Machine Learning Price Optimization for Retail Fuel
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Solution Overview
Problem
Existing price optimization systems for retail fuel stores are not effective due to frequent price changes and inaccuracies in predicting fuel volume sales, lacking flexibility in using a wide range of data inputs, and being non-extensible to different operational types.
Innovation Solution
A price optimization system utilizing machine learning algorithms and processing in a distributed computing environment, capable of quick autonomous updates, which processes various data feeds to generate accurate fuel pricing based on multiple variables and factors, including competitor reactions, through a cloud-based computing service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional price optimization systems are used for retail fuel stores, then the system structure is simple, but the system cannot handle frequent price changes and has low prediction accuracy for fuel volume sales
Solution Approach 1:
The patent replaces traditional mechanical pricing systems with machine learning algorithms and artificial intelligence to process price optimization. The system uses computational models including neural networks, decision trees, and regression analysis to predict fuel volume sales and determine optimal prices, substituting manual or rule-based pricing mechanisms with advanced computational intelligence.
Solution Approach 2:
The patent employs a composite analytical approach by integrating multiple machine learning models (neural networks, decision trees, regression analysis) and combining various data sources (historical sales data, competitor pricing, market conditions, fuel costs) to create a comprehensive price optimization system that leverages the strengths of different analytical methods.
2Adaptability or versatility
If traditional price optimization systems are used, then the data processing capability is limited, but the system cannot flexibly use a wide range of data inputs
Solution Approach 1:
The patent creates a universal price optimization platform that can process multiple types of data inputs including historical sales data, competitor pricing information, market conditions, fuel costs, and promotional data. The machine learning framework is designed to accommodate various data sources and can be applied across different retail fuel store contexts, making the system highly adaptable and versatile.
Solution Approach 2:
The patent introduces machine learning algorithms as intermediaries that bridge raw diverse data inputs and actionable pricing decisions. These algorithms serve as mediators that process, analyze, and transform multiple data sources into optimized price recommendations, enabling flexible data integration without requiring complex direct processing of each data type.
3Productivity
If manual price optimization methods are used, then the system is easy to understand, but the system cannot provide quick autonomous updates throughout the day
Solution Approach 1:
The patent implements an autonomous price optimization system that automatically collects data, processes information through machine learning models, and generates pricing recommendations without human intervention. The system continuously monitors market conditions and competitor pricing, performing self-updates throughout the day based on real-time data inputs, eliminating the need for manual price adjustments.
Solution Approach 2:
The patent enables continuous price optimization by implementing real-time data processing and continuous machine learning model execution. The system operates continuously throughout the day, constantly analyzing new data inputs and updating pricing recommendations, ensuring uninterrupted optimal pricing rather than periodic or batch-based updates.
4Measurement precision
If traditional prediction models are used, then the computational requirements are low, but the system has inaccuracy in predicted fuel volume sales
Solution Approach 1:
The patent replaces simple statistical prediction models with advanced machine learning algorithms including neural networks, decision trees, and regression analysis. These computational models process large volumes of historical and real-time data to generate accurate fuel volume sales predictions, substituting low-power but inaccurate methods with high-power sophisticated analytical engines.
Data Source
AI summary
A method for price optimization for a product or service at a store using machine learning is disclosed. The method includes determining a price through a reaction model, positioning model, and a forecast model. The reaction model includes determining the probabilities of a competitors' pricing reaction due to the store's price changes. The positioning model includes determining conditional probabilities for attaining an objective and/or sub-objective based on store and competitor's data. The conditional probabilities are used for generating a price proposal for achieving the objective and/or sub-objective. The forecast model provides a forecast for factors such as volume sale using machine learning.