🍕 How do ChatGPT, Gemini, Claude, and DeepSeek LLM’s Segment User Intent?

Search engines, those deliverers of eager shoppers in vast amounts are all in on AI.  AI algorithms govern the pieces and the whole of a shopper’s topic search on Google or ChatGPT. Just this week, Google announced its improved Google AI Mode Search tool — Google’s search tool powered up by Google Gemini.

In response, SEO pros and content strategists need some studies in AI concepts and techniques to create winning content.

As a travel company owner, you’ll likely find this baffling so I’ll try to uncover the complexity and make it as simple as possible.

This post explores how AI-powered search engines may use AI to understand keyword search queries and content better – to deliver the content that each user actually wants — perfectly satisfying each user’s unique intent. In essence, we want to master both Google’s machine-level AI (search engine SEO) and the LLM GPTs AI intelligence to produce high-ranking, most valuable content for our specific audiences.

Briefly, LLM GPTs (AI search engines) can help us:

• content and keyword brainstorming
• enhance content and improve impact
• organize and structure content better
• segment content to serve user segments
• slice up content topics
• help us with content pillars, clusters,
• streamline tasks
• help advise content production for you (insights)

I’ve been using these techniques organically for decades to brainstorm content, keywords and SEO strategy.  What’s different now is the LLM GPT’s research power and teaching skills to give us more insight into how Google handles topics. This helps us design content more precisely to capture more search traffic, leads and sales.

As advocates of advanced SEO strategy, we want to know how we might use AI SEO knowledge, to design and strategize our content so it resonates with both Google search and the LLM GPTs intent segmentation system. They’re two different things, but we have to weld them together to produce high-performing content. I hope you’re with me on this!

There are 4 key tasks for us to solve:

(1) segment user intent
(2) slice up content
(3) create content that fuses both
(4) optimize it for Google rankings

🍕 Slicing Up the User Intent Pie

Today, we’re slicing up searcher’s interest in a topic like it was a pie. For instance, when searchers type in “best travel destinations” each one has different interests or intent.  Thus we’re tapping into pieces of the search traffic. Increasingly, you can rank number one for your targeted high-volume keyword phrase, and still receive limited impressions and clicks. This is because your site isn’t being shown to all searchers — only to a slice of that audience.

If you just want that one audience slice you like, then you’ll have to fight harder for it. And if you want more, you’ll have understand how AI creates all those other slices, then craft content for each of them too.

Questions? What topic clusters should we use, how do we structure our articles, and how should we write our copy? How do we get found on Google or Bing search.  And how do we get into the Google AI overviews? How do we get found in ChatGPT or Gemini? Lots of things to explore when it comes to AI SEO and AI content strategy. And it all comes down to whether the AI system sees your content as most significant.

Just a note to avoid confusion, since this complex, is that we want to use LLM GPTs to improve our rankings/visibility on Google/Bing search, and, to improve our content strategy.

Pure LLMs (like GPT-4, Gemini, or DeepSeek) do not inherently use a Knowledge Graph to cluster content or segment user intent—they rely solely on statistical language patterns learned during training. However, when deployed in real-world applications (like Google Search or Bing), they’re often paired with Knowledge Graphs to improve accuracy. 

Okay, so it’s academic, and we don’t need to solve all of that — just use both so we can win the ranking wars and create market-winning content.

Analyzing Searcher Intent — To Create High-Ranking Content

Importantly to us is that AI-powered search engines don’t just match keywords—they model a searcher’s intent by analyzing behavioral, linguistic, and contextual signals. For a broad query like “USA travel destinations,” modern LLMs (Chatbots such as Gemini, Deepseek, ChatGPT, Co-Pilot, or Claude/Anthropic) dissect intent into latent segments—often in ways even searchers don’t consciously realize.

Most SEOs see user intent in terms of the searcher’s quest for:

1. Information – education and orientation to a topic
2. Navigation – accessing great travel websites and content
3. Transaction – booking a vacation or flight or subscribing to a club.
4. Commercial – pre-trip vacation planning, but not quite ready to buy

This basic intent slicing isn’t sufficient.  The AI-powered search engines can see into user-intent much more deeply to segment and refine a search request using:

• Entity recognition: LLMs identify key entities in the query, such as locations, price ranges, or specific amenities.
• Contextual analysis: Modern LLMs examine:

* from previous interactions in the conversation
* the users location and time context when available
* the seasonal relevance (winter vs. summer vacation preferences)

• Query expansion: LLMs internally generate related concepts to understand the full scope of intent, such as “resorts,” “packages,” “family-friendly,” or “adults-only.”

Google Knows a Lot About Search Behavior

As a traditional ranking-based search engine, Google knows a fair bit about you—the type of content you viewed, keywords used, content you enjoyed, and perhaps even transactions you made. That knowledge (data) helps them serve up the most relevant ads and search results. If you’re shopping for products on Google shopping, they can show more relevant products for sale, and they may know what you’re likely to buy next.

And they know more!  They can see which content a particular traveler type likes and what that content is all about (the content is indexed in its database). They can slice it up into clusters, funnel stage, content desired, anchor text and so much more (there’s a lot of ways). With respect to content strategy and SEO, this knowledge can help us create better content for each traveler type, and optimize it so Google believes your content is the most relevant, trusted and authoritative.

As an example, let’s say a traveler types “all inclusive vacations” into Google.  What are they really wanting or expecting?  Are they driven by price, destination, family amenities, exclusive adults-only, warm sunny climate, Caribbean or Mediterranean, best value, cruises/resorts, or included tours and drinks? People do type in these general phrases, and Google’s trying to get each type of user to the content that’s right for them. We need to know this and build the content Google is looking for, and craft it so travelers love it too.

Note: Google search and ChatGPT are two different types of search engine. Our primary concern is how Google uses AI.

Diving into AI SEO

Perhaps then, a clever SEO Content Strategist can craft content that powerfully satisfies these “intent slices.” Then we won’t be limited to a small slice, as many websites are. The trick is to make our content intensely relevant to our ideal audience of one, but also keep it relevant for other types of customers. A travel agency for instance can serve a broad audience, so why would it want to be typecast and limited? More leads, more sales!

Let’s take a look at how AI-LLM powered search engines might process queries. First, let’s review based on the usual content typologies strategists use when we try to create content that has the attributes Google wants and readers want.

1. Content Pillars (Core Themes) – Why? To Cover Broad Intent Categories

Pillars are the foundational topics that broadly define the subject. For “best travel destinations,” the LLM might recognize:

• Leisure & Tourism (e.g., beaches, resorts)

• Adventure Travel (e.g., hiking, safaris)

• Cultural & Historical (e.g., Rome, Kyoto)

• Budget-Friendly (e.g., backpacking hotspots)

• Luxury Travel (e.g., Maldives, Swiss Alps)

Why? This ensures the response covers all major angles of travel intent, rather than assuming the user only wants one type.

2. Content Clusters (Subtopics) – Why? To Group Related Queries & Depth

Each pillar branches into clusters—more specific subtopics that refine intent. These may differ than what we believe the subtopics and related keywords are. For example:

Pillar: Cultural & Historical Travel →

• “Best ancient ruins to visit”

• “Top cities for art lovers”

• “UNESCO World Heritage Sites”

Why? Clusters help the LLM anticipate follow-up questions or related interests, providing a more nuanced response.

3. Content Categories (Structured Taxonomies) – Why? To Align With Search Patterns

LLMs may use pre-defined categories (like those in knowledge graphs) to classify destinations by attributes. These may differ from what we believe are the categories:

• By Region: “Best travel destinations in Europe”

• By Season: “Best winter travel destinations”

• By Traveler Type: “Best solo travel destinations”

Why? Users often refine searches by these filters, so the LLM preemptively structures data to match.

4. Content User Intent Signals – Why? To Personalize Responses

The LLM also detects implicit signals in the query:

• Informational Intent: “What are the best travel destinations?” → Provides a broad list.

• Commercial Intent: “Best all-inclusive travel destinations” → May emphasize booking-ready options.

• Local Intent: “Best travel destinations near me” → Prioritizes proximity.

Why? Intent parsing ensures the response aligns with what the user actually wants (inspiration vs. immediate booking).

5. Semantic Relationships – Why? To Capture Indirect Queries

LLMs understand related concepts that don’t explicitly mention “destinations”:

• “Where to go for a romantic honeymoon?” → Infers luxury/secluded destinations.

• “Places with the best street food” → Maps to cultural/top foodie destinations.

Why? Users don’t always phrase queries perfectly—semantic links help bridge gaps.

Example Output for “best travel destinations”

An LLM might structure its response like this:

1. By Pillar:

   • Adventure: Patagonia, New Zealand

   • Cultural: Kyoto, Rome

   • Beach Leisure: Bali, Seychelles

2. By Category:

   • Budget: Vietnam, Portugal

   • Luxury: Bora Bora, Santorini

3. By Intent:

   • Planning tips: “Best time to visit…”

   • Inspiration: “Hidden gem destinations…”

How LLMs Deconstruct Broad Queries

When you search “USA travel destinations,” the AI doesn’t see a single intent—it sees a probability distribution of possible intents, shaped by:

a) Query Context & Modifiers

• Implicit cues in phrasing with the related content topic you might choose.
  • “Best USA travel destinations” → “Ranked listicles” (Top 10, “must-see”)
  • “Hidden gem USA travel destinations” → “Offbeat/niche” (Local secrets, anti-tourist)
  • “USA travel destinations for families” → “Filtered by audience” (Kid-friendly, safety)
  • “USA travel destinations in winter” → “Seasonal/activity-based” (Skiing, Christmas)

b) User Profile & History

• Past searches (e.g., if you often click on road trip content, AI skews toward “USA road trip stops”).
• Location (e.g., a Texan gets “Austin to New Orleans road trips”; a Londoner gets “First-time USA highlights”).

c) SERP Engagement Patterns

• If 80% of users clicking on “USA travel destinations” then refine to “national parks,” AI infers a latent subtopic cluster.

d) Entity & Sentiment Analysis

• Mentions of “relaxing” vs. “adventure” trigger different sub-intents.
• Brand bias (e.g., searches ending in “Reddit” → forum-style results).

The 6 Major Intent Segments for “USA Travel Destinations” (And How to Target Them)

Based on LLM training data and observed user behavior, broad travel queries fracture into distinct intent clusters:

Intent Segment	User Goal		Content Strategy
1. Topical Overview	“Give me a basic overview starting point”		Listicles (e.g., “Top 10 Most Visited US Cities”)
2. Niche/Alternative	“I’ve done the basics—what’s next?”		Deep dives (e.g., “Underrated Appalachian Trail Towns”)
3. Audience-Filtered	“I have specific needs”		Guides like “USA Travel for Solo Female Backpackers”
4. Experience-Driven	“I want a feeling, not just a place”		Thematic content that builds a vibe (e.g., “Most Cinematic Small Towns”)
5. Logistics-First	“How do I make this happen?”		Trip itineraries, budget breakdowns, visa tips
6. Social Proof/Reviews	“What do real travelers say?”		UGC roundups, Reddit-style honesty

Example LLM Inference Chain:

• Query: “USA travel destinations”
  • User history: Recently searched “best hiking gear” → weights “Experience-Driven: Outdoor” higher.
  • Location: Denver → boosts “Rocky Mountain road trips.”
  • Click pattern: Skews toward long-form blogs over videos → serves text-heavy results.

How to Align Content With LLM-Inferred Segments

a) Map Your Content to Hidden Intents

• Use tools like:
  • Google’s “People also ask” (reveals related subtopics).
  • Perplexity.ai (shows how AI breaks down broad queries).
  • SparkToro (uncovers audience jargon/clusters).
• Basically, map out the different user intents in a cluster chart with arrows to related content topics

b) Signal Segment Relevance to AI

• For Niche Segments: Use modifiers in headers (“For Digital Nomads: Quiet, Picturesque US Towns With Fiber Internet”).
• For Experience-Driven: Load text with sensory language (“The fragrant smell of larch pine made our hike…”).
• For Social Proof: Embed Reddit/forum snippets (AI detects UGC as a trust signal).

c) Leverage “Latent Semantic Anchors”

LLMs map concepts relationally. Pepper content with intent-relevant entities:

• Logistics segment → “flight deals,” “peak season crowds,” “train passes.”
• Alternative segment → “crowd-free,” “local secrets,” “untouristy.”

Key Takeaway

For a phrase such as “Best travel destinations in the USA” (or any broad topic), winning content doesn’t just cover the subject.  It preemptively mirrors how LLMs slice the intent pie and connects the keywords, subtopics, and related activities.

Action Step:
Run your target query through Perplexity.ai + Google SGE, and note:

• What subtopics appear?
• What content formats dominate?
• Are there gaps in intent coverage you can own?

AI-Driven Intent Analysis: “All Inclusive Vacations”

This query is highly commercial with layered intent signals. Below is how modern LLMs (like Gemini, Claude, or GPT-4o) segment its meaning—and how to craft content that aligns with each hidden sub-intent.

Example LLM Intent Segmentation for “All Inclusive Vacations”

AI models parse this query by user goal, commerciality, and audience filters. Here’s the breakdown:

A) Dominant Intent Clusters

Intent Segment	User Goal	Content Opportunities
1. Top-Rated Deals	“Best value all-inclusive resorts”	Listicles (“Top 10 AI Resorts in Mexico for 2024”)
2. Budget-First	“Cheapest all-inclusive options”	Guides with price tiers, hidden fees exposés
3. Family-Friendly	“All-inclusive with kids’ clubs”	Roundups of resorts by age group (toddlers vs. teens)
4. Adults-Only/Luxury	“Posh, no-kids AI resorts”	Boutique/high-end focus (e.g., “Sandals vs. Secrets”)
5. Destination-Specific	“All-inclusive in Jamaica vs. Cancún”	Head-to-head comparisons by location
6. First-Timer FAQs	“What’s really included?”	Myth-busting (“All-Inclusive Resorts: The Fine Print”)

 

B) Latent Semantic Signals

LLMs detect subtle modifiers in searches:

• “All inclusive vacations with flights“ → Airfare bundling intent, savings conscious, convenience.
• “All inclusive vacations for couples“ → Adults-only/romantic focus.
• “All inclusive vacations reviews“ → User-review trust signals.

C) Behavioral Bias in Results

• Mobile searches: Shorter stays, last-minute deals.
• Desktop searches: Longer planning cycles, premium brands, navigational, transactional, commercial, final decision – booking via desktop/laptop often with someone else present.

How AI Ranks Content for This Query

 1. Commercial vs. Informational Tension

• High commercial intent: Google prioritizes booking sites (Expedia, Booking.com) and resort pages.
• Opportunity for publishers: Create comparison guides that bridge info-to-booking (e.g., “Which All-Inclusive Chain is Right For You?”).

 2. Entity Recognition

AI weights:

• Brands (Sandals, Beaches, Riu).
• Destinations (Cancún, Punta Cana, Maldives).
• Inclusions (“free alcohol,” “kids stay free” “watersports”).

 3.  Google’s SERP Feature Wars

• “People also ask”: Heavy on “Are all-inclusive vacations worth it?” → Create a cost-benefit analysis.
• “Reviews” carousel: Dominate with aggregated ratings (e.g., “Best-Rated AI Resorts by Travelers”).

How to Own Intent Segments

A) For Commercial Intent (Booking Focus)

• Keyword Strategy:
  • “all inclusive vacation packages with airfare”
  • *”best adults-only all inclusive resorts 2025″*
• Content Format: package comparison tables (e.g., “Costco Travel vs. CheapCaribbean”).

B) For Informational Intent (Trust Building)

• Angle Examples:
  • ” All-Inclusive Resorts to Avoid” (hidden fees, cultural impact).
  • “All-Inclusive vs. DIY Vacations: Which Actually is More Affordable?”
• Format: Long-form + video (AI blends media for complex intents) for hosted or YouTube tie in.

C) For Niche Audiences

• Micro-Segments:
  • “All-inclusive resorts for solo travelers”
  • “Vegan-friendly all-inclusive hotels”
• Tone: Match audience vibes (e.g., luxury = aspirational; budget = scrappy/value-driven).

Future of AI and “All Inclusive Vacations”

• Personalized Deal Rankings: AI may soon customize resort rankings based on your past stays (e.g., “You liked X, so try Y”).
• Dynamic Pricing SERPs: Real-time price comparisons embedded in search results.
• Voice Search Shifts: *”Find me a kid-free all-inclusive under $2k”* → direct booking integrations.

Next Steps

In summary, we have to respond to the fact that an AI search engines can slice up content like a pizza pie, serving up results it believes are relevant to each searcher. They’re using in-depth intent signals, and our quest is to understand those signals in general and then design content to serve niches, categories, and segments.

And it’s not a simple task and requires significant mental gymnastics. You’ll be served best by creating cluster charts and flow charts to develop a picture of what Google sees as it evaluates your pages and website. It’s best to nail your core audience of one, so you’re definitely honing in on your best customer prospects.

Good luck developing your AI SEO strategy!

See more on travel SEO and travel marketing topics.

 * title image courtesy of Freepik.com