Optimal Unit Product Value Computation for Low-Data Products
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Solution Overview
Problem
Companies face challenges in determining an optimal range of unit product value for products with insufficient product consumption data, as traditional methods rely on demand curves that cannot be generated with insufficient sales data.
Innovation Solution
A method and system that cluster products with similar names using natural language processing and K-means clustering, mapping products with insufficient data to those with sufficient data based on distance and unit price variation, and using demand curve data from the latter to compute an optimal price range for the former.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demand curve methods are used to determine optimal product value, then the method is simple and direct, but it cannot be applied to products with insufficient consumption data (less than three data points)
Solution Approach 1:
The patent introduces an intermediary mechanism (clustering algorithm) that maps products with insufficient data to products with sufficient data through cluster identification. This mediator enables the transfer of demand curve information from well-documented products to under-documented products, resolving the contradiction between measurement precision and adaptability.
Solution Approach 2:
The patent creates a copy of demand curve information from products with sufficient data and applies it to products with insufficient data through clustering. By copying pricing patterns from similar products within the same cluster, the system achieves accurate optimal price determination for products that would otherwise lack sufficient data for traditional analysis.
2Measurement precision
If products are clustered and mapped to transfer data, then optimal price can be determined for products with insufficient data, but the system complexity increases
Solution Approach 1:
The patent implements a universal clustering framework that handles multiple product types and data scenarios through a single unified algorithm. The cluster identification and mapping mechanism serves multiple functions: grouping similar products, transferring demand curve data, and determining optimal prices, thereby managing system complexity through multi-functionality.
Solution Approach 2:
The patent transforms the problem from individual product analysis to cluster-based analysis by changing the fundamental parameter from single-product data points to multi-product cluster patterns. This parameter change simplifies the overall system complexity by leveraging aggregated cluster information rather than requiring complex individual product modeling for each under-documented product.
3Quantity of substance
If cluster-based mapping is used to determine optimal price, then data collection resources are saved, but the computation and clustering process requires additional processing
Solution Approach 1:
The patent performs preliminary clustering and mapping operations that create reusable demand curve templates for product clusters. By pre-establishing these cluster relationships and demand patterns, the system eliminates the need for additional data collection for individual products, saving resources while the initial computation investment pays off through reusable templates.
Solution Approach 2:
The patent merges computation efforts by processing multiple products simultaneously through cluster-based analysis rather than individually. By combining products into clusters and performing unified demand curve analysis at the cluster level, the system reduces total computation time compared to processing each product separately, while also eliminating the need for additional data collection resources.
Data Source
AI summary
Techniques for computing optimal range of unit product value for products having insufficient product consumption data are described. In an example, a plurality of identifiers corresponding to names of a plurality of products may be determined. Based on the plurality of identifiers, two or more clusters may be generated. Within each cluster, a first identifier associated with a product having insufficient product consumption data and a second identifier associated with a product having sufficient product consumption data is determined. Further, based on demand curve data for the product associated with the second identifier, an optimal range of a unit product value for the product associated with the first identifier is computed.