Funnel Query Pathway: A Framework for Measuring AI Visibility

The most common question I’ve fielded in 2026 is deceptively simple: how do we actually measure this? How do we know if our brand is surfacing inside ChatGPT? How do we confirm whether Perplexity is recommending us? And how do we determine if the grounding work we completed last quarter for AI Mode made any real difference? The honest answer is that no one has cracked this yet.

If someone offers you a polished dashboard claiming to track AI visibility across search, assistive, and agent surfaces simultaneously, they are selling a snapshot that amounts to little more than an educated guess. The conventional advice , monitor queries you think people might ask or adapt existing search keywords , falls short. Prebuilt keyword lists favor what’s easy to track, what maps to existing marketing efforts, or what would be ideal if audiences behaved predictably. The visibility question itself is valid, but the demand for a precise numerical answer is misguided.

The measurement challenge, as the industry currently frames it, borrows from the wrong discipline. Brands hunting for a perfect AI-era visibility KPI are chasing something that doesn’t exist and never will. The solution lies in a methodology rooted in how economists measure complex, opaque systems. My approach is the Funnel Query Pathway, and it serves three functions simultaneously: strategy, measurement, and analysis.

Marketers want a dashboard number that tracks week over week for a specific query on a specific engine, just as search delivered for two decades. Search could provide that because the surface was finite, rankings were stable, clicks were measurable, and the journey was observable. Assistive and agential surfaces offer none of those guarantees. We now operate in a fundamentally different environment that demands different questions, different signals, and different proof.

Why AI visibility is a macro measurement problem

My background in economics and statistical analysis at Liverpool John Moores University makes the shape of this problem familiar. It mirrors what happens when a discipline that works at one scale tries to function at a larger scale where its instruments break down. Microeconomics vs. macroeconomics is the classic example. A corner shop can measure inventory precisely, but a central bank cannot measure inflation with the same accuracy. Both disciplines are correct at their respective scales, but neither set of tools works in the other’s environment. My proposed discipline is not macroeconomics applied to brands; it is the macro instinct applied to AI-era brand measurement.

AI surfaces are macro for three structural reasons. First, opacity: the system’s internal state is unobservable. Modern LLMs cannot fully explain their decisions, just as central banks cannot observe every transaction. I call this brand-user-algorithm (BUA) opacity: users cannot see the alternatives the algorithm rejected, brands cannot see the journey within walled gardens, and algorithms cannot fully introspect on their own reasoning.

Second, personalization acts as the AI-era equivalent of heterogeneous agents. Each user receives a different answer because the engine factors in varying context. Third, the explosion of possibilities extends beyond seven engines. Surfaces now include apps (Copilot in Word, ChatGPT inside Slack, Perplexity in Comet), operating systems (Copilot in Windows, Apple Intelligence in macOS and iOS), and hardware (Lenovo Copilot+ laptops, Samsung Galaxy AI, Meta Ray-Bans). Ambient research becomes a major entry mode, with AI surfacing recommendations unprompted based on context.

This is where the Funnel Query Pathway lives. It is not an evolution of keyword mapping or a pimped-up intent-based methodology. Because it operates at the macro level, it is fundamentally different.

The unit of measurement is a cohort

Most practitioners running keyword campaigns think they group queries by intent, but they often group by category instead. A typical Google Ads campaign might place every Phuket hotel query into one ad group, assuming “Phuket hotels” represents a logical intent group. It does not. “Phuket hotels” defines the destination, but the buyer searching for “5-star hotels in Phuket” and the buyer searching for “cheap hotels in Phuket” share only a location. They have different budgets, decision criteria, conversion paths, and downstream behavior. Grouping them produces an ad group whose performance averages across two cohorts that should never have been combined.

Categories group things. Cohorts group people. Intent is about people, not things. Google engineers tell me this is the most common mistake in AI Max and Performance Max campaigns. The algorithm routing a prospect does not ask, “What category is this query in?” It asks, “What cohort does this user belong to, with what intent?”

The intersection of cohort and intent defines the node

A cohort is a group of people who behave similarly given a specific stimulus: XL men, luxury travelers, parents shopping for kids. Each cohort has a durable identity that persists across time and context. An intent is the situational vector that crosses through the cohort at a moment: buying a shirt, booking a hotel, kitting out a child for summer. Every cohort carries many intents across a lifetime, and the same intent spans many cohorts across the market.

The intersection of cohort and intent defines a node in the Funnel Query Pathway tree. XL men buying a shirt in winter is a node. Luxury travelers booking a hotel for next month is a node. Parents shopping for kids’ shorts for summer is a node. Cohort alone does not work because XL men buying pajamas behave differently from XL men buying office shirts. Intent alone does not work because luxury travelers booking Bali behave differently from budget travelers booking Bali. The intersection is where behavioral coherence lives, and that coherence makes the node trackable in opaque AI surfaces.

A query qualifies for tracking when both cohort and intent are legible in it. “Men’s red shirt from Uniqlo” surfaces a man shopping for clothes (cohort) and buying a red shirt (intent), with the brand named as the commercial destination. Both axes are clear. “Hotels in Bali” surfaces an intent but hides the cohort (luxury, business, budget, honeymoon, family, backpacker), so it cannot function as a node. Narrow it to “cheap hotels in Bali,” and the budget cohort emerges alongside the intent, qualifying the query for the funnel query pathway. The test is behavioral coherence, not specificity.

Build the funnel query pathway from the conversion moment upward

The funnel query pathway does not track what users actually type. It tracks what the cohort would ask given the intent. Every query in the tree is a theoretical representative of cohort behavior at the buying moment, not an empirical record of individual users. This is the macro discipline in practice. We do not research search volume for these queries because they are not necessarily queries anyone has typed. We construct them by reasoning forward from cohort plus intent, building the ideal pathway a representative member of the cohort would walk.

Once a query passes the test, it becomes your starting point. The funnel query pathway builds upward from there, mirroring the funnel flip at the query level. AI-era acquisition starts at the conversion moment and projects upward because the algorithm forward-calculates the conversion path from intent, not from awareness. Start with the ideal branded BOFU query for one cohort with one intent, then project upward through evaluation questions, then upward again through awareness questions.

Example: Building one funnel query pathway tree from a single Uniqlo query

Take Uniqlo as the brand and “men shopping for clothes” as the cohort. The intent defines the buying moment: men buying a shirt, men buying winter outerwear, men buying gym kit. Each is a node. Start with one: buying a red shirt. The branded bottom-of-funnel query that fits the cohort-intent intersection is “men’s red shirt from Uniqlo.” That is the conversion node. Five to 10 variations fit the same intersection: “men’s Uniqlo Oxford shirt,” “Uniqlo men’s smart shirt,” “men’s red dress shirt Uniqlo,” “Uniqlo men’s casual red shirt.” Pick the most useful one for your business and build upward.

Next, find the middle-of-funnel branches that would land at your ideal BOFU query. For “men’s red shirt from Uniqlo,” look for evaluation queries the same man would ask before arriving at the branded buying moment. The cohort is still men shopping for clothes, the intent is still buying a red shirt, and the brand is not named yet: “Best red shirt for men,” “Red shirt for office work,” “Where to buy a quality red Oxford shirt,” “Which red shirt looks best with chinos,” “Affordable men’s red shirts that don’t fade,” “Red shirts for men under €50,” “Best affordable clothing brands for men,” “Minimalist menswear brands with color ranges,” “Where to buy quality basics for men online,” “Best affordable men’s shirt brands.” Ten branches, all the same cohort, all the same intent, all logically routing to the ideal BOFU commercial query.

Top-of-funnel branches that would land at each middle-of-funnel query are broader awareness questions the same man would ask even earlier. For “best red shirt for men”: “Can men wear red shirts to work,” “How to add color to a man’s wardrobe,” “Shirt color rules for office wear,” “How many shirts should a man own,” “Which shirt colors suit men with what skin tone,” “What color clothing would make me stand out in a crowd.” That is one 60-query funnel query pathway. As a rule of thumb, 60 queries is a reasonable number from a budget-versus-insights perspective. The macro approach does not require granularity to measure effectively.

The important thing is that these 60 queries all route to one branded buying moment for one cohort with one intent. Do it again with another intent inside the same cohort (men buying winter outerwear, men buying office trousers), then another cohort (women shopping for clothes, with the intent of buying pajamas, branded BOFU “women’s pajamas Uniqlo”). The tracking surface becomes a forest of trees, accumulated as the methodology runs.

AI routing uses the same math as Google Ads bidding

I discovered this while running keynotes and workshops for Google Marketing Live in Asia Pacific this month, in conversations with senior Google engineers about how Gemini routes recommendations. The math Gemini runs to decide which answer to surface next is the same math Google Ads has been running to decide which ad to serve next: forward-calculate the probability that this cohort, with this intent, lands at a conversion, and pick the path most likely to get them there. Every practitioner who has bid on a campaign in the last 15 years has been working with that probability calculation. This is the most useful framing the funnel query pathway can inherit, because it explains why the cohort-with-intent unit aligns with the engine’s internal logic.

The engine is not tracking categories or queries in isolation. It is running a funnel pathway probability calculation on cohort plus intent. Every node you populate teaches the engine which path is the fastest way to get this user to the best solution.

Ads includes profit margin. Organic does not. The operational formula in Ads is cohort x intent x conversion rate x profit margin. Google holds all four because the advertiser provides the commercial information needed to optimize bidding. The auction maximizes expected profit because Google has the inputs to calculate it. The operational formula in organic is cohort + intent + conversion rate. Profit margin drops out because the engine does not have the commercial information. It does not know your gross margin on a red shirt versus pajamas, and it does not optimize for your bottom line. It optimizes for user satisfaction, which is its own proxy for engine-level commercial outcome.

The principle holds across both surfaces: cohort + intent + conversion rate is the unit AI algorithms work with best. What differs is the precision of the conversion estimate. In organic, conversion is inferred from behavioral patterns. In Ads, it is measured from advertiser-provided data. The macro discipline operates in organic where micro precision is unavailable. Micro precision operates in Ads where it is available. The funnel query pathway tree works on both. Populate it once, and use it for organic content, Ads campaign structure, and analytical insights across both.

The 15-gate model

In the 15-gate model I have built, the AI engine pipeline runs 10 binary gates: Discovered, Selected, Crawled, Rendered, and Indexed (DSCRI), handled by the bot and invisible to the algorithm. Annotated, Recruited, Grounded, Displayed, and Won (ARGDW), handled by the algorithm and invisible to the bot. Our framework extends another five gates after Won: Onboarded, Performed, Integrated, Devoted, and Codified (OPIDC), handled by post-transaction operations that serve people, invisible to both bot and algorithm. Fifteen gates total, each a binary checkpoint where the brand either survives or does not.

Nobody inside the system sees the whole chain. Only the brand does. Won itself has three flavors depending on surface: the imperfect click in traditional search, the perfect click in assistive engines, and the agentic click in assistive agents. The funnel sits on the display gate. The user’s journey from question to purchase moves through three phases at display: awareness, consideration, and decision. Phases are continuous human positions. Gates are binary machine checkpoints. The funnel query pathway tracks the queries the user submits across these three phases, with the branded buying-moment query landing at the decision phase that triggers Won.

Step 1: Start at the bottom of the funnel

Identify the queries your ideal customer profile (ICP) would ideally submit using your brand name at the moment they are ready to buy. The emphasis is on “ideally.” Keyword research asks what people actually type. The funnel query pathway asks what the cohort with this intent would ideally ask the engine just before they purchase from you, with your brand name in the query: branded, bottom-of-funnel, intent-confirmed, cohort-coherent.

Calibrate the specificity to the cohort definition. “Men’s red shirt from Uniqlo” fits the broad cohort of men shopping for clothes. “Men’s extra-large red shirt from Uniqlo” fits a sizing sub-cohort that behaves differently because size availability constrains the consideration set. Either is fine. Pick the cohort level where you want to operate, then operate consistently upward within the branches of your tree. Generic keyword research will not surface these queries because keyword tools optimize for volume, and cohort-with-intent queries are usually low volume by design. You have to know your cohort well enough to write them down yourself. If you cannot write five, your ICP work needs more depth before this methodology produces useful results.

Step 2: Project the pathway upwards

Each bottom-of-funnel query branches into multiple middle-of-funnel queries (evaluation questions the same cohort would ask before arriving at the buying moment), each of which branches into multiple top-of-funnel queries (awareness questions that would come even earlier). Build out gradually, one bottom-of-funnel query at a time. The funnel flip operates at the query level: generation starts at the conversion query and projects upward, rather than starting at top-of-funnel awareness and hoping the buyer arrives at conversion.

Granularity is cohorts x intents. Tracking is a budget call. The question of how many trees to build has one answer: as many as the team can populate. The question of how many trees to track has one answer: as many as give you statistically meaningful data. The starting unit is one cohort with one intent. Men shopping for clothes, with the intent of buying a red shirt. That is one tree, around 60 queries. Add intents inside the same cohort (XL men buying winter outerwear, office trousers, and gym kit). Add cohorts (XL women, parents). Cohorts times intents gives the tree count. The numbers scale with the budget: 1 cohort x 1 intent = 1 tree (60 queries), 3 cohorts x 5 intents = 15 trees (900 queries), 5 cohorts x 10 intents = 50 trees (3,000 queries), 10 cohorts x 10 intents = 100 trees (6,000 queries).

What changes with resolution is the precision of the diagnosis. Track three trees, and you have a low-resolution read on three cohort-with-intent intersections. Track 100, and you have a high-resolution read on most of your buying landscape. Both are defensible macro reads because macro is about defining your methodology and scope to reliably read direction and rate of change, rather than specific values. This methodology means you can start small and build out. Start tracking three Funnel Query Pathways for your most profitable ICP this month, then add another next month. Group them, and you can compare like with like starting today using a macro approach that scales and survives over time.

Populate the tree, and you teach the engine the conversion path

The shaping mechanism is what makes the funnel query pathway more than a measurement methodology. The engine routes recommendations by predicting what comes next for the cohort with the intent. When the brand feeds the AI with content that builds logically structured funnel query pathways and answers each node, the engine learns the chain: which awareness questions belong to this cohort, which evaluation questions follow them, and which branded buying-moment query is the conversion answer. For obvious pathways like red shirts, the algorithms already have the pathways ingrained. For less popular pathways, the engine has no opinion, and you have every opportunity to shape its perception.

Since the engine is an active participant in the funnel alongside the user, it can form a predictive map. The path it surfaces for any prospect in the cohort is the path the brand trained. Shaping is not a side effect. It is the compounding mechanism. It means the brand stops competing for individual query rankings and starts engineering the inference paths the engine forward-calculates from. The competitor optimizing query by query is optimizing against a model the engine has already moved past.

The deeper move: Mapping the funnel query pathway into every webpage

The methodology can sit beside the website as a tracking document, and that works. But the deeper move is mapping the funnel query pathway into your strategy, both on-site and off-site. Every node in every tree corresponds to a query the engine surfaces for the cohort. Every query needs a passage that answers it. Every page names the cohort it is serving. Every passage names the intent that might bring the cohort there and clearly outlines the next step in the cohort’s conversion path. Top-of-funnel pages route toward evaluation pages. Middle-of-funnel pages route toward branded buying-moment pages. Bottom-of-funnel pages close the conversion.

If you can align the content across your brand’s digital footprint to the forward-calculation logic the engine is already running , cohort, intent, awareness layer, evaluation layer, conversion layer , then when the engine forward-calculates the next step for any user in the cohort, your site is one of the few places that has the complete chain laid out. The probability calculation tilts in your favor. Build all the funnel query pathways for your ICP, and you are teaching the machine exactly what the path looks like for every cohort-intent intersection you serve, while encouraging it to bring the subset of its users who are your ideal audience right to your door.

One framework for strategy, measurement, and analysis

The funnel query pathway does three jobs simultaneously. Strategy: populate every node of the tree with content that proves the answer at that phase of the buying journey: awareness content at the top, evaluation content in the middle, and the branded conversion moment at the bottom. Stop running content generation as a calendar against a keyword list. Start engineering paths that represent your ICP’s buying journey.

Measurement: run the same funnel query pathways across the three modes (search, assistive, and agent) and the engines (Google, ChatGPT, Perplexity, Claude, Copilot, Siri, Alexa, etc.). You cannot track every surface those engines appear on (Copilot in Word, ChatGPT in Slack, Apple Intelligence in iOS, and Copilot+ on a Lenovo laptop are all closed contexts that do not allow rank-tracking). But every surface runs the same underlying engine, so your tracking extrapolates to every surface each engine sits inside.

Analysis: use the pattern of where the brand surfaces and where it does not across the funnel query pathway, by mode and by engine, as the macro view you can rely on for a like-for-like comparison over time.

What you actually get from the funnel query pathway

Here is what you actually get from running the funnel query pathway: a quarter-after-quarter read of whether AI is recommending your brand to the right people at the right moment. You see direction, momentum, and a record of what is working. You build, you measure, you analyze, and you adjust. Then you do it again next quarter. The brands that start this discipline now will be the ones AI knows by name in three years.

Pick one cohort , the most strategically important if you have several. Pick one intent inside that cohort. Write five to 10 branded bottom-of-funnel queries that cohort-with-intent would ideally submit at the buying moment (“men’s red shirt from Uniqlo” in our example). Pick one and map upward: five to 15 middle-of-funnel queries that would land at it, then three to 10 top-of-funnel queries that would land at each of those. You now have one tree, somewhere between 50 and 200 queries.

Run strategy, measurement, and analysis on the funnel query pathway branches. Strategy: do you have pages and passages that address each of the nodes? Fill the gaps. Measurement: run the tree across engines and document where the brand surfaces. Analysis: where are the gaps clustered, which node is weakest, and which engines are recruiting most consistently? Build out the content that fills the gaps in your ICP funnel query pathways, and track that set of queries monthly. You will see results, and you will be able to measure them.

AI-era optimization is about defining your methodology, picking your ICP and tracking, and building and strategizing with a macro mindset. That is the subject of the next article in this series. This is the 14th piece in my AI authority series.