Zone-Based Client Count Forecasting Using Adaptive Predictive Models
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Solution Overview
Problem
Organizations face challenges in efficiently forecasting client counts in different zones to improve customer satisfaction and resource utilization, particularly in variable customer environments like retail stores and airports, as existing methods lack accuracy and efficiency in predicting future client numbers.
Innovation Solution
A process that utilizes historical and current client location data to forecast client counts in specific zones by selecting predictive methods based on signal strength information from wireless access points, employing techniques such as linear regression, exponential smoothing, and autoregressive integrated moving average models to generate accurate predictions several hours ahead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting methods are used, then the system is simple to implement, but the forecasting accuracy is low
Solution Approach 1:
The patent segments the forecasting problem by dividing the venue into multiple zones and applying different predictive methods to different zones based on their specific characteristics. This allows the system to improve overall forecasting accuracy by tailoring the approach to each zone's unique patterns rather than using a single generic method for the entire venue.
Solution Approach 2:
The system dynamically selects predictive methods based on real-time conditions and historical performance. It adapts the forecasting approach by choosing from multiple predictive methods (e.g., exponential smoothing, ARIMA, neural networks) depending on the current situation, making the system flexible and responsive to changing patterns in client count data.
Solution Approach 3:
The patent changes key parameters such as the lookback period, prediction horizon, and smoothing constants based on historical performance and current conditions. By optimizing these parameters dynamically, the system improves forecasting accuracy without requiring a complete redesign of the forecasting architecture.
2Measurement precision
If multiple predictive methods are evaluated, then the prediction accuracy improves, but the computational time increases
Solution Approach 1:
The system applies partial evaluation by not always running all predictive methods for every forecast. Instead, it selectively applies multiple methods only when needed based on confidence levels, data availability, and performance thresholds, reducing unnecessary computational overhead while maintaining accuracy where it matters most.
Solution Approach 2:
The patent performs preliminary evaluation of predictive methods using historical data to identify which methods work best for different zones and time periods. This pre-computed knowledge is stored and reused, avoiding the need to evaluate all methods from scratch each time a forecast is needed, thus significantly reducing computational time.
Solution Approach 3:
The system uses feedback from past forecast performance to refine future computations. By analyzing prediction errors and adjusting parameters based on actual outcomes, the system learns which methods and parameter settings yield the best results, reducing the need for extensive re-evaluation and improving efficiency over time.
3Reliability
If historical data from multiple zones is analyzed, then the forecasting reliability improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the multi-zone data processing by treating each zone independently with zone-specific parameters and predictive methods. This segmentation allows the system to leverage data from multiple zones for improved reliability while managing complexity through modular, zone-specific processing rather than attempting to analyze all zones simultaneously as a single complex system.
Solution Approach 2:
The system implements a universal forecasting framework that can handle multiple zones with different characteristics using the same core architecture. The multi-functional platform adapts to various zone types and data patterns, providing reliable forecasting across diverse zones without requiring separate complex systems for each zone.
Data Source
AI summary
An efficient process is provided that exploits features in historical and current client location data to forecast client counts of different zones up to several hours ahead. These features may be obtained from correlations of client counts of multiple zones in a recent and long period of time. These features may also be combined using techniques that choose the best performing method for a particular dataset and a particular lookahead time. This process provides better forecast/prediction on the zone-based client count data, and is very useful in customer analytics which can now show the future predicted value. This can help the analytics customers to plan their operations based on the location analytics.