Neural Email Subject Generation for Personalized Campaigns
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Solution Overview
Problem
Existing automated solutions for generating email subject lines are impersonal and fail to attract individual user attention, despite the importance of concise and focused subject lines in electronic marketing campaigns.
Innovation Solution
A neural network-based method that processes email content and customer profile data to generate personalized subject line recommendations, incorporating named entity recognition and sentiment analysis, while filtering inappropriate content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated solutions focus solely on textual content of email body, then automation efficiency is improved, but personalization and user attention are worsened
Solution Approach 1:
The patent combines multiple data sources including textual content, image data, campaign context, and historic profile information into a unified training dataset for the neural network model, enabling both automation and personalization to coexist
2Ease of operation
If subject lines are made concise and focused, then recipient attention is improved, but information completeness is worsened
Solution Approach 1:
The neural network model extracts the most salient and attention-grabbing information from the comprehensive email content (including text and images) and formulates it into concise subject lines that capture essential meaning without unnecessary details
3Manufacturing precision
If more data processing is performed on email content, then subject line quality is improved, but processing time and complexity are worsened
Solution Approach 1:
The patent replaces traditional rule-based or template-based subject line generation mechanisms with a neural network model that learns patterns from data, achieving higher quality results while the model handles the complexity internally through automated feature extraction and processing
Data Source
AI summary
A computer based method for an electronic marketing campaign from a customer to a contact receives a campaign having an email message body and a historic profile of a previous campaign by the customer. The email message body includes text and image data. The email message body is preprocessed based upon the campaign and the historic profile to produce campaign training data. A neural network learning model is trained with the campaign training data. The neural network provides a subject line recommendation inference, and named entity recognition is performed on the subject line recommendation.