The Impact of Generative AI on Personalized Content Marketing in E-Commerce

Abstract

This research investigates the effectiveness of Generative Artificial Intelligence (GenAI) in creating hyper-personalized marketing content compared to traditional rule-based personalization. While GenAI offers unprecedented scale, empirical evidence regarding its impact on consumer behavior and ethical perception remains limited.

Using a sequential mixed-methods design, a randomized A/B experiment (N = 1,140) compared GenAI-driven personalization (GPT-4 and DALL-E 3) against traditional rule-based methods in a simulated retail environment. Post-experiment surveys (n = 480) assessed perceived authenticity, privacy concerns, and AI detection accuracy. Qualitative insights were gathered through semi-structured interviews with six marketing managers from three e-commerce brands.

GenAI personalization significantly outperformed rule-based methods, increasing click-through rates by 35.2% (24.6% vs. 18.2%; $p = 0.005$) and conversion rates by 38.1% (11.6% vs. 8.4%; p = 0.032). Time-on-site increased by 16.3 seconds (Cohen’s d = 0.49). However, AI disclosure negatively impacted brand authenticity (d = -0.64), trust (d = -0.65), and purchase intention ($d = -0.55$). While 77.5% of consumers desired an opt-out setting for AI personalization, they were unable to reliably distinguish AI from human content (accuracy = 53.3%). Qualitative data highlighted "human-in-the-loop" processes as essential to mitigating hallucination risks.

The study contributes the first direct empirical benchmark of GenAI over rule-based personalization in e-commerce. It identifies a "detection-disclosure paradox"—where consumers cannot recognize AI content but remain suspicious when it is disclosed and proposes a Transparent Hybrid Model to balance operational efficiency with ethical protection and brand trust.

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