Inactive User Reminding Method for Cloud Service Re-engagement
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Solution Overview
Problem
There is no effective solution to re-engage inactive users of cloud services who have not utilized their accounts within a predefined period, leading to potential user churn.
Innovation Solution
A reminding method and device that determines inactive users and assesses their arousable state, sending reminding information to awaken them to reuse cloud service functions by monitoring usage status, acquiring communication records, and sending alerts through contact information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If no reminding operation is performed on inactive users, then the system maintains simplicity and avoids unnecessary operations, but user churn increases and service utilization decreases
Solution Approach 1:
The system automatically monitors user activity status, determines arousable states, and sends reminding information without human intervention. The server self-manages the entire process from detecting inactive users to sending targeted reminders, eliminating the need for manual operations while improving service utilization.
Solution Approach 2:
The system changes the state parameter of inactive users from 'inactive' to 'arousable' or 'non-arousable' based on monitoring criteria such as communication records and usage patterns. This parameter change enables differentiated treatment of users, allowing the system to send reminders only to those likely to respond, thereby improving productivity without excessive complexity.
2Productivity
If reminding information is sent to all inactive users, then more users may be re-engaged, but resource waste increases and user experience deteriorates due to irrelevant reminders
Solution Approach 1:
The system applies different qualities of reminder operations to different users based on their individual arousable states. Users deemed 'arousable' receive reminding information, while those deemed 'non-arousable' do not. This localized differentiation ensures resources are spent only on users likely to respond, improving re-engagement rates while minimizing resource waste.
Solution Approach 2:
Instead of sending reminders to all inactive users (excessive action), the system performs partial action by sending reminders only to the subset of inactive users who are in an arousable state. This partial action approach avoids resource waste on users unlikely to respond while still achieving effective re-engagement.
3Measurement precision
If the system monitors and analyzes user communication records to determine arousable state, then reminder accuracy improves, but system complexity and processing time increase
Solution Approach 1:
The system extracts only the necessary information from user communication records to determine arousable state, rather than analyzing all available data. By taking out only the relevant communication patterns and usage indicators needed for the assessment, the system achieves accurate detection while keeping the monitoring mechanism relatively simple.
Solution Approach 2:
The system performs preliminary monitoring and analysis of user communication records continuously in the background, so that when a user becomes inactive, the arousable state has already been determined. This preliminary action ensures high detection accuracy without requiring complex real-time analysis at the moment of sending reminders.
Data Source
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AI summary
The present disclosure relates to a reminding method and device. The reminding method includes: determining (S101) an inactive user; judging (S102) whether the inactive user is in an arousable state; and if the inactive user is not in the arousable state, performing no operation; and if the inactive user is in the arousable state, sending (S103) reminding information to the inactive user. Through the embodiments of the present disclosure, the inactive user may be awakened timely.