Dynamic Pricing Rules via Interpolated Elasticity Functions
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Solution Overview
Problem
Existing price optimization tools in retail environments are suggestive and lack guarantees of success, leading to undesirable volatility for retailers, as they bear the risk of price adjustments, which can result in losses if market demand does not meet predicted unit sales increases.
Innovation Solution
A system and method for generating dynamic pricing rules by receiving transaction information, interpolating market price elasticity functions, and optimizing them to calculate updated price-volume break points, shifting the risk from retailers to collective shoppers and allowing for real-time price adjustments based on observed market demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If retailers set lower prices to increase unit sales for price-sensitive products, then revenue potential increases, but retailer risk increases due to possible demand forecast errors
Solution Approach 1:
The patent introduces a third-party service provider as an intermediary that performs demand forecasting and price optimization calculations. This mediator uses specialized algorithms and market data to determine optimal prices, transferring the analytical burden and risk from the retailer to the service provider. The retailer pays for the service but avoids the risk of poor pricing decisions, as the intermediary assumes responsibility for the accuracy of demand predictions and pricing recommendations.
Solution Approach 2:
The system enables automated self-service pricing through computer-generated price recommendations that retailers can implement without manual market analysis. The algorithm automatically monitors sales data, adjusts prices in real-time, and optimizes revenue without requiring retailer expertise in demand forecasting. This self-service approach reduces retailer risk by eliminating human error in price setting while maintaining profit maximization.
2Reliability
If retailers maintain stable prices to avoid risk, then business stability is maintained, but revenue optimization is reduced
Solution Approach 1:
The patent implements dynamic pricing that automatically adjusts prices based on real-time demand conditions, product lifecycle stage, competitor pricing, and inventory levels. Rather than maintaining static prices, the system continuously optimizes pricing to maximize revenue while adapting to changing market conditions. This dynamic approach allows retailers to capture additional revenue opportunities without sacrificing stability, as the system automatically manages price fluctuations based on objective data rather than subjective judgment.
Solution Approach 2:
The system changes multiple pricing parameters simultaneously including base price, discount levels, promotional pricing, and price elasticity coefficients. By adjusting these parameters based on demand forecasts and market conditions, the system optimizes revenue while maintaining business stability through controlled, data-driven changes rather than arbitrary price adjustments.
3Ease of operation
If retailers use traditional price optimization tools to set prices based on predicted unit sales, then pricing decisions can be made, but success is not guaranteed and volatility increases
Solution Approach 1:
The patent implements continuous feedback loops where actual sales data is compared against predicted sales, and the algorithm learns from discrepancies to improve future predictions. The system monitors pricing decisions, measures actual outcomes, and adjusts its forecasting models accordingly. This feedback mechanism increases reliability by continuously validating and improving the accuracy of price recommendations, ensuring that success rates improve over time rather than remaining static or uncertain.
Solution Approach 2:
The system performs preliminary demand forecasting and scenario analysis before implementing price changes. By simulating potential outcomes of different pricing strategies and selecting the optimal approach in advance, the system increases the likelihood of successful price adjustments. This preliminary action allows retailers to test pricing strategies virtually before committing to actual price changes, reducing the risk of failed price adjustments and increasing confidence in pricing decisions.
Data Source
AI summary
Systems and computer-readable media for generating dynamic pricing rules to govern offered price-volume break points. Initial target price-volume break points are offered during a time window. For each transaction of the product, transaction information including the transacted price, quantity, and identifying information of the purchaser is received. Based on a sales trend determined from the transaction information, the time window and the offered price can be updated. After the expiration of the time window, redemptions are generated for each purchaser of the product, based at least in part on the final quantity sold during the time window and the offered price-volume break points. A market price elasticity function is interpolated from observed market price elasticities at each price-volume break point. Using the market price elasticity function, dynamic pricing rules are optimized and updated and then used to calculate updated price-volume break points to be offered for the product.