ChatGPT Referrals to E-Commerce Websites: Do LLMs Outperform Traditional Channels?

Abstract

We investigate the performance of organic Large Language Model traffic (oLLM) versus traditional digital channels in e-commerce. Analyzing 12 months of first-party data from 973 websites with $20 billion combined revenue, we examine over 50,000 transactions from ChatGPT referrals alongside 164 million transactions from traditional channels. Using regression models that account for data sparsity, we assess financial metrics (conversion rate, average order value, revenue per session) and engagement metrics (bounce rate, session duration, page views). Results contradict widespread expectations of oLLM superiority. Organic LLM traffic underperforms all traditional channels except paid social media across key financial metrics. While oLLM achieves favorable bounce rates, indicating relevance, it generates lower conversion rates and revenue per session than Google’s paid and organic search channels. The performance gap persists across extensive robustness checks varying aggregation periods, observation thresholds, and website samples. Despite current underperformance, oLLM shows positive trajectories. Conversion rates improved over the observation period, though declining average order values offset some gains. Time-trend analyses suggest gradual convergence with traditional channels, but projections indicate oLLM will not achieve parity with organic search within the next year. These findings challenge narratives of LLMs as immediate “Google killers” while suggesting potential for long-term channel evolution.