As AI visibility shifts from a theoretical concept to a measurable marketing channel, data is beginning to reveal how LLM-driven traffic behaves compared to traditional search. A recent study by Visibility Labs analyzed 12 months of GA4 data (January – December 2025) across 94 eCommerce stores to quantify the performance of ChatGPT vs. Organic Search across commercial-intent traffic.
Is AI Search More Valuable? ChatGPT Conversion Data Analyzed
For Operyn users tracking AI citations and brand mentions, these findings provide a benchmark for the commercial value of AI visibility.
Research Methodology
To ensure comparability between ChatGPT vs. Organic Search, the study utilized the following filtering framework:
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Data Source: 12 months of GA4 data (Jan–Dec 2025) from 94 eCommerce stores.
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Sample Size: The analysis covered 9.46M organic sessions ($32.1M revenue) vs. 135k ChatGPT sessions ($474k revenue).
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Inclusion/Exclusion Criteria: Researchers intentionally excluded homepage traffic (to remove branded bias) and blog post traffic (to remove low-intent informational queries).
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Target: The focus remained strictly on commercial-intent traffic, which is defined as visitors actively looking to evaluate and purchase products.
Key Findings in ChatGPT vs. Organic Search Performance
1. Higher Intent: ChatGPT Converts 31% Better
The study found that ChatGPT traffic converted at an average rate of 1.81%, compared to 1.39% for non-branded organic search.
This conversion advantage is likely due to the “compressed” buyer’s journey. Unlike traditional search, where users often browse multiple results, ChatGPT users frequently refine their needs through dialogue before clicking. By the time a user follows a link from an LLM to a site, they have already moved past the awareness and consideration stages.
2. Growth and Volume Trends
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Explosive Growth: ChatGPT-referred visits grew by 1,079% over the 12-month period, largely accelerated by the introduction of shopping carousels in April 2025.
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Market Share: Despite this growth, ChatGPT remains a small fraction of total traffic. In Q4 2025, non-branded organic search traffic was still 47x larger than ChatGPT traffic.
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Revenue Contribution: While ChatGPT generated 1.48% of total non-branded organic search revenue for the full year ($474K vs. $32.1M), its impact is accelerating. The data shows its revenue share grew to 2.2% in the second half of 2025, which is a significant upward trend in commercial contribution.
3. Financial Metrics: Lower AOV, Higher RPS
The quality of the traffic presents a nuanced financial profile:
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Average Order Value (AOV): ChatGPT traffic had a 14.3% lower AOV ($204 vs. $238 for organic).
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Revenue Per Session (RPS): Because the conversion rate is significantly higher, the net value per visitor remains higher for AI. ChatGPT sessions generated $3.65 per session, a 10.3% lift over organic search ($3.30).
The “Dark” AI Funnel
The research highlights a critical tracking limitation: GA4 referral data likely undercounts the true impact of LLMs.
The study suggests a common user behavior:
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User receives a product recommendation in ChatGPT.
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User leaves ChatGPT and searches for that specific brand or product in Google.
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The purchase is attributed to branded organic search, masking the original AI influence.
Because of this “leakage,” the study recommends implementing post-purchase surveys to capture the true origin of the customer journey. This attribution gap explains why a structured measurement framework for AI-Generated Traffic is required to separate direct LLM referrals from AI-influenced branded search.
Diagnostic Takeaway for Operyn Users
The data confirms that while AI traffic volume is currently a small percentage of total search, the intent quality of that traffic is superior. From that observation emerges three structural shifts.
First, AI-referred traffic behaves like compressed bottom-funnel demand. Conversion rates are higher, revenue per session is higher, and users arrive with clearer purchase intent. Even at low volume, the commercial density of this traffic is disproportionate.
Second, direct LLM referrals likely understate total AI influence. If users receive recommendations inside ChatGPT and later convert through branded search, traditional analytics will attribute the revenue to organic search rather than AI. This creates a visibility gap between influence and measurable sessions.
Third, AI visibility appears to precede measurable traffic growth. The jump from a 1.48% yearly revenue equivalent to 2.2% in H2 2025 suggests that AI exposure compounds before it becomes materially visible in traffic dashboards.
This reframes the problem. AI visibility is not just another acquisition channel. It is an upstream influence layer that compresses intent and redistributes attribution. Brands measuring only sessions and last-click revenue will systematically undercount its impact.
For Operyn users, the implication is structural. If AI systems influence brand selection before the click, then measuring citations, mentions, and representation consistency becomes as important as measuring traffic. Without visibility-level analytics, revenue allocation decisions will be based on incomplete data.
AI traffic may still be small in volume. Its influence is not.
