Measuring GEO Success – New Metrics and Tools

In the era of Generative Engine Optimization (GEO), marketers need to rethink how they define and measure success. Traditional SEO metrics like search rankings, click-through rates, and organic traffic volume are no longer the sole barometers of performance.

Instead, success in GEO is about being part of the answer – ensuring that AI-driven search tools reference your brand and content in their responses. This article explores the new metrics and tools for measuring GEO success, from tracking how often your site is cited in AI answers (your “reference rate” ) to monitoring brand sentiment in AI-generated content, and how to adapt your KPIs and processes accordingly.

From Clicks to “References”

For decades, SEO success was gauged by where your site ranked on a search engine results page and how many users clicked through. In the GEO landscape, visibility is measured in references, not just rankings. Large language model (LLM) search interfaces (like chat-based answers and AI summaries) don’t display a list of blue links for users to click – they synthesize information into a direct answer.

That means your brand’s presence depends on whether the AI includes you in its answer, rather than how high you appear on a SERP. In short, “reference rate” – how often your content or brand is cited as a source in AI-generated answers – becomes a key metric of success ( ^[1] ). As one industry expert put it, “It’s no longer just about click-through rates, it’s about reference rates” ( ^[1] ).

GEO entails optimizing for what the model chooses to mention, not just optimizing for a position in traditional search results ( ^[2] ). This shift fundamentally changes how we define online visibility. In the past, a marketer might celebrate a #1 Google ranking that drove thousands of clicks. Now, imagine an AI assistant (like Google’s Search Generative Experience or ChatGPT) answers a user’s question with a paragraph that mentions your brand or quotes your content. Even if the user never clicks a link, your brand has achieved visibility within the answer itself. These “zero-click” interactions are increasingly common. In fact, even before generative AI, over half of Google searches ended without any click to a website as users found answers directly on the results page ( ^[3] ).

Generative AI has amplified this trend by providing rich answers that often preempt the need for a click. Thus, getting mentioned by the AI – as a cited source, a footnote, or even an uncited brand name in the narrative – can be as valuable as getting a click.

One emerging metric in GEO is the reference rate, which measures the frequency of your brand/content being referenced by AI. This could take several forms: 

1. Explicit citations: e.g. your webpage is cited as a source with a hyperlink. Google’s AI Overviews (formerly SGE) often include a handful of source links in an “According to [Site]…” format or a “Learn more from…” section. Bing Chat likewise footnotes its statements with numbered citations linking to websites. If your page appears in those cited sources, that counts as a reference. 
2. Implicit mentions: Sometimes an AI model will mention a brand or product in its answer without a formal citation. For instance, a ChatGPT response (with default settings) might say “Brand X is known for affordable pricing” based on its training data, even if it doesn’t link to Brand X’s site. Such an uncited mention still indicates the model has included your brand in the answer, which contributes to brand awareness (and can prompt the user to search you out separately). 
3. Suggested content and follow-ups: Some AI search experiences suggest related topics or follow-up questions. If your brand appears in those, it’s another form of reference. For example, if a user asks an AI, “What’s the best project management software?” and the AI’s answer lists a few options including your product (with or without a link), that inclusion is a win for GEO. In GEO, we are essentially shifting from “Did the user click my link?” to “Did the AI mention my brand or content?”.

An immediate implication is that brand awareness via AI becomes critical. If an AI frequently references your site as an authority, it not only drives any direct AI referral traffic, but also boosts your brand’s mindshare. Users may see your name in an AI summary and later navigate to your site or Google your brand (this is analogous to how appearing in a featured snippet could raise awareness even among non-clickers). A recent analysis by Ahrefs underscores this dynamic: in a study of 3,000 sites, 63% of websites had at least one visit from an AI source over a short period, yet on average only 0.17% of total traffic came directly from AI chatbots ( ^[7] ) ( ^[8] ).

The takeaway is that while AI-driven visits are currently a small fraction of traffic, a majority of brands are already being touched by AI in some way – often via mentions that may not immediately show up as a click. In other words, you might be getting “reference traffic” (influence and visibility) even when you’re not getting click traffic.

Over 60% of websites in a 2025 study received at least some traffic from AI chatbots. However, the average share of total traffic from AI was only ~0.17%, indicating that AI-driven visibility often doesn’t translate into large click volumes – many user queries are answered without a click ( ^[7] ) ( ^[8] ). This underscores the importance of measuring references (mentions in AI answers) in addition to traditional click metrics.

Because of this shift, SEO practitioners are now talking about metrics like “AI Reference Rate” or “AI Share of Voice.” These terms describe the proportion of relevant AI answers that include your brand. For example, if out of 100 popular questions in your industry, your site is referenced in 10 of the AI-generated answers, you have a 10% share of voice in that AI domain.

Some early case studies illustrate why this matters: in one instance, a B2C eyewear brand (Warby Parker) was found to command a 29% share of voice on ChatGPT for a set of eyewear-related queries, outperforming competitors like Zenni and Eyebuydirect ( ^[9] ). However, the same brand had a weaker presence in Google’s generative search results (Gemini AI), highlighting that reference rates can vary significantly by platform ( ^[10] ).

GEO success means ensuring you are “the answer” across multiple AI platforms, not just one. Finally, it’s worth noting that being referenced by AI is not only a search visibility issue but also an authority signal. Users implicitly trust information presented by these AI assistants. If your content is what the AI delivers, it carries a halo of credibility. Studies show that content referenced by AI models tends to be perceived as more trustworthy by users (some SEO agencies term this a “GEO trust rate,” noting that people see cited sources as 2–3× more trustworthy than content that wasn’t mentioned at all) ( ^[11] ) ( ^[12] ).

In essence, a high reference rate not only puts you in front of the user, but also positions you as a credible source in the eyes of that user. This is analogous to how a top ranking conferred credibility in the old SEO paradigm – now an AI citation confers credibility in the conversational search paradigm. In summary, as search evolves into AI-driven conversations and summaries, our KPIs must evolve too. Marketers should track how often and where their brand is popping up in AI outputs (“references”), treating those instances as the new impressions.

A mention in an AI answer is akin to an ad impression or a brand mention on social media – a touchpoint that can influence the user’s journey even if no immediate click occurs. The following sections will dive into the tools and techniques to monitor these references and other new performance indicators, ensuring you can quantify and improve your GEO impact.

AI Visibility Tracking: Tools for Brand Presence in AI Answers

Given the importance of being referenced by AI, a new class of tools and features has emerged to track brand presence across AI search platforms. In the past, SEO tools focused on tracking your search rankings and estimating clicks.

Now, “AI visibility” trackers help you monitor if, when, and how your content appears in AI-generated answers. Marketers should incorporate these new tools alongside traditional analytics (like Google Search Console) to get a complete picture of search visibility in the AI era. Search Console and AI: First, let’s consider what Google itself provides. Google Search Console (GSC) remains essential – it captures your organic search performance and, increasingly, some AI performance. Google’s new “AI Mode” in Search (the conversational generative search feature accessible to users) generates clicks and impressions that are now counted in Search Console reports.

Notably, Google updated GSC in mid-2025 to clarify that if your page is included in an AI-generated answer, it counts as an impression in your performance data ( ^[13] ). Similarly, any click on a link within the AI answer counts as a click in GSC ( ^[14] ). Follow-up AI queries are counted as separate searches. This means your GSC impressions might already reflect AI overview appearances. However, Google does not yet provide a filter to isolate AI-specific impressions or clicks ( ^[15] ).

In other words, if your traffic or CTR dropped after the Search Generative Experience rollout, you can see the effect in GSC totals, but you can’t easily separate “AI Overview impressions” from “regular snippets impressions.” It’s all blended.

This lack of granularity makes third-party tools invaluable for explicitly tracking AI visibility beyond what GSC shows. 

AI Visibility Platforms: Several SEO software providers and startups have launched tools to address this gap. Two prominent examples are Ahrefs’ Brand Radar and Semrush’s AI Toolkit, both introduced in 2024–2025 to help marketers quantify their presence in AI search results.

Ahrefs Brand Radar

This tool is designed as an “AI search visibility” tracker. It collects AI mentions of your brand across various LLM platforms to show how often and where you appear ( ^[16] ). Initially in beta, Brand Radar focused on Google’s AI Overviews (powered by the Gemini model) and has since expanded. As of mid-2025, it can track if your brand is being mentioned in Google’s generative search summaries, OpenAI’s ChatGPT, and Perplexity AI ( ^[17] ).

With Brand Radar, you can enter your brand or domain and see data like: 

• AI Overview mentions: e.g. how many Google AI Overview snapshots cited your site, and for which queries. 
• ChatGPT mentions: what questions people asked ChatGPT that led it to mention your brand, and in what context (Brand Radar indexes a sample of ChatGPT Q&A content to find brand names) ( ^[18] ). 
• Perplexity citations: since Perplexity is an AI search engine that always cites its sources, Brand Radar can tell you if your site has been cited in Perplexity’s answers. 
• Competitive comparisons: Brand Radar also offers competitive share of voice – you can see, for example, how often your brand vs. a competitor’s brand appears in AI answers for your industry. It even helps discover “hidden competitors” – domains that frequently get cited by AI on topics of interest, which you might not have considered as SERP competitors ( ^[19] ) ( ^[20] ). A key metric here is the “AI impressions” or “AI mention count” – essentially, how many times your brand showed up in the AI results sample. Ahrefs has introduced concepts like Competitive AI Share (what portion of AI mentions in your niche belong to you vs competitors) ( ^[21] ) ( ^[19] ). For example, if in the topic “running shoes” AI answers cited Nike 40% of the time and Adidas 30% and your brand 5%, that quantifies your share of voice in AI for that topic.

Brand Radar’s interface also tracks trends over time, so you can see if your AI visibility is rising or falling month to month. It even includes a “web visibility” index (traditional organic mentions on the web) and a “search demand” index (how search volume for your brand is trending) to correlate with your AI presence ( ^[22] ) ( ^[23] ). All of this augments the data from Google Search Console by focusing on where in AI ecosystems your brand appears, not just how many clicks you got. 

Semrush AI Toolkit

Semrush, another leading SEO platform, launched an AI Toolkit that automatically analyzes your brand’s presence across multiple AI platforms ( ^[24] ). It scans conversation data from ChatGPT, Google’s AI search (Gemini/AI Mode), Perplexity, and even niche platforms to determine:

• How visible is your brand in AI-driven search results? (It provides a “Market Share” percentage of AI search visibility ( ^[25] ).)
• How that visibility breaks down by platform – e.g., maybe you have strong presence in ChatGPT answers but weak in Google’s AI, or vice versa ( ^[10] ).
• Sentiment and context of mentions (more on that in the next section).
• The types of queries that lead to your brand being mentioned (informational, comparison, etc.).

For example, Semrush’s toolkit can show that “Brand X appears in 20% of AI-generated answers about [category] on ChatGPT, 10% on Google, and 5% on Perplexity.” It might visualize this as a share-of-voice chart by platform ( ^[26] ). In one case study, the tool revealed that Warby Parker (an eyewear retailer) had far stronger visibility on ChatGPT than on Google’s SGE – implying their content was tuned well for conversational AI but needed improvement for Google’s AI summaries ( ^[10] ).

This kind of insight is invaluable for prioritizing optimization efforts (you might need different tactics to win visibility on different AI engines). Additionally, Semrush’s AI Toolkit incorporates query intent analysis for AI search. It can categorize the intents behind questions mentioning your brand (e.g. “research vs. purchase intent”) ( ^[27] ) and help you understand what AI-driven consumers are looking for. It also offers competitive benchmarking – comparing your share of AI mentions and the sentiment of those mentions against competitors across platforms ( ^[28] ). The toolkit is essentially bringing classic SEO analytics (market share, sentiment, competitive analysis) into the AI answer space ( ^[29] ).

Beyond these, a number of specialized startups have appeared with a pure focus on LLM visibility: 

• Profound (TryProfound.com) – Focuses on analyzing how AI models talk about your brand. It claims to have sophisticated tech for sentiment analysis and even detecting AI hallucinations about your brand ( ^[30] ). Profound and similar tools use methods like synthetic queries (pre-programmed questions) to probe AI models and see what answers come up, then aggregate that data. 
• Daydream (withdaydream.com) – An agency tool that, in partnership with a platform called Scrunch AI, monitors client brands in AI search results ( ^[31] ) ( ^[32] ). Daydream emphasizes tracking by prompt and persona – meaning they can see how a brand fares for different question phrasing and even different user personas. They provide analytics on citation frequency and positioning within answers ( ^[33] ). For instance, does the AI mention your brand as the top recommendation or just a footnote? Daydream’s blog notes that they track things like “how your brand performs by prompt, topic, and persona” and “citation tracking & source analysis” as part of their service ( ^[32] ). 
• Good[ie] (HiGoodie.com) – Another entrant that reportedly helps manage model outputs about your brand. It can analyze prompts and help optimize how an AI might respond about you ( ^[34] ).
• BrandScanner is a new Generative Engine Optimization Platform
  that helps marketing professionals master the new era of AI-driven search with comprehensive GEO and AI SEO tools. It supports the optimization of a brand’s visibility in generative AI systems, monitors presence across LLMs, and helps brands to stay ahead in the age of AI search.

All these tools share a common goal: make the invisible visible.

They take the black-box outputs of AI systems and turn them into measurable data points for marketers – how often your brand appears, where it appears, what words surround it, and how that compares to competitors. This is analogous to how early SEO tools made Google’s opaque rankings trackable.

Crucially, these AI visibility trackers should be used alongside traditional tools. Google Search Console and Analytics still tell you about traffic and on-site behavior.

By combining them, you get a fuller story:

• GSC/Analytics: Are AI-driven changes affecting my traffic? (e.g. a drop in clicks for certain queries might coincide with the rollout of AI summaries.)
• AI visibility tools: Are we mentioned even when not clicked? (e.g. maybe clicks fell, but Brand Radar shows you were actually cited in the AI overview. Fewer clicks might simply mean the AI answered the question with your info without sending the user to you – a bittersweet outcome.)
• Together: If you see, for example, that you’re cited a lot by AI but getting few clicks, you may adjust your KPIs to value that brand exposure or find ways within the AI context to prompt clicks (perhaps by having compelling content that encourages the user to “learn more”).

On the other hand, if you’re not appearing in AI answers at all and also losing traffic, that’s a sign you need to ramp up GEO efforts. It’s also important to track which AI platforms matter most for your audience.

Recent data indicates that three AI platforms currently drive the bulk of referral traffic : ChatGPT, Google’s AI (Gemini/SGE), and Perplexity AI account for 98% of AI-sourced traffic to websites ( ^[35] ) ( ^[36] ). ChatGPT alone was the origin of about 50% of detectable AI referrals in early 2025, with Perplexity around ~30% and Google’s SGE (Gemini) about ~18% ( ^[37] ).

This “big three” suggests that focusing on those platforms will cover most scenarios ( ^[36] ). However, each platform has a different style: e.g. ChatGPT (especially with browsing or plugins enabled) can directly cite and even click links, Perplexity always cites sources and tends to favor research-oriented content, while Google’s AI Overviews draw from high-authority web content and often favor well-established sources ( ^[38] ) ( ^[39] ). Bing’s AI (Bing Chat) is another notable platform, effectively an OpenAI GPT-4 variant that cites sources; while not broken out in the above stats, many SEO experts consider Bing Chat in the same category – and any tool that tracks “ChatGPT” answers may capture similar info for Bing, since Bing’s output is also reference-rich.

Marketers should also keep an eye on emerging AI search entrants. For example, Anthropic’s Claude has been mentioned as being built into products like Apple’s Safari for AI Q&A ( ^[40] ) – meaning in the near future, a user asking their iPhone a question might get a Claude-powered answer with citations.

If you operate in markets like China, Baidu’s ERNIE Bot integrates generative answers into search results, which introduces a whole parallel of GEO for Baidu. The tools for tracking those non-English AI mentions are nascent (and often internal to those markets), but the concept remains: you need to monitor your brand’s presence in whichever AI systems your customers use. In Europe or other regions, local players or open-source LLM-based search tools might gain traction, and keeping tabs on those (even if via manual testing) could give an edge.

In summary, AI visibility tracking is now an essential complement to traditional SEO monitoring.

You’ll want to answer questions like: 

1. How often is my site/brand being cited by AI, and is that trending up or down? 
2. Which AI platforms mention me the most (or least)? 
3. What queries or topics tend to include my brand in AI answers? (This can inform content strategy – you might discover AI loves citing one of your blog posts, suggesting that format or topic resonates with LLMs.) 
4. Who are my “AI competitors”? (These might be different from your normal SEO competitors. For instance, you might usually compete with Site A on Google, but find that in AI answers a government FAQ or a niche forum is what’s getting cited instead.) 
5. Is my overall share of voice in AI answers improving as I implement GEO tactics? (For example, after adding structured data or new FAQs, does Brand Radar show more mentions of you in the following months?)

By combining data from GSC (for clicks/impressions) with AI mention data from new tools, you get a fuller visibility picture. For example, BrandScanner’s visibility tracker analyzes a topic cluster of questions relevant to a specific industry to calculate a share-of-voice.

An AI mention without a click still has value; a click without an AI mention means you came via traditional route. The ultimate goal is to maximize both – ensuring you are both mentioned by the AI and enticing the user to click through when appropriate. But even when the AI provides a complete answer, being referenced keeps you in the game (and maybe in the user’s consideration set for later).

Brand Sentiment and Context in AI Outputs

It’s not just if you’re mentioned by AI that matters – it’s also how you’re mentioned. In human conversations, context and tone shape perception, and the same is true for AI-generated content. 

Are AI summaries presenting your brand accurately and favorably? 

Measuring brand sentiment and context in AI outputs is a new frontier of reputation management. Marketers need to ensure that when AI discusses their brand, it’s painting the right picture and not inadvertently damaging the brand with misinformation or negative framing. 

Sentiment Analysis in AI Mentions: Leading SEO platforms have begun incorporating sentiment analysis specifically for AI-generated references. For example, the Semrush AI Toolkit evaluates whether mentions of your brand in AI answers are positive, neutral, or negative ( ^[41] ). It can give an overall sentiment score for your brand based on a sample of AI Q&A, and even identify the key drivers of sentiment ( ^[42] ). In one case, Semrush reported that Warby Parker had about 88% positive sentiment in AI mentions – the AI often described the brand in a favorable light, citing its strengths like affordable prices and home try-on program ( ^[43] ). The negative mentions (the remaining ~12%) touched on things like limited in-store selection ( ^[43] ).

This kind of analysis is incredibly useful: it tells you what aspects of your brand the AI is emphasizing. Are the AIs highlighting your selling points or zeroing in on an old criticism? If an AI consistently describes your product as “expensive” or “difficult to use,” that’s clearly a problem – it might deter users before they even reach your site. Conversely, if AI assistants are extolling your key benefits (perhaps from reviews or content they’ve been trained on), that’s valuable positive exposure. 

Tracking sentiment allows you to quantify this. Some GEO monitoring tools create dashboards that break down the share of positive vs negative descriptors used alongside your brand ( ^[41] ). Marketers should monitor shifts in this sentiment over time or differences between platforms. It’s possible, for instance, that ChatGPT’s older training data might mention some past issue with your product that has since been resolved, whereas a real-time system like Perplexity (which fetches current info) might reflect newer, more positive reviews. Knowing that discrepancy can inform your content strategy (e.g., do more PR to push fresh positive content that AI will pick up) or even direct engagement (perhaps issuing clarifications via content if an AI is propagating a misconception). BrandScanner offers an advanced sentiment analysis that tracks a brand’s perception across major AI platforms, providing detailed scores and trend analytics over time.

Accuracy and Hallucinations: Hand-in-hand with sentiment is accuracy. AI models can “hallucinate” – in other words, fabricate or err in the details. This can be harmless (like slightly misquoting a stat) or it can be seriously damaging (like incorrectly stating a fact about your company). There have been early incidents of AI chatbots providing false information about individuals or businesses. For example, a chatbot might mix up two companies with similar names or might regurgitate an outdated piece of information (such as “Brand X’s CEO resigned amid scandal” when in reality that was a different company or long in the past). Monitoring tools like BrandScanner’s accuracy monitoring can help flag such occurrences. 

Some specialized platforms (like Profound or others) claim to identify hallucinations about your brand ( ^[44] ) – essentially catching when the AI says something that isn’t supported by known data. While it’s tricky to automate fully, one can at least periodically audit AI outputs for factual accuracy regarding your brand. If you discover common errors (for instance, the AI often gets your pricing or product specs wrong), you may need to update your content or FAQs to clarify those points or even use techniques like structured data to feed correct info. A proactive approach some companies use is fine-tuning or prompt-testing models for brand-specific Q&A.

As noted in the Andreessen Horowitz analysis, new platforms for GEO are fine-tuning their own versions of models specifically to see how brands are portrayed ( ^[45] ). They simulate typical user prompts about the brand (e.g., “Is [Brand] reliable?”, “What does [Brand] do?”, “Compare [Brand] vs [Competitor]”), and then they analyze the output for tone and accuracy. The outputs can then be scored for sentiment or checked against fact. If the AI’s answer says something like “[Brand] is one of the more expensive options and has limited customer support hours”, a brand might recognize both a perception issue (expensive) and possibly an accuracy issue (maybe their support hours aren’t limited anymore).

Some tools even allow you to input a desired brand positioning and see how close or far the AI’s answers are from that ideal. For example, if you want to be known as “innovative and affordable,” you’d hope AI descriptions use those or similar words. If not, it suggests your content and PR might not be emphasizing the right messaging, or that negative content is outweighing positive in the model’s training mix. 

“Model perception” is now something to cultivate: one SEO strategist phrased it as ensuring “the model remembers you in the right way.” In other words, how your brand is encoded in the AI’s knowledge is the new battleground ( ^[46] ). 

Context and Framing: Beyond sentiment polarity, context matters. Are you being mentioned as a top recommendation or as an also-ran? Is the AI summarizing your content accurately or taking it out of context? These qualitative aspects require looking at the actual AI answers. For instance, let’s say a user asks, “What are the downsides of Product A?” If your company makes Product A, you’d be very interested in what the AI says. It might list some cons that are exaggerated or outdated. Through prompt-testing, you discover the AI answer references an old review or a competitor’s blog that paints your product in a harsh light. That’s a cue to create fresh content addressing those points or to seek more positive coverage to outweigh the negative.

Conversely, if the AI is asked, “Which product is better, [Yours] or [Competitor]?” – how does it respond? Many AIs will try to give a balanced view, but the details it chooses to mention can sway the reader. Monitoring these comparative answers is crucial for marketers. If the AI consistently highlights your strengths (e.g. “[Your Product] has a more intuitive interface and lower cost” ), great. If it highlights weaknesses, you know what you need to improve or clarify in your materials. A real-world example: the A16Z GEO report shared a case where Canada Goose (an apparel brand) analyzed how often and in what context LLMs referenced it ( ^[47] ).

The interesting find was that beyond just product features, the brand wanted to know if AIs would “spontaneously mention” Canada Goose when talking about winter jackets in general – an indicator of brand prominence ( ^[47] ). For them, it wasn’t only about sentiment but about whether the brand name surfaces at all without being directly prompted. This is another angle of context: unaided brand recall by AI. If people ask generally for recommendations (“best winter coat brands”), does the AI include you? If not, you have a brand awareness issue in the AI’s “mind.” If yes, is it associating the right qualities with you (e.g. durability, luxury, etc.)? 

Tools for sentiment & context: As mentioned, many of the AI tracking tools incorporate some sentiment analysis. Semrush’s toolkit, for instance, not only labels mentions as positive/neutral/negative but also identifies key sentiment drivers – essentially the topics or features that lead to positive or negative talk ( ^[42] ).

In the Warby Parker example, the home try-on program and affordability drove positive sentiment, whereas limited selection and weak in-store presence were negatives ( ^[43] ). If such a report were about your brand, that’s gold: it’s telling you what messaging to amplify and what objections to mitigate in the eyes of AI (which often mirror common consumer perceptions).

Other tools like the startup Goodvie/Goodie have pitched themselves as doing “model sentiment and prompt analysis” ( ^[34] ). This implies they might let you input a series of prompts and then they analyze not just if you’re mentioned but also the tone (perhaps by scoring the adjectives used, etc.). Similarly, Daydream’s integration with Scrunch AI boasts sentiment tracking so you can “discover how AI perceives your brand and track shifts over time.” ( ^[48] ).

The ability to track shifts over time is important. Perhaps you launch a new product or a crisis hits your brand – how quickly and in what way does AI content reflect that? If sentiment plunges after a wave of bad news, you’ll see it in AI answers too. Or if you invest in thought leadership content and expert articles, you might see over months that AI answers become more favorable (because those positive signals have been ingested into the model’s knowledge). Watching these trend lines can validate the impact of your GEO and PR efforts. 

Actioning the insights: Measuring sentiment and context in AI outputs isn’t just for curiosity – it should feed back into strategy: If inaccuracies are found, correct them at the source. That might mean updating your website (AI like Bing and Perplexity might retrieve updated info), issuing press releases or FAQs, or even directly providing feedback to AI providers if possible.

For instance, OpenAI has a feedback system for users to flag incorrect info; enough flags might influence retraining. Google’s SGE might slowly improve if the underlying web content becomes more accurate and consistent. If sentiment is negative due to genuine issues (e.g., many reviews complain about one aspect), that’s a business insight to fix the product or customer experience. If sentiment is negative due to outdated or skewed info, that’s an SEO/PR task: produce content to set the record straight. If sentiment is positive – promote it! Ensure those positive points (that AI is picking up) are reinforced on your site and marketing materials.

Also, you might leverage the fact that AI likes certain content from your site to double down on that content strategy. If your brand isn’t being mentioned when it should (e.g., AI often mentions competitors in answers but not you), that indicates you might need to build more authority. This could mean creating high-quality content that others cite (so the AI sees your name associated with the topic), improving your site’s prominence (so the AI’s web crawl finds and trusts your content), or engaging in more traditional brand-building (since some models might be biased toward well-known names ( ^[38] )).

In fact, an Ahrefs analysis in mid-2025 found that Google’s AI Overview tends to favor brands that have a lot of web mentions (correlation was high between web mention count and appearance in AI results), whereas ChatGPT and Perplexity were less brand-biased ( ^[49] ) ( ^[50] ). Google’s AI seems to lean on brand authority as a trust signal (much like its search did) ( ^[39] ). This means if you’re a lesser-known brand competing with giants, you may find Google’s AI less likely to mention you until you gain more overall prominence (backlinks, mentions, etc.), whereas other AI tools might be more “egalitarian” in citing niche sources.

This insight again ties back to sentiment and context: Google might mention you primarily in contexts where you’re already recognized as a top authority (so your strategy might be to become a known authority through digital PR, etc.), whereas for a platform like Perplexity, even a single excellent article from you could get cited for a specific query.

To wrap up, measuring sentiment and context in AI outputs is about safeguarding and shaping your brand’s narrative in this new medium. You should strive not only for a high reference rate, but also for a positive reference quality : The tone should align with your brand image (e.g., AI calls you “reliable and innovative” rather than “cheap” or “basic” unless those are intentional brand positions). The facts should be correct (no lingering outdated info).

The associations should be favorable (e.g., you’re mentioned alongside other reputable brands, or for the product features you excel in). Through regular monitoring – using tools and manual spot-checks – you can catch issues early. In the same way companies have set up Google Alerts for their brand name on the web, they are now setting up AI alerts : essentially tracking when an AI mentions them and analyzing the output.

By doing so, you maintain control over your brand’s representation in AI-driven channels and can adjust your GEO tactics to keep that representation positive.

Conversion and Engagement Shifts in the AI Era

The rise of AI-generated answers is not only altering how we get visibility; it’s also changing the nature of the traffic that does reach our websites. Marketers may notice shifts in traditional metrics like total organic traffic, conversion rates, on-site engagement, and lead quality as AI becomes the first touchpoint. In this section, we explore how GEO impacts user behavior and downstream conversions – and why quality may trump quantity going forward. 

Traffic Volume vs. Intent: One immediate effect of AI answers is a potential decline in raw organic clicks for certain queries. If a user’s question is fully answered in an AI snippet, they might not feel the need to click any result. Early data from Search Engine Land and others have predicted significant drops in organic clicks (some estimates ranged from 18% up to 64% for certain queries) due to SGE-style answers occupying the top of the SERP ( ^[51] ) ( ^[52] ).

Indeed, an internal Google study noted that when the AI snapshot is present, the classic results get pushed far down, often below the fold on mobile ( ^[53] ). However, focusing only on the loss of clicks can be misleading. It is crucial to ask: which clicks are being lost? Typically, it’s the quick fact-finding or superficial queries – the kind where the user just needed a definition, a simple piece of advice, or a list of options. When those low-intent, informational needs are satisfied by AI, the clicks that remain are often the higher-intent visits : users who have follow-up questions, want to dive deeper, or evaluate options in detail.

In other words, the AI may filter out the top-of-funnel browsers, leaving you with more mid- or bottom-of-funnel visitors. For example, someone searching “How to unclog a drain” might get the step-by-step from an AI overview and never click, whereas someone searching “Plumber near me cost” or “Order BrandX drain snake” likely has a stronger intent and might click through to sites for specifics.

Marketers are observing that while total visit counts from organic search could plateau or drop, the engagement and conversion rates of those who do click can rise. One digital agency analysis suggested that as SGE rolls out, “this could lead to increased conversion rates and a higher quality of organic search traffic.” ( ^[54] ). Why?

Because the people who make it past the AI answers to your site are self-selecting as more motivated. They either weren’t fully satisfied with the summary or are far enough along in their journey that they need the detail/transaction your site offers. In practice, you might see metrics like: Longer average session duration or lower bounce rate from organic users, because those who click genuinely want more information or are closer to decision-making. Higher conversion rate (be it form fills, product purchases, etc.) from organic, as casual information-seekers have been siphoned off by the AI. Your site visits might be fewer but “meatier.”

A concrete example comes from anecdotal evidence in the tech sector: Vercel, a developer platform, found that around 10% of their new sign-ups were coming through ChatGPT’s referrals ( ^[55] ). This implies that users who learned about Vercel via ChatGPT (likely through code suggestions or answers mentioning Vercel) were clicking through and converting at a meaningful rate.

Those referred users presumably already had high intent – ChatGPT might have recommended Vercel as a solution, so by the time the user hit the site, they were primed to sign up. This kind of referral might be low in volume but high in conversion efficiency.

Another study by Ahrefs noted a subtle but important trend:

on average, keywords that trigger AI Overviews had higher zero-click rates (users not clicking anything) but when looking at the same keywords before vs after AI was introduced, the presence of AI slightly decreased the zero-click rate ( ^[56] ) ( ^[57] ). In other words, some users who previously might not have clicked anything at all were now clicking on an AI-cited source. This suggests that AI answers can generate curiosity or credibility that draws certain users to click through for more (especially if the AI snippet includes an intriguing tidbit or partial info).

So, while AI overviews reduce the need for many users to click, they also inspire a subset of users to click specific sources. The net effect on clicks is complex, but from a conversion standpoint, those who do click may be more informed (they’ve read a summary already) and thus further down the funnel. 

Shifting KPIs: Given these dynamics, companies are beginning to adjust their KPIs and success metrics for organic search in the AI era: Instead of obsessing purely over total organic sessions, more attention is paid to conversion-related metrics : lead volume from organic, revenue from organic, ROI per visit, etc. If you lose 20% of your organic traffic but your leads or sales remain the same, it means the traffic you lost was largely informational window-shoppers. Your marketing success might still be intact (or even improved in efficiency). 

Brand visibility metrics (like reference rate, as discussed) become a parallel KPI. Even if traffic drops, you can show stakeholders that “We are being mentioned in 30% of AI answers about [product category] – that’s brand exposure we wouldn’t have otherwise.” You might track how that correlates with branded searches or direct traffic.

For example, maybe your direct visits or brand-name searches increase because people saw you mentioned by AI and later come directly to you. These indirect conversions might not be immediately obvious, but they are part of GEO success. (One hint of this was mentioned in the Ahrefs traffic study: they noted that AI conversions may be underestimated because a user could be influenced by an AI answer and convert later without a traceable click ( ^[58] ).

For instance, an AI might recommend a specific product, and the user goes straight to Amazon or the brand’s site later to buy it – no referral tag, but the AI answer started the chain.) 

On-site behavior metrics : Are pages seeing longer dwell time? Is the scroll depth deeper? These can signal that the nature of organic visits is now more serious. If someone already got the basics from AI, when they come to you they likely want the nitty-gritty – and indeed might spend more time consuming that content or evaluating products.

All this is to say, marketers should pivot from a pure volume mindset to a quality mindset for organic reach. High-level executives might initially panic seeing a drop in Google traffic due to AI. It’s our job to contextualize that with quality metrics. For example: “Yes, traffic on these how-to articles fell after AI answers started showing, but the traffic we’re still getting is 2× more likely to convert. Also, our brand is still present in those AI answers as a cited authority, which has intangible value and likely drives some direct visits.” 

Engagement and Funnel Position: When basic questions are answered by AI, users coming in might be later in the funnel: Someone who asks an AI, “What are the top 5 project management tools?” gets a quick list (with perhaps your brand included). The user then asks follow-up, “Which of those is best for a small team?” The AI elaborates, maybe citing some comparison from your blog. By the time they click your site, they might be at the consideration/evaluation stage rather than the discovery stage.

So, when they land on your page, they might immediately seek free trial info or pricing – because they already know who you are and some pros/cons, courtesy of the AI introduction. Alternatively, they might have only heard of you through the AI’s answer. This means your site needs to capture them quickly with what you want them to do (this is where good UX and on-page prompts matter more than ever).

If AI delivers a user to you, that user has a certain expectation set by the question they asked. Ensure the landing content aligns with that intent (this hasn’t changed from SEO best practices, but the questions might be more specific now).

Marketers should monitor if the landing page mix for organic traffic changes. Are fewer people entering on your generic blog posts and more entering on product pages or deep comparison pages? If so, that suggests AI handled the generic stuff, and users click when they’re ready for specifics. You might see organic homepage entrances drop (because AI answered “who is Company X” so they didn’t click your homepage) but product page entrances rise (they clicked on a product link from the AI because they wanted details or to buy). Keeping an eye on this can validate the idea that the remaining clicks are more intent-rich. 

Conversion Rate Optimization (CRO) Adjustments: With potentially more qualified traffic, you might achieve better conversion rates – but don’t leave it to chance. Double down on CRO for organic entry pages. If visitors now are later-stage, ensure your calls-to-action meet them. For example, if a significant chunk of organic visitors are coming after an AI recommendation, they might be primed to sign up – so make that process smooth and obvious on the landing page.

On informational pages that still get traffic, consider that those visitors likely have very specific questions that maybe the AI couldn’t fully satisfy (or else they wouldn’t be there). It may help to include clear FAQ sections, or interactive elements, to serve those advanced needs.

Also, consider measuring micro-conversions that reflect engagement, not just macro conversions (sales/leads). Micro-conversions like newsletter sign-ups, time spent on site, number of pages viewed, or even interactive elements used (like a product compare tool, calculator, etc.) can show that even if absolute users are fewer, they’re engaging more deeply. These are good signs that your content is attracting a more invested audience. 

Adjusting marketing mix and expectations: If you do find that AI is cutting a big chunk of your trivial-queries traffic, you might reallocate some effort: Perhaps invest less in writing basic how-to articles that an AI will likely summarize, and more in very original, expert or data-rich content that AI might cite or that users will seek out for depth. (This aligns with the broader strategy of creating content AI can’t easily generate on its own.

Focus on branding. If fewer people click to discover you, you want to ensure your name is recognizable and trusted when the AI mentions it. Off-page GEO becomes relevant: building authority through PR, communities, etc., so that when an AI chooses sources, it favors or at least includes you. This indirectly helps conversion because users are likelier to click known brands in the AI citations.

A study by Nielsen decades ago showed brand familiarity boosts click-through and conversion, and a similar effect likely happens in AI results – if the user sees a source they know (or have seen referenced often), they might gravitate to it. This is speculation but supported by Google’s bias toward brands ( ^[39] ); big brands get referenced and likely get the clicks that do happen. So smaller players need to work harder to build that credibility. 

Case in point – quality over quantity: Let’s illustrate with a hypothetical scenario: Before AI, your site got 1,000 organic visits/month, converting 20 leads (2% conversion). After AI overviews roll out, you drop to 700 visits/month. At first, this looks bad. But upon analysis, those 700 visits convert 21 leads (3% conversion). Your total leads even ticked up slightly, and conversion rate improved markedly. This suggests the AI filtered out 300 visits that were unlikely to convert anyway.

Additionally, you find that your brand was cited in 100 AI answers that month – exposures that might not have immediate clicks but contribute to brand awareness. So while old-school metrics would show a 30% traffic drop, the smarter interpretation is that you maintained or even improved performance with fewer, more qualified visitors while also gaining “mindshare impressions” via AI.

Explaining this to stakeholders is crucial so that they understand GEO success isn’t about raw traffic growth in the same way SEO was, but about maintaining outcomes (sales, leads) and presence in a changing search landscape. Another forward-looking metric is engagement shift : if AI handles simple queries, perhaps the traffic that ends up on your site is looking for interactive or value-added experiences (things an AI can’t do well, like provide tools, live data, personal consultations, etc.). Measuring usage of those features (like how many use your online calculator or start a live chat) can show that your site is capturing the users who need more than a static answer. 

ROI and Attribution Considerations: With AI playing middleman, attribution might get muddy. A user could be influenced by an AI recommendation and later come through organic search by typing your brand (making it look like a direct or branded search conversion, when the assist was AI). To the extent possible, gather anecdotal feedback: ask your leads or customers how they heard about you. You might start hearing “I was chatting with ChatGPT and it suggested your tool,” or “I saw your product mentioned in the new Bing.” This qualitative insight can reinforce that AI references are driving real business (just indirectly). Some companies have even started including an “How did you hear about us?” option for “AI assistant (e.g., ChatGPT, Bing AI, etc.)” in forms to track this emerging channel.

In summary, don’t panic if you see fewer clicks – instead, measure what those clicks do, and measure how your brand is surfacing in AI-driven pathways that don’t result in a click. You may need to adjust your success benchmarks : for instance, instead of saying “we aim to grow organic traffic 10%,” you might say “we aim to maintain organic lead volume, while also achieving X% presence in top AI answers for our category.” Internally, this is a big mindset shift, but it’s one that forefront marketers are adopting. They understand that in the AI era, being the answer is just as valuable as getting the click, and the traffic that does come through is more valuable than ever.

Continuous Testing and Experimentation: Adapting to the GEO Frontier

Generative AI in search is still a fast-evolving field. What gets your content referenced today might not work tomorrow after the next model update. Likewise, new AI features and platforms are emerging rapidly.

Therefore, a culture of continuous testing and learning is vital for GEO success. Marketers should establish processes to regularly experiment, monitor, and refine their GEO strategies – in essence, to treat GEO optimization as an ongoing cycle, much like traditional SEO where you continuously tweak and observe rankings. Here we discuss how to set up that test-and-learn framework for GEO. 

Experimenting with Content Changes: One practical approach is to make targeted content optimizations and then see if AI responses reflect the change. For example, suppose you notice that Google’s AI overview for a query about your industry never cites your site. You decide to update one of your relevant pages with a new section (perhaps a concise answer to that query, with clear phrasing and maybe schema markup). Once that page is reindexed, you watch to see if the AI overview now starts pulling from it. This can be done by manually triggering the AI overview (if you have SGE access) or by checking a tool like Brand Radar for that query in the next data refresh.

If you succeed and your site becomes one of the cited sources, that’s a win – and a learning that the change made a difference. If not, that’s also instructive: maybe the content needs further improvement or a different approach.

Similarly, you might add an FAQ section to key pages, anticipating common questions users ask AI, and then later ask those questions to ChatGPT or Bing to see if your new FAQ content gets used in the answer. If you see phrases from your FAQ mirrored in the AI’s answer, bingo – your content is influencing the model output.

SEO professionals have started doing this kind of testing routinely: essentially, feeding likely prompts to various AI systems after each significant site change. This is analogous to how one might do A/B testing on webpages; here you’re testing how the “AI audience” responds to content tweaks. 

Synthetic Prompt Testing: Some GEO agencies formalize this through synthetic prompt testing frameworks. They will create a list of representative user queries (prompts) relevant to the business – covering various funnel stages and question phrasings – and then programmatically or manually run those prompts on a schedule across different AI engines (ChatGPT, Bing Chat, Bard/Gemini, Claude, Perplexity, etc.). The outputs are then parsed to check for brand mentions, citations, sentiment, etc. Monstrous Media Group, for instance, describes that they use test queries to evaluate whether a client’s content is being referenced in AI responses, and if not, they adapt the content ( ^[59] ).

This iterative testing approach allows you to measure improvement: e.g., last quarter, our brand showed up in 2/20 test prompts on ChatGPT; after optimizing content and building some links, it shows up in 5/20 prompts. It’s a controlled way to gauge progress in a world where we can’t directly see “rankings.”

There are even tools emerging to automate prompt testing and result analysis (some SEO suites might add “Prompt Rank Tracker” features in the future). But you don’t need to wait for fancy tools – you can DIY by periodically querying AI with key questions and logging the results.

Many SEO teams now have a standing monthly or bi-weekly task: “Check our presence on AI answers for top use-case questions.” This could be as simple as using ChatGPT’s browsing tool or Bing’s chat and seeing if your site/article is mentioned or linked. 

Model Updates = New Algorithm Updates: Keep in mind that AI models update (or are replaced/upgraded) periodically, akin to Google’s algorithm updates. OpenAI might release a new GPT version, Google will update its Gemini model, etc. Each update could change how content is interpreted or which sources are favored. For example, maybe an update significantly improves citing recent information, so suddenly sites with the freshest content gain an edge in AI answers. Or an update fixes a prior hallucination issue, altering how your brand is described.

Because of this, you should:

• Monitor industry news on AI model updates (OpenAI, Google, Anthropic, etc. often announce major changes). After a known update, re-run your prompt tests. See if your reference rate or answer patterns changed. It’s reminiscent of how an SEO might check their rankings after a Google core update – here you check your AI mentions after a model update.
• Have a plan to quickly respond. If an update wipes out many of your mentions (maybe the AI is now citing Wikipedia for everything, hypothetically), you may need to adjust strategy (perhaps focus on being the source that Wikipedia or other high-authority sites cite, or provide more unique data that stands out). An insightful quote from the a16z GEO report: “With every major model update, we risk relearning (or unlearning) how to best interact with these systems… LLM providers are still tuning the rules behind what their models cite.” ( ^[60] ). This underlines the importance of staying agile and not assuming today’s tactics will always work. Just as SEO had its Panda, Penguin, and countless other updates that required course-corrections, the GEO world will have its own twists and turns. 

Cross-Platform Testing: Don’t limit testing to one AI platform. Each has its quirks: Test Google’s AI (SGE/Gemini) for how it constructs overviews with your content. Are there queries where it could cite you but isn’t? Why might that be (is your content not as crawlable or is another site just more directly answering the question)? Test Bing Chat – it might use a different style. Does it find your content? Bing often provides up to 3–4 citations per response; see if you’re among them for relevant questions.

Test voice assistants or multimodal AI if relevant. For instance, if you care about Alexa or Siri (which might integrate more generative capabilities), how do they respond about your brand? This might not be as open to custom prompts yet, but keep an eye on it as these assistants evolve. Test international or domain-specific AIs if you have markets there. If you operate in, say, Japan, you might test Naver’s HyperCLOVA or other local AI tools with relevant prompts (though accessing some might be tricky without native accounts).

The idea is to not be blindsided by an AI that’s popular in a region you serve. 

Internal Process and Collaboration: Implementing continuous GEO testing can be made part of your ongoing SEO or content workflow: 

1. Schedule regular reviews: e.g., a monthly “AI Visibility Report” that your team generates. It could include data from Brand Radar/Semrush on brand mentions, plus qualitative observations from manual tests. This keeps GEO on the radar (pun intended) in the same way monthly SEO reports do. 
2. Interdisciplinary input: GEO straddles SEO, content, PR, and even product. Consider having your customer support or sales team glance at AI outputs too – they might catch something like the AI giving outdated pricing or feature info. That insight can trigger a content update or a clarification on your site. 
3. Brainstorm experiments: Encourage the team to hypothesize and test. For example, “We aren’t being mentioned for [topic]. What if we publish a unique research study on that topic – will the AI pick it up?” Then do it and watch. Or, “Our competitor is always cited for definition of X. Let’s create a more comprehensive definition and see if we can replace them.” Treat it like scientists running lab experiments. One particular tactic is embedding likely user prompts into your content. This means literally taking common questions users ask AI and including them (and their answers) on your site – in a FAQ section, in headings, etc. By doing so, you increase the chance the AI will use your text when that question is asked. After embedding, you test again: ask the AI that question, see if your phrasing appears. This is a direct way to validate the impact of prompt-oriented content optimization. 
4. Learning from Failures and Successes: If you test something and it doesn’t work – that’s still a learning. GEO is new for everyone, so sharing lessons (even internally) is valuable. Maybe you find that adding a certain schema markup got your content picked up by the AI overview (there have been theories that schema like HowTo or FAQ might influence SGE). Or you find that the AI ignored your page until it gained a few external links, at which point it started citing it – suggesting authority threshold matters. These insights help refine your approach. Also, keep an ear to the ground: SEO forums, case studies (like the Diggity Marketing case study where an agency got a 2,300% increase in AI-driven traffic ( ^[61] )), and industry webinars are now popping up specifically about GEO experiments. For example, some practitioners share how they got a featured snippet and an AI mention by structuring content in a certain way. Being part of that conversation and knowledge-sharing will accelerate your success. The landscape in 2024/2025 is such that everyone is experimenting, so there’s a lot of community learning to tap into (and contribute to). 
5. Governance and Ethical Testing: In your rush to optimize for AI, maintain ethical standards. Avoid trying to trick the AI with spammy tactics (like stuffing a ton of keywords in hidden text hoping the AI training picks it up – that could backfire or be considered manipulation). Another post covered avoiding black-hat tactics; it applies here in testing too. If you use a tool to mass-query an AI, be mindful of the platform’s usage policies. Some AI platforms might restrict automated querying. Use official APIs or approved methods if you want to scale up prompt testing, to avoid being blocked. 
6. Closing the Loop: Finally, ensure there is a feedback loop from what you learn back into your strategy: If a test shows improvement, operationalize it (e.g., “We tested adding summaries at the top of articles; it got us cited more on Perplexity. Let’s roll this out across more content.”). If you discover new queries that people ask AIs (maybe via tools or from logs if available), feed that into content planning. Incorporate AI visibility as a goal in your content briefs. For instance, when creating a piece, define which AI queries you hope it will answer, and structure it accordingly (Q&A format, clear sourcing, etc.). Then after publish and indexing, test those queries to see if it worked. Just as early SEO had an experimental nature (“Let’s try this keyword density or that title tag and see what ranks”), early GEO demands creativity and iteration. By establishing a rigorous yet flexible testing regimen, you’ll keep your GEO efforts aligned with the moving target of AI algorithms. The companies that excel will be those that learn faster and adapt continuously – turning GEO from a one-time project into a sustained capability. 

In conclusion, measuring GEO success requires both old and new lenses : using some traditional analytics in new ways (focusing on conversion quality, using Search Console’s data with an AI filter in mind) and embracing brand-new tools and metrics (reference rate, AI share of voice, AI sentiment). It’s a more complex picture of performance, but a richer one. By tracking references, sentiment, and engagement shifts – and by relentlessly testing and learning – online marketing professionals can ensure that their brands not only survive but thrive in the age of AI-driven search.