Vehicle Recommendation System Using Machine Learning for Profitability
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Solution Overview
Problem
Current methods for recommending vehicle products and services to customers are inefficient and prone to human bias, often failing to account for factors that influence purchasing decisions, leading to potential sales losses due to inappropriate product or service offerings and lengthy processing times.
Innovation Solution
A computer-based system utilizing machine learning models to evaluate customer data and vehicle information, identifying causal factors influencing purchasing decisions and automating the recommendation of profitable vehicle products, services, and loan terms, thereby reducing human bias and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual methods are used to recommend vehicle products and services, then human judgment and flexibility are maintained, but the process is time-consuming and prone to human bias
Solution Approach 1:
The patent replaces the manual mechanical process of human analysis with an automated computer-based system that uses machine learning models. The system automatically retrieves customer data, evaluates purchasing behavior patterns, and generates product recommendations without human intervention, thereby eliminating human bias and significantly reducing processing time while maintaining or improving recommendation quality.
2Measurement precision
If comprehensive customer data analysis is performed to improve recommendation accuracy, then purchasing behavior prediction improves, but system complexity increases
Solution Approach 1:
The patent segments the complex data analysis process into distinct functional modules: a data retrieval module that collects customer information from multiple sources, a machine learning evaluation module that analyzes purchasing behavior patterns, and a recommendation generation module that outputs product suggestions. This modular segmentation manages system complexity by organizing functions into separate, manageable components while maintaining comprehensive data analysis capabilities.
3Productivity
If traditional sales approaches are used to maximize dealership profitability, then potential sales may be lost due to inappropriate product offerings, but automated systems may lack nuanced understanding of customer needs
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning models are trained on historical customer data and purchasing outcomes. The system continuously learns from actual customer responses and purchasing decisions, refining its understanding of customer needs and preferences. This feedback loop enables the automated system to improve its adaptability over time while maintaining high productivity and profitability through data-driven recommendations.
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. The system may determine financial data associated with the customer identifier and vehicle data associated with a vehicle. The system may determine loan information associated with the customer identifier and the vehicle. The system may determine a product or service associated with the vehicle. The system may determine a first value indicative of a probability that the customer will purchase the vehicle and the product or service based on the loan information. The system may determine a second value indicative of a profitability of a purchase of the vehicle and the product or service based on the loan information. The system may determine respective indications of the vehicle, the product or service, and the loan information to a user device for presentation.