Pricing Power and Risk Score Calibrator for B2B Data Scarcity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Business-to-business (B2B) markets face challenges in price optimization due to data scarcity, poor customer segmentation, and the need for customized solutions, which existing classical approaches cannot effectively address, leading to inefficiencies and high consulting costs.
Innovation Solution
A system for calibrating Pricing Power and Risk scores is introduced, using a combination of quantitative and qualitative factors to provide negotiation guidance and price allocation data, reducing the reliance on rich transaction data and minimizing the need for extensive consulting, through components like segment pricing power and risk reconcilers, aggregators, and calibrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical price optimization approaches are used in B2B markets, then price management can be performed, but the effectiveness is poor due to data scarcity and lack of transaction history
Solution Approach 1:
The patent introduces an intermediary mapping model that connects product-level transactions to customer-level pricing outcomes. This mediator enables price optimization at the customer level even when individual transaction data is sparse, by aggregating and mapping transaction information through the intermediary model to infer customer price sensitivity and elasticity.
Solution Approach 2:
The system enables organizations to self-generate customer-level pricing insights by internally mapping their own transaction data through the provided framework. Rather than relying on external consulting or pre-existing rich databases, the system allows organizations to transform their available transaction history into customer-level price optimization capabilities through the mapping model.
2Measurement precision
If customized optimization solutions are developed for each B2B customer, then pricing accuracy improves, but consulting costs and implementation complexity increase significantly
Solution Approach 1:
The patent creates a universal mapping model framework that can be applied across multiple customers and product lines without requiring custom development for each case. The same core mapping methodology works universally across different B2B contexts, reducing implementation complexity while maintaining pricing accuracy through standardized yet adaptable processes.
Solution Approach 2:
The system segments the pricing optimization problem into manageable components: transaction-level data collection, product-customer mapping relationships, and customer-level aggregation. This segmentation allows the complex customization need to be broken down into standardized modules that can be systematically applied across different customers while maintaining accuracy.
3Ease of operation
If top-down price segmentation approaches are applied, then price management is simplified, but customer segmentation quality deteriorates due to non-homogeneous behavior within segments
Solution Approach 1:
The patent implements dynamic customer segmentation that evolves based on actual transaction data and observed customer responses to price changes. Rather than static top-down segments, the system continuously refines customer groupings based on empirical evidence from mapped transactions, allowing segments to adapt and improve in accuracy over time while maintaining operational simplicity.
Data Source
AI summary
A power and risk score calibrator is provided, which receives quantitative power and risk scores for each quantitative segment, and receives qualitative power and risk scores for each qualitative segment. The qualitative segment and the qualitative power and risk scores are defined by a user. The system generates consolidated segments. Then consolidated power and risk scores for each of the consolidated segments are generated, respectively. The gaps between the qualitative power and risk scores and the consolidated power and risk scores are reconciled. From these reconciliations, adjustment factors are generated, which are applied by pricing power and risk value calibrators. The system may also perform a drill down to explain the gap between the qualitative scores and the consolidated scores.