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Artificial intelligence-driven integrated system for comprehensive email marketing automation

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 15 No. 5 (2025)

DOI
10.11591/ijece.v15i5.pp4875-4888
ISSN
2722-2578, 2088-8708
Published
2025-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Right in the context of digital marketing, this paper presents a comprehensive integrated system that combines the latest artificial intelligence advancement – large language models and diffusion models – to generate marketing email subjects and content that result in higher engagement. The system uses finetuned large language models for compelling email subject generation and finetuned Stable Diffusion model for visually appealing and convincing email content images creation. For the latter, both knowledge graphs and vector embeddings have been incorporated to improve contextual relevance. Experimental results demonstrated significant improvement in all engagement metrics that marketers rely on, including 46% growth in open rates, 56% higher click-through rates, and an 51% boost in conversion rates, all compared to human generated content. The unified approach presented by this paper outperforms standalone models and human-generated content in terms of engagement, as the comparative analysis shows. We also discuss the ethical considerations related to content bias and personalization boundaries, alongside challenges faced in this type of projects, such as computation demands and probable solutions. Finally, this paper proposes future directions to be taken, including expansion to other digital marketing channels, the use of other advanced artificial intelligence techniques, and the development of real-time content adaptation mechanisms based on user feedback.

Collaboration network of the authors

18 authors · 35 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Abdelmalek Essaâdi University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mohammed BouhormaAbdelmalek Essaâdi University3
Amina SamihAbdelmalek Essaâdi University2
Amine BelahbibAbdelmalek Essaâdi University1
Mohamed BouhormaAffiliation unknown1
Othman Bakkali YedriAbdelmalek Essaâdi University1
Abdelali SlimaniAbdelmalek Essaâdi University1
Fatiha ElouaaiAbdelmalek Essaâdi University1
Hicham Gibet TaniAbdelmalek Essaâdi University1
Chaker El AmraniAbdelmalek Essaâdi University1
Amine BelahbibeAbdelmalek Essaâdi University1
Anasse HanafiAbdelmalek Essaâdi University1
Bahra MohamedAbdelmalek Essaâdi University1
Ihab MoudhichAbdelmalek Essaâdi University1
Younès AlaouiAbdelmalek Essaâdi University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Artificial intelligence-driven integrated system for comprehensive email marketing automation</dc:title><dc:creator>Loukili, Soumaya</dc:creator><dc:creator>Elaachak, Lotfi</dc:creator><dc:creator>Ghadi, Abderrahim</dc:creator><dc:creator>Fennan, Abdelhadi</dc:creator><dc:description>&lt;jats:p&gt;Right in the context of digital marketing, this paper presents a comprehensive integrated system that combines the latest artificial intelligence advancement – large language models and diffusion models – to generate marketing email subjects and content that result in higher engagement. The system uses finetuned large language models for compelling email subject generation and finetuned Stable Diffusion model for visually appealing and convincing email content images creation. For the latter, both knowledge graphs and vector embeddings have been incorporated to improve contextual relevance. Experimental results demonstrated significant improvement in all engagement metrics that marketers rely on, including 46% growth in open rates, 56% higher click-through rates, and an 51% boost in conversion rates, all compared to human generated content. The unified approach presented by this paper outperforms standalone models and human-generated content in terms of engagement, as the comparative analysis shows. We also discuss the ethical considerations related to content bias and personalization boundaries, alongside challenges faced in this type of projects, such as computation demands and probable solutions. Finally, this paper proposes future directions to be taken, including expansion to other digital marketing channels, the use of other advanced artificial intelligence techniques, and the development of real-time content adaptation mechanisms based on user feedback.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2025-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v15i5.pp4875-4888</dc:identifier><dc:identifier>10.11591/ijece.v15i5.pp4875-4888</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 15 No 5 (2025); 4875</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/39331/18456</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>