traffic from Generative AI / LLMs (like ChatGPT, Perplexity, Claude, Google Gemini/AI Mode, and more) is exploding. At Adobe we've 

 foresees a 50% decrease in brands' organic search traffic by 2028. And how 

 has seen an 800% increase in LLM referral traffic between Q3 and Q4 of 2024. Even 

 has shown a 3,500% increase in traffic to US retail sites from generative AI sources.

Everyone is also asking the same question:

how can I confidently identify this LLM traffic? Sure, I'm being told that referrals are increasing, I'm seeing organic search referrers are dropping, but what are my options for identifying this traffic?

There are many LLMs and there are many ways to prompt those LLMs. Just off the top of my head:

2. ChatGPT MacOS/Windows/iOS/Android Apps

4. Perplexity MacOS/Windows/iOS/Android Apps

10. Microsoft Copilot on the web (there's multiple domains for this for some reason)

11. Copilot MacOS/Windows/iOS/Android Apps (and again, somehow I've got two different Copilot apps installed on my Mac, so...)

And as you either already know, learned from my 

Web traffic can be identified in two ways.

 information provides websites the exact URL of the previous page that linked the user to your web page. This is extremely valuable information - it's what identifies that a user cam from a social media page, or a search engine, or if it's missing, entered your webpage in directly. Over the past few decades, referrer data has degraded. Google was the first one to purposefully degrade it - in the name of privacy they removed the keyword that was searched on Google.com so marketers could no longer see the term. Instead, we can now only see that the user searched on Google, and landed on the website, because of this referrer data. Is removing it from the referring URL actually helping consumer privacy? Decide for yourself as you learn about..

 can be stuffed with valuable information about the referring page that marketers can use to attribute behavior back to. For example, if I share this link on 

: https://blog.ericmatisoff.com/?cid=bluesky then I can use my web analytics tool to align the clickthrough and any subsequent behavior back to that link, because I included 

 in the URL. Of course, this doesn't help for organically clicked links - whether search results or social media posts or anything that the website owner doesn't own - because as a marketer, I can't demand that every single person on the Internet uses a unique query parameter. So generally, these query parameters are used to differentiate Paid and Owned links, but not Earned. Paid links like Paid Search, Paid Social, Display, Marketing Emails can put anything into their URLs, including query parameters. This means that Paid Search Ads on Google can have query parameters in them that identify the keyword that the visitor searched in them. I guess consumer privacy matters less if they click a paid ad... The same can be said for Owned links - these are links on other parts of the web that the brand owns. Whether unpaid social posts from the brand or other domains that are linking, query parameters can help attribute behavior back to them.

That's the right question to ask - so what? Well, since these are the only two ways to identify referring traffic, it must also be the only way that marketers can identify traffic from those 11+ LLMs I have listed above. Sounds easy, right? Just get a list of the domains and use them to identify LLM-driven traffic. Boom. blog post done?

But what about the apps? Well, apps can't pass referrers to your browser the way that websites can. And since these apps are sending links that are neither Paid nor Owned, we just have to hope that the LLMs are sending information in query parameters out of the kindness of their hearts? Well guess what, some of them are. Sometimes. And others aren't. Usually.

Nope. Or at least there's barely any consistency across the different LLMs, in fact, there isn't even significant consistency across links within a single LLM.

Well they're not completely inconsistent. They're a little bit consistent in that when the LLMs that send query parameters do feel like sending query parameters, they use 

. So this means if chatgpt adds a query parameter to the landing page that it's sending visitors to, it'll look like: https://blog.ericmatisoff.com/?

I've seen perplexity use this same utm_source query parameter, but for them it'd look like: https://blog.ericmatisoff.com/?

So I set out on a mission to try and track whether or not Referrers or Query Parameters are getting set across these LLMs. It's messy business, because the LLMs each have multiple ways of using them (.com, OS apps, mobile apps...) and multiple pay tiers (does Pro set different parameters than Plus?), AND even multiple places that they link to websites (directly inline in the response vs the "sources" of the response, and probably more).

This means that sometimes, chatgpt.com includes the utm_source query parameter, sometimes it doesn't. Yet Gemini, Claude, and Copilot have never sent a query parameter in my testing.

Either way, I set out on this mission to try and figure out what is being set consistently, what is being set inconsistently, and what is totally missing.

Here's a first stab at organizing the results of my research. I'm going to keep testing and occasionally update this table as I find more consistency.

Microsoft Copilot at https://m365.cloud.microsoft

See what I mean? A ton of inconsistency. And honestly kind of crazy  that Google doesn't have a Query Parameter. I mean, this means it's just getting bucketed into Organic Search? That's koo koo.

For ChatGPT, it seems that they're angling to use referrals with the utm_source=chatgpt.com query parameter to 

. I'm going to keep testing with multiple prompts to see if the query parameter only shows for these types of use cases. More on this soon.

Anyway - I've got a number of different prompts that I'm running to test these out and I end up clicking each of the different links in the LLM response. This means that the "sometimes" responses in the Query Parameters answers above means that some links have it, some don't, sometimes those same links do have the query parameter on some prompts, and sometimes in other prompts, links in the same location don't have the query parameter. 

In fact, yes. This article has so far only focused on traffic that is referred to websites from LLMs. But what about when an LLM is scraping content from your site in order to provide answers to prompts? That's a whole other consideration. I'll give you the CliffsNotes. 

Wouldn't it be great to know what information LLMs are scraping from your site? Giving you, essentially, a glimpse directly into the type of content being used to reply to LLM prompts? Of course it would be! But there's a challenge - LLMs use crawlers that generally don't trigger JavaScript. And this means that your average web analytics platform won't even know they're there. Web logs and CDN logs do, however, still have this behavioral data. All you need is access to those logs, and then to know which user-agents each of the LLMs are using (here are 

) and then you can see which pages on your site are being accessed.

Is this easy? Oh heck no. But that's where technologies like 

 come in to play - to make that data more accessible. 

Oh and one more thing - Operators. We've already started to see these newfangled things where Operators or Agents are doing the bidding of the user based on a prompt. 

ChatGPT Agent (as Operator is now called)

 can do exactly this. Prompt "build me an agenda for an upcoming trip to Chicago on my Google Calendar and book some reservations for dinner each night". The agent will then go to my calendar, ask me to log in, then find the trip, confirm it with me, then find some dinner reservation ideas, check with me to see if I'm interested, then book them on my behalf. Now THIS experience - this uses JavaScript and will be captured in web analytics tools. Well, assuming the user-agent isn't blocked by any bot-blocking services that you're using. So it's worth looking into this by checking your Data Feeds and your bot data to understand what is and isn't filtered out, and then to analyze it.

Yeah, I know, it's a lot. I hope the information is still helpful for you, even if all over the place - because there's just so much changing and so rapidly. Keep an eye out for 

content from my Adobe colleague, Brian Au

, who helped with this article and will be publishing content on these topics and more.

If you've got other LLMs that you'd like me to test send them my way. Next up is testing iOS apps too. 

If you're seeing consistently different results, send them my way too. This latest tech wild west is changing rapidly.

Go forth and analyze that LLM referral traffic (if you can), you Rockstar you!

Identifying Traffic from Generative AI / LLMs

Surprise! Just when you were starting to get excited for Adobe Summit London I’m thrilled to announce the return of Analytics Rockstar to London! This fun session is sure to be a highlight, bringing together the brightest minds in Adobe Analytics to share their top tips and tricks. If you’ve attended Summit in-person or virtually before, then you might have witnessed this fun, fast-paced, and informative breakout session where real Adobe Analytics users share their top tips and tricks. At minimum - sign up for the 

Is 2025 the year you decide to climb up on stage to share your own special tips and tricks?

To be considered for the Analytics Rockstar competition, you must submit your two best Adobe Analytics or Adobe Customer Journey Analytics tips using this 

. To give some context for the kinds of tips we’re looking for, here’s what each panelist presented at 

 (Supervisor, Digital Analytics, Cardinal Health) discussed Accelerating Your ICID Results With Precision, and Dynamically Enhancing Your ICID.

 (Optimization Manager Analytics, Torstar) provided tips on Reporting By Content Data, and Campaigns/Marketing Channels For Mobile Apps.

 (Sr. eCommerce Analyst, Staples Canada) shared tips on Calculating Weighted Comps, and Using Regressions to Estimate Missing Data.

 (Sr. Consultant, Digital Analytics & Insights, BlastX Consulting) offered tips on the Journey Canvas For Winning Or Losing With Your Redesign, and Game-Winning Marketing Channels.

We’re looking for tips that can help your peers in digital analytics to uncover new and deeper insights or perform their daily tasks more efficiently or effectively. Ultimately, your judges will be the session attendees in the audience as they will be given the opportunity to vote for their favorite tips live in the session. Ideas don’t have to be overly complicated or technical to be well-received, and you may be surprised how beneficial your ideas could be to others.

 will be a great opportunity to establish your personal brand within the industry, share your expertise with grateful peers, and even win some cool prizes! 

 must be received by Wednesday, June 25, 2025. We’ll reach out to all of the applicants the following week with next steps.

🏴󠁧󠁢󠁥󠁮󠁧󠁿 London Analytics Rockstar 🎤

So here's something cool that I did recently in Customer Journey Analytics that I couldn't keep to myself. In my implementation of the Adobe Experience Platform Web SDK on adobestore.com, I have this issue: lots of products have multiple sizes. Here's an example, the 

Here's why products like these cause me, and analysts like you, headaches: when a person visits the page, they haven't selected a product size. So Product Views are attributed to a Product named: 

But then when the user adds the product to cart, checks out, and purchases, we have the sizing information. Unfortunately the data layer (or CSS that I'm scraping 🙈) has both the product name and size during those events. For example: 

If you've tried to analyze retail products before, I bet you'll know what this means. Metrics are getting sent to two different product names!

So if I want to create a conversion rate of Orders per Product View ... I can't!

 - Customer Journey Analytics Data Views are here! 🦸🏻

Of course, I could use Classifications (called Lookup Datasets in CJA) to solve for this, but that's cumbersome and requires effort. I want a quick, retroactive fix that I can set and forget.

So my thinking was: what if I create a Regex that consolidates each of these together and strips the sizing information out? That way all of the Metrics will align to the Product Name without the size!

[here's a secret: my regex skills aren't what they once were. So I used 

 to help me build this, and it worked seamlessly!]

So I added a Regex Substring to the Dimension:

(just be sure to test it. For adobestore.com, for products that have sizing, we always capture it in parentheses at the end.)

And you want to see how it worked out? Of course you do!

Isn't that beautiful? Notice how all the Metrics line up correctly and each in a single row of data. So I could create conversion rates, calculated metrics, segments, and more with ease!

And yes, of course I made a separate Dimension (hooray for duplicating Components in Data Views) that focuses on Product Sizing:

Super cool, isn't it? And of course this isn't limited to just sizes or just product names. Any Dimension in CJA that acts this way could easily be consolidated with a little help from our Regex friends.

Go forth and consolidate those Product Names (or whatever!), you Rockstar you!

Analytics Rockstar 🤘

Using Regex to Consolidate Product Variations in CJA

More Stories

Identifying Traffic from Generative AI / LLMs

🏴󠁧󠁢󠁥󠁮󠁧󠁿 London Analytics Rockstar 🎤