Entity-based SEO is a content optimization strategy based on concepts, relationships, and context rather than isolated keyword phrases. Search engines identify entities – different concepts, people, places or things – and connect them across the knowledge graph to interpret meaning and determine current authority.
This approach reflects a fundamental shift in the way search systems work. Google no longer simply matches text; It represents how concepts relate to each other and evaluates whether content makes a meaningful contribution to a subject’s broader ecosystem. As large language models like ChatGPT and Gemini increasingly influence the way information emerges, the strength of entity signals determines which sources are cited, referenced, and ranked.
This guide covers what entities are in search engine optimization, how they differ from keywords, where to find the ones that matter, how to structure content based on entity relationships, and how to measure whether the strategy is working.
Table of contents
What are entities in SEO?
Entities are different concepts, people, places or things that search engines identify and connect in the knowledge graph. These relationships help systems interpret meaning rather than relying on exact match phrases.
Search engines use entities to understand how topics are related. When content makes these connections clear, visibility improves across multiple related searches – not just one main term.
An entity represents much more than a word or phrase on a page – it encompasses the entire context surrounding a concept. For example, HubSpot is an organizational unit linked to CRM software, marketing automation and content strategy, while email marketing is linked to newsletters, automation platforms and lead nurturing units. These relationships act as semantic signals that help Google understand how topics fit together. Google uses entities to understand and associate content in the Knowledge Graph.
Entity relationships allow search engines to evaluate relevance even if a page does not contain an exact match keyword. Here lies the semantics SEO shows its strength: Google connects entities via the Knowledge Graph, which determines whether a page makes a meaningful contribution to the broader ecosystem of a topic. This 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 these concepts. Entities carry context, relationships, and attributes, while keywords reflect wording. This distinction helps search engines understand the meaning and not just the text.
The Knowledge Graph connects brands, tools, topics, and attributes through entity connections in ways that keywords alone cannot capture. This explains why pages often rank for multiple related search queries, even if they don’t contain exact keyword matches. A page optimized for “Email Automation” can also rank for “AI Marketing Workflows” if both concepts have strong semantic connections.
Entities also act as confirmed facts in search systems. Keywords provide superficial signals, but entities carry meaning. This structural difference is why entity-driven content often ranks for multiple related searches.
Carolyn ShelbyMain SEO at Yeastoffers a different perspective. “Keyword SEO basically works on a flat map, while entity SEO lives in three-dimensional space,” she explains. “At the retrieval level, LLMs treat concepts, brands, authors and facts like stars, grouped into constellations determined by topic and relevance.”
In this model, queries move along a trajectory through semantic space that is determined by the wording of the question. The entities included in AI-generated responses are those with sufficient “gravity” – the well-established, strongly connected concepts that LLMs recognize as authoritative in their training data.
As Shelby puts it, “Keywords simply help you appear on the map; entities determine whether you are ‘bright enough’ to be selected.”
For example, if you’re optimizing for a “content marketing strategy,” an entity-based approach connects that topic to related concepts like “editorial calendars,” “buyer personas,” and “content distribution channels.” These are not just related keywords, but different entities that form a knowledge network.
Google understands that someone looking for a content strategy will likely need information about planning tools, audience research, and publishing processes. Search engines use these entity relationships to deliver comprehensive results that match user intent, not just pages that repeat the search term.
|
aspect |
Keywords |
Entities |
|
definition |
Phrases, words, or search queries entered into search engines |
Unique 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 current authority |
|
SEO impact |
Optimized for specific search terms |
Strengthens visibility for related topics and intent-based queries |
An entity-focused content strategy helps Google and AI-powered search engines understand how brands fit into broader topics—not just what terms they rank for.
Why entity-based SEO is important for content and SEO marketers
Entity-based SEO strengthens topical depth, improves relevance across clusters, and helps search engines interpret how content fits into broader topic areas. Rather than relying on isolated keywords, entity relationships show how concepts are connected to each other – a signal that is important to both SERPs and AI-generated answers.
Accordingly Research by Fractl66% of consumers believe AI will replace traditional search within five years, and 82% already find AI search more helpful than traditional SERPs. As Kelsey LibertCo-founder of Fraktlstates: “This highlights the need for marketers to prioritize GenAI brand visibility over keyword optimization because keyword strategies are a thing of the past while knowledge graphs define your current and future brand visibility.”
When a page consistently references the entities most relevant to a topic—such as “content operations,” “CMS governance,” or “editorial planning”—search systems gain a clearer understanding of its position within a semantic neighborhood. These relationships help build current authority by showing how concepts within a cluster reinforce each other.
Entity mapping also shapes the internal linking strategy. Connecting pages through shared entities strengthens the relationships that the knowledge graph expects in a well-structured cluster. As HubSpot’s Semantic Search Guide notes, structured relationships help search engines evaluate the depth and context of a topic.
Business-driven planning improves editorial strategy by reducing duplication and clarifying where new content is needed. Topics like “content audit frameworks,” “AI-assisted writing,” or “internal content quality standards” may have overlapping keywords, but they represent different entities. Incorporating these entities into briefs and planning documents ensures that each article contributes something unique to a cluster.
This approach depends on how HubSpot’s Content Hub supports content operations. Content Hub centralizes business-driven briefings, editorial governance and cluster mapping, making it easier to maintain consistency across a growing library of pages and ensuring topics are connected the way search systems expect.
Entity-focused content also improves discoverability in AI systems that rely on conceptual relationships to identify authoritative sources and reconstruct information. As large language models play a larger role in representing results, strong entity signals provide additional visibility beyond traditional SERPs.
Taken together, these benefits make entity-based SEO a fundamental layer of modern content strategy – one that improves discoverability, demonstrates expertise, and supports performance across both search and AI-driven channels.
How to Find Companies for SEO
Entities form the backbone of a modern SEO strategy, but finding the right ones requires first understanding what search engines already recognize. Google’s Knowledge Graph contains millions of interconnected concepts – and effective content strategies leverage 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 business strategy starts with a simple question: What is the main theme and who needs to find it?
Marketing automation could be the core theme for a SaaS company, which naturally branches out into related units like CRM integration, email workflows, and lead scoring. These are not random connections, but rather the actual problems and solutions that the audience is looking for.
HubSpots AEO Graders offers a reality check here that shows how AI systems currently interpret branded content in ChatGPT, Perplexity and Gemini. AEO Grader analyzes brand presence in AI search using entity signals. It’s one thing to assume that certain entity connections exist – it’s another to see what the AI actually recognizes.
Step 2: Search search results and Wikipedia for proven entities.
Google already shows which entities are important via search functions. The “People Also Ask” fields, knowledge panels, and related searches aren’t just helpful features – they’re a roadmap of discovered entity relationships.
Wikipedia deserves special attention because it feeds directly into Google’s Knowledge Graph. The blue links in the first few paragraphs of a Wikipedia article reveal entity connections that Google trusts. An article about email marketing links on marketing automation, CRM systems and open rates. Each link essentially says, “These concepts are related.”
Tools like Ahrefs and SEMrush are built on this foundation. Their analyzes confirm which entities appear most frequently in high-ranking content and transform qualitative observations into measurable patterns.
Step 3: Extend entity maps with semantic analysis tools.
Once the founding units are clear, it’s time to find the gaps and connections that competitors may be missing. This is where special tools earn their living.
Google’s Natural Language API
Google’s Natural Language API reads each piece of content and identifies which entities it contains – invaluable for checking whether existing content hits the right semantic markers.
Ahrefs and Semrush
Ahrefs And Semrush have evolved beyond keyword research to include entity recognition and semantic clustering that show how topics in the knowledge graph are related. Your content gap analyzes specifically highlight business opportunities that competitors are ranking for.
Clearscope and SurferSEO
Clearscope And SurferSEO Take a different perspective and analyze what makes top-notch content successful from a business perspective. They bring to light the supporting concepts—the tools, people, and subtopics—that give the content real thematic depth.
HubSpots Nexus (internal)
For HubSpot’s internal content teams, there’s also Nexus – a proprietary tool that transforms the company’s approach to entity mapping.
Killian KellyAI 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 embeds were getting in the SEO and AEO space, but no one had a practical way to use them in a real content strategy,” explains Kelly.
Nexus models how AI systems like ChatGPT and Google’s AI Mode interpret search intent and analyzes semantic relationships across entire content libraries. The tool generates topic scores that show exactly which pages match the target entities and where coverage gaps exist.
“Nexus helps us visualize how topics, subtopics, and entities in our content are connected,” notes Kelly. “We can run a key topic through Nexus and immediately see an overall score of the topic – along with the pages that semantically match that entity and what areas we are missing overall.”
HubSpot’s team conducts monthly key topic reviews across Nexus to assess semantic coverage, identify competing pages, and identify gaps. These insights feed directly into content descriptions, consolidation priorities, and cleanup decisions. The tool maps queries and topics to content almost instantly – a job that used to take weeks – and does it based on data rather than human guesswork.
The optimization feedback loop makes the effect measurable. Once the team closes gaps and strengthens coverage, they can come back months later to see how topic scores have improved and whether entity signals have become stronger across the cluster. This takes entity-based SEO from theory to a trackable, iterative process that shows exactly where content investments are paying off.
Step 4: Create topic clusters around entity relationships.
Once the entities are identified, the real work begins: organizing them into clusters that make sense to both search engines and readers. The strongest clusters represent the natural relationships that already exist between concepts.
A strong cluster starts with a pillar page that covers a broad topic like “AI marketing.” Supporting pages then address specific aspects: AI content creation, chatbots for customer service, predictive analytics for campaigns. Each piece reinforces the other through internal links and shared context, creating what search engines recognize as current authority.
Keeping everything organized as content libraries grow is a practical challenge. Content Hub Addresses this issue with pre-written short descriptions and automated internal linking, ensuring consistency across dozens or hundreds of related pages. When each new article strengthens the entire entity map rather than existing in isolation, true authority emerges.
Pro tip: HubSpot’s SEO recommendation tool makes this visual, showing exactly where internal links between column and cluster content are missing, and transforms 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. Although not mandatory for entity SEO success, schema acts like a translator – it explicitly states what each entity is and how it is connected to others.
For a page through HubSpot Content Hub, the schema tells Google exactly what is what:
- “HubSpot Content Hub” is a software product.
- “HubSpot” is the organization behind it.
- “Entity-based SEO” is a topic covered in the content.
A simple one JSON-LD Example looks like this:
Free tools like Google’s Structured Data Markup Helper Generate this code automatically and the Rich search results test confirmed it works before release. The payoff? Increased 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 transform entity discoveries into a structured editorial strategy by mapping the relationship between concepts and reinforcing those relationships through content. Entities form the foundation of these clusters and link related ideas through shared context, internal linking, and consistent thematic framing.
Effective clusters reflect the way people explore topics: they start with a broad concept and then move on to increasingly specific subtopics. Entity relationships naturally guide this progress by showing which concepts belong together and how deep each area should go.
This is what effective entity-based clustering looks like in practice:
|
Core topic of the pillar (entity) |
Supporting units/subtopics |
Content type |
Goal/intention |
Example of an internal link |
|
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 leads back to the CRM pillar page and links to the others where appropriate |
|
Marketing automation |
Email sequences, A/B testing, segmentation, personalization |
Blog posts, e-books, video walkthroughs |
Move readers from awareness to consideration |
“Email Sequences” post links to “A/B Testing Best Practices” and the main “Marketing Automation Tools” mainstay. |
|
Data integration |
API management, ETL processes, data hygiene, data governance |
Case studies, how-to articles, white papers |
Build trust and authority |
Each supporting part is linked to the “Data Integration Strategy” pillar and links to relevant “CRM” or “Automation” posts |
Clusters are most useful when incorporated directly into content creation. Each entity becomes a content opportunity with clear intent and a defined set of internal links. For example, an email sequences page makes a natural connection to A/B testing, lead nurturing, and the broader pillar of marketing automation. These connections follow patterns that readers expect and are rewarded by search engines.
HubSpots Content Hub Operationalizes this structure at scale by converting entity insights into reusable short templates and maintaining editorial consistency across growing 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 that are missing from existing content, these gaps become an integrated roadmap for future editorial planning and quarterly content development.
Pro tip: For more tips and strategies, check out these SEO best practices.
How to measure and report on an entity-based SEO strategy
Track cluster-level performance in Google Search Console.
Google Search Console Provides the most direct view of business-driven progress. Instead of isolating searches at the keyword level, monitor impressions and clicks across entire clusters of pages tied to a common concept. The increasing visibility of these interconnected pages signals that Google understands the entities’ connections and views the site as an authoritative source within that domain.
Evaluate internal link density and relationship mapping.
Entity-rich websites have tight internal linkage between related topics. As clusters grow, the density and consistency of these links help search systems understand how concepts reinforce each other. HubSpots Content Hub Automatically displays related pages and suggests internal links to ensure supporting content links to pillar pages and relevant subtopics. Over time, a semantic network emerges 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 are based on structured context rather than keyword matching. The increase in these rankings shows that search engines can clearly interpret the meaning of the page and its relationship to other concepts.
Connect business performance to engagement and results.
An entity’s authority often correlates with stronger behavioral metrics. As clusters mature, increasing impressions are typically accompanied by higher engagement, longer time-on-page, and more consistent conversion paths. When search systems understand the relationships between topics, content appears in more relevant contexts – leading to better downstream performance.
Use AI Search Grader for new visibility signals.
HubSpots AI search grader adds a forward-looking dimension by showing how a brand appears in AI-driven search environments such as ChatGPT, Gemini and Perplexity. These insights can be used to determine whether entity signals are strong enough for LLM-based retrieval and where additional semantic reinforcement may be required.
Frequently asked questions about entity-based SEO
Are entities the same as keywords?
No. Entities are different from keywords because entities have context and relationships. Keywords are text strings that reflect how people search, while entities are the underlying concepts that these strings refer to. For example, “CRM platform” is a keyword; HubSpot is an entity that represents a specific product and organization. Entities help search systems understand meaning and context rather than just matching text.
Do I need a schema to benefit from Entity SEO?
Schema markup is helpful, but not necessary for entity SEO. Schema markup makes entities unique to search engines. It provides explicit, machine-readable definitions of the entities on a page and their relationship to each other. 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 like Google’s Natural Language API, Ahrefs, and SEMrush reveal entities that are often linked to a primary concept. Wikipedia, People Also Ask panels, and related searches also reveal trusted entity connections. Internal linking further strengthens these relationships by depicting how concepts within a cluster support each other.
How do entities affect rankings?
When Google detects strong entity coverage, visibility improves across multiple related searches rather than just one term. Entity-driven pages often show consistent growth across entire clusters because search systems understand how each part fits into a broader topic.
What is the best way to measure entity SEO results?
Monitor impressions, clicks, and ranking trends for entity-centric clusters in Google Search Console. Track internal link development and SERP feature visibility to assess whether semantic authority is increasing. HubSpots AEO grader shows how clearly brand entities appear in 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 when appropriate, and maintaining consistent terminology across assets improves machine interpretation. HubSpots Content Hub supports this by standardizing briefs and reinforcing entity-oriented patterns across content libraries.
Switch from keywords to entity-based SEO.
Entity-based SEO reflects how modern search engines interpret content based on context and relationships. When these relationships are clear, visibility improves in both traditional search and AI-generated experiences.
Content Hub Makes this structure scalable by identifying entities, templating short descriptions, and maintaining semantic consistency across large content ecosystems. AEO grader shows how entity signals behave in AI environments like ChatGPT and Gemini – visibility that will become increasingly important as search evolves.
Switching from keywords to entities changed my approach to content strategy. As clusters formed around natural relationships rather than isolated terms, it became clear why Google rewards content that connects ideas. The best-performing pieces weren’t the ones that were stuffed with keywords – it was the ones that showed how concepts are related.
As AI plays a larger role in information gathering, creating content around entities ensures long-term visibility and credibility. The goal goes beyond ranking individual search queries; The focus is on producing content that gains authority through real expertise, meaningful relationships and a clear semantic structure.