Blending Promotion Effects for Retail Demand Forecasting
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Solution Overview
Problem
Current demand forecasting methods for retail products are either too granular and unstable, leading to inaccurate results due to insufficient data at the item/store level, or too aggregate, losing store sensitivity and accuracy for individual stores, making it difficult for retailers to manage inventory effectively.
Innovation Solution
A computerized system that combines aggregate and granular promotion effect values using regression analysis to generate a combined promotion effect value, which characterizes the sales and profitability impact of promotions at a specific item/store level, improving forecast accuracy by weighing statistical confidence values from both levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If promotion effects are estimated directly at the item/store level, then store sensitivity and granularity are improved, but data insufficiency causes estimation instability and inaccuracy
Solution Approach 1:
The patent combines aggregate-level promotion effect estimates with granular item/store-level estimates to create a blended promotion effect value. This merging allows the system to leverage the stability of aggregate data while incorporating store-specific sensitivity, resolving the contradiction between granularity and reliability through data integration at multiple levels.
2Reliability
If promotion effects are estimated at an aggregate level, then data stability and accuracy are improved, but store sensitivity is lost
Solution Approach 1:
The patent applies local quality by adjusting the promotion effect estimation to reflect local store characteristics while maintaining the stability of aggregate estimates. The system weights and combines aggregate-level results with item/store-specific factors, ensuring that each location receives a customized estimation that preserves both overall accuracy and local sensitivity.
3Measurement precision
If granular estimation is used to account for demographic and geographical differences, then store-specific accuracy is improved, but the number of item/store/week/promotion intersections increases complexity
Solution Approach 1:
The patent segments the estimation process into two distinct components: aggregate-level promotion effects and item/store-specific adjustments. This segmentation allows the system to handle the complexity of numerous item/store/week/promotion intersections by processing them in modular stages, reducing overall system complexity while maintaining granular accuracy.
Data Source
AI summary
Systems, methods, and other embodiments are disclosed that are configured to characterize an effect on sales of a retail item due to a sales promotion. In one embodiment, first sales data for the retail item is retrieved from a plurality of stores that have applied the sales promotion for the retail item. Second sales data for the retail item is retrieved from a single store that has applied the sales promotion for the retail item. A combined promotion effect value is generated based on the first sales data and the second sales data. The combined promotion effect value characterizes an effect on sales of the retail item as sold by the single store due to the sales promotion.