Real-Time Dynamic Pricing System for Small Merchants
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Solution Overview
Problem
Small merchants lack real-time data to determine optimal prices for their products, making it difficult for them to compete with larger merchants who have access to historical sales data, and existing pricing solutions only analyze a narrow set of transaction data.
Innovation Solution
A system that collects and analyzes historical sales data from multiple merchants to determine a real-time optimal price for a product by using a weighted average of prices and sales velocity, allowing for automatic price adjustments within set boundaries to prevent loss or price gouging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If merchants use traditional static pricing models, then pricing simplicity is maintained, but ability to respond to real-time market dynamics deteriorates
Solution Approach 1:
The patent introduces a payment network system as an intermediary that collects, aggregates, and analyzes transaction data from multiple merchants. This intermediary process enables small merchants to access real-time pricing intelligence without needing to build complex data collection and analysis systems themselves, thus improving adaptability while managing complexity through externalization
Solution Approach 2:
The system implements continuous feedback loops where transaction data from multiple merchants is constantly collected, analyzed to determine optimal prices, and fed back to merchants in real-time. This feedback mechanism enables dynamic pricing adjustments based on actual market conditions, resolving the contradiction between adaptability and complexity by automating the feedback process
2Quantity of substance
If small merchants collect and analyze their own sales data, then data availability for pricing analysis improves, but data quantity and quality deteriorate due to insufficient sales volume
Solution Approach 1:
The patent merges transaction data from multiple independent merchants through the payment network system. By combining data across the ecosystem, the system achieves sufficient data volume and diversity that no single merchant could obtain alone, while maintaining pricing analysis accuracy through aggregated market intelligence rather than individual merchant data
Solution Approach 2:
The payment network system serves multiple functions: it processes payments, collects transaction data, analyzes pricing patterns across diverse merchants and products, and provides pricing recommendations. This multi-functional approach enables the system to overcome individual merchant data limitations by leveraging universal market data from the entire ecosystem
3Speed
If merchants manually adjust prices frequently, then responsiveness to market changes improves, but operational efficiency deteriorates
Solution Approach 1:
The system enables self-service pricing where merchants configure their pricing parameters and boundaries once, then the system automatically adjusts prices in real-time based on market conditions without requiring manual intervention. This automates the price adjustment process, achieving high speed responsiveness while maintaining merchant productivity by eliminating repetitive manual tasks
Solution Approach 2:
The patent implements dynamic pricing where prices automatically adjust in real-time based on changing market conditions, sales velocity, and competitive data. This dynamic system replaces static manual pricing with automated real-time adjustments, achieving both high speed responsiveness and operational efficiency through computer-based automation
4Productivity
If automated pricing systems are implemented without boundaries, then pricing optimization improves, but risk of financial loss or price gouging increases
Solution Approach 1:
The system implements preliminary anti-action by establishing pre-configured price boundaries (minimum and maximum prices) before automated pricing begins. These boundaries prevent harmful outcomes such as financial loss or price gouging by constraining the automated pricing algorithm within safe and ethical limits, thus maintaining both optimization efficiency and pricing reliability
Data Source
AI summary
Transaction data across a plurality of merchants may be analyzed as a data stream in real time to determine an optimal price for a product in real time. Transaction data and product data corresponding to a plurality of purchase transactions for a product of the product data may be stored as the transactions are completed. The product data may include an item identification and an item price and the transaction data may correspond to purchase transactions between a plurality of customer computer systems and a plurality of merchant computer systems. Each purchase transaction may include an item sale price and a merchant identifier. Real-time pricing data may then be determined from a combination of coefficients corresponding to the product data and the transaction data. The item price may be revised for the product based on the pricing data.