Bayesian-Greedy Pricing Strategy for B2B Revenue Optimization
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Solution Overview
Problem
In business-to-business (B2B) pricing scenarios, existing technologies face challenges such as high data heterogeneity, noise accumulation, spurious correlations, and computational inefficiencies, leading to inaccurate predictive models and high costs of failure due to the uncertainty in buyer behavior and willingness to pay, necessitating a system for real-time price optimization.
Innovation Solution
The implementation of a Bayesian-greedy pricing strategy using approximate Bayesian inference to update belief parameters and optimize revenue estimation by integrating uncertainty into pricing decisions, allowing for efficient learning and adaptation in noisy data environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional pricing models are used with heterogeneous B2B data, then the system can process large amounts of data, but the models become vulnerable to noise accumulation and spurious correlations leading to inaccurate predictions
Solution Approach 1:
The patent transforms the pricing problem into a parameter estimation problem by modeling the buyer's willingness to pay as a random variable with unknown distribution. Instead of directly predicting binary outcomes, the system estimates parameters (mean and variance) of the underlying demand distribution, which provides more stable and accurate predictions while filtering out noise from heterogeneous data.
Solution Approach 2:
The patent introduces an intermediary statistical model layer between the heterogeneous data and the pricing decisions. By using probabilistic models to represent buyer behavior and inserting this intermediary layer, the system mediates between raw data and predictions, filtering out spurious correlations and noise while retaining meaningful patterns.
2Reliability
If a pricing strategy is optimized for long-term performance, then the model can learn from historical data, but the seller cannot learn quickly enough from new deals in the short term
Solution Approach 1:
The patent implements dynamic pricing by continuously updating the estimated demand distribution parameters as new deal outcomes become available. The system adapts the mean and variance parameters in real-time based on new data, allowing it to learn quickly from recent deals while maintaining the reliability of long-term patterns. This dynamic adjustment enables the system to respond to changing buyer behavior without forgetting valuable historical insights.
Solution Approach 2:
The system incorporates feedback loops where the outcomes of new deals (accepted or rejected) are fed back into the statistical model to update the demand distribution parameters. This feedback mechanism allows the model to continuously refine its predictions based on actual performance, enabling both rapid learning from new data and maintenance of long-term strategic optimization.
3Measurement precision
If the seller uses detailed negotiation data for pricing decisions, then the pricing can be optimized, but the computational complexity and processing time increase
Solution Approach 1:
The patent extracts the essential information from complex negotiation data by focusing on the binary outcome (accepted/rejected) and the price offered, rather than processing all detailed negotiation interactions. By taking out only the critical elements needed for parameter estimation, the system achieves pricing optimization with reduced computational complexity and without losing predictive accuracy.
Solution Approach 2:
The system segments the pricing problem into separate components: estimating the mean of the demand distribution and estimating the variance. This segmentation allows each component to be handled with appropriate computational methods, simplifying the overall computation while maintaining pricing optimization accuracy. The segmented approach enables efficient processing even with detailed negotiation data.
Data Source
AI summary
The present invention relates to systems and methods for optimizing bidding in a business-to-business environment. Initially the observed outcomes for n deals are received, and the belief parameters for these n deals are calculated. The Bayes-greedy price is then calculated and presented to a buyer. The buyer's response is collected and an optimal variance parameter based on the buyer's response is generated. The belief parameters for these n+1 deals are also updated. This process may be repeated for additional deals.