Revenue Optimization via Customer Demand Curves
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Solution Overview
Problem
Current revenue optimization methods for merchants fail to account for individual customer purchasing behavior patterns, particularly in response to price discounts, leading to suboptimal pricing strategies that do not maximize revenue.
Innovation Solution
The system generates supply and demand curves using a normalized unit pricing structure to determine the unit elasticity point, optimizing pricing and discounting for individual customers or subsets by analyzing their willingness to buy and sell across a range of products, employing a networked environment with computing devices and data preprocessing services to predict optimal pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional revenue optimization methods are used, then implementation is simple, but revenue maximization is not achieved due to ignoring individual customer purchasing behavior patterns
Solution Approach 1:
The patent segments the customer base into individual customer profiles, each with their own demand curves and pricing characteristics. Instead of applying a uniform pricing strategy to all customers, the system divides the market into discrete customer segments and analyzes each segment's price elasticity independently, enabling personalized pricing optimization.
Solution Approach 2:
The system changes the pricing parameter from a fixed or uniform value to a dynamic, customer-specific value. By calculating the unit elasticity point for each customer's demand curve, the system determines the optimal price point that maximizes revenue for that specific customer, thereby changing the pricing parameter based on individual customer characteristics.
2Productivity
If personalized pricing strategies are implemented, then revenue is maximized, but system complexity increases due to generating and analyzing supply and demand curves for individual customers
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing demand curves and unit elasticity points for each customer before actual pricing decisions are needed. This advance preparation allows the merchant to quickly apply optimized pricing without performing complex calculations in real-time, reducing the computational burden during transaction processing.
Solution Approach 2:
The patent creates simplified copies or representations of complex customer behavior patterns through demand curves and elasticity points. Instead of analyzing raw transaction data repeatedly, the system generates simplified mathematical models (demand curves) that capture essential purchasing behavior, making subsequent pricing decisions computationally efficient.
3Measurement precision
If unit elasticity point analysis is performed for each customer, then pricing accuracy is improved, but computational requirements and data processing time increase
Solution Approach 1:
The system calculates unit elasticity points and generates demand curves in advance, before actual pricing decisions are required. This preliminary computation stores the results of complex analyses, allowing rapid retrieval and application during transactions without repeating time-consuming calculations.
Solution Approach 2:
The system dynamically adjusts pricing based on each customer's specific demand characteristics while maintaining computational efficiency. By using pre-calculated but customer-specific elasticity points, the system achieves dynamic, personalized pricing without the computational overhead of real-time curve fitting for each transaction.
Data Source
AI summary
Disclosed are various embodiments for determining discounts that optimize revenue for customers or customer subsets. Transaction records that are associated with a customer and a merchant are obtained. Each transaction record identifies a corresponding purchase of a quantity of one of multiple items by the customer from the merchant at a corresponding unit purchase price. A normalized unit purchase price is generated for each transaction record by normalizing the respective unit purchase price relative to a respective unit retail price at the time of purchase. A demand curve is generated for the customer for the items based at least in part on the transaction records and the normalized unit purchase prices. The demand curve identifies a quantity of units that the customer is predicted to purchase at a range of normalized unit purchase prices.