Cluster-Based Cross-Elasticity Estimation for Retail Demand Forecasting
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Solution Overview
Problem
Retail forecasting systems struggle to accurately estimate cross-elasticity, which is essential for determining the full costs and benefits of price changes, due to difficulties in identifying product groups, data limitations, smaller magnitude of cross-price effects, collinearity, and reconciling elasticity estimates with economic constraints.
Innovation Solution
The system generates cluster-based price-elasticity values by clustering products based on product descriptions and preliminary elasticity values, using natural language processing and cosine similarity algorithms, and then refines these values by applying elasticity estimation regression algorithms and modifying them based on demand attributes and collinearity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional models attempt to estimate cross-elasticity values for many product pairs, then more comprehensive cross-elasticity coverage is achieved, but measurement precision deteriorates due to erroneously identifying insignificant cross-elasticity estimates as significant
Solution Approach 1:
The patent segments the estimation process into two distinct stages: (1) screening phase using a computationally efficient model to identify potential cross-elasticity relationships, and (2) refinement phase using a more accurate but computationally intensive model only for the subset of product pairs identified in stage 1. This segmentation allows comprehensive coverage while maintaining precision by applying rigorous estimation only where needed.
Solution Approach 2:
The patent performs preliminary filtering of product pairs using a fast approximation model before applying the more accurate estimation model. This preliminary action reduces the search space and eliminates obviously insignificant relationships early, preventing false positives from propagating through the full estimation process.
2Reliability
If the system estimates cross-elasticity for all product pairs, then complete price change impact analysis is achieved, but computational complexity becomes infeasible due to thousands of potential cross-elasticity values
Solution Approach 1:
The patent divides the computational task into manageable segments: first computing own-price elasticity for individual products, then using these results as inputs for cross-elasticity estimation between product pairs. This segmentation reduces the computational burden from estimating all cross-elasticity values simultaneously to a sequential two-pass approach.
Solution Approach 2:
The patent implements a two-stage estimation process where the first stage uses a simplified model to identify promising product pairs, and the second stage applies full estimation only to this reduced subset. This partial action approach estimates cross-elasticity for fewer product pairs with high confidence, achieving reliable price change impact analysis without the prohibitive cost of exhaustive estimation.
3Ease of operation
If retailers promote items without cross-elasticity estimates, then promotion implementation is simple, but the ability to determine full costs and benefits of price changes deteriorates
Solution Approach 1:
The patent introduces an intermediary forecasting system that automatically computes cross-elasticity estimates and integrates them with promotion planning tools. This intermediary layer handles the complex computational tasks, allowing retailers to access comprehensive price change impact information without directly managing the computational complexity themselves.
Data Source
AI summary
Techniques for generating a retail forecasting model from product-cluster-based estimated elasticity values to forecast the effects of price changes on the demand for a set of products are disclosed. A system generates cluster-based price-elasticity values for a set of products by applying a set of regressive elasticity-estimation algorithms to a set of product data and clustering products based on product descriptions and estimated price-elasticity values. The system uses the cluster-based price-elasticity values for the products to generate the retail forecasting model.