Geofence Notification System Using Threshold Filtering
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Solution Overview
Problem
Traditional coupon systems are underutilized as an information source for improving customer profiles, which limits their effectiveness in targeted advertising, as they do not efficiently gather data on customer preferences and behaviors.
Innovation Solution
A method that filters geofence users within a defined area, compares the count of qualified users to a threshold, and activates a notification process to output personalized offers, using Natural Language Processing and machine learning to predict user responses and adjust notification strategies based on user behavior and location data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional coupon systems are used to gather customer information, then customer profile data can be collected, but the systems are underutilized and do not efficiently gather data on customer preferences and behaviors
Solution Approach 1:
The system implements feedback loops where customer responses to notifications are captured and used to continuously refine customer profiles. The notification system tracks user interactions, purchases, and location data, feeding this information back into the profiling algorithm to improve future targeting accuracy and data collection efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-segmenting customers into groups based on existing profile data before sending notifications. This allows the system to efficiently target specific customer segments with relevant offers, improving both data gathering efficiency and reducing information loss by focusing on high-value interactions.
2Reliability
If targeted advertising is improved through better customer profiles, then advertising effectiveness increases, but the complexity of processing and analyzing customer data increases
Solution Approach 1:
The system divides the customer base into distinct segments based on profile attributes, behaviors, and preferences. This segmentation allows for simplified processing of large datasets by handling smaller, more homogeneous groups, while maintaining high advertising effectiveness through targeted messaging for each segment.
Solution Approach 2:
The system dynamically adjusts notification parameters such as timing, content, and delivery channel based on customer profile attributes and real-time behavior data. This parameter optimization improves advertising effectiveness while managing complexity through rule-based adjustments rather than exhaustive analysis of all possible variables.
3Productivity
If notifications are sent to all geofence users, then maximum customer engagement is achieved, but resource waste occurs when too many notifications are sent
Solution Approach 1:
The system applies different notification strategies to different geographic locations and customer segments within the geofence. Rather than uniform notification delivery, it tailors the approach to local conditions and customer characteristics, improving engagement rates while reducing resource waste on low-probability recipients.
Solution Approach 2:
The system sends notifications to a selective subset of customers within the geofence rather than all users. By using threshold-based filtering and predictive analytics, it identifies the optimal partial audience that is most likely to engage, achieving high productivity with reduced resource consumption compared to universal notification delivery.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: filtering in geofence users within an area of a geofence to determine a count of qualified in geofence users within an area of a geofence; comparing the count of qualified in geofence users within an area of a geofence to a threshold and activating a notification sending process based on a result of the comparing; based on the activating and outputting to the qualified users a notification based on the filtering; and disabling the notification sending process based on a disabling criteria being satisfied.