Token-Set Impact Analysis for Communication Content Selection
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Solution Overview
Problem
Selecting effective text content for communications, such as email subject lines, is often guesswork-intensive and challenging due to the complexity of combining different text elements, making it difficult to determine their impact on target outcomes like open rates.
Innovation Solution
A system that analyzes historical communications to determine the impact of token-sets (unigrams, bigrams, trigrams) by generating composite performance parameters, recommending high-performing token-sets for new communications using machine learning and bucket-based analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If text content for communications is selected based on guesswork, then selection effort is reduced, but the impact on target outcomes (e.g., open rates) is suboptimal
Solution Approach 1:
The system implements feedback by analyzing historical communication data and user responses to determine the actual impact of different text contents. This feedback loop enables the system to learn from past performance and continuously improve text content selection, replacing guesswork with data-driven decisions that reliably enhance target outcomes like open rates
Solution Approach 2:
The patent replaces the mechanical/manual process of guesswork-based text selection with an automated machine learning system. The ML model automatically analyzes historical data, evaluates token-set impacts, and generates text content recommendations, substituting human intuition with computational analysis that provides both efficiency and reliability
2Measurement precision
If the impact of text content on target outcomes is determined through analysis, then selection accuracy improves, but the technical complexity and effort increase
Solution Approach 1:
The system segments text content into discrete token-sets (individual words, phrases, or grammatical units) that can be independently analyzed. This segmentation allows the ML model to evaluate the impact of each token-set separately on target outcomes, providing precise measurement without requiring complex analysis of entire text blocks, thus managing system complexity while maintaining accuracy
3Adaptability or versatility
If different text contents are combined in a single communication, then communication effectiveness may improve, but determining the impact of each text element becomes more challenging
Solution Approach 1:
The system introduces an intermediary ML model that acts as a mediator between the complex combination of text contents and the target outcome measurement. The model processes the entire communication and attributes impact to individual token-sets through learned patterns, making it possible to measure the contribution of each text element even when multiple contents are combined, thus maintaining versatility while enabling precise measurement
Data Source
AI summary
Disclosed are techniques for determining the impact of including a token-set (e.g., text in the form of unigrams, bigrams, or trigrams) in a communication on a target outcome. More particularly, the present disclosure relates to techniques for determining the impact of the token-set based on, for example, the token-sets included in previous communications transmitted to user devices and the corresponding user responses to those previous communications.