Reference Price Framework for Pricing Strategy
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Solution Overview
Problem
Professionals and firms lack reliable methods to determine competitive and revenue-maximizing pricing strategies for transactional services due to a lack of accurate and up-to-date market information, leading to difficulties in evaluating pricing behavior and identifying top and bottom performers.
Innovation Solution
A computer-assisted method using reference prices to segment and analyze pricing episode data, determining a reference price based on predetermined levels or percentiles, and calculating revenue opportunities to provide guidance for future pricing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If professionals charge discretionary prices based on market judgment, then they can reward client loyalty and encourage new business, but they lack reliable information to determine competitive and revenue-maximizing pricing strategies
Solution Approach 1:
The patent segments pricing episode data into multiple groupings based on price predictive parameters such as transaction type, account size, asset type, and time period. This segmentation allows professionals to compare their pricing against relevant peer groups rather than a single aggregate benchmark, providing more accurate market information for discretionary pricing decisions.
Solution Approach 2:
The system provides feedback to professionals through reference prices, revenue opportunity calculations, and performance evaluations that show how their pricing compares to peers. This feedback loop enables professionals to adjust their discretionary pricing strategies based on actual market data while maintaining adaptability to individual client needs.
2Productivity
If firms monitor pricing behavior to maximize revenue and identify top performers, then they can improve organizational performance, but the culture of secrecy and complex pricing formulas make pricing information opaque and难以 to analyze
Solution Approach 1:
The patent introduces a computer-assisted modeling system as an intermediary that processes complex pricing episode data and translates it into meaningful reference prices and performance metrics. This intermediary handles the complexity of data analysis, allowing firms to monitor pricing behavior effectively without requiring professionals to understand or simplify their pricing formulas.
Solution Approach 2:
The system replaces manual pricing analysis and evaluation with automated computer-assisted modeling. The computational system processes pricing data, determines reference prices, calculates revenue opportunities, and evaluates professional performance, substituting mechanical human analysis with automated computational processes that can handle complexity efficiently.
3Loss of energy
If professionals charge higher prices to maximize revenue, then they can increase their earnings, but they risk losing clients who perceive the prices as too high
Solution Approach 1:
The patent calculates revenue opportunities as the difference between reference prices and actual discretionary prices, identifying partial opportunities for price increases. Rather than recommending maximum price increases, the system provides targeted adjustments based on how much higher peers are charging for similar transactions, allowing professionals to capture additional revenue while maintaining client acceptance.
Data Source
AI summary
A method of computer-assisted price modeling is provided, which uses a reference price to assist in evaluating discretionary pricing of transactional services provided (i) by a professional among his own transactions or (ii) by a target professional among others' transactions. A universe of pricing episode data is segmented into groupings based on price predictive parameters. These data are then arranged in each grouping according to price. A reference price is determined within each grouping based on a predetermined level, rank or percentile. This reference price can then be used to evaluate the pricing episode data and provide various calculations or comparisons, including the revenue opportunity that could have been obtained by pricing at the reference price.