In 1994, I wrote a thesis arguing that search would become the most powerful way to understand and serve consumer interests. Seeing search as a reflection of real human needs felt radical then. Most marketers were still locked into push-based models: interrupt, repeat, blast, convert. I believed then that findability would outpace visibility and that search would become the connective tissue between buyer intent and business growth.
Few wanted to hear it.
While presenting that thesis at an academic event in St. Petersburg, Russia, a year later, I was introduced to Search Theory, a microeconomic framework that gave my intuition structure. It explained why buyers and sellers often miss each other: the friction of finding the right information at the right time in the right context.
Fast-forward three decades, and the world is catching up. Google’s own chief economist, Hal Varian, has shown that search data can predict real-world economic trends. AI systems now prioritize semantic depth, intent alignment, and answer authority.
Today, AI-driven search environments, zero-click results, and next-question prediction are forcing us to confront what we should have all along: the need to deeply understand consumer intent and reduce the friction between question and answer.
The systems have evolved. The stakes are higher.
And the truth I wrote thirty years ago is more relevant than ever.
So the question is: will we finally listen?
The Insight That Was Too Early: From 1994 to Now
In 1994, before Google even existed, I argued for a new direction for international marketing that was based on what I was experiencing with my disaster preparedness products company in real-time:
“Search will be the best opportunity companies have to engage interest-driven prospects who have self-identified their category, brand, and/or product interest somewhere in the world.”
Back then, as they are now, most brands were obsessed with media spend, reach, and control. Listening to users, especially by analyzing what they typed into early search At the time, that idea felt contrarian. Marketing was still dominated by interruption, reach, and broadcast media. But I believed and now have seen confirmed over decades that search is the most honest form of consumer behavior data we have. People reveal their needs, desires, fears, and intentions in search queries—often more truthfully than in surveys or interviews.
My thesis laid out three guiding principles—now more urgent than ever:
- Findability will become the most significant trend in customer behavior.
- Search influences every phase of the decision journey: consideration, demand, and competitive switching.
- Search marketing can remove friction by precisely matching content to what people are actively trying to learn or decide.
These weren’t written as SEO tactics but calls to build marketing around real human intent.
That call is no longer optional.
Fast forward to today: the marketing world has finally caught up. But the environment has changed. Instead of pages and keyword rankings, we now face AI overviews, semantic search, and agent-driven answers.
To stay competitive, we must not only apply those early principles—we must upgrade them with new tools from Search Theory, probabilistic modeling, and intent mapping.
A Chance Encounter with Search Theory
In 1995, while presenting this thesis at an economics conference in St. Petersburg, Russia, I was introduced to Search Theory — a microeconomic framework that immediately resonated with my thinking. The Dean of the School of Economics explained the connection to me over dinner and far too many celebratory shots of vodka. I have not been able to shake this connection and the potential raw power of eliminating the friction between buyers and sellers.
Developed by George Stigler, Search Theory explores how individuals make decisions when facing imperfect information and costly searches. Its core idea:
Friction in the search process impacts outcomes.
In digital marketing, that friction is:
- Poorly structured content
- Mismatched landing pages
- Shallow or disconnected answers
- A lack of contextual relevance
Search Theory gave my thesis a mathematical backbone. What I had seen as marketing insight was, in fact, aligned with economic principles. We weren’t just writing content. We were managing to minimize friction.
This friction—whether informational, navigational, or cognitive—acts as a tax on decision-making. The more effort consumers exert to find or trust an answer, the less likely they are to convert, return, or recommend. Every unnecessary click, unclear term, or disconnected content fragment delays progress and increases drop-off risk.
This is where AI becomes transformative. By predicting intent, surfacing contextually relevant content, and reducing redundancy, AI is actively removing the friction that Search Theory identifies as economically inefficient. In doing so, it shortens the path to value, improves match quality, and accelerates the conversion cycle.
In economic terms, friction is inefficiency. In marketing terms, it’s lost revenue. The brands that structure for reduction in friction—and therefore decision velocity—will be the ones that dominate this new era.
And no one had put it into these terms, until now.
Using Google Data to Predict Economic Behavior
This academic link became even more tangible last year when I seriously considered pursuing a Ph.D. in Economics. I planned to focus on adapting Search Theory not just to predict economic trends but also to enable cross-border demand discovery and product development. While traditional research has examined the frictions of physical cross-border trade, little work has addressed the far more scalable—but equally complex—digital equivalents.
I was inspired by Google’s Hal Varian’s Okun lecture, “Nowcasting with Google Trends,” where I spoke to the Yale Economics department about using Google data to model real-time economic behavior. Varian emphasized that search queries are more revealing than surveys because they reflect what people are actually curious about, often in moments of vulnerability, urgency, or genuine intent. For example, surges in searches for “unemployment benefits” or “used cars” can foreshadow economic shifts before they show up in monthly reports. In essence, Varian formalized what many marketers overlooked: search is not just a marketing signal—it’s a real-time behavioral economic dataset.
Why Search Has Changed—And Why You Must Think Differently Now
Traditional SEO logic assumed a one-to-one match between query and page. That logic has been eroded:
- AI agents now summarize content and remix answers, often removing attribution or context
- Google’s Search Generative Experience (SGE) and AI overviews fan out from a query into semantically adjacent questions
- Zero-click results, rich snippets, and voice search often resolve intent before a user ever lands on your site
Content must do more than be relevant to thrive in this new reality. It must be:
- Semantically complete enough to be the central node for a topic
- Predictively structured to match the likely chain of questions a user (or AI) will follow
- Authoritative and trusted enough to be cited or summarized by search engines as the best match
This is where search theory and intent modeling come together.
Applying Search Theory to Digital Content Strategy
As previously noted, Search Theory describes the friction, cost, and decision-making logic behind the search for information.
In commerce, it explains how buyers and sellers fail to find each other due to transaction costs or incomplete information. Simply put, searchers may struggle to find what they need because getting good information takes effort, time, or access to the right source.
In search marketing, this friction shows up as:
- Incomplete or shallow content that doesn’t help a user feel confident in the next step
- Fragmented pages across conditions, symptoms, and brands with no connective tissue
- Missed “hand-offs” between content types that stall the user’s decision journey
By modeling search as a process of probabilistic matching, we reframe content from static pages into information ecosystems that reduce friction and maximize alignment.
This shift in thinking demands a change from answering queries to answering journeys.
Introducing the Consumer Intent Modeling Framework
At its core, this framework helps align what people are searching for with what your business wants to achieve—across any industry.
To guide strategy in today’s environment, I use a simple yet powerful model that categorizes search behavior into three universal intent segments:
Problem Aware (Symptom Aware)
The person knows something is wrong, but not exactly what. They’re trying to name the issue or understand the cause.
Example: “Why won’t my laptop charge?” or “sharp pain in lower back”
Marketing objective: build trust and provide education.
Solution Aware (Condition Aware)
They’ve identified the general category of the solution or condition, but haven’t picked a product or provider.
Example: “best CRM software for small business” or “how to treat sciatica”
Marketing objective: evaluate and recommend.
Brand Aware
They know about your brand or product and are evaluating it.
Example: “HubSpot vs. Salesforce” or “Is Advil safe during pregnancy?”
Marketing objective: differentiate, reassure, and convert.
This framework is not limited to healthcare or tech. It applies to any category where buyers must navigate decisions. Whether you’re selling software, supplements, financial services, or kitchen appliances, understanding which phase the user is in allows you to match your content and message to their level of clarity, not yours.
For each of these stages, brands should define:
- Searcher Questions – What are they asking and why?
- Intent Type – What stage of the buying or decision journey?
- Business Objective – What action or conversion do you want?
- Content Role – What unique value or insight can your brand offer?
- Device Nuance – Does this change if they are on a desktop, tablet or mobile device?
By structuring content around these three segments, you create a findable, relevant, and trustworthy answer system—no matter what business you’re in.
Searcher Discovery Journeys: Turning Intent into Opportunity
For the past fifteen years, I’ve used a Searcher Discovery Journeys model to map how people move from problem recognition to brand selection. These journeys help reveal what consumers understand, need, and want at each phase of their decision-making process—not just what they type into the search bar.
One of the most complex implementations was for a consumer product segment, where I analyzed over 50 million global search queries to uncover intent signals across dozens of markets. The result was a roadmap of unmet needs, behavioral patterns, and market-specific opportunities. This work generated tens of millions of site visits, drove measurable brand growth, and earned multiple industry awards.
The core lesson: search data isn’t just a reflection of interest—it’s a scalable proxy for global market demand. When you reduce friction and align with how people actually search and think, you unlock both visibility and action.
Discovery Journeys reveal how people move through phases of awareness, solution exploration, and brand evaluation. However, insight alone isn’t enough. To act on this intelligence, brands must translate these journeys into a structured content architecture that mirrors real behavior. This means aligning your site—not to your org chart or product taxonomy—but to how consumers actually think, question, and decide.
It’s not just about having the right answers. It’s about structuring them so that both users and AI systems can find, follow, and trust them.
Building a Semantic and Predictive Content Architecture
Content must evolve beyond isolated, static pages to thrive in today’s AI-powered search environment. Traditional content silos organized by internal structures or outdated keyword groupings—no longer match how people search or how AI systems interpret intent.
Modern systems like Google’s query fan-out explore the next likely question, not just the current one. AI agents summarize answers across multiple sources and value semantic depth, logical pathways, and intent alignment over simple keyword relevance. We must understand and infer the why behind a search and ensure that we have content that answers any potential user need related to our products or services.
To meet these demands, your content strategy must function as an interconnected system of:
- Semantic Content Clusters – authoritative groupings of related information that signal expertise
- Intent Connectors – internal pathways that guide users (and AI) through a coherent, stage-based journey
- Predictive Structures – anticipatory answers and linked content that reflect how real people think, search, and decide
This isn’t just about optimization. It’s about being understood—by both human users and machine agents. The following principles define how to build an intent-aligned content architecture that reduces friction, improves visibility, and accelerates decision-making.
Key Principles of Intent-Aligned Content Architecture
To earn authority and guide decision-making in a predictive, AI-mediated environment, your content must follow these principles:
Core Phrase Coverage
For each stage of intent—problem, solution, or brand—ensure your content covers the essential semantic phrases users are likely to search for.
Think: causes, solutions, comparisons, features, risks, benefits. Each core concept should be addressed in depth, ideally on a single, authoritative page per topic.
Content Assignment
Every phrase or cluster of related queries should have a clear Primary Landing Page (PLP).
If queries for similar intent types (e.g., “how to fix X” and “X troubleshooting”) are ranking across different pages, you have a fragmentation problem. Unify or realign them to improve clarity and authority.
“Next Question” Thinking
Don’t stop at the initial query—anticipate the follow-up.
If someone searches “best small business accounting software,” the next logical questions might be:
→ “Is it easy to use?”
→ “Does it work with QuickBooks?”
→ “How much does it cost?”
If someone searches a brand name, expect follow-ups like:
→ “Is [brand] worth it?”
→ “What’s the difference between [brand A] and [brand B]?”
→ “Are there hidden fees?”
Design your content and internal links to answer forward, not just in place.
Intent-Driven Internal Linking
Move beyond flat navigation. Your links should guide users through their decision-making journey, from identifying the problem to evaluating solutions to validating product fit.
Use contextual cues, not just menu structures, to connect pages logically across awareness levels.
Depth Over Dumping
Avoid “everything and the kitchen sink” content blocks that overload pages with disorganized keywords.
AI systems favor structured depth and semantic clarity over quantity.
Cover what matters most—completely and cohesively.
The Strategic Role of Content as Authority Infrastructure
This isn’t just an SEO exercise—it’s a business alignment and trust strategy.
By modeling intent and matching your content structure to user (and AI) expectations, you become:
- Easier to find
- More likely to be cited
- More trusted by both humans and machines
This is especially critical for healthcare, financial services, and regulated industries. Google’s emphasis on E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) demands that you not just answer queries but structure your knowledge ecosystem to reinforce experience and expertise that increases your overall trustworthiness for both humans and bots.
Search engines and AI agents are no longer just indexing pages—they assess topic-level authority.
The Vision Was Right—Now the Stakes Are Higher
That thesis from 30 years ago wasn’t just a prediction but a roadmap. It described a future where search becomes the connective tissue between buyer intent and brand strategy. Back then, it was a choice few marketers made. Today, it’s no longer optional.
In a world where search is predictive, conversational, and AI-mediated, old keyword playbooks no longer apply. Content that doesn’t guide—dies.
To succeed now, brands must:
- Think in probabilities, not just rankings
- Build information networks, not just isolated pages
- Serve intent chains, not just individual queries
- Structure semantic ecosystems, not just publish content
This is the new playbook for digital growth:
Search Theory – to reduce friction and match more efficiently
Intent Modeling – to guide users through decision journeys
Semantic Structuring – to signal authority and completeness
Predictive Navigation – to align with how AI and users move through questions
When you master all four, you are chosen, not just found. The brand that best understands and anticipates consumer interest wins.
This is not a tactical checklist. It’s a strategic imperative. When you align content with interest, authority with intent, and next-question logic with experience, you don’t just drive traffic.
You build trust.
You earn relevance.
You become the answer.
