LLM-Generated Ads: From Personalization Parity to Persuasion Superiority
Elyas Meguellati, Stefano Civelli, Lei Han, Abraham Bernstein, Shazia Sadiq, Gianluca Demartini

TL;DR
This study shows that large language models can generate persuasive advertisements that outperform human-created content across key psychological principles, marking a shift from mere personalization to persuasive superiority.
Contribution
The paper demonstrates that LLMs not only match human performance in personalized ads but also surpass humans in universal persuasion, highlighting their potential for advertising innovation.
Findings
LLMs achieved statistical parity with humans in personalized ads.
AI-generated ads significantly outperformed humans in persuasion across four principles.
Even with detection penalties, AI ads remained more persuasive than human ads.
Abstract
As large language models (LLMs) become increasingly capable of generating persuasive content, understanding their effectiveness across different advertising strategies becomes critical. This paper presents a two-part investigation examining LLM-generated advertising through complementary lenses: (1) personality-based and (2) psychological persuasion principles. In our first study (n=400), we tested whether LLMs could generate personalized advertisements tailored to specific personality traits (openness and neuroticism) and how their performance compared to human experts. Results showed that LLM-generated ads achieved statistical parity with human-written ads (51.1% vs. 48.9%, p > 0.05), with no significant performance differences for matched personalities. Building on these insights, our second study (n=800) shifted focus from individual personalization to universal persuasion,…
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Taxonomy
TopicsAI in Service Interactions · Digital Marketing and Social Media · Media Influence and Health
