The Measurement Trap<\/strong><\/h2>\n\n\n\nWe are a generation raised on clicks.<\/p>\n\n\n\n
Our performance dashboards glow with charts and line graphs charting unique sessions, bounce rates, time on site. Every month we squint at referral breakdowns and organic lifts, trying to understand what moved the needle and what missed. And through all this, we\u2019ve built our strategies, and our careers, on the promise that visibility is proxied by traffic, and traffic is a proxy for value.<\/p>\n\n\n\n
But what if those proxies are starting to fail<\/a>?<\/p>\n\n\n\nAI engines like ChatGPT, Perplexity, Claude, and Google’s own AI Overviews and AI Mode don\u2019t behave like traditional search engines. They don\u2019t operate on ten blue links. They aren\u2019t structured to pass the user off to a publisher via click. They are designed to answer<\/em>. Which, by their nature, means they are principally zero-click environments<\/em>.<\/p>\n\n\n\nWe should not be surprised that they send little traffic.<\/p>\n\n\n\n
We should be concerned that we\u2019re still using traffic as our primary measure of relevance.<\/p>\n\n\n\n
If your mental model says, \u201cvalue comes from traffic,\u201d then 1% LLM traffic seems like a distraction. But if your model instead considers \u201cvalue comes from influence,\u201d then we have to confront the possibility that we are looking at the wrong signal.<\/p>\n\n\n\n
A Restaurant Without a Keycard<\/strong><\/h2>\n\n\n\nWhere Google has historically functioned as a bridge (input on one side, destination on the other) ChatGPT and similar tools offer more self-contained experiences. In the same way that calculators didn\u2019t send you to arithmetic websites, LLMs are not built (at least primarily) to send you to pages that contain the answer. They are built to be the place where the answer lives.<\/p>\n\n\n\n
Which means we\u2019re dealing with a zero-click interface. And zero-click interfaces break our attribution models. Because the thing we\u2019ve always used to detect success – the click – is no longer part of the interaction.<\/p>\n\n\n\n
Clumsy analogy time.<\/p>\n\n\n\n
Let\u2019s say you run a restaurant, a busy little spot with a loyal customer base. For years, you\u2019ve tracked guest traffic using a digital keycard system. Every customer swipes in when they arrive.<\/p>\n\n\n\n
You know who\u2019s coming, how often, at what times. Perhaps there\u2019s even a component of these keycards that allows you to see, with some precision, how a customer found you (let\u2019s call this a URL parameter). You\u2019ve built dashboards. You\u2019ve run campaigns. You\u2019ve A\/B tested the dinnertime menu based on Monday Night Football. It\u2019s not perfect data, but it\u2019s yours. You trust it.<\/p>\n\n\n\n
Then, one day, you realize the keycard system is creating friction. People forget their cards. The tech is buggy. So you remove it. Guests still come. The dining room still fills. The waitstaff stays busy. The register rings. But now, you don\u2019t know how many<\/em> people walked through the door. You can\u2019t see the foot traffic anymore.<\/p>\n\n\n\nWould you assume your restaurant is empty?<\/p>\n\n\n\n
Of course not.<\/p>\n\n\n\n
You would recognize that your measurement tool no longer reflects the reality of your business. The absence of telemetry is not the absence of behavior. It\u2019s just the absence of data. And smart operators would adapt. They\u2019d find new proxies. They\u2019d watch the register. They\u2019d listen to servers. They\u2019d ask diners how they heard about the place.<\/p>\n\n\n\n
Yet in marketing, we often fail to make this distinction.<\/p>\n\n\n\n
We look at the clickstream and decide it is<\/em> the stream. We look at referral traffic and believe it defines influence. We forget that when the medium changes, the signals we\u2019re trained to look for may vanish – not because the influence is gone, but because it\u2019s flowing through a new vessel.<\/p>\n\n\n\nThe same number of people might still be \u201cwalking through the door\u201d (discovering your product, forming a preference, making a purchase) but the door is now metaphorical. Maybe they came in through a side entrance. Maybe they read a summary on Perplexity and Googled your brand name instead. Maybe they asked ChatGPT, got a strong answer, remembered your company, and typed it in directly a week later.<\/p>\n\n\n\n
The interface is different. So the path is different. And so the old map, the one that traces everything from organic search to landing page to lead form, is no longer as helpful as we want it to be, at least when it comes to LLMs. <\/p>\n\n\n\n
The hard part is: we still want it to be.<\/p>\n\n\n\n
We want clean lines. Measurable outcomes. Attribution you can show your CFO<\/a>. But we\u2019re entering a domain where the influence is upstream of measurable behavior. We are operating in a dark forest, lit only by scattered self-reports, directional brand visibility scores against self-selected prompts, referral traffic presenting only the tip of the iceberg, and a whole lot of faith shadows.<\/p>\n\n\n\nBut if we continue to mistake the absence of clicks for the absence of presence, we will miss what\u2019s actually happening.<\/p>\n\n\n\n
Influence Without Telemetry<\/strong><\/h2>\n\n\n\nOur own research at Omniscient revealed that about half of B2B buyers are starting their purchase journeys in an LLM interface<\/a>. That number will likely rise.<\/p>\n\n\n\nThese prompts aren\u2019t always high intent. In fact, many begin as problem-aware explorations:<\/p>\n\n\n\n
\n- \u201cHow do I reduce churn in a PLG motion?\u201d<\/li>\n\n\n\n
- \u201cBest ways to manage unstructured data?\u201d<\/li>\n\n\n\n
- \u201cHow do other companies structure their analytics teams?\u201d<\/li>\n<\/ul>\n\n\n\n
They sound like diary entries. And often, that\u2019s exactly what these tools are being used as – private, probabilistic journals, personalized for each user. Sometimes they cite sources. Often they don\u2019t. But make no mistake: they are shaping decisions.<\/p>\n\n\n\n
In fact, recent research conducted by Omniscient\u2019s Cate Dombrowski found that<\/a> a significant percentage of the time (roughly \u2153 of the time for problem aware queries), LLMs are recommending brands without being explicitly asked. <\/p>\n\n\n\nOur own data shows this discrepancy. Roughly 5% of our website traffic comes from LLMs, which pales in comparison to our traffic from Google search.<\/p>\n\n\n\n![\"\"](\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\")
<\/figure>\n\n\n\nSimilarly, we see a handful of leads coming in directly from LLMs each month (using click-based attribution in HubSpot and Google Analytics). <\/p>\n\n\n\n
However, self-reported attribution tells a completely different story, with 30-40% of inbound leads stating they found us via an LLM. This is probably an underestimate, as many do not write anything on the web form, yet when asked in our discovery call how they found us, they will often say \u201coh, I was asking ChatGPT how to do\u2026and I found you.\u201d <\/p>\n\n\n\n
Just because you can\u2019t see the click, doesn\u2019t mean the influence didn\u2019t happen.<\/p>\n\n\n\n
LinkedIn\u2019s Lesson (or Chris Walker Redux)<\/strong><\/h2>\n\n\n\nLet\u2019s take a detour.<\/p>\n\n\n\n
Consider LinkedIn. How much website traffic does your average post drive?<\/p>\n\n\n\n
For most B2B companies, the answer is: not much. A few clicks here, a spike there.<\/p>\n\n\n\n
Yet we invest time, money, and effort into creating content on the platform, not for website traffic, but for presence. For narrative shaping. For recall. For community. And sometimes, to shitpost. <\/p>\n\n\n\n
We accept, intuitively, that LinkedIn is a powerful tool for brand influence, even if it rarely triggers a session in GA4. This is what Chris Walker talked about for years<\/a>. And today, most marketers are aware of this and treat LinkedIn social appropriately. <\/p>\n\n\n\nTowards Brand Gravity<\/strong><\/h2>\n\n\n\nThis isn\u2019t an argument against SEO. I don\u2019t even care what we call AI search<\/a>. <\/p>\n\n\n\nIn fact, I believe, and we\u2019ve seen, that great SEO is often great AEO (Answer Engine Optimization), with some marginal tactical differences that I\u2019ve discussed at length on this newsletter. <\/a><\/p>\n\n\n\nIf you\u2019re creating great content, garnering high quality backlinks, have a technically sound and performant website with clean information architecture, this will translate well into AI search. <\/p>\n\n\n\n
The key strategic difference is that LLMs require a broader remit.<\/p>\n\n\n\n
Imagine you\u2019re at a cocktail party. Someone asks the group, \u201cHey, does anyone know a good tool for managing remote teams?\u201d A few people offer answers. One person says, \u201cWe\u2019ve had a great experience with Basecamp.\u201d Another says, \u201cI think Notion added something like that.\u201d A third mentions, \u201cHonestly, Notion is great. It also does X, Y, and Z.\u201d<\/p>\n\n\n\n
Later that week, the person who asked the question might Google \u201cNotion,\u201d visit the site, poke around, and maybe even sign up. When they do, the analytics dashboard will show it as direct traffic, or perhaps branded search. But the real origin, the seed of influence, was the conversation (which, again, was influenced upstream of measurable behavior). <\/p>\n\n\n\n
This is what AI engines are becoming.<\/p>\n\n\n\n
They are scaled cocktail parties – probabilistic, personalized, and trained on the exhaust of the public internet. When someone asks, \u201cWhat\u2019s the best CRM for a B2B startup?\u201d<\/em>, the engine draws a myriad sources, including your website, but also from competitive blog posts, forum threads, help documents, product comparisons, YouTube transcripts, social posts, feature pages, and more.<\/p>\n\n\n\nIn short: the answer space is trained on the ambient residue of what people have said about you<\/em>, not just what you\u2019ve published yourself.<\/p>\n\n\n\nThis is the new game.<\/p>\n\n\n\n
Not just to rank, but to be recallable & recommended.<\/em><\/p>\n\n\n\nWe call this brand gravity<\/a>.<\/p>\n\n\n\nThe pull your company exerts across the market, not through traffic volume, but through narrative density (and accuracy). When someone in your category types a prompt into an LLM, are you likely to show up? Not because you\u2019ve gamed the prompt, but because your presence is so persistent, so clear, so associated with the problem space that the engine can\u2019t help but mention you?<\/p>\n\n\n\n
That\u2019s the goal.<\/p>\n\n\n\n
And this kind of visibility isn\u2019t built through keyword targeting. It\u2019s built through ecosystem saturation. Through off-page content, product clarity, and repeated mentions in credible sources. Through clarity in your own materials and resonance in the words of others. It\u2019s PR. It\u2019s word-of-mouth. It\u2019s technical documentation. It\u2019s third-party reviews and customer quotes and \u201csomeone on Hacker News mentioned you once\u201d posts. It\u2019s the damned Surround Sound SEO thing I came up with in 2018<\/a>, but now evident and broadening in scope. <\/p>\n\n\n\nYour brand\u2019s presence in AI engines is less about keywords and more about gestalt. Are you known? Are you understood? Are you the brand people name in the digital cocktail party of a prompt?<\/p>\n\n\n\n
Rewriting the Playbook: Adapting to the Invisible Shift<\/strong><\/h2>\n\n\n\nSo we adapt.<\/p>\n\n\n\n
Not by abandoning measurement entirely, but by broadening our lens and by acknowledging that attribution is no longer linear<\/a>, no longer clean, and certainly no longer just about what shows up in last-click dashboards.<\/p>\n\n\n\nWe start asking different questions.<\/p>\n\n\n\n
\n- How often is our brand recommended in LLM outputs?\n
\n- Are the recommendations accurate and positive?<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- How many leads self-report ChatGPT (or other AI engines) as the discovery source?<\/li>\n\n\n\n
- Where are we being recommended, and by whom?<\/li>\n\n\n\n
- What third-party sites shape the training data for AI models?<\/li>\n\n\n\n
- What is our citation share for prompts that matter to our target customer base?<\/li>\n\n\n\n
- What stories are circulating in the corpus that represent our category?<\/li>\n<\/ul>\n\n\n\n
We experiment with new metrics<\/strong>: brand visibility in LLMs, citation share, brand mentions in citations (or Surround Sound SEO), qualitative perception audits, forum sentiment mining and social listening. None of these are perfect, but together, they provide a directional sense of presence<\/em>.<\/p>\n\n\n\nWe double down on messaging<\/strong>: making sure our product is described clearly, consistently, and distinctively across every possible surface. If a model can\u2019t figure out what you do, neither will your buyer.<\/p>\n\n\n\nWe build distributed presence<\/strong>: ensuring our expertise and narrative live not just on our domain, but in the places that shape answers (Reddit, Wikipedia, Quora, niche blogs, review sites, YouTube, etc.).<\/p>\n\n\n\nWe prioritize depth over volume<\/strong>. The infinite content treadmill loses some of its appeal when the game becomes coherence, not cadence. Scarce, high-quality, hard-to-fake content (original research, voice of customer, technical deep dives) becomes a differentiator.<\/p>\n\n\n\nAnd most importantly, we get comfortable operating in a world of incomplete information<\/strong>.<\/a><\/p>\n\n\n\nWe look at the register (pipeline, revenue) and if it\u2019s holding steady, we resist the urge to panic just because the clickstream has diminished. We trust the forest even when we can\u2019t see every tree.<\/p>\n\n\n\n
Malte Landwehr said it plainly in a recent LinkedIn comment<\/a>:<\/p>\n\n\n\n\n\u201cI fully agree that betting on AI search should not mean completely neglecting traditional search engines. Especially since at least 50% of AEO (or GEO or AI SEO) is just traditional SEO anyways.<\/em><\/p>\n\n\n\nBut I want to add one perspective to the 2-3% of Google traffic number. I believe that is the wrong metric. 35% to 40% of Google searches lead to a click. With ChatGPT, AI Mode & Co, this number is much lower. Somewhere in the 1% to 5% range. (Source: SimilarWeb, Semrush, Growth Memo, Promptwatch, Peec AI, etc.)<\/em><\/p>\n\n\n\nIf you receive 1% of your clicks from ChatGPT that could mean 30% of relevant questions are already asked there.<\/em><\/p>\n\n\n\nI have seen cases where 20% of new leads self-identified as \u201cI found you via ChatGPT\u201d when ChatGPT was less than 1% of traffic.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\nIf there\u2019s a single question to guide our next phase of marketing strategy, it\u2019s this:<\/p>\n\n\n\n
When your buyer sits down at the proverbial cocktail party – or types a question into the glowing rectangle – is your name the one that comes up?<\/p>\n\n\n\n
Want more insights like this?\u00a0<\/a>Subscribe to Field Notes<\/a><\/p>\n\n\n\n<\/p>\n","protected":false},"excerpt":{"rendered":"
If a tree falls in the forest, does it make…<\/p>\n","protected":false},"author":3,"featured_media":6943,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[184,25],"tags":[],"wf_post_folders":[],"class_list":{"0":"post-6941","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-field-notes","8":"category-seo"},"yoast_head":"\n
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AI engines like ChatGPT, Perplexity, Claude, and Google’s own AI Overviews and AI Mode don\u2019t behave like traditional search engines. They don\u2019t operate on ten blue links. They aren\u2019t structured to pass the user off to a publisher via click. They are designed to answer<\/em>. Which, by their nature, means they are principally zero-click environments<\/em>.<\/p>\n\n\n\n We should not be surprised that they send little traffic.<\/p>\n\n\n\n We should be concerned that we\u2019re still using traffic as our primary measure of relevance.<\/p>\n\n\n\n If your mental model says, \u201cvalue comes from traffic,\u201d then 1% LLM traffic seems like a distraction. But if your model instead considers \u201cvalue comes from influence,\u201d then we have to confront the possibility that we are looking at the wrong signal.<\/p>\n\n\n\n Where Google has historically functioned as a bridge (input on one side, destination on the other) ChatGPT and similar tools offer more self-contained experiences. In the same way that calculators didn\u2019t send you to arithmetic websites, LLMs are not built (at least primarily) to send you to pages that contain the answer. They are built to be the place where the answer lives.<\/p>\n\n\n\n Which means we\u2019re dealing with a zero-click interface. And zero-click interfaces break our attribution models. Because the thing we\u2019ve always used to detect success – the click – is no longer part of the interaction.<\/p>\n\n\n\n Clumsy analogy time.<\/p>\n\n\n\n Let\u2019s say you run a restaurant, a busy little spot with a loyal customer base. For years, you\u2019ve tracked guest traffic using a digital keycard system. Every customer swipes in when they arrive.<\/p>\n\n\n\n You know who\u2019s coming, how often, at what times. Perhaps there\u2019s even a component of these keycards that allows you to see, with some precision, how a customer found you (let\u2019s call this a URL parameter). You\u2019ve built dashboards. You\u2019ve run campaigns. You\u2019ve A\/B tested the dinnertime menu based on Monday Night Football. It\u2019s not perfect data, but it\u2019s yours. You trust it.<\/p>\n\n\n\n Then, one day, you realize the keycard system is creating friction. People forget their cards. The tech is buggy. So you remove it. Guests still come. The dining room still fills. The waitstaff stays busy. The register rings. But now, you don\u2019t know how many<\/em> people walked through the door. You can\u2019t see the foot traffic anymore.<\/p>\n\n\n\n Would you assume your restaurant is empty?<\/p>\n\n\n\n Of course not.<\/p>\n\n\n\n You would recognize that your measurement tool no longer reflects the reality of your business. The absence of telemetry is not the absence of behavior. It\u2019s just the absence of data. And smart operators would adapt. They\u2019d find new proxies. They\u2019d watch the register. They\u2019d listen to servers. They\u2019d ask diners how they heard about the place.<\/p>\n\n\n\n Yet in marketing, we often fail to make this distinction.<\/p>\n\n\n\n We look at the clickstream and decide it is<\/em> the stream. We look at referral traffic and believe it defines influence. We forget that when the medium changes, the signals we\u2019re trained to look for may vanish – not because the influence is gone, but because it\u2019s flowing through a new vessel.<\/p>\n\n\n\n The same number of people might still be \u201cwalking through the door\u201d (discovering your product, forming a preference, making a purchase) but the door is now metaphorical. Maybe they came in through a side entrance. Maybe they read a summary on Perplexity and Googled your brand name instead. Maybe they asked ChatGPT, got a strong answer, remembered your company, and typed it in directly a week later.<\/p>\n\n\n\n The interface is different. So the path is different. And so the old map, the one that traces everything from organic search to landing page to lead form, is no longer as helpful as we want it to be, at least when it comes to LLMs. <\/p>\n\n\n\n The hard part is: we still want it to be.<\/p>\n\n\n\n We want clean lines. Measurable outcomes. Attribution you can show your CFO<\/a>. But we\u2019re entering a domain where the influence is upstream of measurable behavior. We are operating in a dark forest, lit only by scattered self-reports, directional brand visibility scores against self-selected prompts, referral traffic presenting only the tip of the iceberg, and a whole lot of faith shadows.<\/p>\n\n\n\n But if we continue to mistake the absence of clicks for the absence of presence, we will miss what\u2019s actually happening.<\/p>\n\n\n\n Our own research at Omniscient revealed that about half of B2B buyers are starting their purchase journeys in an LLM interface<\/a>. That number will likely rise.<\/p>\n\n\n\n These prompts aren\u2019t always high intent. In fact, many begin as problem-aware explorations:<\/p>\n\n\n\n They sound like diary entries. And often, that\u2019s exactly what these tools are being used as – private, probabilistic journals, personalized for each user. Sometimes they cite sources. Often they don\u2019t. But make no mistake: they are shaping decisions.<\/p>\n\n\n\n In fact, recent research conducted by Omniscient\u2019s Cate Dombrowski found that<\/a> a significant percentage of the time (roughly \u2153 of the time for problem aware queries), LLMs are recommending brands without being explicitly asked. <\/p>\n\n\n\n Our own data shows this discrepancy. Roughly 5% of our website traffic comes from LLMs, which pales in comparison to our traffic from Google search.<\/p>\n\n\n\n Similarly, we see a handful of leads coming in directly from LLMs each month (using click-based attribution in HubSpot and Google Analytics). <\/p>\n\n\n\n However, self-reported attribution tells a completely different story, with 30-40% of inbound leads stating they found us via an LLM. This is probably an underestimate, as many do not write anything on the web form, yet when asked in our discovery call how they found us, they will often say \u201coh, I was asking ChatGPT how to do\u2026and I found you.\u201d <\/p>\n\n\n\n Just because you can\u2019t see the click, doesn\u2019t mean the influence didn\u2019t happen.<\/p>\n\n\n\n Let\u2019s take a detour.<\/p>\n\n\n\n Consider LinkedIn. How much website traffic does your average post drive?<\/p>\n\n\n\n For most B2B companies, the answer is: not much. A few clicks here, a spike there.<\/p>\n\n\n\n Yet we invest time, money, and effort into creating content on the platform, not for website traffic, but for presence. For narrative shaping. For recall. For community. And sometimes, to shitpost. <\/p>\n\n\n\n We accept, intuitively, that LinkedIn is a powerful tool for brand influence, even if it rarely triggers a session in GA4. This is what Chris Walker talked about for years<\/a>. And today, most marketers are aware of this and treat LinkedIn social appropriately. <\/p>\n\n\n\n This isn\u2019t an argument against SEO. I don\u2019t even care what we call AI search<\/a>. <\/p>\n\n\n\n In fact, I believe, and we\u2019ve seen, that great SEO is often great AEO (Answer Engine Optimization), with some marginal tactical differences that I\u2019ve discussed at length on this newsletter. <\/a><\/p>\n\n\n\n If you\u2019re creating great content, garnering high quality backlinks, have a technically sound and performant website with clean information architecture, this will translate well into AI search. <\/p>\n\n\n\n The key strategic difference is that LLMs require a broader remit.<\/p>\n\n\n\n Imagine you\u2019re at a cocktail party. Someone asks the group, \u201cHey, does anyone know a good tool for managing remote teams?\u201d A few people offer answers. One person says, \u201cWe\u2019ve had a great experience with Basecamp.\u201d Another says, \u201cI think Notion added something like that.\u201d A third mentions, \u201cHonestly, Notion is great. It also does X, Y, and Z.\u201d<\/p>\n\n\n\n Later that week, the person who asked the question might Google \u201cNotion,\u201d visit the site, poke around, and maybe even sign up. When they do, the analytics dashboard will show it as direct traffic, or perhaps branded search. But the real origin, the seed of influence, was the conversation (which, again, was influenced upstream of measurable behavior). <\/p>\n\n\n\n This is what AI engines are becoming.<\/p>\n\n\n\n They are scaled cocktail parties – probabilistic, personalized, and trained on the exhaust of the public internet. When someone asks, \u201cWhat\u2019s the best CRM for a B2B startup?\u201d<\/em>, the engine draws a myriad sources, including your website, but also from competitive blog posts, forum threads, help documents, product comparisons, YouTube transcripts, social posts, feature pages, and more.<\/p>\n\n\n\n In short: the answer space is trained on the ambient residue of what people have said about you<\/em>, not just what you\u2019ve published yourself.<\/p>\n\n\n\n This is the new game.<\/p>\n\n\n\n Not just to rank, but to be recallable & recommended.<\/em><\/p>\n\n\n\n We call this brand gravity<\/a>.<\/p>\n\n\n\n The pull your company exerts across the market, not through traffic volume, but through narrative density (and accuracy). When someone in your category types a prompt into an LLM, are you likely to show up? Not because you\u2019ve gamed the prompt, but because your presence is so persistent, so clear, so associated with the problem space that the engine can\u2019t help but mention you?<\/p>\n\n\n\n That\u2019s the goal.<\/p>\n\n\n\n And this kind of visibility isn\u2019t built through keyword targeting. It\u2019s built through ecosystem saturation. Through off-page content, product clarity, and repeated mentions in credible sources. Through clarity in your own materials and resonance in the words of others. It\u2019s PR. It\u2019s word-of-mouth. It\u2019s technical documentation. It\u2019s third-party reviews and customer quotes and \u201csomeone on Hacker News mentioned you once\u201d posts. It\u2019s the damned Surround Sound SEO thing I came up with in 2018<\/a>, but now evident and broadening in scope. <\/p>\n\n\n\n Your brand\u2019s presence in AI engines is less about keywords and more about gestalt. Are you known? Are you understood? Are you the brand people name in the digital cocktail party of a prompt?<\/p>\n\n\n\n So we adapt.<\/p>\n\n\n\n Not by abandoning measurement entirely, but by broadening our lens and by acknowledging that attribution is no longer linear<\/a>, no longer clean, and certainly no longer just about what shows up in last-click dashboards.<\/p>\n\n\n\n We start asking different questions.<\/p>\n\n\n\n We experiment with new metrics<\/strong>: brand visibility in LLMs, citation share, brand mentions in citations (or Surround Sound SEO), qualitative perception audits, forum sentiment mining and social listening. None of these are perfect, but together, they provide a directional sense of presence<\/em>.<\/p>\n\n\n\n We double down on messaging<\/strong>: making sure our product is described clearly, consistently, and distinctively across every possible surface. If a model can\u2019t figure out what you do, neither will your buyer.<\/p>\n\n\n\n We build distributed presence<\/strong>: ensuring our expertise and narrative live not just on our domain, but in the places that shape answers (Reddit, Wikipedia, Quora, niche blogs, review sites, YouTube, etc.).<\/p>\n\n\n\n We prioritize depth over volume<\/strong>. The infinite content treadmill loses some of its appeal when the game becomes coherence, not cadence. Scarce, high-quality, hard-to-fake content (original research, voice of customer, technical deep dives) becomes a differentiator.<\/p>\n\n\n\n And most importantly, we get comfortable operating in a world of incomplete information<\/strong>.<\/a><\/p>\n\n\n\n We look at the register (pipeline, revenue) and if it\u2019s holding steady, we resist the urge to panic just because the clickstream has diminished. We trust the forest even when we can\u2019t see every tree.<\/p>\n\n\n\n Malte Landwehr said it plainly in a recent LinkedIn comment<\/a>:<\/p>\n\n\n\n \u201cI fully agree that betting on AI search should not mean completely neglecting traditional search engines. Especially since at least 50% of AEO (or GEO or AI SEO) is just traditional SEO anyways.<\/em><\/p>\n\n\n\n But I want to add one perspective to the 2-3% of Google traffic number. I believe that is the wrong metric. 35% to 40% of Google searches lead to a click. With ChatGPT, AI Mode & Co, this number is much lower. Somewhere in the 1% to 5% range. (Source: SimilarWeb, Semrush, Growth Memo, Promptwatch, Peec AI, etc.)<\/em><\/p>\n\n\n\n If you receive 1% of your clicks from ChatGPT that could mean 30% of relevant questions are already asked there.<\/em><\/p>\n\n\n\n I have seen cases where 20% of new leads self-identified as \u201cI found you via ChatGPT\u201d when ChatGPT was less than 1% of traffic.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n If there\u2019s a single question to guide our next phase of marketing strategy, it\u2019s this:<\/p>\n\n\n\n When your buyer sits down at the proverbial cocktail party – or types a question into the glowing rectangle – is your name the one that comes up?<\/p>\n\n\n\n Want more insights like this?\u00a0<\/a>Subscribe to Field Notes<\/a><\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" If a tree falls in the forest, does it make…<\/p>\n","protected":false},"author":3,"featured_media":6943,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[184,25],"tags":[],"wf_post_folders":[],"class_list":{"0":"post-6941","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-field-notes","8":"category-seo"},"yoast_head":"\nA Restaurant Without a Keycard<\/strong><\/h2>\n\n\n\n
Influence Without Telemetry<\/strong><\/h2>\n\n\n\n
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![\"\"](\"https:\/\/beomniscient.com\/wp-content\/uploads\/image-204-1024x618.png\")
<\/figure>\n\n\n\nLinkedIn\u2019s Lesson (or Chris Walker Redux)<\/strong><\/h2>\n\n\n\n
Towards Brand Gravity<\/strong><\/h2>\n\n\n\n
Rewriting the Playbook: Adapting to the Invisible Shift<\/strong><\/h2>\n\n\n\n
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