AI search strategy: A guide for modern marketing teams

Search no longer rewards keywords alone — it rewards clarity. Large language models now read, reason, and restate information, deciding which brands to quote when they answer. An AI search strategy adapts content for that shift, focusing on being understood and cited, not just ranked and clicked.

Structured data defines entities and relationships; concise statements make them extractable; CRM connections turn unseen visibility into measurable influence. Clicks may decline, but authority doesn’t. In AI search, every sentence becomes a new point of discovery.

This article explores what an AI search strategy is and how content marketers and SEOs can implement an effective one. Readers will also learn how to measure success and the tools that can help. Check your AI visibility with HubSpot’s AEO Grader to see how AI systems currently represent your brand.

Table of Contents

• What is an AI search strategy?
• Where Inbound Marketing Fits
• AI Search Strategy for Content Marketers and SEOs
• How Loop Marketing Integrates With Your AI Search Strategy
• How to Measure AI Search Strategy Success
• How HubSpot’s AEO Grader Can Help
• Frequently Asked Questions About AI Search Strategy

What is an AI search strategy?

An AI search strategy is a plan to optimize content for AI-powered search engines and answer engines. An AI search strategy aligns content with how large language models (LLMs) and answer engines interpret, summarize, and attribute information.

Traditional SEO optimizes for rankings and clicks; AI search optimization focuses on eligibility and accuracy so that when AI systems generate an answer, they can recognize, quote, and correctly attribute a brand. This kind of AI search optimization ensures machine learning systems can interpret your brand’s authority and present it accurately across AI Overviews, chat results, and voice queries.

In practice, that means structuring content so every paragraph can stand alone as a verifiable excerpt. Sentences should use clear subjects, defined relationships, and unambiguous outcomes. Schema markup confirms what each page represents — its entities, context, and authorship — while consistent naming helps AI systems map those entities across the web.

This approach reframes SEO fundamentals for the LLM era. Topics, intent, and authority remain essential, but the unit of optimization shifts from the page and its keywords to the paragraph and its relationships.

The Building Blocks of AI Search

Large language models interpret not just words, but the relationships between concepts — what something is, how it connects, and who it comes from. Three foundational elements make that possible: entities, schema, and structured data. Together, these determine whether AI systems can recognize, understand, and cite a brand’s expertise.

Entities: How AI Defines “Things”

An entity is a clearly identifiable thing — a person, company, product, or idea. If keywords help humans find information, entities help machines understand it.

Example:

• Entity: HubSpot (Organization)
• Related entities: Marketing Hub (Product), AEO Grader (Tool), Marketing Against the Grain (Creative Work)

When entity names appear consistently across content and structured data, AI systems can unify them into a single node in their knowledge graphs so that a brand is interpreted as one coherent source.

Schema: How AI Reads the Context

Schema is a type of structured data that uses a shared vocabulary (like Schema.org) to label what’s on a page. It tells search engines and AI models exactly what kind of content they’re seeing — an article, a product, an FAQ, an author, and more.

Examples:

• Adding FAQPage schema clarifies that the section answers specific questions.
• Adding Organization schema connects your brand to official profiles and logos.

Without schema, AI must infer meaning; with it, the developers state meaning explicitly.

Structured Data: How AI Connects the Dots

Structured data refers to any information arranged for machine readability. That includes JSON-LD schema markup and visible structures like tables, bulleted lists, and concise TL;DR summaries. These formats help models extract and relate ideas efficiently.

Structured data improves content eligibility and interpretability for AI search engines. For marketers, structured data forms the technical foundation of Answer Engine Optimization (AEO), making content more eligible for AI Overviews, knowledge panels, and chat citations.

How AI Changes Discovery

Search used to work like a race: crawl, index, rank. Now, it works more like a conversation. LLMs read, extract, and restate what they understand to be true. Visibility still matters, but the rules have changed.

Clarity is now the new authority signal. AI systems surface statements they can quote confidently — sentences that express a clear subject, predicate, and object. The most citable content isn’t the longest but the clearest.

Eligibility now comes before position. Before a model can recommend a brand, it must recognize it. That recognition depends on consistent entities, clean schema, and structured formats such as FAQs, tables, and summaries.

The goal has shifted from outranking competitors to earning inclusion in the model’s reasoning — writing statements precise enough that AI can reliably reference and attribute them.

Dimension	Old SEO (pre-AI)	AI Search (LLM era)
Primary goal	Rankings, CTR	Citations, mentions, eligibility in AI Overviews
Optimization unit	Keyword → Page	Entity / Relationship → Paragraph
Formatting cues	Long sections, link architecture	Summaries, tables, FAQs, short standalone chunks
Authority signals	Backlinks, topical breadth, EEAT	Factual precision, schema, entity consistency, EEAT
Measurement	Sessions, positions, CTR	AI impressions, brand mentions, assisted conversions
Iteration loop	Publish → Rank → Click	Structure → Extract → Attribute → Refine

What “Zero-Click” Really Means

AI search strategy prioritizes earning citations from large language models and optimizing for zero-click results. But zero-click doesn’t mean zero value. It means the first moment of influence happens before anyone visits your site. When AI systems quote your definition or summarize your advice, your brand still earns awareness — it just happens off-site.

In this model, trust builds through representation, not traffic. The goal is to connect the invisible touchpoints to real outcomes.

• AI impressions show how often your ideas appear in AI results.
• Entity mentions confirm how accurately the models recognize your brand.
• Assisted conversions reveal when that early visibility leads to engagement or revenue.

When these signals feed into a CRM, visibility becomes measurable. Recognition — not just clicks — becomes the proof of value.

Where Inbound Marketing Fits

Inbound marketing still anchors the strategy, but the first moment of connection moves upstream. A table, a TL;DR, or a one-sentence definition can now introduce a brand within an AI experience. From there, the familiar lifecycle continues: capture interest, deliver value, nurture, convert, and retain.

The shift is in how teams connect those off-site impressions to real results. That connection depends on visibility data, structured content, and CRM attribution working together. HubSpot’s ecosystem supports that stitching in practical ways:

• AEO Grader reveals how brands appear across AI systems and highlights visibility and sentiment gaps.
• Content Hub ensures templates, content briefs, and modules support consistent structured data and defined entities.
• Marketing Hub enables multi-channel tracking and allows experiments with new entry and conversion paths.
• Smart CRM captures contacts influenced by content, tracks assisted conversions, and links those signals to stage and revenue outcomes.

The fundamentals haven’t changed: Be useful, be clear, be consistent. The difference is that the first win now happens in a sentence, not a search ranking.

AI Search Strategy for Content Marketers and SEOs

An AI search strategy for content marketers and SEOs focuses on clarity, structure, and measurable visibility. The process unfolds in five practical stages:

1. Audit current AI visibility.
2. Structure content for answer engines.
3. Optimize for citations over clicks.
4. Operationalize and automate.
5. Attribute and iterate.

Each stage builds on the last, creating a repeatable system that turns structured clarity into discoverability — and discoverability into influence measurable within a CRM.

Step 1: Audit current AI visibility.

Every AI search strategy starts with understanding how the brand appears across AI environments. HubSpot’s AEO Grader establishes that visibility baseline by querying leading AI engines (GPT-4o, Perplexity, Gemini) to analyze how they describe, position, and cite a brand in synthesized answers.

Source

The report focuses on five measurable areas:

• AI Visibility Score. Frequency and prominence of a brand’s inclusion in AI-generated results.
• Contextual Relevance. How accurately AI engines associate the brand with key topics and use cases.
• Competitive Positioning. How the brand appears relative to peers (Leader, Challenger, or Niche Player).
• Sentiment Analysis. Tone and credibility of AI references to the brand across contexts.
• Source Quality. Credibility of the external sources AI systems rely on when representing the business.

Together, these indicators provide a top-level view of brand representation in AI search. AI Search Grader diagnoses AI search visibility and optimization gaps. Marketing teams receive a snapshot of how clearly AI understands and communicates their identity.

Step 2: Structure content for answer engines.

In this new format, the content’s structure becomes the primary delivery vehicle for ideas and positioning. Think of each heading as a micro-search intent. Beneath it, the first 2–3 sentences should provide a direct answer that can stand alone in AI summaries. This pattern mirrors how LLMs read pages: segment by segment, not end to end.

Practical structure principles to incorporate in the strategy include:

• Lead with clarity. Open with a plain-language answer before adding background or nuance.
• Use TL;DR or summary blocks. Brief recaps under each H2 make information easier to extract for answer engines.
• Keep paragraphs compact. Short sections (roughly 50–100 words) maintain readability for both humans and models.
• Show relationships visually. Tables, numbered lists, and bullet points help AI systems map entities and connections.
• Add schema at the template level. Apply Article, FAQ, or other structured data to the full page so that intent and entities are clear to crawlers and AI systems alike.

HubSpot’s Content Hub enables this structure through AI-assisted content briefs, reusable templates, and module-based schema fields. Together, structure and schema make information easier to interpret, cite, and reuse across AI-driven discovery.

Step 3: Optimize for citations, not clicks.

Traditional SEO optimized content for rankings. AI search optimizes for credibility, meaning your paragraph earns the right to appear in the model’s reasoning chain. That credibility depends on your language’s consistency and verifiability.

LLM citations happen when:

• Entities are clearly named.
• Facts are precise and locatable.
• Relationships are clarified.
• Paragraphs are self-contained.

Use these patterns within paragraphs to write toward a citation:

• [Tool] helps [audience] [achieve goal] through [method].
• [Process] improves [metric] when [condition].
• [Feature] reduces [pain point] for [persona].

A model can extract this information and attach attribution reliably. That’s what moves a line of text from “invisible background noise” to “cited authority.”

Step 4: Operationalize and automate.

An AI search strategy becomes sustainable when automation and consistency support it. Within HubSpot’s connected ecosystem, each tool reinforces the broader AI search optimization process:

• Content Hub – Centralizes briefs, templates, and schema fields to keep structure and metadata consistent.
• Marketing Hub – Runs campaign tests and optimizes CTAs and formats for low-click environments.
• Smart CRM – Unifies marketing and sales data so attribution connects structured content to lifecycle progress.
• Breeze Assistant – Accelerates ideation and content outlining for conversational format.

Together, these tools turn AEO from a one-time project into a repeatable system: structure, publish, measure, refine.

Start this process with HubSpot’s Content Hub and Marketing Hub for free.

Step 5: Attribute and iterate.

An AI search strategy works best as a continual system. The goal is to connect what your content earns in AI environments to what it drives in your CRM. Marketing teams then repeat that process with each update. Over time, this loop turns structured visibility into measurable growth — the practical outcome of a scalable AI SEO strategy.

Start by running the AEO Grader on core pages monthly. Use those results to identify where AI search results improved (and where they didn’t). Refine what works, adjust what doesn’t, and measure again. Over time, this rhythm turns AI visibility into a continuous cycle of structure, validation, and growth.

How Loop Marketing Integrates With Your AI Search Strategy

Loop Marketing is HubSpot’s four-stage operating framework for growth in the AI era. It operationalizes AI search optimization by combining brand clarity, data precision, and continuous iteration within HubSpot’s AI ecosystem.

Source

Stage 1: Express — Define your brand identity.

The Express stage builds clarity. AI tools can generate content, but they can’t replicate perspective or tone. Consistent naming, style, and messaging strengthen entity accuracy so models recognize and attribute a brand correctly across summaries and search results.

Stage 2: Tailor — Personalize your approach.

The Tailor stage aligns content with audience intent. Unified CRM data reveals patterns that inform relevance and timing. Personalization ensures that when AI systems surface content, it resonates with context and feels built for each reader.

Stage 3: Amplify — Extend your reach.

The Amplify stage broadens discoverability across channels. Structured content, distributed through multiple formats, reinforces authority signals that help AI systems and human audiences encounter a brand consistently. Cross-channel repetition turns structure into recognition.

Stage 4: Evolve — Improve through feedback.

The Evolve stage transforms performance data into iteration. Visibility insights and assisted conversions inform what to update and where to focus. Each cycle sharpens accuracy and efficiency, creating a self-learning system that compounds.

Loop Stage	Purpose	Connection to AI Search
Express	Define a brand identity	Strengthens entity accuracy for AI citation
Tailor	Personalize by data	Aligns content to user intent and context
Amplify	Distribute widely	Expands authority signals across channels
Evolve	Analyze and optimize	Feeds insights back into structured updates

How to Measure AI Search Strategy Success

Measuring AI search strategy performance requires blending traditional SEO metrics with new signals from AI visibility and CRM attribution. Measurement goes beyond traffic and into how machine learning SEO systems interpret, quote, and credit expertise.

AI search performance is measured by AI impressions, assisted conversions, and engagement depth. When teams link visibility, structure, and CRM attribution, they can see how AI exposure yields measurable results. HubSpot’s 2025 AI Trends for Marketers report found that 75% of marketers report measurable ROI from AI initiatives, primarily through improved efficiency and insight.

Core Metrics for AI Search Performance

Metric	What it measures	Why it matters
Assisted Conversions	Deals or contacts influenced by a content asset, even without a direct click	Shows how early-stage content contributes to revenue
Schema Coverage	Share of key pages with valid Article, FAQ, or Organization markup	Improves eligibility for AI and answer-engine visibility
Entity Consistency	Uniform naming for brand, product, and author entities	Ensures correct recognition and citation in AI summaries
AI Visibility	How often a brand appears in AI-generated results (AEO Grader, Gemini, Perplexity)	Expands reporting beyond clicks to include AI exposure
Engagement Depth	Time on page, scroll rate, and repeat sessions from structured content	Indicates quality of engagement after AI discovery

Emerging or Stretch Metrics

These indicators point toward where attribution is heading, not where it is today. AI visibility data doesn’t directly integrate into CRM or analytics platforms (yet), so these signals work best as experimental metrics that provide directional insight.

• AI Share of Voice – Frequency of brand mentions versus competitors in AI results.
• AI-Informed Pipeline – Revenue influenced by AI-discovered contacts.
• Brand Recall via Entity Health – Consistency of brand phrasing in AI outputs.
• Lifecycle Velocity – Speed of movement through CRM stages after AI exposure.

Making AI Visibility Measurable

An AI search strategy becomes measurable by relying on the systems that already prove marketing performance. Today, HubSpot supports practical measurement through assisted conversions, engagement depth, and structured-data visibility — all available inside Smart CRM and Marketing Hub. AEO Grader adds narrative and competitive context, showing how AI systems describe the brand. Together, these signals create a repeatable framework for improvement, while newer AI-specific metrics continue to evolve.

How HubSpot’s AEO Grader Can Help

HubSpot’s AEO Grader analyzes how leading AI engines describe a brand when answering real user queries. Instead of measuring clicks or rankings, the Grader evaluates brand visibility, narrative themes, sentiment, and competitive standing inside AI-generated responses. It reveals how AI systems characterize a company in synthesized answers and whether that representation aligns with the brand’s goals.

AEO visibility depends on how consistently and accurately AI engines summarize your brand. The Grader turns those qualitative signals into structured indicators that highlight strengths, gaps, and opportunities to improve AI-era discoverability.

Source

What the AEO Grader Evaluates

The AEO Grader report includes three primary dimensions related to a brand’s AI search visibility.

Metric	What it checks	Why it matters
AI Visibility / Share of Voice	How often a brand appears in AI-generated answers across GPT-4o, Gemini, and Perplexity	Shows relative brand presence in synthesized AI results and category conversations
Brand Narrative & Sentiment	The tone, themes, and language AI engines use when describing the brand	Highlights which storylines shape perception and how credibility or expertise is framed
Source Credibility & Data Richness	The authority and completeness of external sources AI engines reference	Reveals whether models rely on strong, reliable information or weak/noisy sources

Run this audit consistently (quarterly or monthly) to get a clear timeline of how AI systems shift their descriptions, introduce new competitors, or adjust sentiment. Tracking these changes over time shows whether your brand is gaining clarity and relevance or losing ground in AI-generated narratives.

Frequently Asked Questions About AI Search Strategy

How long does it take to see results from an AI search strategy?

Most teams start seeing movement within a few weeks of implementing structural updates, like adding schema or tightening TL;DR sections. But sustainable visibility usually takes three to six months.

AI systems surface new content quickly, but actual results depend on model refresh cycles and the consistency of your updates. HubSpot’s 2025 AI Trends for Marketers Report shows that AI adoption speeds up content production and experimentation, giving teams more frequent opportunities to refine and update structured content — a key factor in improving AI visibility.

Do I need to rebuild my entire content library for AI search?

No, you can evolve what you already have. Start by modernizing your highest-performing pages — the 20% that drives most of your organic or assisted conversions.

Add Article and FAQ schema (using built-in blog templates or custom modules), clarify entities (brand, author, product), and insert concise TL;DRs under each major heading. Then, move outward through supporting pages. This incremental approach builds visibility faster and avoids overwhelming your team.

Which structured data should I implement first?

Start with structured data that helps AI systems interpret both content and context. At the content layer, use visible structure: tables, bulleted lists, and short Q&A sections under each heading. At the metadata layer, apply Schema.org markup, starting with Article, FAQPage, and Organization. These schema types clarify what the page covers and whom it represents.

How do I prove value to leadership when clicks are declining?

Zero-click environments require conversion paths that do not rely on traditional clicks. They show influence, not traffic. Traditional analytics miss the visibility your brand gains when AI systems cite or summarize your content.

Connect visibility to revenue with the following tools:

• AEO Grader, which shows brand presence and sentiment in AI results.
• HubSpot Smart CRM, which shows contact and deal movement influenced by AI-discovered content.
• Marketing Hub, which showcases conversions and engagement depth.

What’s the best way to keep AI search work sustainable?

AI search optimization stays sustainable when it’s folded into your normal reporting cycle.

• Run AEO Grader audits on a consistent cadence (monthly or quarterly) to track how AI systems describe your brand and competitors.
• Use Content Hub templates and custom modules to keep structured data and schema fields current.
• In Smart CRM, log or import the insights from each audit so engagement and lifecycle metrics can be reviewed alongside AI visibility trends.

Does Loop Marketing replace inbound marketing?

Inbound marketing still forms the foundation. Loop Marketing builds on it to meet the realities of AI-era discovery. Where inbound organizes around a linear funnel, Loop Marketing creates a four-stage cycle — Express, Tailor, Amplify, Evolve — that keeps your brand message adaptive across channels and AI systems.

Do I have to use HubSpot products to implement an AI search strategy?

No, but HubSpot’s connected tools make implementation easier. You can apply AEO principles manually, but HubSpot’s ecosystem streamlines the process:

• AEO Grader surfaces brand visibility, narrative, sentiment, and competitive gaps across AI systems.
• Content Hub centralizes creation, supports schema-ready templates, and includes AI-assisted content features.
• Marketing Hub and Smart CRM track engagement and convert signals into revenue outcomes. You can also import or tag AI visibility data manually for full-funnel attribution.

According to HubSpot’s 2025 AI Trends for Marketers Report, 98% of organizations plan to maintain or increase AI investment this year. Connected tools simply speed up progress.

How will I know if AI systems recognize my brand?

Use AEO Grader to see how AI systems describe your brand and where you appear in category-level answers. Then, test key topics directly in assistants like Gemini, ChatGPT, and Perplexity to see how individual pages are referenced.

Make AI search strategy a system, not a sprint.

AI search has reshaped how visibility works, but the fundamentals still apply: Clarity earns trust, and structure earns reach. Winning marketers will build systems that connect visibility to measurable outcomes.

HubSpot’s AEO Grader makes AI visibility tangible. It reveals how generative search systems describe a brand — what they highlight, how often it appears, and how the story compares to competitors. These insights help marketing teams see where their message lands inside AI-driven discovery and where clarity or coverage needs work.

AI search has become measurable not by clicks, but by presence and perception. The smartest way to improve both is by understanding how AI already represents your brand.

Get a free demo of HubSpot’s Breeze AI Suite and Smart CRM and see how HubSpot connects AI visibility, structure, and attribution.

from Marketing https://blog.hubspot.com/marketing/ai-search-strategy

Search no longer rewards keywords alone — it rewards clarity. Large language models now read, reason, and restate information, deciding which brands to quote when they answer. An AI search strategy adapts content for that shift, focusing on being understood and cited, not just ranked and clicked.

Structured data defines entities and relationships; concise statements make them extractable; CRM connections turn unseen visibility into measurable influence. Clicks may decline, but authority doesn’t. In AI search, every sentence becomes a new point of discovery.

This article explores what an AI search strategy is and how content marketers and SEOs can implement an effective one. Readers will also learn how to measure success and the tools that can help. Check your AI visibility with HubSpot’s AEO Grader to see how AI systems currently represent your brand.

Table of Contents

• What is an AI search strategy?
• Where Inbound Marketing Fits
• AI Search Strategy for Content Marketers and SEOs
• How Loop Marketing Integrates With Your AI Search Strategy
• How to Measure AI Search Strategy Success
• How HubSpot’s AEO Grader Can Help
• Frequently Asked Questions About AI Search Strategy

What is an AI search strategy?

An AI search strategy is a plan to optimize content for AI-powered search engines and answer engines. An AI search strategy aligns content with how large language models (LLMs) and answer engines interpret, summarize, and attribute information.

Traditional SEO optimizes for rankings and clicks; AI search optimization focuses on eligibility and accuracy so that when AI systems generate an answer, they can recognize, quote, and correctly attribute a brand. This kind of AI search optimization ensures machine learning systems can interpret your brand’s authority and present it accurately across AI Overviews, chat results, and voice queries.

In practice, that means structuring content so every paragraph can stand alone as a verifiable excerpt. Sentences should use clear subjects, defined relationships, and unambiguous outcomes. Schema markup confirms what each page represents — its entities, context, and authorship — while consistent naming helps AI systems map those entities across the web.

This approach reframes SEO fundamentals for the LLM era. Topics, intent, and authority remain essential, but the unit of optimization shifts from the page and its keywords to the paragraph and its relationships.

The Building Blocks of AI Search

Large language models interpret not just words, but the relationships between concepts — what something is, how it connects, and who it comes from. Three foundational elements make that possible: entities, schema, and structured data. Together, these determine whether AI systems can recognize, understand, and cite a brand’s expertise.

Entities: How AI Defines “Things”

An entity is a clearly identifiable thing — a person, company, product, or idea. If keywords help humans find information, entities help machines understand it.

Example:

• Entity: HubSpot (Organization)
• Related entities: Marketing Hub (Product), AEO Grader (Tool), Marketing Against the Grain (Creative Work)

When entity names appear consistently across content and structured data, AI systems can unify them into a single node in their knowledge graphs so that a brand is interpreted as one coherent source.

Schema: How AI Reads the Context

Schema is a type of structured data that uses a shared vocabulary (like Schema.org) to label what’s on a page. It tells search engines and AI models exactly what kind of content they’re seeing — an article, a product, an FAQ, an author, and more.

Examples:

• Adding FAQPage schema clarifies that the section answers specific questions.
• Adding Organization schema connects your brand to official profiles and logos.

Without schema, AI must infer meaning; with it, the developers state meaning explicitly.

Structured Data: How AI Connects the Dots

Structured data refers to any information arranged for machine readability. That includes JSON-LD schema markup and visible structures like tables, bulleted lists, and concise TL;DR summaries. These formats help models extract and relate ideas efficiently.

Structured data improves content eligibility and interpretability for AI search engines. For marketers, structured data forms the technical foundation of Answer Engine Optimization (AEO), making content more eligible for AI Overviews, knowledge panels, and chat citations.

How AI Changes Discovery

Search used to work like a race: crawl, index, rank. Now, it works more like a conversation. LLMs read, extract, and restate what they understand to be true. Visibility still matters, but the rules have changed.

Clarity is now the new authority signal. AI systems surface statements they can quote confidently — sentences that express a clear subject, predicate, and object. The most citable content isn’t the longest but the clearest.

Eligibility now comes before position. Before a model can recommend a brand, it must recognize it. That recognition depends on consistent entities, clean schema, and structured formats such as FAQs, tables, and summaries.

The goal has shifted from outranking competitors to earning inclusion in the model’s reasoning — writing statements precise enough that AI can reliably reference and attribute them.

Dimension	Old SEO (pre-AI)	AI Search (LLM era)
Primary goal	Rankings, CTR	Citations, mentions, eligibility in AI Overviews
Optimization unit	Keyword → Page	Entity / Relationship → Paragraph
Formatting cues	Long sections, link architecture	Summaries, tables, FAQs, short standalone chunks
Authority signals	Backlinks, topical breadth, EEAT	Factual precision, schema, entity consistency, EEAT
Measurement	Sessions, positions, CTR	AI impressions, brand mentions, assisted conversions
Iteration loop	Publish → Rank → Click	Structure → Extract → Attribute → Refine

What “Zero-Click” Really Means

AI search strategy prioritizes earning citations from large language models and optimizing for zero-click results. But zero-click doesn’t mean zero value. It means the first moment of influence happens before anyone visits your site. When AI systems quote your definition or summarize your advice, your brand still earns awareness — it just happens off-site.

In this model, trust builds through representation, not traffic. The goal is to connect the invisible touchpoints to real outcomes.

• AI impressions show how often your ideas appear in AI results.
• Entity mentions confirm how accurately the models recognize your brand.
• Assisted conversions reveal when that early visibility leads to engagement or revenue.

When these signals feed into a CRM, visibility becomes measurable. Recognition — not just clicks — becomes the proof of value.

Where Inbound Marketing Fits

Inbound marketing still anchors the strategy, but the first moment of connection moves upstream. A table, a TL;DR, or a one-sentence definition can now introduce a brand within an AI experience. From there, the familiar lifecycle continues: capture interest, deliver value, nurture, convert, and retain.

The shift is in how teams connect those off-site impressions to real results. That connection depends on visibility data, structured content, and CRM attribution working together. HubSpot’s ecosystem supports that stitching in practical ways:

• AEO Grader reveals how brands appear across AI systems and highlights visibility and sentiment gaps.
• Content Hub ensures templates, content briefs, and modules support consistent structured data and defined entities.
• Marketing Hub enables multi-channel tracking and allows experiments with new entry and conversion paths.
• Smart CRM captures contacts influenced by content, tracks assisted conversions, and links those signals to stage and revenue outcomes.

The fundamentals haven’t changed: Be useful, be clear, be consistent. The difference is that the first win now happens in a sentence, not a search ranking.

AI Search Strategy for Content Marketers and SEOs

An AI search strategy for content marketers and SEOs focuses on clarity, structure, and measurable visibility. The process unfolds in five practical stages:

1. Audit current AI visibility.
2. Structure content for answer engines.
3. Optimize for citations over clicks.
4. Operationalize and automate.
5. Attribute and iterate.

Each stage builds on the last, creating a repeatable system that turns structured clarity into discoverability — and discoverability into influence measurable within a CRM.

Step 1: Audit current AI visibility.

Every AI search strategy starts with understanding how the brand appears across AI environments. HubSpot’s AEO Grader establishes that visibility baseline by querying leading AI engines (GPT-4o, Perplexity, Gemini) to analyze how they describe, position, and cite a brand in synthesized answers.

Source

The report focuses on five measurable areas:

• AI Visibility Score. Frequency and prominence of a brand’s inclusion in AI-generated results.
• Contextual Relevance. How accurately AI engines associate the brand with key topics and use cases.
• Competitive Positioning. How the brand appears relative to peers (Leader, Challenger, or Niche Player).
• Sentiment Analysis. Tone and credibility of AI references to the brand across contexts.
• Source Quality. Credibility of the external sources AI systems rely on when representing the business.

Together, these indicators provide a top-level view of brand representation in AI search. AI Search Grader diagnoses AI search visibility and optimization gaps. Marketing teams receive a snapshot of how clearly AI understands and communicates their identity.

Step 2: Structure content for answer engines.

In this new format, the content’s structure becomes the primary delivery vehicle for ideas and positioning. Think of each heading as a micro-search intent. Beneath it, the first 2–3 sentences should provide a direct answer that can stand alone in AI summaries. This pattern mirrors how LLMs read pages: segment by segment, not end to end.

Practical structure principles to incorporate in the strategy include:

• Lead with clarity. Open with a plain-language answer before adding background or nuance.
• Use TL;DR or summary blocks. Brief recaps under each H2 make information easier to extract for answer engines.
• Keep paragraphs compact. Short sections (roughly 50–100 words) maintain readability for both humans and models.
• Show relationships visually. Tables, numbered lists, and bullet points help AI systems map entities and connections.
• Add schema at the template level. Apply Article, FAQ, or other structured data to the full page so that intent and entities are clear to crawlers and AI systems alike.

HubSpot’s Content Hub enables this structure through AI-assisted content briefs, reusable templates, and module-based schema fields. Together, structure and schema make information easier to interpret, cite, and reuse across AI-driven discovery.

Step 3: Optimize for citations, not clicks.

Traditional SEO optimized content for rankings. AI search optimizes for credibility, meaning your paragraph earns the right to appear in the model’s reasoning chain. That credibility depends on your language’s consistency and verifiability.

LLM citations happen when:

• Entities are clearly named.
• Facts are precise and locatable.
• Relationships are clarified.
• Paragraphs are self-contained.

Use these patterns within paragraphs to write toward a citation:

• [Tool] helps [audience] [achieve goal] through [method].
• [Process] improves [metric] when [condition].
• [Feature] reduces [pain point] for [persona].

A model can extract this information and attach attribution reliably. That’s what moves a line of text from “invisible background noise” to “cited authority.”

Step 4: Operationalize and automate.

An AI search strategy becomes sustainable when automation and consistency support it. Within HubSpot’s connected ecosystem, each tool reinforces the broader AI search optimization process:

• Content Hub – Centralizes briefs, templates, and schema fields to keep structure and metadata consistent.
• Marketing Hub – Runs campaign tests and optimizes CTAs and formats for low-click environments.
• Smart CRM – Unifies marketing and sales data so attribution connects structured content to lifecycle progress.
• Breeze Assistant – Accelerates ideation and content outlining for conversational format.

Together, these tools turn AEO from a one-time project into a repeatable system: structure, publish, measure, refine.

Start this process with HubSpot’s Content Hub and Marketing Hub for free.

Step 5: Attribute and iterate.

An AI search strategy works best as a continual system. The goal is to connect what your content earns in AI environments to what it drives in your CRM. Marketing teams then repeat that process with each update. Over time, this loop turns structured visibility into measurable growth — the practical outcome of a scalable AI SEO strategy.

Start by running the AEO Grader on core pages monthly. Use those results to identify where AI search results improved (and where they didn’t). Refine what works, adjust what doesn’t, and measure again. Over time, this rhythm turns AI visibility into a continuous cycle of structure, validation, and growth.

How Loop Marketing Integrates With Your AI Search Strategy

Loop Marketing is HubSpot’s four-stage operating framework for growth in the AI era. It operationalizes AI search optimization by combining brand clarity, data precision, and continuous iteration within HubSpot’s AI ecosystem.

Source

Stage 1: Express — Define your brand identity.

The Express stage builds clarity. AI tools can generate content, but they can’t replicate perspective or tone. Consistent naming, style, and messaging strengthen entity accuracy so models recognize and attribute a brand correctly across summaries and search results.

Stage 2: Tailor — Personalize your approach.

The Tailor stage aligns content with audience intent. Unified CRM data reveals patterns that inform relevance and timing. Personalization ensures that when AI systems surface content, it resonates with context and feels built for each reader.

Stage 3: Amplify — Extend your reach.

The Amplify stage broadens discoverability across channels. Structured content, distributed through multiple formats, reinforces authority signals that help AI systems and human audiences encounter a brand consistently. Cross-channel repetition turns structure into recognition.

Stage 4: Evolve — Improve through feedback.

The Evolve stage transforms performance data into iteration. Visibility insights and assisted conversions inform what to update and where to focus. Each cycle sharpens accuracy and efficiency, creating a self-learning system that compounds.

Loop Stage	Purpose	Connection to AI Search
Express	Define a brand identity	Strengthens entity accuracy for AI citation
Tailor	Personalize by data	Aligns content to user intent and context
Amplify	Distribute widely	Expands authority signals across channels
Evolve	Analyze and optimize	Feeds insights back into structured updates

How to Measure AI Search Strategy Success

Measuring AI search strategy performance requires blending traditional SEO metrics with new signals from AI visibility and CRM attribution. Measurement goes beyond traffic and into how machine learning SEO systems interpret, quote, and credit expertise.

AI search performance is measured by AI impressions, assisted conversions, and engagement depth. When teams link visibility, structure, and CRM attribution, they can see how AI exposure yields measurable results. HubSpot’s 2025 AI Trends for Marketers report found that 75% of marketers report measurable ROI from AI initiatives, primarily through improved efficiency and insight.

Core Metrics for AI Search Performance

Metric	What it measures	Why it matters
Assisted Conversions	Deals or contacts influenced by a content asset, even without a direct click	Shows how early-stage content contributes to revenue
Schema Coverage	Share of key pages with valid Article, FAQ, or Organization markup	Improves eligibility for AI and answer-engine visibility
Entity Consistency	Uniform naming for brand, product, and author entities	Ensures correct recognition and citation in AI summaries
AI Visibility	How often a brand appears in AI-generated results (AEO Grader, Gemini, Perplexity)	Expands reporting beyond clicks to include AI exposure
Engagement Depth	Time on page, scroll rate, and repeat sessions from structured content	Indicates quality of engagement after AI discovery

Emerging or Stretch Metrics

These indicators point toward where attribution is heading, not where it is today. AI visibility data doesn’t directly integrate into CRM or analytics platforms (yet), so these signals work best as experimental metrics that provide directional insight.

• AI Share of Voice – Frequency of brand mentions versus competitors in AI results.
• AI-Informed Pipeline – Revenue influenced by AI-discovered contacts.
• Brand Recall via Entity Health – Consistency of brand phrasing in AI outputs.
• Lifecycle Velocity – Speed of movement through CRM stages after AI exposure.

Making AI Visibility Measurable

An AI search strategy becomes measurable by relying on the systems that already prove marketing performance. Today, HubSpot supports practical measurement through assisted conversions, engagement depth, and structured-data visibility — all available inside Smart CRM and Marketing Hub. AEO Grader adds narrative and competitive context, showing how AI systems describe the brand. Together, these signals create a repeatable framework for improvement, while newer AI-specific metrics continue to evolve.

How HubSpot’s AEO Grader Can Help

HubSpot’s AEO Grader analyzes how leading AI engines describe a brand when answering real user queries. Instead of measuring clicks or rankings, the Grader evaluates brand visibility, narrative themes, sentiment, and competitive standing inside AI-generated responses. It reveals how AI systems characterize a company in synthesized answers and whether that representation aligns with the brand’s goals.

AEO visibility depends on how consistently and accurately AI engines summarize your brand. The Grader turns those qualitative signals into structured indicators that highlight strengths, gaps, and opportunities to improve AI-era discoverability.

Source

What the AEO Grader Evaluates

The AEO Grader report includes three primary dimensions related to a brand’s AI search visibility.

Metric	What it checks	Why it matters
AI Visibility / Share of Voice	How often a brand appears in AI-generated answers across GPT-4o, Gemini, and Perplexity	Shows relative brand presence in synthesized AI results and category conversations
Brand Narrative & Sentiment	The tone, themes, and language AI engines use when describing the brand	Highlights which storylines shape perception and how credibility or expertise is framed
Source Credibility & Data Richness	The authority and completeness of external sources AI engines reference	Reveals whether models rely on strong, reliable information or weak/noisy sources

Run this audit consistently (quarterly or monthly) to get a clear timeline of how AI systems shift their descriptions, introduce new competitors, or adjust sentiment. Tracking these changes over time shows whether your brand is gaining clarity and relevance or losing ground in AI-generated narratives.

Frequently Asked Questions About AI Search Strategy

How long does it take to see results from an AI search strategy?

Most teams start seeing movement within a few weeks of implementing structural updates, like adding schema or tightening TL;DR sections. But sustainable visibility usually takes three to six months.

AI systems surface new content quickly, but actual results depend on model refresh cycles and the consistency of your updates. HubSpot’s 2025 AI Trends for Marketers Report shows that AI adoption speeds up content production and experimentation, giving teams more frequent opportunities to refine and update structured content — a key factor in improving AI visibility.

Do I need to rebuild my entire content library for AI search?

No, you can evolve what you already have. Start by modernizing your highest-performing pages — the 20% that drives most of your organic or assisted conversions.

Add Article and FAQ schema (using built-in blog templates or custom modules), clarify entities (brand, author, product), and insert concise TL;DRs under each major heading. Then, move outward through supporting pages. This incremental approach builds visibility faster and avoids overwhelming your team.

Which structured data should I implement first?

Start with structured data that helps AI systems interpret both content and context. At the content layer, use visible structure: tables, bulleted lists, and short Q&A sections under each heading. At the metadata layer, apply Schema.org markup, starting with Article, FAQPage, and Organization. These schema types clarify what the page covers and whom it represents.

How do I prove value to leadership when clicks are declining?

Zero-click environments require conversion paths that do not rely on traditional clicks. They show influence, not traffic. Traditional analytics miss the visibility your brand gains when AI systems cite or summarize your content.

Connect visibility to revenue with the following tools:

• AEO Grader, which shows brand presence and sentiment in AI results.
• HubSpot Smart CRM, which shows contact and deal movement influenced by AI-discovered content.
• Marketing Hub, which showcases conversions and engagement depth.

What’s the best way to keep AI search work sustainable?

AI search optimization stays sustainable when it’s folded into your normal reporting cycle.

• Run AEO Grader audits on a consistent cadence (monthly or quarterly) to track how AI systems describe your brand and competitors.
• Use Content Hub templates and custom modules to keep structured data and schema fields current.
• In Smart CRM, log or import the insights from each audit so engagement and lifecycle metrics can be reviewed alongside AI visibility trends.

Does Loop Marketing replace inbound marketing?

Inbound marketing still forms the foundation. Loop Marketing builds on it to meet the realities of AI-era discovery. Where inbound organizes around a linear funnel, Loop Marketing creates a four-stage cycle — Express, Tailor, Amplify, Evolve — that keeps your brand message adaptive across channels and AI systems.

Do I have to use HubSpot products to implement an AI search strategy?

No, but HubSpot’s connected tools make implementation easier. You can apply AEO principles manually, but HubSpot’s ecosystem streamlines the process:

• AEO Grader surfaces brand visibility, narrative, sentiment, and competitive gaps across AI systems.
• Content Hub centralizes creation, supports schema-ready templates, and includes AI-assisted content features.
• Marketing Hub and Smart CRM track engagement and convert signals into revenue outcomes. You can also import or tag AI visibility data manually for full-funnel attribution.

According to HubSpot’s 2025 AI Trends for Marketers Report, 98% of organizations plan to maintain or increase AI investment this year. Connected tools simply speed up progress.

How will I know if AI systems recognize my brand?

Use AEO Grader to see how AI systems describe your brand and where you appear in category-level answers. Then, test key topics directly in assistants like Gemini, ChatGPT, and Perplexity to see how individual pages are referenced.

Make AI search strategy a system, not a sprint.

AI search has reshaped how visibility works, but the fundamentals still apply: Clarity earns trust, and structure earns reach. Winning marketers will build systems that connect visibility to measurable outcomes.

HubSpot’s AEO Grader makes AI visibility tangible. It reveals how generative search systems describe a brand — what they highlight, how often it appears, and how the story compares to competitors. These insights help marketing teams see where their message lands inside AI-driven discovery and where clarity or coverage needs work.

AI search has become measurable not by clicks, but by presence and perception. The smartest way to improve both is by understanding how AI already represents your brand.

Get a free demo of HubSpot’s Breeze AI Suite and Smart CRM and see how HubSpot connects AI visibility, structure, and attribution.

via Perfecte news Non connection

Entity-based SEO: An explainer for SEOs and content marketers

Entity-based SEO is a content optimization strategy built around concepts, relationships, and context rather than isolated keyword phrases. Search engines identify entities — distinct concepts, people, places, or things — and connect them through the Knowledge Graph to interpret meaning and determine topical authority.

This approach mirrors a fundamental shift in how search systems work. Google no longer simply matches text; it maps how concepts relate to one another and evaluates whether content meaningfully contributes to a subject’s broader ecosystem. As large language models like ChatGPT and Gemini increasingly shape how information surfaces, the strength of entity signals determines which sources get cited, referenced, and ranked.

This guide covers what entities are in SEO, how they differ from keywords, where to find the ones that matter, how to structure content around entity relationships, and how to measure whether the strategy works.

Table of Contents

• What are entities in SEO?
• Why Entity-Based SEO Matters for Content and SEO Marketers
• How to Find Entities for SEO
• How to Plan Topic Clusters With SEO Entities
• How to Measure and Report on Entity-Based SEO Strategy
• Frequently Asked Questions About Entity-Based SEO

What are entities in SEO?

Entities are distinct concepts, people, places, or things that search engines identify and connect within the Knowledge Graph. These relationships help systems interpret meaning instead of relying on exact-match phrases.

Search engines use entities to understand how topics connect. When content makes those connections clear, visibility improves across multiple related queries — not just one primary term.

An entity represents far more than a word or phrase on a page — it encompasses the full context surrounding a concept. For example, HubSpot is an organizational entity linked to CRM software, marketing automation, and content strategy, while email marketing connects to newsletter, automation platform, and lead nurturing entities. These relationships function as semantic signals that help Google understand how topics fit together. Google uses entities to understand and connect content in the Knowledge Graph.

Entity relationships allow search engines to evaluate relevance even when a page doesn’t contain an exact-match keyword. This is where semantic SEO shows its strength: Google connects entities through the Knowledge Graph, which determines whether a page meaningfully contributes to a topic’s broader ecosystem. That system-level understanding makes entity-based SEO essential for visibility in both traditional and AI-powered search.

How are entities different from keywords?

Entities represent concepts; keywords represent the language people use to search for those concepts. Entities carry context, relationships, and attributes, while keywords reflect phrasing. This distinction helps search engines understand meaning, not just text.

The Knowledge Graph links brands, tools, topics, and attributes through entity connections in ways that keywords alone cannot capture. This explains why pages often rank for multiple related queries even when they don’t contain exact keyword matches. A page optimized for “email automation” may also rank for “AI marketing workflows” when both concepts share strong semantic ties.

Entities also function as confirmed facts within search systems. Keywords provide surface signals, but entities carry meaning. This structural difference is why entity-led content often ranks across multiple related searches.

Carolyn Shelby, principal SEO at Yoast, offers another perspective. “Keyword SEO is basically working on a flat map, while entity SEO lives in three-dimensional space,” she explains. “In the retrieval layer, LLMs treat concepts, brands, authors, and facts like stars clustered in constellations determined by topic and relevance.”

In this model, queries move through semantic space along a trajectory shaped by how the question is phrased. The entities that get pulled into AI-generated answers are the ones with enough “gravity” — the well-established, strongly connected concepts that LLMs recognize as authoritative within their training data.

As Shelby puts it, “Keywords just help you appear on the map; entities determine whether you ‘shine brightly’ enough to be selected.”

For instance, when optimizing for “content marketing strategy,” an entity-based approach connects that topic to related concepts like “editorial calendar,” “buyer personas,” and “content distribution channels.” These aren’t just related keywords — they’re distinct entities that form a knowledge network.

Google recognizes that someone searching for content strategy likely needs information about planning tools, audience research, and publishing workflows. Search engines use these entity relationships to deliver comprehensive results that match user intent, not just pages that repeat the search phrase.

Aspect	Keywords	Entities
Definition	Phrases, words, or queries typed into search engines	Distinct concepts, people, places, or things recognized by search engines
Example	“best CRM tools”	“HubSpot,” “Salesforce,” “Customer Relationship Management”
Focus	Text string matching	Context and relationships
Used For	Targeting short-term rankings	Building long-term topical authority
SEO Impact	Optimizes for specific search phrases	Strengthens visibility for related topics and intent-based queries

Content strategy focused on entities helps Google and AI-powered search engines understand how brands fit into broader topics — not just which terms to rank for.

Why Entity-Based SEO Matters for Content and SEO Marketers

Entity-based SEO strengthens topical depth, improves relevance across clusters, and helps search engines interpret how content fits within broader subject areas. Instead of relying on isolated keywords, entity relationships show how concepts connect — a signal that matters for both SERPs and AI-generated answers.

According to research from Fractl, 66% of consumers believe AI will replace traditional search within five years, and 82% already find AI search more helpful than traditional SERPs. As Kelsey Libert, co-founder at Fractl, notes, “This highlights the need for marketers to prioritize GenAI brand visibility over keyword optimization, because keyword strategy is a thing of the past, while knowledge graphs will define your current and future brand visibility.”

When a page consistently references the entities most relevant to a subject — such as “content operations,” “CMS governance,” or “editorial planning” — search systems gain a clearer understanding of its place within a semantic neighborhood. These relationships help build topical authority by showing how concepts reinforce one another within a cluster.

Entity mapping also shapes the internal linking strategy. Connecting pages through shared entities reinforces the relationships the Knowledge Graph expects to see in a well-structured cluster. As HubSpot’s semantic search guide notes, structured relationships help search engines evaluate the depth and cohesion of a topic.

Entity-led planning improves editorial strategy by reducing duplication and clarifying where new content is needed. Topics such as “content audit frameworks,” “AI-assisted drafting,” or “internal content quality standards” may share overlapping keywords, but they represent distinct entities. Incorporating those entities into briefs and planning documents ensures each article contributes something unique to a cluster.

This approach aligns with how HubSpot’s Content Hub supports content operations. Content Hub centralizes entity-led briefs, editorial governance, and cluster mapping, making it easier to maintain consistency across a growing library of pages and ensure topics connect the way search systems expect.

Entity-focused content also improves retrievability in AI systems, which rely on conceptual relationships to identify authoritative sources and reconstruct information. As large language models play a greater role in surfacing results, strong entity signals provide additional visibility beyond traditional SERPs.

Together, these benefits make entity-based SEO a foundational layer of modern content strategy — one that improves discoverability, clarifies expertise, and supports performance across both search and AI-driven channels.

How to Find Entities for SEO

Entities form the backbone of modern SEO strategy, but finding the right ones starts with understanding what search engines already recognize. Google’s Knowledge Graph contains millions of interconnected concepts — and effective content strategies tap into these existing relationships rather than creating new ones from scratch.

Here’s a practical approach to discovering and organizing entities for any content strategy.

Step 1: Start with clear goals and core topics.

Every strong entity strategy begins with a simple question: What’s the main topic, and who needs to find it?

Marketing automation might be the core topic for a SaaS company, which naturally branches into related entities like CRM integration, email workflows, and lead scoring. These aren’t random connections — they’re the actual problems and solutions that audiences search for.

HubSpot’s AEO Grader offers a reality check here, showing how AI systems currently interpret brand content across ChatGPT, Perplexity, and Gemini. AEO Grader analyzes brand presence in AI search using entity signals. It’s one thing to assume certain entity connections exist — it’s another to see what AI actually recognizes.

Step 2: Mine search results and Wikipedia for proven entities.

Google already shows which entities matter through search features. The “People also ask” boxes, Knowledge Panels, and related searches aren’t just helpful features — they’re a roadmap of recognized entity relationships.

Wikipedia deserves special attention since it feeds directly into Google’s Knowledge Graph. The blue links in a Wikipedia article’s opening paragraphs reveal entity connections Google trusts. An article about email marketing links to marketing automation, CRM systems, and open rates. Each link essentially says, “These concepts are related.”

Tools like Ahrefs and Semrush build on this foundation. Their analyses confirm which entities appear most frequently in top-ranking content, converting qualitative observations into measurable patterns.

Step 3: Expand entity maps with semantic analysis tools.

Once the foundation entities are clear, it’s time to find the gaps and connections that competitors might be missing. This is where specialized tools earn their keep.

Google’s Natural Language API

Google’s Natural Language API reads any piece of content and identifies which entities it contains — invaluable for checking whether existing content hits the right semantic marks.

Ahrefs and Semrush

Ahrefs and Semrush have evolved beyond keyword research, now offering entity recognition and semantic clustering that reveal how topics connect in the Knowledge Graph. Their content gap analyses specifically highlight entity opportunities that competitors rank for.

Clearscope and SurferSEO

Clearscope and SurferSEO take a different angle, analyzing what makes top-ranking content successful from an entity perspective. They surface the supporting concepts — the tools, people, and subtopics — that give content true topical depth.

HubSpot’s Nexus (Internal)

For HubSpot’s internal content teams, there's also Nexus — a proprietary tool that’s transforming how the company approaches entity mapping.

Killian Kelly, AI search technical strategist at HubSpot, developed Nexus to bridge a critical gap between theory and operational reality. “I came up with the idea for Nexus after seeing how much attention vector embeddings were getting in the SEO and AEO space, but no one had a practical way to use them in real content strategy,” Kelly explains.

Nexus models how AI systems like ChatGPT and Google’s AI Mode interpret search intent, analyzing semantic relationships across entire content libraries. The tool generates topic scores revealing exactly which pages align with target entities and where coverage gaps exist.

“Nexus helps us visualize how topics, subtopics, and entities connect across our content,” Kelly notes. “We can run a key topic through Nexus and instantly see an overall topic score — along with which pages align semantically with that entity and which areas we’re missing altogether.”

HubSpot’s team runs key topics through Nexus monthly to evaluate semantic coverage, identify competing pages, and spot gaps. Those insights feed directly into content briefs, consolidation priorities, and pruning decisions. The tool maps queries and topics to content almost instantly — work that used to take weeks — and does it based on data, not human guesswork.

The optimization feedback loop makes the impact measurable. Once the team fills gaps and strengthens coverage, they can return months later to see how topic scores have improved and whether entity signals have strengthened across the cluster. This turns entity-based SEO from theory into a trackable, iterative process that shows exactly where content investments pay off.

Step 4: Build topic clusters around entity relationships.

With entities identified, the real work begins: organizing them into clusters that make sense to both search engines and readers. The strongest clusters map the natural relationships that already exist between concepts.

A strong cluster starts with a pillar page covering a broad entity like “AI marketing.” Supporting pages then dive into specific aspects: AI content generation, chatbots for customer service, predictive analytics for campaigns. Each piece reinforces the others through internal links and shared context, creating what search engines recognize as topical authority.

Keeping everything organized as content libraries grow presents a practical challenge. Content Hub addresses this through templated briefs and automated internal linking, maintaining consistency across dozens or hundreds of related pages. When every new article strengthens the overall entity map instead of existing in isolation, real authority builds.

Pro tip: HubSpot’s SEO recommendations tool makes this visual, showing exactly where internal links are missing between pillar and cluster content, turning abstract entity relationships into actionable improvements.

Step 5: Reinforce with structured data.

Schema markup is the final layer that makes entity relationships crystal clear to search engines. While not mandatory for entity SEO success, schema acts like a translator — explicitly stating what each entity is and how it connects to others.

For a page about HubSpot Content Hub, schema tells Google exactly what’s what:

• “HubSpot Content Hub” is a software product.
• “HubSpot” is the organization behind it.
• “Entity-based SEO” is a topic covered within the content.

A simple JSON-LD example looks like this:

Free tools like Google’s Structured Data Markup Helper generate this code automatically, and the Rich Results Test confirms it’s working before publication. The payoff? Better chances of appearing in rich snippets, AI-generated answers, and knowledge panels — the high-visibility spots that drive real traffic.

How to Plan Topic Clusters With SEO Entities

Topic clusters turn entity discoveries into a structured editorial strategy by mapping how concepts relate and reinforcing those relationships through content. Entities form the foundation of these clusters, linking related ideas through shared context, internal linking, and consistent topical framing.

Effective clusters mirror how people research subjects: beginning with a broad concept and moving into increasingly specific subtopics. Entity relationships naturally guide this progression by showing which concepts belong together and how deep each area should go.

Here’s what effective entity-based clustering looks like in practice:

Core Pillar Topic (Entity)	Supporting Entities / Subtopics	Content Type	Goal / Intent	Internal Linking Example
Customer Relationship Management (CRM)	Contact Management, Lead Scoring, Sales Forecasting, Pipeline Automation	Blog posts, tutorials, comparison guides	Educate and attract top-funnel traffic	Each subtopic links back to the CRM pillar page and cross-links to the others where relevant
Marketing Automation	Email Sequences, A/B Testing, Segmentation, Personalization	Blog posts, ebooks, video walkthroughs	Guide readers from awareness to consideration	“Email Sequences” post links to “A/B Testing Best Practices” and the main “Marketing Automation Tools” pillar
Data Integration	API Management, ETL Processes, Data Hygiene, Data Governance	Case studies, how-to articles, whitepapers	Build trust and authority	Each supporting piece links up to the “Data Integration Strategy” pillar and references relevant “CRM” or “Automation” posts

Clusters become most useful when they directly inform content creation. Each entity turns into a content opportunity with clear intent and a defined set of internal links. For example, a page about email sequences naturally connects to A/B testing, lead nurturing, and the broader marketing automation pillar. These connections follow patterns that readers expect and search engines reward.

HubSpot’s Content Hub operationalizes this structure at scale by transforming entity insights into reusable brief templates and maintaining editorial consistency across expanding content libraries. Whether the output is a blog post, case study, or video, the platform helps ensure each piece strengthens the broader entity map.

Clusters also help identify gaps. When competitors rank for entity relationships missing from existing content, those gaps become a built-in roadmap for future editorial planning and quarterly content development.

Pro tip: Check out these SEO best practices for more tips and strategies.

How to Measure and Report on Entity-Based SEO Strategy

Measuring entity-based SEO focuses on whether search engines recognize and reward topical authority across related concepts, not on the performance of individual keywords. The strongest indicators show growth across clusters, improved semantic coverage, and greater visibility in the SERP features that rely on contextual understanding.

Track cluster-level performance in Google Search Console.

Google Search Console provides the most direct view of entity-led progress. Instead of isolating keyword-level queries, monitor impressions and clicks across entire clusters of pages tied to a shared concept. Rising visibility across these interconnected pages signals that Google understands the entity relationships and is treating the site as an authoritative source within that domain.

Evaluate internal link density and relationship mapping.

Entity-rich sites demonstrate tight internal linking between related topics. As clusters grow, the density and consistency of these links help search systems understand how concepts reinforce each other. HubSpot’s Content Hub automatically surfaces related pages and suggests internal links, ensuring supporting content connects back to pillar pages and to relevant subtopics. Over time, this creates a semantic network that signals depth and authority.

Monitor SERP features influenced by entity clarity.

Entity-optimized content is more likely to appear in featured snippets, knowledge panels, and AI-generated answer boxes — all of which rely on structured context rather than keyword matching. Increases in these placements show that search engines can clearly interpret the page’s meaning and its relationship to other concepts.

Connect entity performance to engagement and outcomes.

Entity authority often correlates with stronger behavioral metrics. As clusters mature, rising impressions typically appear alongside higher engagement, stronger time-on-page, and more consistent conversion paths. When search systems understand the relationships between topics, the content surfaces in more relevant contexts — driving better downstream performance.

Use AI Search Grader for emerging visibility signals.

HubSpot’s AI Search Grader adds a forward-looking dimension by showing how a brand appears across AI-driven search environments such as ChatGPT, Gemini, and Perplexity. These insights help determine whether entity signals are strong enough for LLM-based retrieval and where additional semantic reinforcement may be needed.

Frequently Asked Questions About Entity-Based SEO

Are entities the same as keywords?

No. Entities differ from keywords because entities have context and relationships. Keywords are text strings that reflect how people search, while entities are the underlying concepts that those strings refer to. For example, “CRM platform” is a keyword; HubSpot is an entity representing a specific product and organization. Entities help search systems understand meaning and context rather than matching text alone.

Do I need schema to benefit from entity SEO?

Schema markup is helpful but not required for entity SEO. Schema markup disambiguates entities for search engines. It provides explicit, machine-readable definitions of the entities on a page and how they relate to one another. Schema increases clarity for search engines and often improves visibility in featured snippets, knowledge panels, and AI-generated summaries.

How do I find related entities for my topic?

Tools such as Google’s Natural Language API, Ahrefs, and Semrush surface entities commonly associated with a primary concept. Wikipedia, People Also Ask panels, and related searches also reveal trusted entity connections. Internal linking further reinforces those relationships by mapping how concepts support one another within a cluster.

How do entities affect rankings?

When Google recognizes strong entity coverage, visibility improves across multiple related queries rather than just one term. Entity-driven pages often show consistent growth across entire clusters because search systems understand how each piece fits within a broader topic.

What’s the best way to measure entity SEO results?

Monitor impressions, clicks, and ranking trends for entity-aligned clusters in Google Search Console. Track internal link development and SERP feature visibility to assess whether semantic authority is increasing. HubSpot’s AEO Grader shows how clearly brand entities appear across AI search experiences.

How can I make my content more AI-friendly using entities?

Clear definitions, consistent naming conventions, and structured internal links make entity relationships explicit for AI models. Breaking up dense paragraphs, using schema markup where appropriate, and maintaining consistent terminology across assets improves machine interpretation. HubSpot’s Content Hub supports this by standardizing briefs and reinforcing entity-aligned patterns across content libraries.

Shift from keywords to entity-based SEO.

Entity-based SEO reflects how modern search engines interpret content through context and relationships. When those relationships are clear, visibility improves across both traditional search and AI-generated experiences.

Content Hub makes this structure scalable by identifying entities, templatizing briefs, and maintaining semantic consistency across large content ecosystems. AEO Grader shows how entity signals perform in AI environments such as ChatGPT and Gemini — visibility that’s increasingly important as search continues to evolve.

The shift from keywords to entities changed my approach to content strategy. When clusters formed around natural relationships rather than isolated terms, it became clear why Google rewards content that connects ideas. The strongest performers weren’t the pieces packed with keywords — they were the ones that demonstrated how concepts relate.

As AI plays a bigger part in information retrieval, building content around entities ensures long-term visibility and credibility. The goal extends beyond ranking for individual queries; it centers on producing content that earns authority through genuine expertise, meaningful relationships, and clear semantic structure.

from Marketing https://blog.hubspot.com/marketing/entities-seo

Entity-based SEO is a content optimization strategy built around concepts, relationships, and context rather than isolated keyword phrases. Search engines identify entities — distinct concepts, people, places, or things — and connect them through the Knowledge Graph to interpret meaning and determine topical authority.

This approach mirrors a fundamental shift in how search systems work. Google no longer simply matches text; it maps how concepts relate to one another and evaluates whether content meaningfully contributes to a subject’s broader ecosystem. As large language models like ChatGPT and Gemini increasingly shape how information surfaces, the strength of entity signals determines which sources get cited, referenced, and ranked.

This guide covers what entities are in SEO, how they differ from keywords, where to find the ones that matter, how to structure content around entity relationships, and how to measure whether the strategy works.

Table of Contents

• What are entities in SEO?
• Why Entity-Based SEO Matters for Content and SEO Marketers
• How to Find Entities for SEO
• How to Plan Topic Clusters With SEO Entities
• How to Measure and Report on Entity-Based SEO Strategy
• Frequently Asked Questions About Entity-Based SEO

What are entities in SEO?

Entities are distinct concepts, people, places, or things that search engines identify and connect within the Knowledge Graph. These relationships help systems interpret meaning instead of relying on exact-match phrases.

Search engines use entities to understand how topics connect. When content makes those connections clear, visibility improves across multiple related queries — not just one primary term.

An entity represents far more than a word or phrase on a page — it encompasses the full context surrounding a concept. For example, HubSpot is an organizational entity linked to CRM software, marketing automation, and content strategy, while email marketing connects to newsletter, automation platform, and lead nurturing entities. These relationships function as semantic signals that help Google understand how topics fit together. Google uses entities to understand and connect content in the Knowledge Graph.

Entity relationships allow search engines to evaluate relevance even when a page doesn’t contain an exact-match keyword. This is where semantic SEO shows its strength: Google connects entities through the Knowledge Graph, which determines whether a page meaningfully contributes to a topic’s broader ecosystem. That system-level understanding makes entity-based SEO essential for visibility in both traditional and AI-powered search.

How are entities different from keywords?

Entities represent concepts; keywords represent the language people use to search for those concepts. Entities carry context, relationships, and attributes, while keywords reflect phrasing. This distinction helps search engines understand meaning, not just text.

The Knowledge Graph links brands, tools, topics, and attributes through entity connections in ways that keywords alone cannot capture. This explains why pages often rank for multiple related queries even when they don’t contain exact keyword matches. A page optimized for “email automation” may also rank for “AI marketing workflows” when both concepts share strong semantic ties.

Entities also function as confirmed facts within search systems. Keywords provide surface signals, but entities carry meaning. This structural difference is why entity-led content often ranks across multiple related searches.

Carolyn Shelby, principal SEO at Yoast, offers another perspective. “Keyword SEO is basically working on a flat map, while entity SEO lives in three-dimensional space,” she explains. “In the retrieval layer, LLMs treat concepts, brands, authors, and facts like stars clustered in constellations determined by topic and relevance.”

In this model, queries move through semantic space along a trajectory shaped by how the question is phrased. The entities that get pulled into AI-generated answers are the ones with enough “gravity” — the well-established, strongly connected concepts that LLMs recognize as authoritative within their training data.

As Shelby puts it, “Keywords just help you appear on the map; entities determine whether you ‘shine brightly’ enough to be selected.”

For instance, when optimizing for “content marketing strategy,” an entity-based approach connects that topic to related concepts like “editorial calendar,” “buyer personas,” and “content distribution channels.” These aren’t just related keywords — they’re distinct entities that form a knowledge network.

Google recognizes that someone searching for content strategy likely needs information about planning tools, audience research, and publishing workflows. Search engines use these entity relationships to deliver comprehensive results that match user intent, not just pages that repeat the search phrase.

Aspect	Keywords	Entities
Definition	Phrases, words, or queries typed into search engines	Distinct concepts, people, places, or things recognized by search engines
Example	“best CRM tools”	“HubSpot,” “Salesforce,” “Customer Relationship Management”
Focus	Text string matching	Context and relationships
Used For	Targeting short-term rankings	Building long-term topical authority
SEO Impact	Optimizes for specific search phrases	Strengthens visibility for related topics and intent-based queries

Content strategy focused on entities helps Google and AI-powered search engines understand how brands fit into broader topics — not just which terms to rank for.

Why Entity-Based SEO Matters for Content and SEO Marketers

Entity-based SEO strengthens topical depth, improves relevance across clusters, and helps search engines interpret how content fits within broader subject areas. Instead of relying on isolated keywords, entity relationships show how concepts connect — a signal that matters for both SERPs and AI-generated answers.

According to research from Fractl, 66% of consumers believe AI will replace traditional search within five years, and 82% already find AI search more helpful than traditional SERPs. As Kelsey Libert, co-founder at Fractl, notes, “This highlights the need for marketers to prioritize GenAI brand visibility over keyword optimization, because keyword strategy is a thing of the past, while knowledge graphs will define your current and future brand visibility.”

When a page consistently references the entities most relevant to a subject — such as “content operations,” “CMS governance,” or “editorial planning” — search systems gain a clearer understanding of its place within a semantic neighborhood. These relationships help build topical authority by showing how concepts reinforce one another within a cluster.

Entity mapping also shapes the internal linking strategy. Connecting pages through shared entities reinforces the relationships the Knowledge Graph expects to see in a well-structured cluster. As HubSpot’s semantic search guide notes, structured relationships help search engines evaluate the depth and cohesion of a topic.

Entity-led planning improves editorial strategy by reducing duplication and clarifying where new content is needed. Topics such as “content audit frameworks,” “AI-assisted drafting,” or “internal content quality standards” may share overlapping keywords, but they represent distinct entities. Incorporating those entities into briefs and planning documents ensures each article contributes something unique to a cluster.

This approach aligns with how HubSpot’s Content Hub supports content operations. Content Hub centralizes entity-led briefs, editorial governance, and cluster mapping, making it easier to maintain consistency across a growing library of pages and ensure topics connect the way search systems expect.

Entity-focused content also improves retrievability in AI systems, which rely on conceptual relationships to identify authoritative sources and reconstruct information. As large language models play a greater role in surfacing results, strong entity signals provide additional visibility beyond traditional SERPs.

Together, these benefits make entity-based SEO a foundational layer of modern content strategy — one that improves discoverability, clarifies expertise, and supports performance across both search and AI-driven channels.

How to Find Entities for SEO

Entities form the backbone of modern SEO strategy, but finding the right ones starts with understanding what search engines already recognize. Google’s Knowledge Graph contains millions of interconnected concepts — and effective content strategies tap into these existing relationships rather than creating new ones from scratch.

Here’s a practical approach to discovering and organizing entities for any content strategy.

Step 1: Start with clear goals and core topics.

Every strong entity strategy begins with a simple question: What’s the main topic, and who needs to find it?

Marketing automation might be the core topic for a SaaS company, which naturally branches into related entities like CRM integration, email workflows, and lead scoring. These aren’t random connections — they’re the actual problems and solutions that audiences search for.

HubSpot’s AEO Grader offers a reality check here, showing how AI systems currently interpret brand content across ChatGPT, Perplexity, and Gemini. AEO Grader analyzes brand presence in AI search using entity signals. It’s one thing to assume certain entity connections exist — it’s another to see what AI actually recognizes.

Step 2: Mine search results and Wikipedia for proven entities.

Google already shows which entities matter through search features. The “People also ask” boxes, Knowledge Panels, and related searches aren’t just helpful features — they’re a roadmap of recognized entity relationships.

Wikipedia deserves special attention since it feeds directly into Google’s Knowledge Graph. The blue links in a Wikipedia article’s opening paragraphs reveal entity connections Google trusts. An article about email marketing links to marketing automation, CRM systems, and open rates. Each link essentially says, “These concepts are related.”

Tools like Ahrefs and Semrush build on this foundation. Their analyses confirm which entities appear most frequently in top-ranking content, converting qualitative observations into measurable patterns.

Step 3: Expand entity maps with semantic analysis tools.

Once the foundation entities are clear, it’s time to find the gaps and connections that competitors might be missing. This is where specialized tools earn their keep.

Google’s Natural Language API

Google’s Natural Language API reads any piece of content and identifies which entities it contains — invaluable for checking whether existing content hits the right semantic marks.

Ahrefs and Semrush

Ahrefs and Semrush have evolved beyond keyword research, now offering entity recognition and semantic clustering that reveal how topics connect in the Knowledge Graph. Their content gap analyses specifically highlight entity opportunities that competitors rank for.

Clearscope and SurferSEO

Clearscope and SurferSEO take a different angle, analyzing what makes top-ranking content successful from an entity perspective. They surface the supporting concepts — the tools, people, and subtopics — that give content true topical depth.

HubSpot’s Nexus (Internal)

For HubSpot’s internal content teams, there's also Nexus — a proprietary tool that’s transforming how the company approaches entity mapping.

Killian Kelly, AI search technical strategist at HubSpot, developed Nexus to bridge a critical gap between theory and operational reality. “I came up with the idea for Nexus after seeing how much attention vector embeddings were getting in the SEO and AEO space, but no one had a practical way to use them in real content strategy,” Kelly explains.

Nexus models how AI systems like ChatGPT and Google’s AI Mode interpret search intent, analyzing semantic relationships across entire content libraries. The tool generates topic scores revealing exactly which pages align with target entities and where coverage gaps exist.

“Nexus helps us visualize how topics, subtopics, and entities connect across our content,” Kelly notes. “We can run a key topic through Nexus and instantly see an overall topic score — along with which pages align semantically with that entity and which areas we’re missing altogether.”

HubSpot’s team runs key topics through Nexus monthly to evaluate semantic coverage, identify competing pages, and spot gaps. Those insights feed directly into content briefs, consolidation priorities, and pruning decisions. The tool maps queries and topics to content almost instantly — work that used to take weeks — and does it based on data, not human guesswork.

The optimization feedback loop makes the impact measurable. Once the team fills gaps and strengthens coverage, they can return months later to see how topic scores have improved and whether entity signals have strengthened across the cluster. This turns entity-based SEO from theory into a trackable, iterative process that shows exactly where content investments pay off.

Step 4: Build topic clusters around entity relationships.

With entities identified, the real work begins: organizing them into clusters that make sense to both search engines and readers. The strongest clusters map the natural relationships that already exist between concepts.

A strong cluster starts with a pillar page covering a broad entity like “AI marketing.” Supporting pages then dive into specific aspects: AI content generation, chatbots for customer service, predictive analytics for campaigns. Each piece reinforces the others through internal links and shared context, creating what search engines recognize as topical authority.

Keeping everything organized as content libraries grow presents a practical challenge. Content Hub addresses this through templated briefs and automated internal linking, maintaining consistency across dozens or hundreds of related pages. When every new article strengthens the overall entity map instead of existing in isolation, real authority builds.

Pro tip: HubSpot’s SEO recommendations tool makes this visual, showing exactly where internal links are missing between pillar and cluster content, turning abstract entity relationships into actionable improvements.

Step 5: Reinforce with structured data.

Schema markup is the final layer that makes entity relationships crystal clear to search engines. While not mandatory for entity SEO success, schema acts like a translator — explicitly stating what each entity is and how it connects to others.

For a page about HubSpot Content Hub, schema tells Google exactly what’s what:

• “HubSpot Content Hub” is a software product.
• “HubSpot” is the organization behind it.
• “Entity-based SEO” is a topic covered within the content.

A simple JSON-LD example looks like this:

Free tools like Google’s Structured Data Markup Helper generate this code automatically, and the Rich Results Test confirms it’s working before publication. The payoff? Better chances of appearing in rich snippets, AI-generated answers, and knowledge panels — the high-visibility spots that drive real traffic.

How to Plan Topic Clusters With SEO Entities

Topic clusters turn entity discoveries into a structured editorial strategy by mapping how concepts relate and reinforcing those relationships through content. Entities form the foundation of these clusters, linking related ideas through shared context, internal linking, and consistent topical framing.

Effective clusters mirror how people research subjects: beginning with a broad concept and moving into increasingly specific subtopics. Entity relationships naturally guide this progression by showing which concepts belong together and how deep each area should go.

Here’s what effective entity-based clustering looks like in practice:

Core Pillar Topic (Entity)	Supporting Entities / Subtopics	Content Type	Goal / Intent	Internal Linking Example
Customer Relationship Management (CRM)	Contact Management, Lead Scoring, Sales Forecasting, Pipeline Automation	Blog posts, tutorials, comparison guides	Educate and attract top-funnel traffic	Each subtopic links back to the CRM pillar page and cross-links to the others where relevant
Marketing Automation	Email Sequences, A/B Testing, Segmentation, Personalization	Blog posts, ebooks, video walkthroughs	Guide readers from awareness to consideration	“Email Sequences” post links to “A/B Testing Best Practices” and the main “Marketing Automation Tools” pillar
Data Integration	API Management, ETL Processes, Data Hygiene, Data Governance	Case studies, how-to articles, whitepapers	Build trust and authority	Each supporting piece links up to the “Data Integration Strategy” pillar and references relevant “CRM” or “Automation” posts

Clusters become most useful when they directly inform content creation. Each entity turns into a content opportunity with clear intent and a defined set of internal links. For example, a page about email sequences naturally connects to A/B testing, lead nurturing, and the broader marketing automation pillar. These connections follow patterns that readers expect and search engines reward.

HubSpot’s Content Hub operationalizes this structure at scale by transforming entity insights into reusable brief templates and maintaining editorial consistency across expanding content libraries. Whether the output is a blog post, case study, or video, the platform helps ensure each piece strengthens the broader entity map.

Clusters also help identify gaps. When competitors rank for entity relationships missing from existing content, those gaps become a built-in roadmap for future editorial planning and quarterly content development.

Pro tip: Check out these SEO best practices for more tips and strategies.

How to Measure and Report on Entity-Based SEO Strategy

Measuring entity-based SEO focuses on whether search engines recognize and reward topical authority across related concepts, not on the performance of individual keywords. The strongest indicators show growth across clusters, improved semantic coverage, and greater visibility in the SERP features that rely on contextual understanding.

Track cluster-level performance in Google Search Console.

Google Search Console provides the most direct view of entity-led progress. Instead of isolating keyword-level queries, monitor impressions and clicks across entire clusters of pages tied to a shared concept. Rising visibility across these interconnected pages signals that Google understands the entity relationships and is treating the site as an authoritative source within that domain.

Evaluate internal link density and relationship mapping.

Entity-rich sites demonstrate tight internal linking between related topics. As clusters grow, the density and consistency of these links help search systems understand how concepts reinforce each other. HubSpot’s Content Hub automatically surfaces related pages and suggests internal links, ensuring supporting content connects back to pillar pages and to relevant subtopics. Over time, this creates a semantic network that signals depth and authority.

Monitor SERP features influenced by entity clarity.

Entity-optimized content is more likely to appear in featured snippets, knowledge panels, and AI-generated answer boxes — all of which rely on structured context rather than keyword matching. Increases in these placements show that search engines can clearly interpret the page’s meaning and its relationship to other concepts.

Connect entity performance to engagement and outcomes.

Entity authority often correlates with stronger behavioral metrics. As clusters mature, rising impressions typically appear alongside higher engagement, stronger time-on-page, and more consistent conversion paths. When search systems understand the relationships between topics, the content surfaces in more relevant contexts — driving better downstream performance.

Use AI Search Grader for emerging visibility signals.

HubSpot’s AI Search Grader adds a forward-looking dimension by showing how a brand appears across AI-driven search environments such as ChatGPT, Gemini, and Perplexity. These insights help determine whether entity signals are strong enough for LLM-based retrieval and where additional semantic reinforcement may be needed.

Frequently Asked Questions About Entity-Based SEO

Are entities the same as keywords?

No. Entities differ from keywords because entities have context and relationships. Keywords are text strings that reflect how people search, while entities are the underlying concepts that those strings refer to. For example, “CRM platform” is a keyword; HubSpot is an entity representing a specific product and organization. Entities help search systems understand meaning and context rather than matching text alone.

Do I need schema to benefit from entity SEO?

Schema markup is helpful but not required for entity SEO. Schema markup disambiguates entities for search engines. It provides explicit, machine-readable definitions of the entities on a page and how they relate to one another. Schema increases clarity for search engines and often improves visibility in featured snippets, knowledge panels, and AI-generated summaries.

How do I find related entities for my topic?

Tools such as Google’s Natural Language API, Ahrefs, and Semrush surface entities commonly associated with a primary concept. Wikipedia, People Also Ask panels, and related searches also reveal trusted entity connections. Internal linking further reinforces those relationships by mapping how concepts support one another within a cluster.

How do entities affect rankings?

When Google recognizes strong entity coverage, visibility improves across multiple related queries rather than just one term. Entity-driven pages often show consistent growth across entire clusters because search systems understand how each piece fits within a broader topic.

What’s the best way to measure entity SEO results?

Monitor impressions, clicks, and ranking trends for entity-aligned clusters in Google Search Console. Track internal link development and SERP feature visibility to assess whether semantic authority is increasing. HubSpot’s AEO Grader shows how clearly brand entities appear across AI search experiences.

How can I make my content more AI-friendly using entities?

Clear definitions, consistent naming conventions, and structured internal links make entity relationships explicit for AI models. Breaking up dense paragraphs, using schema markup where appropriate, and maintaining consistent terminology across assets improves machine interpretation. HubSpot’s Content Hub supports this by standardizing briefs and reinforcing entity-aligned patterns across content libraries.

Shift from keywords to entity-based SEO.

Entity-based SEO reflects how modern search engines interpret content through context and relationships. When those relationships are clear, visibility improves across both traditional search and AI-generated experiences.

Content Hub makes this structure scalable by identifying entities, templatizing briefs, and maintaining semantic consistency across large content ecosystems. AEO Grader shows how entity signals perform in AI environments such as ChatGPT and Gemini — visibility that’s increasingly important as search continues to evolve.

The shift from keywords to entities changed my approach to content strategy. When clusters formed around natural relationships rather than isolated terms, it became clear why Google rewards content that connects ideas. The strongest performers weren’t the pieces packed with keywords — they were the ones that demonstrated how concepts relate.

As AI plays a bigger part in information retrieval, building content around entities ensures long-term visibility and credibility. The goal extends beyond ranking for individual queries; it centers on producing content that earns authority through genuine expertise, meaningful relationships, and clear semantic structure.

via Perfecte news Non connection