Channel Event Demand Prediction Using Location Data Correlation
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Solution Overview
Problem
Conventional approaches to detecting surges in demand are reactionary and fail to incorporate multiple data sources, leading to an incomplete understanding of demand dynamics in channel events.
Innovation Solution
A system and method that utilize user-sourced data and predictive technologies to generate channel analysis data by correlating location data with user-sourced data, enabling real-time and predictive analysis of demand, and executing demand adjustment functions such as dynamic pricing and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches detect demand surges based solely on increase in requests, then the system can respond to demand changes, but the understanding of demand dynamics remains incomplete and reactionary
Solution Approach 1:
The patent combines multiple data sources including user-sourced data, location data, channel events, and historical data into a unified demand prediction model. This merging of diverse information streams enables comprehensive demand analysis that goes beyond simple request counting, allowing the system to understand demand dynamics through correlated patterns across multiple dimensions.
Solution Approach 2:
The system performs preliminary demand prediction by analyzing correlated patterns in user-sourced data, location data, and channel events before actual demand surges occur. This predictive capability allows the system to anticipate demand changes and take proactive measures rather than reacting after demand has already increased.
2Measurement precision
If the system incorporates multiple data sources and predictive technologies, then demand prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex data processing system into distinct functional modules: user-sourced data collection, location data processing, channel event monitoring, correlation analysis engine, and demand prediction model. Each module handles specific data types and processing tasks independently, then integrates results through standardized interfaces, making the overall complex system manageable and maintainable.
Solution Approach 2:
The system introduces intermediary components including data normalization layers, correlation engines, and predictive modeling interfaces that mediate between raw diverse data sources and the demand prediction output. These intermediaries standardize data formats, filter relevant information, and apply analytical algorithms, simplifying the integration of multiple data sources while maintaining high prediction accuracy.
3Productivity
If real-time demand analysis is performed using multiple data sources, then operational efficiency improves, but data processing time and resources increase
Solution Approach 1:
The system implements periodic demand analysis by continuously monitoring data streams at optimized intervals rather than processing every single data point in real-time. The correlation engine periodically aggregates user-sourced data, location data, and channel events over defined time windows, applying predictive models at these periodic intervals to balance responsiveness with processing efficiency.
Solution Approach 2:
The system applies partial processing by focusing computational resources on analyzing only the most relevant and recently changed data elements that have the highest impact on demand predictions. Rather than processing all available data uniformly, the system identifies and prioritizes key data patterns and anomalies, performing detailed analysis only where needed while using simpler methods for routine data.
Data Source
AI summary
A computerized method comprising: receiving channel events comprising (a) user-sourced data provided by users each using a respective mobile device within a channel and (b) location data associated with the respective mobile devices of the users with the channel; generating a correlation between the location data and the user-sourced data; and generating, using a predictive engine, channel analysis data for the channel based on the channel events and the correlation between the location data and the user-sourced data. Other embodiments are disclosed herein.