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It took only a few short years for AI search engines and Google AI Overviews to surpass traditional search engine results pages (SERPs) in popularity. A 2025 survey found that only 8% of users who received an AI summary after a Google search clicked on links to other websites, compared to 15% who would click if they did not see a Google AI Overview.
This indicates a clear need for marketers to create and promote content so it appears in AI overviews. As these short summaries originate from various web sources and are combined to deliver the most accurate answers to inquiries, they typically include citations to support information. These citations serve as reference points and draw traffic that websites would otherwise gain through organic searches.
Now, therefore, it’s every marketer’s goal to track the impact of their content by measuring citations in generative AI query answers on platforms like Google AI Overviews and ChatGPT.
The Measurement Gap: Why Traditional Rank Tracking Is Failing
Search is changing, and the traditional ranking metrics no longer apply. Old-style search engine optimization (SEO) focused on getting pages to rank in positions 1-10 in results. Now, Google users don’t have to dive deeper than the Google AI Overviews or Search Generative Experiences (SGE).
AI search engines now provide complete answers to user queries, whether they’re looking at ChatGPT, Google AI Overviews, or other AI tools. But while these AI-generated answers might take information from your website pages, you can’t rely on traditional content tracking methods to identify referrals. That’s because those traditional methods were designed for website traffic patterns that existed before AEO. That’s especially the case when users now have conversations with AI platforms that make “clicks” harder to attribute.
AI search tracking is the new way to determine your website’s performance. It tracks your AI visibility by measuring your “Share of Model” (SoM). Your SoM refers to the frequency with which large language models (LLMs) that power AI search platforms cite or recommend your brand in their outputs.
Key Methodologies for Measuring AI Visibility
There are now tools available for AI search tracking. With these tools, you can assess your brand visibility on critical LLMs. There are two key aspects to this assessment.
First, there’s monitoring of when your brand appears in AI answers to user questions. Retrieval-augmented generation (RAG) monitoring checks to see which models are using your live website content to answer real-time user queries.
Second, there’s sentiment and context mapping. This goes beyond brand mentions in AI search tracking to analyze the framing of your content. For example, are you a luxury, budget, or expert option in AI output? Mapping can tell you whether your AI framing is aligned with your brand goals.
This kind of AI search tracking relies on key metrics that are now fundamental in the modern era of search.
Share of Model (SoM) vs. Share of Voice (SoV)
Old-style SEO measures rank in search engine results. Modern AI search tracking looks at the frequency of your brand mentions across AI platforms and how you stack up against competitors. The first is Share of Model (SoM) and the second is Share of Voice (SoV).
SoM is, in many ways, the new pie chart: it’s a calculation of your brand’s percentage of citations within a specific topic area across several LLMs. Those models include ChatGPT, Claude, Gemini, and Perplexity.
Beyond these AI search engine mentions is your brand’s performance compared to others in your market. SoV shows how competitive benchmarking lets you measure your brand presence against others within AI responses.
Citation Velocity and Retention
AI search tracking allows you to measure user engagement as well as your brand’s AI search visibility. You can measure how long your brand’s online content is a cited source for an LLM before the model changes to a different online authority. This is your brand’s staying power in an LLM.
There’s also the new click-through rate (CTR) with AI search tracking. It’s now called your link-through rate (LTR) and refers to the number of users who click the hyperlinked citation in an AI-generated answer to find out more about your brand.
The AI Search Tracking Toolkit
There is a growing number of AI search monitoring tools you can use to gather your brand’s AI visibility data, including a burgeoning market of commercial answer engine optimization (AEO) tools.
Although these AI search monitoring tools may prove invaluable to brands, there’s still the necessity of a virtual, on-the-ground “vibe check” to see the tone an LLM takes with your brand identity. At elk Marketing, we still perform these qualitative “vibes-based” audits so you know not only when AI mentions your brand, but how.
Developing an Attribution Model for the AI Era
Early data from 2024 showed a drop in website traffic after the implementation of Google’s AI Overviews. Search engine users might no longer scroll down and click through the SERPs. They might stop at the AI-generated overview or summary, which often cites individual pages.
For marketers, this means they may now earn traffic from a major AI search engine summary instead of a blue link in search page results. Accordingly, they might want to track this new route of traffic referral to calculate their digital marketing return on investment (ROI).
As the AI search tracking era evolves, there are new ways to look at how your content performs and how people find you, and they take AEO into account.
Bridging the Gap Between Search and Conversion
A new attribution model has to analyze how search through AI engines leads to conversion. It has to trace your traffic source back to the halo effect of AI mentions, even after numerous touchpoints for your brand.
The multitouch journey might start with brand mentions in a ChatGPT citation. Users might follow the citation and verify your brand’s legitimacy through your LinkedIn page. If their curiosity or interest is maintained, they might search for you directly. As with traditional multitouch attribution, you can credit this conversion to several sources, but must start with the AI engine.
Some existing tools can help you track AI engine conversions. You can configure Google Analytics 4 (GA4) to identify referral traffic from OpenAI, Perplexity, and other LLMs.
Natural Language Query (NLQ) Analysis
Beyond mapping traffic referral pathways, a modern attribution model should also analyze the queries that lead to your brand mentions. This natural language analysis looks at the conversational questions that people put into engines like ChatGPT.
This is similar to matching long-tail search queries to results page rank in traditional SEO. In the AI engine context, it shows marketing teams what “problem” the AI thinks your brand is best equipped to solve.
Strategic Reporting: Showing Value to the C-Suite
AI search tracking and updated attribution models are the building blocks of a new way to measure the return on investment (ROI) of your content. C-suite executives might need a clearer explanation of how these new measurements show the potential value of content. Demonstrating the value proposition of your content to decision-makers might start with giving them a primer on how users now find and engage with your brand online.
Some new evaluative metrics might include the AI presence scorecard, which looks at authority scores instead of rankings. Instead of cost-per-click, you may also want to measure the cost per citation, where the effectiveness of a piece of content at fueling multiple AI answers is the gauge of your investment return.
Fundamentally, your brand needs to start with baseline AI search tracking now. That way, you are poised to identify long-term growth trends, especially as AI search engines gain new users and upend your brand visibility compared to traditional search.
Tracking Your AI Search Presence for Brand Visibility, TL;DR
Measuring content impact now depends on a new model that combines AI search tracking and conversion attribution back to AI models.
As online search evolves, marketers can’t rely only on third-party tools to analyze brand visibility; the vibe-check of how AI uses your brand is also vital. It’s a new way to monitor how your brand performs online now that click-throughs have been replaced by link-throughs in a conversation with an AI.
Ready to take control of your brand’s AI search presence? Contact us today and let’s build your visibility strategy together.
FAQs
Can I see my “rank” inside ChatGPT?
No, because there is no “ranking” like there is in traditional SEO search. Unlike Google search results, ChatGPT doesn’t have a “page one.” However, you can assess your AI search performance by tracking the probability of brand mentions in response to specific seed prompts.
Does Perplexity show up in my Google Analytics?
Yes, under the category of “referral” traffic. To better track hits to your site from AI agents, consider setting up a segment in GA4 to monitor this activity. It allows you to see when people arrive at your site through a generative answer citation or recommendation.
Why does my AI visibility score keep changing?
AI models are under constant change. LLMs receive regular training from developers, but also learn through user queries and live result retrieval. Their retrieval algorithms are dynamic and responsive to user input. AEO in this early stage is, therefore, highly volatile, and your brand visibility score can change frequently.
Is there a “Search Console” for AI?
Not yet. Analysts still have to use a combination of third-party tools and custom programs to gather and assess data from different LLMs. Once this analytical package is established, brands can use it for ongoing AI search performance monitoring.
What’s the most important metric for AI search?
Right now, marketers focus on citation authority (CA). This measures how often an LLM chooses your brand as the definitive source for a specific fact or recommendation.
