Predictive Pricing Model for Vehicle Purchase Timing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Consumers face difficulties in understanding complex vehicle pricing due to lack of transparent information, with existing solutions providing single-dimensional data that does not account for various factors influencing prices, leading to confusion and biased pricing recommendations.
Innovation Solution
A system and method for predicting the best/worst time to buy a vehicle by aggregating and analyzing data from multiple sources, using predictive models to provide users with projections of discounts over time, presented in a user-friendly format such as price curves or charts, to facilitate informed purchasing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-dimensional pricing data is provided, then data simplicity is improved, but information completeness deteriorates
Solution Approach 1:
The patent transforms single-dimensional pricing data into multi-dimensional analysis by incorporating time as an additional dimension. Historical pricing data is collected and analyzed across multiple time points, enabling the system to provide not just current pricing but temporal trends, best times to buy, and predictive pricing information. This dimensional expansion resolves the contradiction by maintaining data simplicity in presentation while enriching information completeness through temporal depth.
2Measurement precision
If complex negotiated transactions are analyzed in detail, then information accuracy is improved, but consumer understanding deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer that sits between complex transaction data and consumer presentation. The system collects detailed negotiated transaction data from multiple dealers and transactions, processes this complex information through analytical algorithms, and transforms it into simplified consumer-facing metrics such as 'best time to buy,' 'price sensitivity,' and 'recommended pricing.' This intermediary layer maintains high measurement precision in analysis while ensuring ease of operation in consumer understanding.
3Measurement precision
If product lifecycle timing is considered, then pricing accuracy is improved, but data complexity deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming the product lifecycle concept into quantifiable temporal parameters. Instead of qualitatively assessing product lifecycle stage, the system uses historical transaction data to identify specific time patterns, seasonal trends, and lifecycle-phase correlations. These temporal parameters are then integrated into pricing models, allowing the system to provide accurate lifecycle-aware pricing recommendations without requiring consumers to understand complex lifecycle theory.
Data Source
AI summary
In response to a user request for information on the best/worst days in an upcoming time period to buy a commodity, a vehicle data system may determine anticipated daily discounts applicable to the commodity. An example commodity may be a vehicle of a specific configuration. In one embodiment, characteristics of month, day of week, and day of month may be gathered and fed into a Best Day to Buy model to determine, for each day of the time period, a projected daily discount relative to a set price for the commodity. Additional input variables such as incentives and seasonal discounts may be included. From the computed daily discounts, the vehicle data system may determine the best day and/or the worst day to buy and report same to the user.