Category-Level Planogram Optimization via Two-Model Space Efficiency
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Solution Overview
Problem
Current space modeling techniques for retailers are computationally expensive and inaccurate, relying on item-level product data, which is difficult to measure and process effectively, leading to suboptimal product placement and sales impact determination.
Innovation Solution
The implementation of a two-model approach, comprising a space efficiency model and a space optimization model, that generates sales impact values for category-level planograms based on predictors like price, weather, and holiday impact, eliminating the need for item-level data and reducing computational inefficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If item-level product data is used for space modeling, then measurement precision is improved, but device complexity and computational cost increase significantly
Solution Approach 1:
The patent extracts and aggregates item-level data into category-level summaries, retaining only the essential information needed for space modeling. This extraction process reduces computational complexity while maintaining sufficient accuracy for determining sales impact and optimizing product placement.
Solution Approach 2:
The patent changes the level of aggregation from item-level to category-level parameters. By transforming detailed item-level data into summarized category-level metrics (such as total sales, average price, inventory levels), the system reduces computational burden while preserving the core relationships needed for space optimization.
2Measurement precision
If item-level product data is processed, then measurement precision is improved, but loss of time increases due to difficult data processing
Solution Approach 1:
The patent performs preliminary aggregation of item-level data into category-level summaries before conducting space modeling analysis. This preliminary action reduces the volume of data that needs to be processed in detail, thereby reducing overall processing time while maintaining placement accuracy through the preserved category-level relationships.
Solution Approach 2:
By extracting only the essential category-level metrics from item-level data (such as total sales, average price, and inventory trends), the system eliminates the time-consuming processing of redundant item-level details while retaining the information necessary for accurate product placement decisions.
3Device complexity
If category-level data is used for space modeling, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent carefully selects and transforms category-level parameters to maintain their representativeness of item-level behavior. By using aggregated metrics that preserve key relationships (such as category-level sales trends, price elasticity, and inventory turnover), the system achieves sufficient measurement precision without the computational complexity of item-level analysis.
Solution Approach 2:
The patent segments the product catalog into meaningful categories and subcategories, allowing for differentiated analysis at appropriate levels. This segmentation enables the system to apply category-level modeling where sufficient homogeneity exists while maintaining detailed analysis for specific high-value segments, thus balancing precision and complexity.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to improve space modeling identify a category-level planogram length and category-level predictors for a category, apply a first model to transform the category-level planogram length and the category-level predictors for the category to a first-model-compatible category-level planogram length and first-model-compatible category-level predictors, generate a first sales impact value corresponding to the identified category-level planogram length for the category by using the first-model-compatible category-level planogram length and the first-model-compatible category-level predictors, generate a modified category-level planogram corresponding to a second sales impact value for the category, the modified category-level planogram generated by applying a second model to (a) the first sales impact value, (b) at least one constraint, and (c) a candidate category-level planogram of interest, and cause a spatial modification of products corresponding to the category based on the modified category-level planogram.