Graph-Based Price Optimization for Noisy Retail Demand Data
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Solution Overview
Problem
Traditional price optimization methods in retail fail to capture intricate relationships among variables influencing consumer behavior and sales dynamics, leading to suboptimal pricing decisions and missed revenue opportunities due to high signal-to-noise ratios and resource-intensive data requirements.
Innovation Solution
A graph-based machine learning approach that constructs hierarchical graphs to analyze sales patterns, identify relationships among items, and dynamically adjust prices based on real-time market dynamics, incorporating domain knowledge and comprehensive data analysis to enhance forecasting accuracy and inform pricing strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning techniques are used for price optimization, then the system can process available sales data, but the training data has a high signal-to-noise ratio causing the model to overfit to noise and perform poorly
Solution Approach 1:
The patent combines multiple data sources including sales data, product data, promotional data, and external factors (weather, holidays, events) into a unified training dataset. This merging of diverse data types increases the overall signal quality by providing multiple perspectives on demand drivers, thereby reducing the relative impact of noise in any single data source and improving model reliability.
Solution Approach 2:
The system performs preliminary data processing and feature engineering before model training, including aggregating sales data at multiple hierarchical levels (store, region, category), creating derived features (price elasticity estimates, demand patterns), and preprocessing external data sources. This preliminary action ensures that the model receives high-quality, pre-processed training data with reduced noise, improving prediction accuracy from the start.
2Ease of operation
If traditional pricing strategies based on isolated mathematical models are used, then the approach is straightforward and easy to implement, but it fails to capture intricate relationships among variables influencing consumer behavior and sales dynamics
Solution Approach 1:
The system transforms the pricing approach by changing multiple parameters simultaneously: it moves from isolated single-product models to multi-product hierarchical models, from static price elasticity to dynamic demand modeling incorporating external factors, and from manual adjustment to automated machine learning-based optimization. These parameter changes enable capture of intricate relationships while maintaining ease of operation through automation.
Solution Approach 2:
The patent introduces an intermediary machine learning system that mediates between raw data and pricing decisions. This intermediary layer processes complex relationships among multiple variables (product associations, promotional interactions, external factors) and translates them into actionable pricing recommendations, making the complex analysis transparent and easy to operate through standardized interfaces.
3Productivity
If rule-based algorithms and statistical models are used for price optimization, then some improvement over manual pricing is achieved, but the solutions struggle to adapt to evolving market conditions and changing consumer preferences in real-time
Solution Approach 1:
The system implements dynamic pricing by continuously updating the machine learning model with new sales data and external factors. The model adapts to changing market conditions by retraining on recent data, incorporating real-time promotional information, and adjusting to evolving consumer preferences through ongoing analysis of sales patterns and external events, thereby maintaining high productivity and adaptability simultaneously.
Data Source
AI summary
Methods for configuring learning model for price optimization. Item ontologies defining categories, properties, and relationships between multiple items, and sales data for items are used to aggregate sales data for subsets of the items and create sales vectors for the items, A price optimization target for items can be specified as a function of a price vector and the sales vector. A learning model is trained based on the price vectors and the sales vectors to optimize the price of each specific item in accordance with the price optimization target.