Send Time Optimization Tool for Personalized Message Engagement
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Solution Overview
Problem
Current cloud computing systems lack effective tools for predicting optimal send times for messages to individual subscribers, leading to inefficient resource usage and low personalized engagement, as existing solutions rely on historical trends and do not account for individual subscriber preferences or demographic variations.
Innovation Solution
A machine learning-based send time optimization tool using a two-layer non-negative matrix factorization model that predicts optimal send times by analyzing subscriber engagement patterns and historical feedback, providing personalized recommendations and reducing resource consumption by scheduling messages at optimal engagement times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If messages are sent to high volume of subscribers at the same time, then message delivery coverage is improved, but network resource overhead and computational burden increase
Solution Approach 1:
The system performs preliminary analysis of subscriber engagement patterns and historical feedback before message transmission. By pre-calculating optimal send times for individual subscribers based on their engagement behavior, the system schedules messages to be sent at different times rather than all at once, thereby reducing peak network resource overhead while maintaining delivery coverage.
Solution Approach 2:
The system dynamically adjusts message send times for different subscribers based on their individual engagement patterns. Instead of a static batch send approach, the system continuously learns from engagement data and adapts send schedules, optimizing the distribution of message transmission across time to reduce network overhead while maintaining high delivery coverage.
2Device complexity
If messages are sent based on historical trends without personalization, then system complexity is reduced, but subscriber engagement and model accuracy deteriorate
Solution Approach 1:
The system segments the subscriber base into individual units and analyzes engagement patterns for each subscriber separately rather than treating all subscribers as a homogeneous group. This segmentation enables personalized send time recommendations for each subscriber, significantly improving model accuracy and engagement predictions while managing complexity through modular processing of individual subscriber data.
Solution Approach 2:
The system applies local quality by tailoring send time recommendations to each individual subscriber's specific engagement patterns and preferences. Instead of applying a uniform historical trend analysis to all subscribers, the system customizes the analysis for each subscriber, improving model accuracy by capturing individual variations in engagement behavior.
3Quantity of substance
If send time optimization tools pool data across enterprises, then data volume increases, but model accuracy becomes imbalanced
Solution Approach 1:
The system segments data processing by maintaining separate analysis for each enterprise's subscribers rather than pooling data across enterprises. This approach preserves the integrity of enterprise-specific engagement patterns while still enabling personalized recommendations, thereby maintaining balanced model accuracy across different enterprises without the imbalances caused by data pooling.
4Reliability
If A/B testing tools are used for send time optimization, then experimental validation is improved, but time consumption and manual effort increase
Solution Approach 1:
The system performs self-service by automatically analyzing engagement patterns and generating send time recommendations without requiring manual A/B testing setup or execution. The system continuously learns from engagement data and autonomously optimizes send times, eliminating the time-consuming manual efforts associated with traditional A/B testing while maintaining reliable validation through continuous feedback from actual subscriber engagement.
Data Source
AI summary
Disclosed embodiments are related to send time optimization technologies for sending messages to users. The send time optimization technologies provide personalized recommendations for sending messages to individual subscribers taking into account the delay and/or lag between the send time and the time when a subscriber engages with a sent message. A machine learning (ML) approach is used to predict the optimal send time to send messages to individual subscribers for improving message engagement. The personalized recommendations are based on unique characteristics of each user's engagement preferences and patterns, and deals with historical feedback that is generally incomplete and skewed towards a small set of send hours. The ML approach automatically discovers hidden factors underneath message and send time engagements. The ML model may be a two-layer non-linear matrix factorization model. Other embodiments may be described and/or claimed.