Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.20024 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914050225471488 |
|---|---|
| author | Jobson, Deddy Shukla, Muktti Dinh, Phuong Young, Julio Christian Pittoni, Nick Chen, Nina Ginstrom, Ryan |
| author_facet | Jobson, Deddy Shukla, Muktti Dinh, Phuong Young, Julio Christian Pittoni, Nick Chen, Nina Ginstrom, Ryan |
| contents | E-commerce marketplaces make use of a number of marketing channels like emails, push notifications, etc. to reach their users and stimulate purchases. Personalized emails especially are a popular touch point for marketers to inform users of latest items in stock, especially for those who stopped visiting the marketplace. Such emails contain personalized recommendations tailored to each user's interests, enticing users to buy relevant items. A common limitation of these emails is that the primary entry point, the title of the email, tends to follow fixed templates, failing to inspire enough interest in the contents. In this work, we explore the potential of large language models (LLMs) for generating thematic titles that reflect the personalized content of the emails. We perform offline simulations and conduct online experiments on the order of millions of users, finding our techniques useful in improving the engagement between customers and our emails. We highlight key findings and learnings as we productionize the safe and automated generation of email titles for millions of users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20024 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using item recommendations and LLMs in marketing email titles Jobson, Deddy Shukla, Muktti Dinh, Phuong Young, Julio Christian Pittoni, Nick Chen, Nina Ginstrom, Ryan Machine Learning E-commerce marketplaces make use of a number of marketing channels like emails, push notifications, etc. to reach their users and stimulate purchases. Personalized emails especially are a popular touch point for marketers to inform users of latest items in stock, especially for those who stopped visiting the marketplace. Such emails contain personalized recommendations tailored to each user's interests, enticing users to buy relevant items. A common limitation of these emails is that the primary entry point, the title of the email, tends to follow fixed templates, failing to inspire enough interest in the contents. In this work, we explore the potential of large language models (LLMs) for generating thematic titles that reflect the personalized content of the emails. We perform offline simulations and conduct online experiments on the order of millions of users, finding our techniques useful in improving the engagement between customers and our emails. We highlight key findings and learnings as we productionize the safe and automated generation of email titles for millions of users. |
| title | Using item recommendations and LLMs in marketing email titles |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.20024 |