Dynamic Pricing for Physical Stores via Pooled Sales Data
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Solution Overview
Problem
Physical stores face challenges in dynamic pricing due to insufficient historical data for demand analysis and the need to account for the cost of items, unlike online businesses that can leverage historical page view data and auctions.
Innovation Solution
A system that aggregates sales data from multiple physical stores to determine patterns, identifies deviations, and suggests prices based on correlations, including price adjustments through experiments to optimize sales and revenue, while considering merchant costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical stores perform demand analysis using their own historical data, then they can determine optimal prices, but they lack sufficient historical data for accurate analysis
Solution Approach 1:
The patent combines historical data from multiple physical stores to create a pooled dataset that is sufficient for demand analysis. By merging data across stores, the system achieves adequate sample size and statistical significance that individual stores cannot obtain alone, enabling accurate demand pattern recognition and optimal price determination.
2Ease of manufacture
If physical store operators manually determine prices considering costs and market conditions, then they can account for merchant costs, but it increases the resource burden and time required for price determination
Solution Approach 1:
The system enables automatic price determination that performs demand analysis and generates pricing recommendations without requiring manual operator intervention. The automated system analyzes pooled historical data, identifies demand patterns, and calculates optimal prices, freeing operators from time-consuming manual price setting while ensuring costs and market conditions are properly considered.
Solution Approach 2:
The patent replaces manual price determination processes with an automated computational system. Instead of operators manually analyzing market conditions and calculating prices, the system uses demand analysis algorithms and historical data processing to automatically generate pricing recommendations, significantly reducing the time and effort required.
3Productivity
If physical stores use dynamic pricing strategies, then they can optimize sales and revenue, but it requires complex data analysis capabilities that individual stores lack
Solution Approach 1:
The patent combines data analysis capabilities across multiple stores through a centralized or distributed system that pools historical data. This collective approach provides sufficient data volume and variety to perform sophisticated demand analysis and identify pricing patterns, enabling dynamic pricing optimization that would be infeasible for individual stores with limited data.
Solution Approach 2:
The system introduces an intermediary data analysis layer that processes pooled historical data from multiple stores and generates pricing recommendations. This intermediary system handles the complex analytical work, translating raw historical data into actionable pricing insights that physical stores can implement without needing to build complex analysis capabilities themselves.
Data Source
AI summary
Techniques of dynamic pricing for physical stores are described. A server handling purchase transactions of multiple physical stores can aggregate sales data on goods and services and determine a pattern. The server can determine that sales of an item of goods or services at a particular physical store deviate from the pattern at a particular time. Upon determining that sales price of the item significantly correlates to the deviation, the server can determine a suggested price for correcting the deviation. As an experiment, the server can conduct an experiment of selling the item at the suggested price at the particular time for a given time period. The server can present results of the experiment to an operator of the physical store.