Recipient Assignment Modeling for Adaptive Message Template Selection
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Solution Overview
Problem
Existing electronic messaging systems struggle with optimizing message templates for diverse recipient groups, relying on manual rules and intuition, which is inefficient and prone to errors.
Innovation Solution
An adaptive system autonomically assigns recipients to different electronic message templates by using a recipient assignment model that predicts optimal message types based on contextual variables and target behaviors, employing modules for testing, modeling, and managing message delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual rules and intuition are used to segment recipients, then the system is simple to operate, but it is slow, clunky, and prone to errors in judgment
Solution Approach 1:
The system performs self-service by automatically segmenting recipients and assigning message templates without requiring manual user intervention. The machine learning model autonomously analyzes recipient characteristics and message performance data to make segmentation decisions, eliminating the need for users to manually create segments and apply rules.
Solution Approach 2:
The patent replaces the mechanical manual process of creating segments and applying rules with an automated machine learning system. The neural network model processes recipient data and message performance metrics automatically, substituting human intuition and manual rule-based segmentation with computational algorithms that operate at scale.
2Device complexity
If manual rules and intuition are used to segment recipients, then the system structure is simple, but it is prone to errors in judgment
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously learns from message performance data, recipient engagement metrics, and outcome measurements. This feedback loop enables the model to improve its segmentation accuracy over time, reducing judgment errors by adapting to changing recipient behaviors and preferences.
Solution Approach 2:
The patent replaces unreliable manual judgment with automated machine learning algorithms that process data objectively. The neural network model analyzes multiple variables including recipient demographics, message content, and engagement patterns to make consistent, data-driven segmentation decisions free from human bias and error.
3Device complexity
If a one-size-fits-all electronic messaging scheme is used, then the system is simple, but no single message template is optimally engaging to all recipients
Solution Approach 1:
The system applies segmentation by dividing the recipient population into distinct groups based on shared characteristics and message response patterns. The machine learning model identifies segments with similar engagement behaviors and assigns appropriate message templates to each segment, enabling tailored communication strategies for different recipient groups.
Solution Approach 2:
The patent implements local quality by customizing message templates for specific recipient segments rather than using a uniform approach. Each segment receives message content optimized for its unique characteristics, such as preferred message types, optimal send times, and relevant content topics, thereby maximizing engagement for each local group.
4Adaptability or versatility
If multiple message templates are assigned to different recipient segments, then recipient engagement is optimized, but the system becomes complex
Solution Approach 1:
The system handles complexity through self-service automation where the machine learning model autonomously manages the entire process of segment creation, template selection, and message assignment. Users simply provide initial parameters and the system automatically handles the complex tasks of data processing, model training, and real-time decision-making without requiring manual intervention in the complexity management.
Data Source
AI summary
A method for optimally assigning recipients to electronic messages that vary by content. The method comprises testing the varying electronic messages on test recipients for a target behavior and computing a metric corresponding to the target behavior of the recipients. The method further comprises building a recipient assignment model to predict the likelihood a recipient shall perform the target behavior after receiving the varying electronic messages. Untested recipients are then assigned to one of the electronic messages using the model to maximize the likelihood the recipient shall perform the target behavior. The method further comprises sending to each of the untested recipients the optimal variation of electronic message. Related systems are also described.