Product Assortment Segmentation Using Multiple Hierarchies
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Solution Overview
Problem
Current product assortment methodologies rely on single product hierarchy trees, introducing errors and inflexibility in analyzing cross-category impacts, leading to inefficient and resource-intensive decision-making processes for retailers.
Innovation Solution
Utilize multiple product hierarchies representing different views of product groupings to generate a single set of coefficients using linear regression methods, providing a hierarchy-independent analysis of cross-category impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single product hierarchy tree is used for product assortment analysis, then the analysis structure is simple and easy to implement, but the measurement precision and reliability of cross-category impact analysis deteriorates due to introduced errors and inflexibility
Solution Approach 1:
The patent divides the product analysis into multiple separate hierarchy trees, each representing a different category or viewpoint. Instead of using one comprehensive hierarchy that mixes all products, the system segments products into multiple hierarchies (e.g., brand hierarchy, category hierarchy, price point hierarchy) and analyzes each separately, then combines the results. This segmentation eliminates the errors introduced by forcing all products into a single rigid hierarchy structure.
2Ease of manufacture
If a single product hierarchy tree is used, then the implementation is straightforward, but the adaptability and flexibility of the analysis deteriorates
Solution Approach 1:
The patent creates a universal analysis framework that can work with multiple different hierarchy types. The system is designed to accept various hierarchy structures (brand-based, category-based, price-based, etc.) and process them through the same analytical engine. This multi-functionality allows the system to adapt to different analysis needs and perspectives without requiring separate implementation approaches for each type of hierarchy.
Solution Approach 2:
The system dynamically selects and combines different hierarchy trees based on the specific analysis requirements. Rather than being fixed to a single hierarchy structure, the framework can adaptively choose which hierarchies to use and how to weight their contributions to the final analysis, providing flexibility while maintaining ease of implementation through automated selection processes.
3Measurement precision
If multiple product hierarchies are used for analysis, then the measurement precision and debiasing of cross-category impacts is improved, but the device complexity and computational resources required increase
Solution Approach 1:
The patent introduces an intermediary layer that standardizes and harmonizes the outputs from multiple different hierarchy trees. This intermediary component translates results from various hierarchy structures into a common format and coordinate system, making them comparable and combinable. The intermediary layer manages the complexity of integrating multiple hierarchies by providing a unified interface that masks the underlying complexity from the user.
Solution Approach 2:
The system merges results from multiple hierarchy trees through a combination process that weights and integrates the different analyses. Rather than maintaining separate independent analyses, the framework combines the insights from multiple hierarchies to produce a unified product impact assessment, reducing the effective complexity by synthesizing results into a single comprehensive view.
4Reliability
If multiple product hierarchies are used, then the reliability and debiasing of analysis is improved, but the productivity and efficiency of decision-making process deteriorates due to increased computational resources
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing product data into multiple hierarchy trees before the actual impact analysis is needed. The hierarchies are constructed and validated in advance, so that when analysis is required, the system can quickly query and combine pre-organized data rather than building hierarchies from scratch each time. This preliminary organization significantly improves decision-making efficiency while maintaining high reliability.
Solution Approach 2:
The system allows for parameter changes in the hierarchy construction and combination processes, enabling optimization of computational efficiency. By adjusting parameters such as the depth of hierarchy traversal, the number of hierarchies to combine, and the weighting factors, the system can balance reliability and productivity based on specific needs, achieving high reliability when necessary while maintaining efficiency for routine decisions.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for text extraction from a receipt image. An example non-transitory computer readable medium is disclosed comprising instructions that, when executed, cause a machine to at least generate a baseline product hierarchy using product information, calculate categorical impact values for products in the baseline product hierarchy, calculate an average impact value for the baseline product hierarchy using the calculated categorical impact values, calculate a first weighting factor for respective ones of the products based on a comparison between the categorical impact values and the average impact value, calculate a second weighting factor associated with respective ones of the products in the baseline product hierarchy based on sales data, and generate final weighted categorical impact values based on (a) the first weighting factors, (b) the second weighting factors and (c) the categorical impact values corresponding to the respective ones of the products.