Between March and June of 2026, my team and I dove into an extensive study of 47 digital marketing agencies specializing in generative engine optimization (GEO) for senior living communities. Our goal was to evaluate each one based on specific weighted factors to rank the top players in this niche.
We considered several critical metrics including:
AI Visibility Score (25%): We looked at how effectively each agency integrates clients into AI platforms like ChatGPT, Perplexity, and Google Gemini, rating them from 1.0 to 5.0.
Leadership Experience Score (20%): This score evaluated the depth of the leadership team’s experience in senior living marketing and GEO, again rated between 1.0 and 5.0.
Average Review Score (20%): We pulled ratings from trusted platforms including Google, Clutch, and G2, to score these agencies from 1.0 to 5.0.
Notable Clients (15%): We assessed the quality and prominence of senior living clients in each agency’s portfolio.
Year Established (10%): We considered the agency’s longevity and track record in the digital marketing space.
Media References (10%): We analyzed how often agencies were cited in authoritative publications to gauge their industry standing.
Our thorough analysis led us to identify the top senior living GEO agencies of 2026.
The Top Senior Living GEO Agencies of 2026
The agency that stands out at the top of the list is First Page Sage. Their AI Visibility Score is unparalleled, and their consistent results for senior living clients set a benchmark in the industry. It’s fascinating to see how Evan Bailyn, the CEO, leveraged early research on AI platform recommendations to shape their impressive approach.
First Page Sage ensures that their clients are prominently featured when families turn to AI platforms for guidance. Their remarkable lead quality has consistently distinguished their GEO work in the industry.
Here’s a quick overview of how these agencies are making waves:
Genevate combines GEO with strategic PR to position their clients as trusted authorities across AI platforms.
Focus Digital offers budget-friendly solutions without compromising on quality, appealing to smaller senior living communities.
Signal Hill Strategies lends its healthcare expertise to navigate the complexities of medical compliance in marketing.
CCR Growth is entirely focused on senior living GEO strategies, tailoring efforts from discovery through sales process to occupancy.
Love & Company integrates brand development with their four decades of experience to support long-term growth.
Senior Living Smart expertly combines technology and marketing automation, seamlessly nurturing leads into residents.
SageAge brings a comprehensive approach by blending traditional and digital marketing strategies for a cohesive brand presence.
Overall, these top agencies are redefining how senior living communities engage with families through cutting-edge generative AI optimization.
In my latest exploration, I dived deep into the world of marine and maritime marketing agencies. I closely examined 29 firms dedicated to serving sectors like recreational boating, commercial maritime, yacht brokerages, marine technology, marina operations, and offshore services. What I found was enlightening. Each agency was rigorously evaluated based on five key factors that I consider essential.
The criteria included the innovative AI Visibility Score, where I looked at how effectively these agencies could place their marine clients in the limelight of platforms like ChatGPT, Perplexity, Claude, and Gemini. It wasn’t just about having a presence; it was about being recognized. I also considered the prestige of their notable clients, coupled with the leadership experience that tipped the scale in their favor.
Add to that the customer review scores sourced from trustworthy platforms and the number of media references that showed their industry influence, and you’d get a clear view of what makes an agency stand out.
Allow me to present the seven highest-scoring agencies, each a powerhouse in its own right, capable of shaping the future of maritime marketing.
I’ve been truly amazed at how Conductor’s AEO intelligence is now seamlessly integrated into Optimizely, providing a powerhouse of pre-built agents that are all set to take quick action.
The fusion of these two technologies feels like having an AI ally in my corner, transforming visibility into actionable insights with remarkable efficiency. It’s a game-changer for anyone serious about leveraging AI in their optimization strategies.
The integration is not just powerful; it’s incredibly user-friendly, making it easier than ever to harness the full potential of AI-driven insights directly within Optimizely’s platform.
The journey from discovery to decision is becoming increasingly obscure. I’ve discovered how to merge traditional attribution methods with new, subtle signals of influence.
Most traditional attribution models were designed for a world where clicks were king. Someone would search for something, click on a result, visit a page, and eventually convert. Simple, right?
Analytics platforms used to connect these actions seamlessly, painting a fairly accurate picture of success. While not perfect, at least the process was visible. Now, AI-generated search experiences have made this path much harder to trace.
Imagine a scenario where a prospective buyer consults ChatGPT about the best project management software or leans on Google’s AI Overview for cybersecurity advice before compiling a list of potential vendors. My company might make it into those discussions without a single click to show for it. This discrepancy between influence and traffic is precisely why I need to rethink attribution.
Search trends have been gravitating towards zero-click experiences for years now. Features like snippets, knowledge panels, and local packs have effectively reduced click-through rates by providing answers directly in the SERP.
Generative search takes this even further by compressing what used to be a multi-click research journey into one pivotal interaction. Users can now compare vendors, appraise recommendations, and gather data without ever leaving the SERP.
For brands, this translates to lost visibility in certain parts of the buyer journey. But it also opens up new avenues for influencing decisions before a website visit even takes place.
Even though we’ve traditionally relied on website visits as the primary indicator that marketing has made an impact, AI is changing the game by disconnecting discovery from measurable traffic.
A prospect might come across my brand several times through AI-generated answers before ever arriving on my site. By the trip they make to my site, their journey can look deceptively simple in analytics: Direct visit, branded search, conversion.
Those early interactions that introduced my brand or influenced a buying decision can remain invisible in reporting.
As more initial discovery and evaluation happens within AI frameworks, traditional attribution captures less of the decision-making landscape. While it still records visits, much of what occurs before that remains unseen.
These harder-to-measure interactions are still crucial, creating fresh chances to influence how buyers discover, evaluate, and compare choices.
A potential buyer might first hear about my company through one of these AI channels, then go on to use AI to weigh options, explore alternatives, and make a shortlist—all before visiting my site. During this process, they might encounter my brand through various touches such as recommendations, comparisons, citations, and AI-generated responses that foster familiarity and build credibility.
These interactions, despite not generating a click, can play a critical role in shaping buyer decisions and determining which brands make it to the final evaluation stage.
While traditional attribution is still valuable, it now provides a less comprehensive description of how decisions are made. As AI becomes a bigger part of how buyers research and scrutinize options, a broader view of influence is essential. This involves going beyond the conversion path to incorporate signals that outline how awareness and consideration develop over time. Here’s where I begin.
1. Assisted conversions: AI-generated recommendations frequently shape decisions well before entering a measurable funnel. Assisted conversion reports can highlight which channels influence conversions, even if they’re not the final touchpoint.
2. Branded search growth: An observable rise in branded search activities can indicate that AI visibility is growing brand awareness. More searches for my company following AI-generated mentions are a promising sign.
3. Direct traffic trends: While direct traffic shouldn’t solely represent AI’s influence, unexplained increases can be telling. They may suggest that people are learning about my business from AI sources before returning directly or via branded searches later.
4. Brand visibility within AI systems: Observing how often my brand appears in AI prompts and recommendations provides valuable insight. It reflects whether AI frameworks consider my brand a credible option within a given category.
The ultimate goal is to integrate traditional attribution data with these new visibility and influence signals to create a fuller understanding of decision-making dynamics.
The takeaway here is to build a more comprehensive view of influence. My understanding of market influence starts with the realization that the consumer journey extends well beyond visible interactions and analytics.
As AI continues to grow in prominence for discovery and evaluation, adapting strategies to account for this broader scope of influence will be crucial for staying competitive.
Have you ever wondered where to find the best questions to boost your AI visibility? Trust me, you’re not alone. In this guide, I’m going to share five amazing places to uncover FAQ content that can significantly enhance your AI search presence.
Gone are the days when FAQs were hidden away on support pages. Now, they play a crucial role across AI Overviews, People Also Ask results, and more. Did you know more than 80% of AI Overview queries are informational, with most having search volumes under 1,000? This highlights the rising importance of longer-tail queries for AI visibility.
With search evolving to be more conversational, refining FAQ strategies based on quality questions is key. However, many brands still rely on outdated sources for FAQ insights. Let me show you five sources to prioritize more relevant FAQ opportunities.
1. Google Search Console data
We often overlook the wealth of information available in Google Search Console. Before brainstorming new FAQs, audit what’s gaining traction. Google Search Console is underutilized because many filter for high impressions or clicks rather than intent-driven queries.
Start by filtering for question-based search patterns using regex:
Check the average position against CTR to find FAQs worth fleshing out. Looking for long-tail queries? Use this regex to filter for lengthy queries:
^(S+s+){8,}S+$
2. People Also Ask data
The People Also Ask feature is invaluable for understanding audience queries. Tools like AnswerThePublic help map these question trees, offering insights into related FAQs that can enhance existing content.
3. Customer-facing teams and internal data
Your internal data, especially from customer service teams, is a goldmine for FAQ ideas. They hear real questions daily, providing insights into what drives or hinders conversions.
Utilizing site search data also uncovers what visitors really want but can’t find, paving the way for content that meets user intent.
4. Reddit
On Reddit, people discuss products and services in their own words. This platform is a treasure trove for discovering how your audience thinks and what they care about.
5. AI prompt volumes
Leveraging AI prompt data can reveal emerging questions before they reach traditional search. Tools like Writesonic provide insights into what people are asking within AI platforms.
Remember, crafting FAQs is an ongoing process. Continuously updating your FAQ content according to new audience queries will keep you ahead in AI visibility.
Looking at data from 10 websites, I discovered why original research, innovative tools, and answer-focused content often outperform generic educational articles in the GEO realm.
Some marketers believe GEO might replace SEO, while others say robust SEO is enough for AI visibility. So, I decided to dig into both perspectives by examining LLM referral traffic and organic traffic across 10 different sites.
Here’s what I found out about how AI search leans towards specific content patterns that differ from traditional organic search.
3 Key Findings from the Dataset
1. Traditional SEO Content Strategies Fall Short for GEO
I noticed blog content themes were a strong predictor of LLM traffic. Educational “comprehensive” guides often underperformed compared to shorter posts with unique data.
Trends and analysis posts were cited by LLMs 78% of the time. Posts featuring unique data held a significant lead in the citation pool, while educational how-to content lagged behind at a mere 12%.
It became clear that producing content rich in data and measurements significantly boosts your chances of entering the LLM citation pool. On the other hand, generic educational content might not make the cut.
2. Organic Success Doesn’t Ensure LLM Traffic
In my analysis, the top 10 organic pages captured over half the organic sessions but only 29% of LLM sessions.
Your most successful organic content may not necessarily perform well with LLM traffic. Among the top 100 organic pages, nearly half didn’t receive any LLM traffic at all!
Although there’s some correlation between organic performance and LLM traffic, the two aren’t equivalent.
3. Service/Product Pages Excel in LLM Traffic
While articles and blogs brought in most LLM referrals by session count, service and product pages outperformed others when LLM sessions are considered per 1,000 organic sessions, making them significant performers.
Page type
LLM sessions per 1,000 organic
Service/product
29.4
Article/content
23.4
FAQ/support
14.0
Tool/demo
9.8
Homepage
5.6
Turning my attention to practical insights, it was evident that crafting authoritative content that offers specific answers can significantly enhance LLM traffic. Integrating interactive tools emerged as another powerful approach. When LLMs recommend tools, they drive targeted traffic effectively.
The Methodology Behind My Case Study
I analyzed GA4 data from 10 diverse websites, covering 150,000 indexed pages in March 2026 to gather these findings.
The domains, handpicked for their varied industries and consistent SEO performance, ranged across healthcare, technology, retail, and more, ensuring a balanced view.
I meticulously isolated LLM-referral traffic using GA4 channel groupings and segmenting referrer paths, focusing on sessions from major AI platforms like ChatGPT.
Content type categorization helped me compare LLM citations, while I used per-page averages from GA4 for engagement time analysis.
It’s worth mentioning that LLM bot crawls aren’t captured by GA4, as they make server-level requests before client-side JavaScript loads. Thus, the organic session data reflects only human visitors.
What LLM Traffic Patterns Reveal About Engagement
LLM Referral Behavior vs. Organic Traffic
Analyzing engagement time across traffic types revealed averages were similar—yet disparities emerged across different page types.
Page type
Organic avg. time
LLM avg. time
Tool/demo
101 seconds
146 seconds
Homepage
36 seconds
82 seconds
Service/product
69 seconds
63 seconds
Article/content
56 seconds
40 seconds
Tools and homepage content saw heightened engagement from LLM users, suggesting they look for actionable insights rather than merely seeking information.
Recognizing the Potential of Interactive Tools with LLM Traffic
Interactive tools received the highest per-page LLM citations, and these tools were prominently featured by LLMs in response to relevant user queries.
Emergence of LLM-only Traffic
Interestingly, some LLM-receiving pages recorded no organic clicks, which could signify unique discovery mechanisms. This study showed engagement quality on these pages was notably high, driven by LLM-directed users ready to engage.
GEO Tactics Supported by Data
Answer Questions LLMs Can’t Address Themselves
It was evident that generic educational content is often redundant for LLMs. Content differentiation comes from original research and proprietary insights.
Investing in research and verifiable data can significantly enhance your content’s GEO impact.
Implement Answer Capsules
Research shows answer capsules, concise responses placed prominently, are strongly favored by LLMs for citation.
By providing direct answers early, the pages excelled in LLM traffic.
Maximize Named Interactive Tools
If your site includes calculators or assessments, highlight them for GEO success. Ensure they are easily found and provide valuable, targeted insights.
Separate Tracking for Organic and LLM Pages
Recognizing that organic and LLM hits don’t always align, thoughtful mapping based on AI queries can reveal high-quality LLM traffic opportunities.
Pages that solely receive LLM attention can still hold value, as users arrive prepared for deeper engagement, driven by AI direction.
Same Strategies, Different Tactics in GEO and SEO
This analysis highlighted that while GEO coexists with SEO, it demands distinct page tactics. As zero-click searches grow, understanding and leveraging these nuances becomes crucial.
By constructing content that answers specific questions with original data and strategic uses of GEO tactics, you can optimize for both systems. Keep in mind, mastering one does not automatically ensure success in the other.
AI visibility has transformed into a macro measurement challenge, and I’m here to guide you through building a foolproof framework to track recommendation trends effectively.
Through my experiences, I’ve learned that the funnel query pathway (FQP) is the ideal framework for measuring AI visibility. By assessing the FQP quarterly, I can derive actionable strategic insights.
I’ve coined this transformation the micro-macro shift. Traditional micro (ranking) metrics from search engines are no longer sufficient to measure AI visibility due to the opaque nature of AI engines.
In the AI-driven world, we must embrace a macro measurement approach, akin to economics evolving new measurement disciplines for broader economic systems.
The AI landscape operates under a brand-user-algorithm (BUA) opacity, where four layers veil every AI-era brand recommendation process.
The multi-layered opacity impacts everything from brand perception to conversion rates, and understanding this opacity is crucial.
Utilizing micro-strategies in an AI environment is futile. Instead, my focus shifts to macro-level insights, acknowledging that consistency over time is key, not momentary precision.
In 2026, search remains micro, while assistive and agent modes adopt macro approaches. The right measurement strategy for your business hinges on understanding each mode’s environment and data.
Search enables user control with clear metrics. Having been trained in this mode, I recommend maintaining micro strategies for search-based operations, supplemented by macro methodologies.
Assistive recommendations come from engines like ChatGPT. Unfortunately, I can’t see the decision data, making micro assessments impossible and macro the only viable option.
Agents autonomously make purchases, providing a clear but limited view of their decision-making. The conversion insight remains macro, even if initiation is observable.
Given buyers’ ever-changing reliance on different surfaces, adopting a macro approach remains inevitable, ensuring I stay adaptable to any environment they opt into.
As I shift forward with macro metrics, measuring becomes more about trends. Tracking consistent methodologies over eight quarters offers reliable strategic clarity.
In the busy world of AI decision-making, patience and consistency are key to staying ahead. I prioritize stable methodologies to gain competitive insights over time.
Entity optimization might sound like a complex term, but trust me, it’s incredibly powerful when you’re trying to make AI understand your brand better. Essentially, my goal is to help AI see exactly who I am and what I’m about. Let me share more about how you can do the same.
When I optimize entities related to my brand, I start by clarifying what my brand represents. This means ensuring that all my online content clearly reflects my brand’s identity and core values. By creating a strong, consistent message, AI can better understand and categorize my content.
Next, I focus on strengthening associations. This involves connecting my brand with relevant entities and concepts within my industry. When AI detects these connections, it increases my brand’s relevance in related searches.
Finally, driving accurate AI citations is crucial. I make sure that any references to my brand on different platforms are correct and consistent. This helps in building trust with AI, ensuring that it can reliably reference my brand in the right contexts.
An analysis of 200 GPT-5.2 responses revealed that enhanced reasoning increases the citation of sources, deepens research, and boosts early-stage funnel visibility.
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I’ve explored how AI provides a conversational experience through large language models (LLMs) and chatbots. However, I’ve noticed that no one has thoroughly examined the evolution of citations and mentions within these conversations.
By examining data from the Semrush AI Visibility Toolkit, I reviewed 20 buyer journeys across four industries, comparing the high and low reasoning of ChatGPT5.2.
In this analysis, you’ll discover:
How high reasoning cites a vastly different web with only 25.6% domain overlap and which source types gain or lose prominence.
The renewed importance of TOFU content: Brands cited at the Problem stage tend to persist through to the Selection stage under high reasoning.
How to differentiate your prompt tracking by reasoning modes, ensuring your AI visibility reports reflect two distinct systems instead of an average.
Methodology
Data collection utilized the Semrush AI Visibility Toolkit to capture prompts, citations, and fan-out queries generated by ChatGPT for each response.
We executed 100 prompts twice through GPT-5.2, once with minimal reasoning and once with high reasoning, totaling 200 responses.
Prompts covered 20 buyer journeys across four sectors (B2B SaaS, Finance, Consumer Tech, Health/Lifestyle), each consisting of 5 stages: Problem, Exploration, Comparison, Validation, Selection.
The citation rate represents the proportion of prompts where the response cited at least one external source.
The average citation quantifies the sources per cited response.
Fan-out queries are sub-queries the model generates internally for research before responding, surfaced via the Semrush API.
High Reasoning in GPT 5.2 Leads to More Citations and Searches
Activating high reasoning elevates the citation rate from 50% to 68%, nearly doubles the average sources per response (from 2.6 to 4.5), and multiplies fan-out queries by 4.6 times. High reasoning also draws from 173 unique domains versus 127 with minimal reasoning, with 99 domains appearing exclusively under high reasoning.
*Citation Rate signifies the share of prompts where at least one external source is cited.
This grounding is essential. When the model thinks more critically, it increasingly depends on web-based research, significantly impacting brand visibility, although user activation of reasoning remains uncertain.
Query intent provides a clearer indication than user demographics. Even free-tier users can access reasoning, albeit at limited rates, and ChatGPT automatically routes challenging prompts to Thinking mode. The critical question isn’t about affordability but about which prompts trigger reasoning automatically.
Complex comparisons, evaluation frameworks, compliance inquiries, and intricate shopping setups are most likely to invoke reasoning across all users. It’s crucial to categorize your audience by query type rather than paywall status.
High Reasoning Launches More Fan-out Queries in Later Stages
Users navigate problem-solving and purchasing decisions through stages, often within the same conversation. The distinction between minimal and high reasoning is not static; it varies based on the user’s journey stage.
For instance, consider a buyer evaluating CRM software:
Problem: “How do I know if my sales team needs a CRM?”
Exploration: “What types of CRM software exist for B2B SaaS?”
Comparison: “HubSpot vs. Salesforce vs. Pipedrive for a 50-person sales team.”
Validation: “Is HubSpot worth the price for mid-market B2B?”
Selection: “How do I get started with HubSpot Sales Hub?”
The following patterns are consistent across all 20 buyer journeys:
The citation rate increases as users progress through the funnel in both reasoning modes, but early-stage gaps close faster in high reasoning: +35pp at the Problem stage, only +5pp at Validation.
Fan-out queries peak during the Comparison stage, with high reasoning triggering 24 sub-queries per response compared to 5.5 in minimal reasoning. For Selection, these numbers are 15.4 and 2.6, respectively.
Average citations per response culminate during the Comparison stage (9.8 high, 5.8 minimal) and narrow during the Selection stage (4.7 high, 2.6 minimal). The citation pattern resembles an hourglass throughout the funnel.
Aggregately, minimal reasoning triggers 245 search queries over 100 prompts, while high reasoning triggers 1,130. In high reasoning, the model conducts thorough investigations for each prompt, with most research occurring during the Comparison and Selection phases.
What does fan-out look like?
A B2B SaaS prompt that requires high reasoning, like comparing Salesforce, HubSpot, and Pipedrive for a 50-person sales team, breaks down into different queries regarding API rate limits, compliance standards, support tools, pricing tiers, and more. Each aspect requires specific retrieval. The brand that succeeds here will be the one with clean, accessible documentation for each sub-query, not merely ranking for the initial prompt.
The Selection stage features a remarkable variance in per-response queries: between 0 and 40 fan-out queries with the same five-stage cohort. This variance is driven mainly by the specificity of prompts.
Bounded prompts (like “should I finance through the dealer at 0% APR or use a bank?” or “draft an RFP to 3 SEO agencies”) run zero queries since the answer’s structure is predefined. On the other hand, open-ended tasks (“shopping list for a $3,000 home gym” or “which travel card system matches our grocery spending?”) prompt 28 to 40 queries. With no single query type dominating the Selection stage, the model’s research intensity correlates with the degrees of freedom left by the prompt.
For marketers: Capturing early-funnel visibility is highly dependent on reasoning mode. If buyers engage with ChatGPT in reasoning mode, your Problem-stage and Exploration-stage content become more relevant. Otherwise, visibility might only surface during the Comparison stage.
How Reasoning Alters Brand Representation in Conversations
A session with an LLM is more conversational than transactional. Does an initially cited brand endure till the concluding stage? If yes, early-funnel visibility multiplies. If no, each step is an independent battleground.
For minimal reasoning, persistence from the Problem stage to the Selection stage rarely happens. With high reasoning, however, continuous brand presence was recorded in 4 journeys across all 5 stages.
Within individual responses, high reasoning strongly relies on specific sources, with 51 out of 100 high-reasoning responses citing the same domain multiple times versus 26 in minimal reasoning. When committed, high reasoning cites a source repeatedly.
Analyzing brand names mentioned in the text provides a broader perspective. With a relaxed test criterion, persistence was noticeable in 3 high-reasoning sessions and 2 in minimal reasoning: HubSpot through CRM Selection, American Express in Business Credit Cards, and prominent mentions of Sony and Canon in Mirrorless Cameras. Consumer Tech again emerges, albeit without citation persistence, showing dominance through continuous conversation presence.
High reasoning establishes a consistent perception of the solution landscape throughout a session. Crucially, TOFU prompts possess enormous value. A brand appearing at the Problem stage is likely to be present at the Selection stage. Top-of-funnel content transcends mere brand awareness for AI visibility—it’s a predictor of where the model’s reasoning lands at decision-making points.
There are two more significant insights:
All four persistent journeys occur within Finance, indicating persistence thrives on authoritative-source content like regulatory pages and official brand sites, echoing the +28pp lift in Finance.
For marketers focusing on account-based strategies or market creation, visibility in reasoning mode is paramount as it’s the sole mode turning early funnel efforts into selection-stage citations.
Reasoning Mode: A Distinct Search Paradigm
The champions under minimal reasoning differ from those under high reasoning: Three out of four cited domains diverge. The diversity in source types and citation stages is unmistakable.
I’m particularly intrigued by these findings:
Firstly, measurement. It’s imperative to differentiate low and high reasoning in our prompt trackers to avoid oversimplification, as their functions are distinct.
This endeavor may seem costlier, but it significantly enhances prompt tracking accuracy.
Secondly, the relevance of funnel stages. In the latest AI Mode user behavior study, it was observed that users heavily rely on shortlists, much like they do with Google’s top results. It initially appeared that focusing on BOFU prompts to generate shortlists was most strategic.
Nonetheless, TOFU prompts carry substantial benefits due to their persistence potential. Brands entering the buyer journey early can remain present throughout. Mapping buyer journeys and tracking persistence offer the best insights.
This post originally appeared on the author’s website and is reproduced here with permission.
I often get asked in 2026, “How do we measure this?” when it comes to AI visibility.
People want to know if their brand is appearing in ChatGPT or if Perplexity is recommending them. They also wonder if their work on AI grounding last quarter made any impact.
The truth is, the solution doesn’t exist yet. Anyone offering a straightforward dashboard for tracking your brand’s presence in AI spaces across search, assistive, and agent modes is just making an educated guess.
Tracking queries we assume users might ask, or adapting search keywords as a best guess, won’t cut it. These prebuilt lists often miss the mark as they choose easily mapped or ideal scenarios that don’t reflect reality.
The visibility question itself is valid, but the precise answer everyone seeks simply isn’t feasible.
Brands looking for perfect AI-era visibility KPIs are chasing a mirage. Instead, we need a methodology inspired by economic measurement of complex systems—this is where my Funnel Query Pathway comes in.
This unique approach serves as strategy, measurement, and analysis, unlike traditional metrics that were reliable when search rankings were predictable and measurable.
Now, we must rethink our approach in a complex AI landscape, asking new questions and measuring different signals.
I studied economics at Liverpool John Moores University, which gives me a unique perspective on measurement challenges where traditional tools fail at larger scales.
As with macroeconomics dealing with vast, unobservable systems, AI visibility is too opaque and personalized for old tools. We need macro principles to guide AI-era brand measurement.
AI systems have similar structural complexities as macroeconomics:
Opacity hinders visibility into the system’s state, with AI algorithms operating like a black box. Personalization means users receive unique outputs from the same inputs, influencing the visibility paths.
With expanding possibilities across apps, systems, and devices, AI environments now introduce variables that weren’t present in traditional search models.
The Funnel Query Pathway methodology focuses on these macro aspects, shifting away from keyword mapping to a broader approach focused on cohorts and intent at the node level.
AI-era acquisition begins at the conversion moment projected upward, contrary to traditional funnel methods.
