Dynamic Geo-fence Adjusting Range by Product Popularity
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Solution Overview
Problem
Existing geo-fencing methods rely on static boundaries, which fail to optimize customer flow to venues based on product popularity, leading to inefficient resource consumption and potential negative customer sentiment due to incorrect geographic size settings.
Innovation Solution
A dynamic geo-fence system that adjusts its range based on product popularity, using GPS coordinates and mobile device location data to optimize customer flow by expanding or contracting the geographic area according to sales performance and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a static geo-fence with fixed boundaries is used, then the system is simple to implement and manage, but it cannot optimize customer flow based on product popularity, leading to inefficient resource consumption
Solution Approach 1:
The geo-fence system transitions from static boundaries to dynamic boundaries that automatically adjust their geographic range based on real-time product popularity metrics. The system monitors sales data and demand signals, then modifies the geo-fence radius or area to optimize customer attraction and throughput, enabling adaptive resource allocation without manual intervention
Solution Approach 2:
The system changes the geographic parameters (range, area, radius) of the geo-fence based on product popularity metrics. By linking geo-fence dimensions to dynamic product performance data, the system automatically scales the geographic coverage to match demand levels, improving customer flow optimization while maintaining automated operation
2Productivity
If the geo-fence area is increased to attract more customers, then customer throughput increases, but computing overhead and resource consumption increase
Solution Approach 1:
The geo-fence computing resources are dynamically allocated based on actual customer throughput requirements. When product popularity and demand signals indicate higher throughput needs, the system expands the geo-fence area and activates additional computing resources. When demand decreases, the system contracts the geo-fence and reduces resource consumption, achieving efficient resource utilization that matches actual operational needs
Solution Approach 2:
The system implements a feedback loop that continuously monitors customer throughput metrics, product popularity, and computing resource usage. Based on this feedback, the system automatically adjusts geo-fence boundaries and resource allocation to maintain optimal performance. This closed-loop control ensures computing overhead is proportional to actual customer flow requirements rather than operating at fixed maximum capacity
3Productivity
If the geo-fence range is dynamically adjusted based on product popularity, then customer flow is optimized, but the system complexity increases
Solution Approach 1:
The geo-fence system performs self-adjustment by automatically monitoring its own performance metrics and product popularity data, then autonomously modifying its geographic boundaries without external intervention. The system uses built-in sensors and algorithms to detect when optimization is needed and executes the adjustment independently, reducing the operational burden while maintaining high productivity
Data Source
AI summary
Approaches presented herein enable creating a dynamic geo-fence based on a popularity of a product. Specifically, a geo-fence at a venue (e.g., retail outlet, restaurant, ticket office, etc.) is established based on a reference point and an area having a range. A product is associated with the established geo-fence. The range of the area is dynamically modified (i.e., increased or decreased) based on a popularity of the product in order to optimize the flow of customers to the venue.