Geographic Predictive Pricing Using Employment And Demographic Signals
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Solution Overview
Problem
Retail pricing strategies fail to account for dynamic events affecting consumer purchasing power in specific geographic locations, relying on outdated information that does not consider recent employment or demographic changes.
Innovation Solution
A predictive pricing model that utilizes employment and demographic data to dynamically adjust prices in real-time based on recent events, incorporating machine learning to analyze payroll and demographic data for geographic-specific pricing adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical sales records and previous promotional effects are used to determine future pricing, then pricing consistency is maintained, but responsiveness to dynamic events affecting consumer purchasing power is lost
Solution Approach 1:
The patent implements dynamic pricing by continuously updating pricing models with real-time employment data, demographic information, and economic indicators. The system transitions from static historical pricing to dynamic pricing that automatically adjusts based on current conditions affecting consumer purchasing power in specific geographic locations.
Solution Approach 2:
The system performs preliminary analysis of employment trends, demographic changes, and economic events before they significantly impact consumer behavior. By monitoring leading indicators such as employment data and demographic shifts, the pricing model proactively adjusts prices in anticipation of changes in purchasing power rather than reacting to historical outcomes.
2Measurement precision
If real-time employment and demographic data are incorporated into pricing models, then pricing accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces intermediary components including data aggregation layers, preprocessing modules, and machine learning models that mediate between raw employment/demographic data and pricing decisions. These intermediaries transform complex multi-source data into standardized features that can be efficiently processed by pricing algorithms, reducing overall system complexity while maintaining high pricing accuracy.
Solution Approach 2:
The pricing system is segmented into modular components: data collection modules, data processing modules, modeling modules, and execution modules. Each component handles specific tasks independently, allowing the system to manage complexity through functional decomposition while maintaining pricing accuracy through coordinated operation of specialized subsystems.
3Adaptability or versatility
If geographic-specific pricing adjustments are made based on recent events, then local market responsiveness improves, but data requirements increase
Solution Approach 1:
The patent implements a universal data collection framework that gathers multi-purpose data suitable for multiple pricing decisions across different geographic locations. The same employment and demographic data serves multiple functions: identifying geographic segments, predicting purchasing power changes, and determining price elasticities, thereby reducing overall data requirements while enabling localized pricing adjustments.
Solution Approach 2:
The system merges multiple data sources including employment records, demographic information, economic indicators, and historical sales data into a unified pricing model. By combining these data streams and analyzing them collectively through machine learning algorithms, the system achieves comprehensive local market understanding without requiring separate extensive data collections for each factor.
Data Source
AI summary
Systems and methods are described for dynamic and predictive pricing for ecommerce systems and brick-and-mortar retail businesses for a selected geographic location or territory. In one example, a system comprises a computing device that is configured to receive a request to display a network page of an item on a client device. The computing device is further configured to determine a geographic location of the client device and determine a price for the item using a machine learning model based at least in part on the geographic location. The network page is displayed on the client device to include the price of the item.