Email Send-Time Prediction Using Clustering and Thompson Sampling
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Solution Overview
Problem
Existing email communication systems are sender-biased and lack adaptability, leading to suboptimal send times due to heuristic time interval definitions and insufficient data for new users, resulting in biased predictions and reduced engagement.
Innovation Solution
A dynamic and adaptable system using a proprietary tree-based clustering model and reinforcement learning to predict optimized send times for each end user, incorporating cluster and individual user preferences through Thompson Sampling to balance exploration and exploitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical analysis of user's past communication is used to determine optimal send time, then engagement rate is improved, but sender bias is introduced and data points are lost
Solution Approach 1:
The patent segments the time intervals into multiple granular slots throughout the day and uses Thompson Sampling to allocate email sends across these segments. This segmentation allows exploration of previously unexplored time intervals while maintaining engagement optimization through structured data collection across all segments.
Solution Approach 2:
The patent changes the parameter of time interval granularity from coarse heuristic intervals to fine-grained time slots, and changes the optimization parameter from sender-defined intervals to user-specific optimal times determined through exploration and exploitation balance via Thompson Sampling.
2Ease of operation
If heuristic time intervals defined by marketers are used, then implementation is simplified, but sender bias is introduced and all time intervals are not explored
Solution Approach 1:
The system performs self-service by automatically determining optimal send times through Thompson Sampling without requiring marketer-defined heuristic intervals. The system autonomously explores and exploits time interval data, adapting to each user's preferences while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The patent introduces dynamics by transitioning from static marketer-defined time intervals to dynamic user-specific optimal times that adapt over time. The system continuously learns and adjusts send time recommendations based on user engagement patterns, making the system adaptable while keeping implementation simple through automated learning.
3Device complexity
If static time intervals are used for all users, then system complexity is reduced, but user-specific optimization is lost
Solution Approach 1:
The patent applies universality by using a single Thompson Sampling framework that serves multiple functions: it works for new users without historical data, adapts to existing users' preferences, and handles varying engagement patterns across different user segments, all within one unified system that maintains manageable complexity.
Solution Approach 2:
The system implements feedback loops where user engagement responses to emails sent at different times are continuously fed back into the Thompson Sampling model. This feedback mechanism allows the system to learn and optimize user-specific send times while maintaining simple implementation through automated learning and adaptation.
4Loss of energy
If exploration of all time intervals is limited by campaign setup, then data collection is reduced, but computational resources are conserved
Solution Approach 1:
The patent applies partial action by using Thompson Sampling to selectively explore time intervals based on current knowledge and uncertainty. Instead of exhaustively testing all possible time intervals, the system performs partial exploration focused on high-value opportunities, balancing data collection with computational efficiency through intelligent allocation of exploration resources.
Data Source
AI summary
In order to predict an optimized send time for one or more end users in an email campaign, one or more times in a day are identified for sending the email campaign to the one or more end users. The email campaign may then be transmitted at one of the one or more times identified to the one or more end users.