arXiv:2606.09623v1 Announce Type: new Abstract: When running marketing campaigns, retailers must decide which products to promote and which users to target. These decisions are inherently coupled: effective campaigns match users and items with strong mutual affinity into non-overlapping groups of predefined sizes. However, existing approaches assume predefined campaign structure or decouple item selection from user assignment, and cannot discover campaign groupings directly from joint interaction patterns. We therefore formalize this campaign problem as auto-targeting: jointly selecting users
The increasing sophistication of AI models and access to vast user interaction data in e-commerce facilitate the development of more advanced targeting methodologies.
This research outlines a method for optimizing marketing campaign efficacy by directly linking user characteristics with product promotions, leading to higher conversion rates and improved ROI for retailers.
Existing approaches that decouple item selection from user assignment are challenged by a new formalism that jointly optimizes user-item allocation for marketing campaigns.
- · E-commerce retailers
- · AI/ML marketing solution providers
- · Consumers (more relevant ads)
- · Traditional marketing agencies (if not adapting)
- · Less efficient advertising platforms
Retailers will achieve more efficient allocation of marketing spend and higher conversion rates through integrated user-item targeting.
Increased efficiency in marketing could intensify competition in e-commerce, driving innovations in personalization and customer experience.
As AI optimizes targeting, regulatory scrutiny around data privacy and algorithmic bias in consumer profiling may increase significantly.
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Read at arXiv cs.LG