Hierarchical Weighting of Model Parameters for Retail Demand
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Solution Overview
Problem
Economic modeling in retail environments faces challenges in accurately predicting demand for new or low-volume products due to insufficient transaction log data, leading to inaccurate model parameters and statistical noise.
Innovation Solution
A hierarchical weighting system that aggregates and combines model parameters from similar products and stores to generate preliminary parameter data for new products, using a hierarchical categorization of products and stores to determine relevant pre-existing models and weight their parameters accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If model parameters are generated from limited transaction log data for new products, then the model can be created quickly, but the model parameters become inaccurate due to statistical noise
Solution Approach 1:
The patent combines model parameters from multiple similar products (parent products, sibling products, competitor products) to create a weighted average parameter set for the new product. This merging approach allows the system to generate accurate model parameters immediately without waiting for sufficient transaction data to accumulate for the individual new product.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing model parameters for existing products in the database. When a new product is introduced, these pre-computed parameters from similar products are immediately available for aggregation and weighting, eliminating the need to wait for transaction data accumulation.
2Measurement precision
If model parameters are generated by waiting for sufficient transaction log data to accumulate, then the model parameters become accurate, but it takes weeks or months before robust parameters can be generated
Solution Approach 1:
The patent merges transaction data and model parameters from multiple similar products to achieve sufficient statistical robustness immediately. By aggregating data across parent products, sibling products, and competitor products, the system obtains accurate parameter estimates without requiring weeks or months of data accumulation for a single new product.
Solution Approach 2:
The system creates a universal parameter estimation approach that works for any new product by leveraging the hierarchical product structure. The same methodology can be applied across different product categories by selecting appropriate similar products from the database, making the solution universally applicable rather than product-specific.
3Measurement precision
If hierarchical weighting is applied to aggregate model parameters from multiple products, then the accuracy for new products improves, but the system complexity increases
Solution Approach 1:
The patent segments the parameter aggregation process into distinct hierarchical levels: identifying similar products, selecting relevant parameter sets, applying weights based on product similarity, and computing weighted averages. This segmentation breaks down the complex aggregation task into manageable steps that can be implemented systematically.
Solution Approach 2:
The system changes parameters by introducing weight factors that reflect the degree of similarity between products. Instead of simple averaging, the weighted parameter aggregation uses adjustable weights that can be modified based on product category, brand relationship, and market segment, providing flexibility without excessive complexity.
Data Source
AI summary
A computer-implemented method estimates model parameters for a product. The method includes storing transaction data from customer sales in a database. The transaction data includes a product and a store. The database includes a hierarchical categorization of the products or the stores. The method includes generating a model for each product at each store in the database. The models include model parameters. The method includes aggregating first and second sets of model parameters from a first set of products occupying a first node of the hierarchical categorization and a second set of products occupying a second node of the hierarchical categorization, weighting the first and second sets of model parameters, and storing the average of the weighted first and second sets of model parameters in the database as the model parameters for a model of a product.