solution Approach 2:
The system transforms qualitative manual judgments into quantitative parameter-based evaluations by assigning weights to different data factors (credit score, vehicle type, product type, etc.). This parameterization enables automated processing that improves productivity while making the system's decision logic transparent and manageable despite its complexity.
3Productivity
If automated machine-learned models are implemented, then processing speed increases and bias is reduced, but system complexity increases
Solution Approach 1:
The patent segments the complex recommendation system into distinct modular components: credit data processing, vehicle data processing, machine-learned model evaluation, and recommendation generation. Each module handles specific data types and functions independently, which simplifies implementation and maintenance while enabling high-speed automated processing through parallel operation of these segmented components.
Data Source
AI summary
This disclosure describes systems, methods, and devices related to predictive modeling for evaluating vehicles. A device may receive a customer identifier (e.g., a user identification number, a social security number, driver license number, etc.). The device may retrieve credit data associated with the user identifier and vehicle data associated with a vehicle. The device may determine a first weight for the credit data and a second weight for the vehicle data. The device may determine, based on the first weight and the second weight, a value. The device may determine whether or not the value exceeds a profitability threshold. The device may determine a loan information associated with the customer identifier. The device may send a first indication of the product or service to a user device for presentation. The device may send a second indication of the loan information to the second device for presentation.