Elevating Email Marketing with Dynamic Subject Line Generation

Background Information

The introduction of subject line generation aims to optimize email marketing campaigns by developing effective subject lines. These subject lines are designed to increase open rates and improve click-through rates.

Problem Statement

The main problem or challenge faced by the company/organization is the ineffective subject lines in their email marketing campaigns. The existing subject lines fail to capture recipients' attention, resulting in low open rates and poor click-through rates. By optimizing subject lines to be more compelling and engaging, the company can increase open rates, attract more attention to their emails, and ultimately improve the effectiveness of their marketing campaigns. This, in turn, can lead to higher conversion rates, increased customer engagement, and overall business growth.


  • Data Collection and Preparation: Gather and preprocess a dataset of past email subject lines and engagement metrics.
  • Model Selection and Training: Choose and train a machine learning or natural language processing model using the dataset.
  • Feature Extraction: Extract relevant features from subject lines for input into the trained model.
  • Subject Line Generation: Use the trained model to generate subject lines based on desired criteria or input prompts.
  • Evaluation and Refinement: Evaluate generated subject lines and refine the model based on feedback and performance metrics.
  • Integration and Deployment: Integrate the model into the email marketing system for subject line generation, continuously updating it for improved results.


  • The technology used - is NLP, Pegasus pre-train model (Hugging face library), and NER.
  • Performance Metrics used - To evaluate the performance of the subject line generation using Pegasus, several metrics can be considered. These may include metrics such as the novelty of the generated subject lines, their coherence, relevance to the email content, and the overall engagement metrics of the corresponding email campaigns, such as open rates and click-through rates.
  • The dataset was Enron Corpus (for general use cases).
  • For Mailzzy - Re-creating the Mailzzy dataset using NER approaches like extracting Company name, Job Title, Year of Experience, Location, and Duration.
  • The dataset is fine-tuned with the Google/pegasus-aeslc pre-trained model.


The case study demonstrated the effectiveness of using a fine-tuned Pegasus model for generating engaging subject lines in email marketing campaigns.

By leveraging advanced language models, organizations can optimize open rates, enhance recipient engagement, and drive conversions. The findings highlight the relevance and significance of incorporating these technologies in email marketing strategies, offering opportunities for improved campaign performance and business growth.

By staying updated on advancements in natural language processing, organizations can maintain a competitive edge in the dynamic landscape of digital marketing.

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