Reputation-Impacted Rate Determination via Regression
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Solution Overview
Problem
Current systems lack the ability to effectively incorporate online reputation scores into pricing decisions, failing to capture consumer willingness to pay based on product reputation, which is crucial for businesses in industries like hospitality, airlines, and rental cars.
Innovation Solution
A system that generates reputation-impacted rates by analyzing historical reputation and rate information using a multiple linear regression model, combining reputation indices with demand and rate indices to determine optimal pricing, smoothing out noise and accounting for lead times and special events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If online reputation systems are used to generate and publish ratings and reviews, then consumers can make better purchasing decisions, but businesses cannot effectively incorporate these reputation scores into pricing decisions
Solution Approach 1:
The patent segments the pricing decision process into distinct components: a reputation index module that processes reputation data separately, a rate index module that handles historical rate information, and a price determination module that combines them. This segmentation allows reputation information to be systematically integrated into pricing without disrupting existing pricing mechanisms.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a mediator between reputation data and pricing decisions. The reputation index and rate index serve as intermediate representations that translate raw reputation scores and historical rates into comparable metrics, enabling their integration through the multiple linear regression model.
2Measurement precision
If reputation scores are incorporated into pricing models, then consumer willingness to pay can be captured, but the complexity of the pricing system increases
Solution Approach 1:
The patent creates a universal pricing framework using multiple linear regression that can handle multiple input variables (reputation index, rate index, demand index, indicator variables) through a single unified model. This multi-functional approach allows the system to process diverse data types while maintaining a consistent pricing output structure.
Solution Approach 2:
The patent transforms complex reputation data into a standardized reputation index parameter, and similarly transforms historical rates into a rate index parameter. These parameter transformations convert unstructured or semi-structured data into quantifiable metrics that can be directly used in the pricing model, reducing complexity while maintaining precision.
3Measurement precision
If historical rate information and reputation information are analyzed using multiple linear regression, then reputation-impacted rates can be determined, but the computational processing requirements increase
Solution Approach 1:
The patent performs preliminary processing of raw data into standardized indices before the main regression analysis. The reputation index module pre-processes reputation data, and the rate index module pre-processes historical rate information. This preliminary action reduces the complexity of the main computational task by providing cleaned, normalized input data.
Solution Approach 2:
The patent implements a modular processing architecture where only necessary components are activated based on available data. The system can selectively process reputation information, rate information, demand information, or indicator variables depending on what data is available, avoiding unnecessary computational overhead while maintaining accuracy.
Data Source
AI summary
Various embodiments may be generally directed to techniques and an apparatus to generate a plurality of rate indices from the historical rate information for one or more products, each of the rate indices associated with a different lead time, and determine a rate index from the plurality of rate indices associated with an optimal lead time based on a maximum correlation between the rate index and a reputation index, the reputation index based on the historical reputation information for the one or more products. In addition, a multiple linear regression model comprising one or more parameters may be generated using the rate index, the reputation index, and one or more indicator values, the multiple linear regression model may be used to determine a reputation impacted rate for a product.