Personalized Geo-fencing via Location-Specific Affinity
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Solution Overview
Problem
Conventional geo-fencing systems lack personalization, sending targeted messages to users based on proximity rather than affinity, leading to wasted computing resources and a poor user experience due to irrelevant messages.
Innovation Solution
A system that generates personalized geo-fences by determining a user's location-specific affinity for products, creating customized boundaries and transmitting messages only to users with demonstrated interest, using data from mobile devices and online interactions to segment users based on behavior and location similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional geo-fences transmit messages to all users within a geographical boundary, then message coverage is maximized, but resource wastage increases due to sending irrelevant messages to low-affinity users
Solution Approach 1:
The system segments users within a geo-fence based on their affinity scores for specific products. Instead of treating all users uniformly, it divides them into high-affinity and low-affinity groups, transmitting messages only to those above a threshold. This segmentation resolves the contradiction by maintaining message coverage for relevant users while eliminating resource wastage on irrelevant recipients.
Solution Approach 2:
The system applies local quality by customizing message transmission based on individual user characteristics (affinity scores) rather than applying a uniform approach to all users in the geo-fence. Each user receives messages tailored to their specific interest level, optimizing resource allocation while maintaining effective coverage for interested users.
2Productivity
If conventional geo-fences send targeted messages based on proximity, then message transmission volume increases, but user experience deteriorates due to irrelevant messages
Solution Approach 1:
The system segments the user base within geo-fences by calculating affinity scores and applying threshold filtering. This segmentation ensures that message transmission volume is maintained for high-affinity users while eliminating irrelevant messages for low-affinity users, thereby improving user experience without completely sacrificing transmission productivity.
Solution Approach 2:
The system changes the parameter of message transmission by introducing an affinity-based threshold condition. Instead of transmitting to all users based solely on location, it modifies the transmission criterion to include affinity scores, ensuring messages are sent only when both location and interest criteria are met, thus improving user experience while maintaining meaningful transmission volume.
3Loss of energy
If personalized geo-fences are created based on user affinity, then resource efficiency improves, but system complexity increases due to affinity calculation and user segmentation
Solution Approach 1:
The system performs preliminary action by pre-calculating user affinity scores and storing them for future reference. This pre-computation approach reduces real-time complexity during message transmission, as the affinity assessment is already completed. The system prepares user segments in advance, resolving the contradiction between resource efficiency and system complexity by shifting computational load to a preliminary stage.
Solution Approach 2:
The system implements self-service by automatically calculating affinity scores, segmenting users, and determining message transmission eligibility without requiring manual intervention. This automation handles the increased complexity internally, allowing the system to achieve resource efficiency through personalized targeting while managing system complexity through self-managed processes.
4Ease of manufacture
If manual geo-fence boundaries are specified, then setup simplicity is maintained, but personalization capability is lost
Solution Approach 1:
The system performs preliminary action by pre-calculating affinity scores and user segments before message transmission. This pre-computation enables personalized targeting without requiring complex real-time processing, maintaining ease of setup while achieving personalization. The affinity assessment is prepared in advance, allowing simple geo-fence boundaries to work effectively with personalized content.
Solution Approach 2:
The system introduces an intermediary mechanism (affinity scoring system) that bridges simple geo-fence boundaries and personalized message delivery. The affinity score acts as a mediator between location-based targeting and user-specific customization, allowing manual geo-fence setup to maintain simplicity while the intermediary layer adds personalization capability automatically.
Data Source
AI summary
In some embodiments, a computing system determines, based on stored user information retrieved from a mobile user device and associated with a particular user, a location-specific affinity of the particular user for a product at a particular geographical location. The location-specific affinity indicates an interest of the particular user in the product that increases when the particular user is positioned at the particular geographical location. The computing system designs a geo-fence targeted to the particular user based on the location-specific affinity, where messages are transmitted to the mobile user device if the particular user is within a boundary of the geo-fence. The geo-fence defines a geographical area that includes the particular geographical location and that is associated with a provider of the product. The computing system causes a telecommunication server to transmit the message to the user device when the user device is positioned within the designed geo-fence.