Message Timing Optimization via Historical Transaction Analysis
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Solution Overview
Problem
Electronic communications lead to over-messaging, diluting the value and annoying recipients, resulting in messages being ignored, and there is a need to optimize the timing of message transmission to increase readability.
Innovation Solution
A message timing optimization system that uses historical transaction data to determine probability values for subsequent user actions, identifying optimal times to transmit recommendation messages based on these values, thereby reducing the number of messages sent and increasing their effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If messages are sent frequently to ensure user engagement, then message delivery success is improved, but message value is diluted and recipient annoyance increases
Solution Approach 1:
The system performs preliminary analysis of historical transaction data to predict optimal message timing before actually sending messages. By pre-calculating the best send times based on patterns from past transactions, the system avoids sending messages at suboptimal times that would cause annoyance, while ensuring messages are sent at times when users are most likely to engage.
Solution Approach 2:
The system continuously monitors and analyzes historical transaction data to refine its prediction models. This feedback loop allows the system to learn from actual user behavior patterns and adjust its message timing strategies accordingly, improving both delivery success and reducing annoyance over time.
2Object-generated harmful factors
If the number of messages sent is limited to reduce annoyance, then recipient engagement is improved, but the challenge of selecting optimal send time increases
Solution Approach 1:
The system automatically performs the complex analysis of historical transaction data and prediction model execution without requiring manual intervention. The self-service mechanism handles the complexity of timing selection through automated processes, allowing the system to determine optimal send times independently based on learned patterns from transaction history.
Solution Approach 2:
The system changes the parameter of message timing based on predicted user behavior patterns. By dynamically adjusting send times according to the predicted optimal moment derived from historical data analysis, the system simplifies the complexity of timing selection while maintaining high engagement effectiveness.
3Productivity
If messages are sent at optimal times based on prediction models, then message effectiveness is improved, but computing resources for data analysis are consumed
Solution Approach 1:
The system applies partial action by analyzing only the most relevant historical transaction data and using streamlined prediction models rather than processing all available data exhaustively. This selective approach maintains message effectiveness while reducing computing resource consumption to manageable levels.
Solution Approach 2:
The system uses simplified copies or representations of complex transaction patterns through trained prediction models. Instead of processing the full complexity of historical transaction data in real-time, the system uses pre-trained model copies that can quickly generate predictions with minimal computational resources.
Data Source
AI summary
Disclosed are systems, methods, and non-transitory computer-readable media for message timing optimization. A message timing optimization system determines optimized times to transmit messages to users based on historical transaction data. For example, the message timing optimization system may determine an optimal time to transmit a recommendation message to a user to perform a subsequent, such as repurchasing an item, refreshing a password, and the like. The message timing optimization system uses historical transaction data describing previous transactions performed by the user and/or other users to determine probability values indicating the likelihood that a user will perform a subsequent action at various time periods after the user performed an initial transaction.