LLM-referred traffic to online stores grew 6.5x in the 12 months from May 2025 to April 2026, measured on a like-for-like cohort of US brands (about 10% of the dataset) tracked continuously across the full period. That is a 550% increase in a single year, and it happened while total traffic to those same brands stayed flat. The growth is a channel shift, not brand growth.
This is not a forecast; it is a measurement of what already happened, drawn from a first-party dataset of roughly 310 retail brands and about 190 million visitors. We are Alhena AI's data team, and the numbers come from our own on-site shopping and support agents, which observe how visitors arrive, what they ask, and whether they check out. Every figure carries its population and window, because a channel this new is easy to overstate.
Data as of April 2026. Published July 2026. This is a new cohort and a longer window than our earlier State of AI Commerce report; the two are not directly comparable, and we explain why below.
Disclosure: Alhena publishes this research and sells the AI Visibility and AI Shopping products that generated the underlying data. We hold our own numbers to the same evidence standard we would apply to anyone else's, and we print the limitations in full.
Key takeaways
- LLM-referred traffic to online stores grew 6.5x (550%) in 12 months on a stable US cohort, a channel shift rather than portfolio growth.
- The channel converts at 2.68% (US, October 2025 to April 2026), ahead of Google Ads and Meta Ads, at zero media cost.
- ChatGPT is 96.1% of the volume, but Perplexity, Copilot, Gemini, and Claude behave differently enough that a single "LLM" number hides the real picture; measure by provider.
- AI-referred shoppers arrive to compare and to ask for a recommendation, not to open a support ticket; the on-site experience should match that intent.
- Fewer than 1 in 10 US brands hold most of the value today, so the opportunity for everyone else is still open.
Key findings
- 6.5x growth in 12 months. LLM-referred traffic to a stable cohort of US brands grew 6.5x (a 550% increase) between May 2025 and April 2026, while total traffic to those brands stayed flat.
- LLM referrals convert at 2.68%. In the US over the October 2025 to April 2026 window, LLM/AI traffic converted at 2.68%, ranking fourth of 13 tracked channels, above Google Ads (1.87%) and Meta Ads (0.51%), at no media cost.
- ChatGPT is 96.1% of the channel. ChatGPT accounted for 96.1% of US LLM-referred traffic over 12 months. Every other engine combined was under 4%.
- Perplexity shoppers spend 82% more. Perplexity's average order value was $129 versus ChatGPT's $71 (US, October 2025 to April 2026), an 82% gap.
- AI shoppers arrive in research mode. LLM-referred shoppers were 8.9 percentage points more likely to ask a skin or body concern question and 3.9 points more likely to ask a comparison question than non-LLM shoppers.
- The channel is concentrated. Fewer than 1 in 10 US brands in the dataset captured roughly 90% of LLM-referred value in the reliable window. For most brands the channel is still open.
What is in the dataset?
The dataset covers approximately 310 retail brands across the US and EU, about 190 million unique visitors (189.76M), 1.6 million checkouts, and $260.6M in observed checkout volume, from May 2025 through April 2026. Visitor and channel data cover the full 12 months. Conversion data is reliable from October 2025 onward, when our cross-brand checkout instrumentation reached portfolio-wide deployment, so every conversion, AOV, and topic figure below is scoped to that shorter window and labeled as such.
We can join arrival channel to purchase in one system because the data comes from our own on-site agents rather than from crawling the web. That first-party vantage point is the same one behind our first-party AI visibility intelligence: the visitor lands from an AI engine, we see what they do, and whether they check out.
Growth comparisons use a stable cohort: brands active in both May 2025 and April 2026 with at least 1,000 visitors in each month. That is 30 US and 8 EU brands. Holding the brand set constant separates a real channel shift from new-brand onboarding, so these numbers survive every fairness adjustment.
How fast is LLM traffic to online stores growing?
On the stable cohort, LLM-referred traffic grew 6.5x between May 2025 and April 2026, a 550% increase. Over the same period, total visitor volume across those exact brands moved only from 3.31 million to 3.43 million per month. Because the denominator barely moved, the growth is demand moving into a new channel, not a portfolio getting bigger.
Measured as a share of all traffic, LLM referrals went from 0.038% of visitors to 0.237% on the US cohort, a 6.3x increase in channel share (530%). Established channels rarely move share by more than 10 to 20% in a year; a 530% move signals a channel that did not exist at scale a year earlier.
ChatGPT drove almost all of it. On the same cohort, ChatGPT-referred visitors grew 6.6x (1,134 to 7,489 per month) while Perplexity grew 2.2x (111 to 245). The AI referral market is consolidating toward ChatGPT faster than the other engines are entering it.
Across the full 310-brand portfolio, LLM-referred visits grew about 29x year over year. We label that number as gross growth: most of the gap between the cohort's 6.5x and the portfolio's 29x is new brands onboarding to AI-commerce tracking, not per-brand demand. The one defensible number is the cohort-normalized 6.5x, not the 29x headline.
The EU told a quieter story. The 8-brand EU stable cohort grew just 1.1x over the same 12 months (3,907 to 4,325 monthly LLM visitors). This is a US-led shift for now, and we say so plainly rather than blending the two regions into a flattering global average.
Do LLM-referred shoppers actually convert?
They do, at a rate that outperforms most paid media. In the US over the October 2025 to April 2026 reliable window, LLM/AI traffic converted at 2.68%, fourth of 13 tracked channels (those with at least 50,000 visitors). That beat Google Ads at 1.87% and was more than five times Meta Ads at 0.51%, two channels that carry billions in annual spend while the LLM channel costs the brand nothing per referral.
| Channel | Conversion rate |
|---|---|
| SMS | 4.18% |
| 4.03% | |
| Direct / Other | 3.02% |
| LLM / AI | 2.68% |
| Affiliate | 2.22% |
| Google Ads | 1.87% |
| Bing Ads | 1.70% |
| Direct | 1.15% |
| Organic Search | 1.07% |
| Google Shopping | 1.05% |
| Meta Ads | 0.51% |
| TikTok Ads | 0.02% |
Conversion rate by channel, US, October 2025 to April 2026, channels with at least 50,000 visitors. Thirteen channels were tracked; one aggregated "other" bucket is omitted here, so LLM/AI is shown in its true fourth position.
The LLM channel's average order value was about $69 in the US over the reliable window, below the portfolio-wide $156. That reflects the smaller-basket categories AI recommends most today, beauty, skincare, and accessories, rather than a lower-value shopper.
One more pattern deserves care. LLM-referred visitors who engaged with our on-site assistant converted at about 4.3x the rate of those who did not. That is an engaged-versus-unengaged comparison, not a controlled test: shoppers who choose to engage are already further along, so this is an association, not proof the assistant caused the lift.
Which AI engines send the traffic, and which send the money?
ChatGPT dominates volume, but it does not have the highest-value shopper. Over 12 months in the US, it accounted for 96.1% of LLM-referred traffic; every other engine combined made up under 4%. Behaviorally, though, "LLM traffic" is not one audience.
| Engine | Share of US LLM traffic | Average order value | Note |
|---|---|---|---|
| ChatGPT | 96.1% | $71 | Volume leader; almost the entire channel |
| Perplexity | 2.6% | $129 | 82% higher AOV than ChatGPT; research-driven |
| Copilot | 0.7% | $64 | Converts at 2.56%, on par with ChatGPT |
| Gemini | 0.3% | $124 | High AOV, low volume |
| Other LLMs | 0.2% | $164 | Kagi, you.com, Brave, Meta AI, character.ai |
| Claude | 0.1% | $295 | Highest AOV; small sample, directional only |
Share reflects 12-month US LLM traffic. AOV reflects the October 2025 to April 2026 reliable conversion window. Claude's AOV is based on 188 visitors and should be read as directional, not statistical.
Perplexity shoppers spent 82% more per order than ChatGPT shoppers, $129 versus $71, consistent with a research-oriented user base doing comparison work before they click. The pattern extends down the tail: lower-volume engines tend to send higher-AOV shoppers, with Claude's small sample of 188 visitors posting the dataset's highest per-order value at $295.
The quiet story is Copilot. It sent only 0.7% of the traffic, but it converted at 2.56%, essentially on par with ChatGPT and ahead of Bing Ads (1.70%) and Google Ads (1.87%). Microsoft has put a small but commerce-ready referrer into the funnel that most brands do not measure separately.
What do AI-referred shoppers ask for?
AI-referred shoppers arrive pre-qualified and in comparison mode. When they engaged with the on-site assistant, 32.5% asked a comparison question ("which is better," "X versus Y"), compared with 28.6% of non-LLM shoppers, a 3.9-point gap. They have narrowed the field and want the tiebreaker, not an introduction.
The largest behavioral gap is category-specific intent. Among LLM-referred shoppers, 20.4% asked about a skin or body concern such as acne, dryness, or anti-aging, versus 11.5% of non-LLM shoppers, an 8.9-point difference and the widest topic delta in the dataset. They arrive framing their need as a problem to solve, and they want a product match.
| On-site question topic | LLM-referred shoppers | Non-LLM shoppers | Difference |
|---|---|---|---|
| Skin or body concern | 20.4% | 11.5% | +8.9 pts |
| Comparison ("which is better") | 32.5% | 28.6% | +3.9 pts |
| Recommendation ("which should I get") | 3.4% | 2.5% | +0.9 pts |
| Sizing or fit | 5.4% | 9.3% | -3.9 pts |
| Order status ("where is my order") | 1.1% | 1.7% | -0.6 pts |
| Returns or refunds | 0.5% | 1.2% | -0.7 pts |
Based on sampled on-site chat messages from LLM-referred visitors October 2025 to April 2026.
What they do not ask is as telling. LLM-referred shoppers were less likely to ask "where is my order" (1.1% versus 1.7%) or about returns (0.5% versus 1.2%): net-new prospects with pre-purchase questions, not customers with a service problem. They were also likelier to ask for an outright recommendation (3.4% versus 2.5%), wanting the assistant to make the call, not rubber-stamp a choice.
The implication is a design choice: if AI-referred shoppers want comparison and recommendation, greeting them with a support-ticket widget wastes the visit. The playbook for selling inside ChatGPT and Gemini starts with a product-discovery conversation, not a help desk.
Who is capturing LLM traffic today?
The channel is real but concentrated. Of the US brands in the dataset, just 10% received 1,000 or more LLM-referred visitors in the seven-month reliable window, and that group captured roughly 90% of LLM-referred value. The other 90% captured little to none, either because their products do not yet surface in AI recommendations or because their content is not structured for AI shopping research to read.
The brands that captured the channel share a profile. Four of the five top US brands by LLM volume were in categories such as fashion, apparels, footwear, skincare, beauty, health & wellness and home improvement. These are categories where the shopper's question is "is this right for me," not "is this in stock," exactly what an AI engine answers well. We do not name individual brands or publish per-brand traffic or revenue.
Methodology
We classify each visitor by their first on-site session in a month for a given brand, inferring the arrival channel from UTM parameters, click identifiers (gclid, fbclid, ttclid, msclkid), and the referring host. LLM traffic is sub-classified into providers using UTM tags and direct referrers, including chatgpt. Checking both signals matters: in one 40-day platform-wide window, UTM-tagged ChatGPT visits outnumbered referrer-identified ones by roughly ten to one, so referrer-only counting would miss most of the channel.com, perplexity.ai, claude.ai, gemini.google.com, and copilot.microsoft.com, plus a long tail of Kagi, Brave, you.com, Meta AI, and character.ai.
Conversion is a same-month attribution model: a visitor's first session in a month is joined to checkout events for the same anonymized fingerprint in that same month. This deliberately conservative rule undercounts true conversion, because a shopper who lands in October and buys in November is not credited to the channel. The undercount applies uniformly across all channels, so the relative comparisons in the conversion table are fair even though the absolute rates are understated.
For the full account of how we score and sample AI visibility, see our companion AI visibility methodology page and AI search revenue attribution writeup.
Limitations
Here is what the data cannot support. Conversion rates are understated: roughly 25% of checkout events could not be matched to a visitor, and cross-month conversions are dropped entirely. The engaged-versus-passive lift is an association, not a controlled result; shoppers self-select into engagement, so we do not claim the assistant caused the difference. And channel share can move as the engines change their own models and routing. What holds up under every adjustment is the cohort-normalized 6.5x growth and the ordering of the conversion table.
The channel shift already happened
The debate about whether AI will change how people shop is over; the data settled it a year ago. A channel that was a rounding error in May 2025 now converts better than the paid channels most brands spend the most on, and it is compounding. The question now is whether you can measure it, because you cannot optimize a channel you cannot see by provider, product, and revenue.
That measurement problem is the one we built for. We can publish arrival-to-checkout numbers because visibility data, an on-site agent, and purchase outcomes live in a single system, which is what lets Alhena trace AI answers to revenue rather than to mentions. If you want to see which engines, products, and pages send you shoppers and which send you nothing, that is what Alhena AI Visibility measures, down to the product level covered in our guide on SKU-level AI visibility. The brands that start measuring now will be in the top 10%, not the bottom 90%.
About the publisher: Alhena AI, founded in 2022 by ex-LinkedIn and Meta engineers, is an Agentic commerce AI platform on a mission to make online shopping more fun, efficient and social. Alhena's product suite spans AI Shopping Agents, Support Concierge, Voice AI, and AI Visibility (AEO and GEO tracking).
