Adaptive Probability Matrix for Notification Scheduling
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Solution Overview
Problem
Users tend to initially engage with the saver button but eventually decrease their usage, leading to a need for an effective notification schedule to increase engagement and savings.
Innovation Solution
An adaptive probability matrix is used to create a notification schedule based on historical data of button pushes, including time of day, day of week, location, and user preferences, to optimize reminders and encourage user interaction with the saver button.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users are sent frequent notifications to press the saver button, then user engagement and savings increase, but users may become annoyed or desensitized to notifications
Solution Approach 1:
The notification system dynamically adjusts the timing and frequency of notifications based on user behavior patterns learned from historical data. The probability matrix is continuously updated to reflect changing user preferences and engagement levels, making the notification schedule adaptive rather than static.
Solution Approach 2:
The system incorporates feedback loops where user responses to notifications (pressing the button or ignoring it) are fed back into the probability matrix. This feedback mechanism allows the system to learn from user behavior and adjust future notification strategies to optimize engagement while minimizing annoyance.
2Adaptability or versatility
If the notification schedule is highly customized to individual user preferences, then user engagement increases, but system complexity increases
Solution Approach 1:
The system manages complexity by changing parameters in a structured way - using a probability matrix with defined parameters (time of day, day of week, probability thresholds) rather than unlimited customization options. This parameterized approach allows for meaningful customization while maintaining system manageability.
Solution Approach 2:
The system performs self-adjustment by automatically learning user preferences from historical button-push data and generating its own notification schedule without requiring manual configuration. This eliminates the need for complex user setup while still providing highly customized notifications.
3Productivity
If notifications are sent at random times, then system simplicity is maintained, but user engagement and savings effectiveness decrease
Solution Approach 1:
The system performs preliminary analysis of user historical data before generating the notification schedule. By pre-processing button-push timestamps and location data to identify patterns, the system prepares the probability matrix in advance, enabling targeted notifications without adding operational complexity during execution.
Data Source
AI summary
The technology described includes an adaptive probability matrix that creates a notification schedule. The schedule can be used to send reminders to users to press a physical, connected device/button, that when pressed or clicked, causes money to be transferred according to a set of rules (i.e., a “one-click” transfer of funds).