Dynamic B2B Price Modeling with Nash Equilibrium Calibration
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Solution Overview
Problem
Business-to-business (B2B) markets face challenges in price optimization due to data scarcity, poor customer segmentation, and the need for reliable price control and management systems, which are not effectively addressed by existing solutions that rely on data-rich environments and homogeneous customer behavior.
Innovation Solution
The development of systems and methods for continuous learning and calibration in B2B price modeling, utilizing sales data, deal history, competitive behavior, and Nash equilibrium computations to generate optimized prices that can be updated and calibrated dynamically, incorporating user input and market segmentation for effective price control and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical price optimization approaches are used in B2B markets, then price optimization can be achieved in data-rich environments, but the approach fails in B2B markets due to data scarcity and poor customer segmentation
Solution Approach 1:
The patent segments customers into distinct categories (e.g., strategic customers, tactical customers, strategic partners) based on purchase behavior patterns rather than traditional demographic segmentation. This allows the system to apply different pricing strategies to different customer segments, improving optimization accuracy despite limited overall data availability.
Solution Approach 2:
The system dynamically adjusts pricing strategies based on real-time customer behavior patterns and market conditions. The pricing model continuously learns from new data and adapts to changing customer preferences, allowing effective price optimization even in data-poor environments by leveraging the most recent and relevant information.
2Measurement precision
If sizable price changes are used to model customer behavior in B2B markets, then customer behavior can be captured, but large customers may be driven away with significant volume loss
Solution Approach 1:
Instead of using large price changes to model customer behavior, the patent employs subtle parameter adjustments and incremental pricing modifications. This allows the system to capture customer behavior patterns through minor price variations that do not trigger significant customer reactions or volume losses, maintaining both measurement accuracy and sales productivity.
Solution Approach 2:
The system implements continuous feedback loops where pricing decisions are monitored and adjusted based on actual customer responses. This allows the system to learn customer behavior patterns through observation of real-world outcomes rather than relying on large experimental price changes, thereby avoiding the risk of driving away large customers while still achieving accurate behavior modeling.
3Adaptability or versatility
If highly customized optimization solutions are implemented in B2B markets, then pricing can be tailored to unique situations, but significant consulting effort and maintenance cost are required
Solution Approach 1:
The patent develops a universal pricing framework that can be applied across different B2B market segments and industries without requiring custom customization for each specific situation. The system uses generalizable customer segmentation methodologies and pricing algorithms that adapt to different contexts through parameter adjustment rather than structural redesign, significantly reducing implementation and maintenance complexity.
Solution Approach 2:
The system incorporates automated learning mechanisms that continuously improve pricing optimization without requiring manual reconfiguration or extensive consulting intervention. The pricing model automatically adjusts to changing market conditions and customer behaviors through algorithmic learning, reducing the ongoing maintenance effort and consulting requirements associated with highly customized solutions.
Data Source
AI summary
The present invention relates to business to business market price control and management systems. More particularly, the present invention relates to systems and methods for generating price modeling and optimization modules in a business to business market setting wherein price changes are optimized to achieve desired business results.