LLMs and their influence on traffic to a brand’s website are a major topic in our client conversations. Everyone wants to know what’s happening, how they can do better, and what the best practices are.
My recommendation to brands right now is to start with the data and focus on what they can know for sure.
To glean insights into how LLM traffic is influencing key metrics, we analyzed our dataset of LLM prompt referral traffic in Google Analytics across our customer base over the last 13 months (Jan. 1, 2025 to Feb. 7, 2026).Â
We focused on traffic from various LLM models to brand sites and the conversion events closest to true business outcomes. In some cases, that’s a purchase. In others, it’s a generated lead.
When we look at this dataset, four major findings rise to the surface:
- LLM referral traffic is still small.
- LLM traffic is growing fast.
- The sources referenced in responses are shifting.
- LLMs convert at a very high rate compared to other channels
LLM referral traffic is still small
LLM referral traffic accounts for less than 2% of total referral traffic on average, according to our dataset. In other words, fewer than 2 out of 100 visitors to a site come from an LLM referring source.
The range is 0.15%-1.5% of referral traffic coming from various LLMs, including ChatGPT, Perplexity, Gemini, and Claude.
So while this is a major topic of conversation, it isn’t the highest priority for near-term bottom-line impact for many businesses.
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LLM traffic is growing fast
LLMs, as a referral source, are growing quickly, according to our data. Comparing the first half of 2025 with the second half, we saw an average growth rate of 80% in LLM referral traffic.
There was a wide range across the dataset. Some companies saw just 10% growth, while others experienced 300% increases.
Below is the aggregate referral traffic by month in 2025. It shows a steady month-by-month increase, building to 3x referral traffic growth from January to December.
That means it’s not enough to understand your volume of LLM traffic. You also need to monitor the velocity of that growth.
LLMs are expanding as consumer adoption grows, and prompt algorithms keep changing. Between those two variables, you can see dramatic swings that you need to monitor.
Dig deeper: LLM optimization in 2026: Tracking, visibility, and what’s next for AI discovery
Sources referenced in responses are shifting
The sources cited in LLM responses are changing quickly.
Here’s a look at our dataset since September of last year. The data comes from monitoring more than 5,000 prompts and their responses across various LLM APIs, including Gemini,…
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