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Study: ChatGPT referral traffic gains overstated by platform growth

A new study published on arXiv investigates the effectiveness of "Answer Engine Optimization" (AEO) for driving referral traffic from LLMs like ChatGPT. The research, conducted on a YouTube Q&A domain, found that while raw referral growth appeared significant (5.7x), this was largely due to the platform's overall growth (3.5x for untreated pages). After controlling for platform growth, the AEO interventions showed a more modest, though still suggestive, increase in traffic (1.82x). The study emphasizes the importance of separating intervention effects from platform tailwinds to accurately measure AEO's causal impact. AI

IMPACT Highlights the need for careful methodology when evaluating the impact of LLM-driven traffic on web content.

RANK_REASON Academic paper analyzing LLM referral traffic and optimization techniques.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Keisuke Watanabe, Kazuki Nakayashiki ·

    Disentangling Answer Engine Optimization from Platform Growth: A Log-Based Natural Experiment on ChatGPT Referral Traffic

    arXiv:2606.04362v1 Announce Type: cross Abstract: Large language model (LLM) "answer engines" such as ChatGPT now send measurable referral traffic to the open web, and a practice analogous to search engine optimization, here called Answer Engine Optimization (AEO), has emerged. P…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Kazuki Nakayashiki ·

    Disentangling Answer Engine Optimization from Platform Growth: A Log-Based Natural Experiment on ChatGPT Referral Traffic

    Large language model (LLM) "answer engines" such as ChatGPT now send measurable referral traffic to the open web, and a practice analogous to search engine optimization, here called Answer Engine Optimization (AEO), has emerged. Public AEO success stories typically quote large ra…