Introduction: Moving beyond keywords with semantic SEO
I distinctly remember the moment my old SEO playbook stopped working. I had spent weeks refreshing title tags, optimizing H1s, and tweaking keyword density for a client’s core service page. The rankings fluctuated slightly, but the traffic didn’t budge. Meanwhile, a competitor with objectively ‘worse’ optimization was dominating the top spot with a comprehensive guide that barely mentioned my target keyword but covered the topic exhaustively.
The lesson was painful but clear: Google had stopped reading strings of text and started understanding ‘things’—entities. As search engines rely more on Knowledge Graphs and AI-driven summaries, traditional keyword-only tactics are becoming less reliable for US businesses. If you want to secure visibility in 2025, you need a new model.
In this guide, I’m sharing my exact workflow. We will move beyond theory into a repeatable process: how to identify search entities, map them to a content plan, implement the right schema, and measure results that actually impact your bottom line.
Semantic SEO basics: what entities are (and why keywords aren’t enough)
To understand semantic SEO, you have to think less like a lexicographer and more like a librarian connecting dots. Traditional SEO focuses on matching a query string (e.g., ‘best crm software’) to a page containing that string. Semantic SEO focuses on the meaning behind the query and the relationships between concepts.
At the center of this is the entity. An entity is any distinct, well-defined concept—a person (Elon Musk), a place (Phoenix, AZ), an organization (Tesla), or a concept (CRM software). Search engines use entities to understand context. For example, if you search for ‘jaguar,’ Google looks at the surrounding entities (is the user mentioning ‘speed’ and ‘engine,’ or ‘jungle’ and ‘predator’?) to determine if you mean the car or the cat.
While keywords are still the inputs users type, entities are how search engines organize the output. This shift requires us to build topical authority by creating clusters of content around entity hubs, rather than isolated pages targeting isolated keywords.
Quick definition: What is semantic SEO?
Semantic SEO is the practice of optimizing content around topics and entities rather than individual keywords. It involves mapping entity relationships (how Topic A connects to Topic B), using structured data to define those connections explicitly, and building deep content clusters that signal expertise to search engines.
Entity-based SEO vs traditional keyword SEO
Many marketers treat these as synonyms, but the execution is totally different. When I audit a site, I can immediately tell which strategy was used. A keyword-based site has 50 blog posts that all loosely target ‘best marketing tips.’ An entity-based site has a structured library where every page has a distinct job.
| Feature | Traditional Keyword SEO | Entity-Based (Semantic) SEO |
|---|---|---|
| Unit of Optimization | Single Page / Single Keyword | Topic Cluster / Entity Hub |
| Content Structure | Flat (unrelated blog posts) | Hierarchical (Pillar + Cluster pages) |
| Internal Linking | Exact-match anchors (‘click here for SEO’) | Conceptual anchors (‘how semantic search works’) |
| Primary Goal | Rank for a specific phrase | Cover a topic comprehensively |
| Success Metric | Keyword Ranking Position | Entity Visibility & Traffic Quality |
How search engines interpret entities today: Knowledge Graphs, vectors, and AI Overviews
You don’t need a PhD in computer science to do SEO, but you do need a mental model of what’s happening under the hood. I picture it like a giant mind map (the Knowledge Graph) combined with a translator that understands nuance (Vectors).
Knowledge Graph 101 (in plain English)
Think of the Knowledge Graph as a database of facts. It consists of nodes (entities) and edges (relationships). For a local business, like a personal injury lawyer in Phoenix, the graph looks like this:
- Node A: The Law Firm (Organization)
- Edge: is located in
- Node B: Phoenix (Place)
- Edge: offers service
- Node C: Car Accident Defense (Service)
When you use schema markup and clear writing, you are essentially spoon-feeding these connections to Google.
Embeddings/vectors: why ‘meaning’ beats exact-match wording
Vector search allows Google to map concepts mathematically. Words with similar meanings appear close together in this “vector space.” This is why you no longer need to stuff the phrase “cheap car insurance” ten times. If your content comprehensively covers ‘affordability,’ ‘premiums,’ and ‘deductibles,’ the vector embeddings signal to Google that your page is relevant to the user’s intent, even if the exact keyword match is loose.
GEO and AEO: semantic SEO for AI-driven results
This is where things get urgent. Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) are the new frontiers. With AI Overviews appearing in over 50% of search queries by 2025 , the goal isn’t just to rank blue links—it’s to be cited.
AI models (LLMs) prefer content that is structured, authoritative, and factually dense. They struggle with ambiguity. By optimizing for entities, you reduce ambiguity, making it easier for an AI to retrieve your content and present it as the answer.
My semantic SEO workflow to identify and prioritize search entities (step-by-step)
Theory is great, but execution pays the bills. Here is the exact workflow I use to build entity-focused content plans. I skip the expensive enterprise tools here on purpose—you can do 90% of this with Google Search and a spreadsheet. Let’s use a ‘Mid-Market CRM Software’ company as our running example.
Step 1: Start with one core entity and the jobs-to-be-done (intent)
I always start with the primary entity I want to own. In our example, it’s “CRM Software.” But typing that into a brief isn’t enough. I need to map the intent.
I search for the term and look at the top 3 results. Are they product pages? No. They are listicles (“Best CRM Tools for 2025”) and definition guides (“What is CRM?”). If I try to rank a product landing page here, I will fail. The entity “CRM Software” usually triggers an informational or comparative intent. My first step is simply verifying: What type of page does Google associate with this entity?
Step 2: Expand to related entities (attributes, comparisons, problems, alternatives)
Next, I look for the ‘orbiting’ entities. These are the sub-topics that give the main topic context. I look for signals like:
- Attributes: What makes a CRM? (Automation, Contact Management, Lead Scoring).
- Related People/Orgs: Who are the big players? (Salesforce, HubSpot).
- User Problems: What are users trying to solve? (Customer retention, sales pipeline visibility).
I verify this by looking at the ‘People Also Ask’ box. If users are asking “How much does a CRM cost?”, then ‘Price’ is a critical attribute entity I must cover.
Step 3: Validate entities with SERP patterns (what Google is rewarding)
I scan the SERP for visual clues. These are not random; they are evidence of how Google categorizes the topic.
- Knowledge Panel: Indicates Google understands the brand or concept as a distinct fact.
- Local Pack: Indicates a local service entity.
- Video Carousel: Indicates the entity requires visual explanation (common for ‘How-to’ entities).
For our CRM example, I see a ‘People Also Search For’ carousel listing competitors. This tells me comparison is a huge part of this entity’s graph.
Step 4: Build an ‘entity map’ and turn it into a content plan
Now I draw a simple map. The center is my Pillar Page (The Ultimate Guide to CRM for Mid-Market). The spokes are the supporting entities (CRM Pricing, CRM vs ERP, CRM Implementation).
This map becomes my content plan. To scale this effectively, especially when you have dozens of clusters to build, you might use an AI article generator to help draft the initial briefs or outlines based on these entity maps. Whether you write manually or use AI assistance, the key is that every piece of content must have a specific place in the map.
Implementing entity targeting: content structure, internal linking, and schema markup
Once you have your map, you have to put it on the page. This is where most people drop the ball. They write the content but forget to signal the structure to the search engine.
Content: write for entity completeness (definition, attributes, use cases, constraints)
When I edit a draft, I look for ‘entity completeness.’ If we are writing about ‘Lead Scoring’ (a sub-entity of CRM), a weak heading would be “Why it’s good.” A strong, entity-rich heading would be “How Predictive Lead Scoring Models Work.”
I ensure we explicitly define the term early in the content (good for Featured Snippets) and cover its key attributes: costs, benefits, integration requirements, and limitations. I don’t force it, but I make sure the noun count is high—using specific terminology rather than vague fluff like “solutions” or “synergy.”
Internal linking: shift from keyword anchors to entity-based context
Rule of thumb: Stop using “click here” or generic “read more” links. Also, stop obsessing over exact-match keywords like “best crm” for every link.
Instead, link relationships. If I’m writing about CRM implementation, I might write: “Once you have imported your data, you will need to configure your lead scoring rules.” I link the phrase “lead scoring rules” to the dedicated page on that topic. This tells Google: ‘Lead Scoring’ is a component of ‘CRM Implementation’.
Structured data for entity SEO: the schema types that matter most
Schema is your direct line to the bot. It doesn’t guarantee a rich result, but it makes you eligible. For semantic SEO, I focus on schema that defines what things are and how they relate.
| Schema Type | Best Use Case | Critical Properties |
|---|---|---|
| Organization | Homepage / About Page | name, logo, sameAs, contactPoint |
| Article / BlogPosting | Blog posts | headline, author, datePublished, about (mentions main entity) |
| FAQPage | Service / Support pages | question, answer (great for Voice Search/AEO) |
| LocalBusiness | Local service pages | geo, areaServed, openingHours, priceRange |
| HowTo | Step-by-step guides | step, supply, tool |
sameAs and entity consistency: when it helps (and when it’s unnecessary)
The sameAs property in schema is powerful. It allows you to say, “This company on my website is the same as this profile on LinkedIn and this profile on Crunchbase.” It creates a triangle of trust. I only link to authoritative sources I can verify. Don’t spam this field; keep it to your official profiles and major citations (like a Wikipedia page if you have one).
Tracking results in semantic SEO: the KPIs that show entity visibility (not just rankings)
If you only track keyword rankings, you will miss the bigger picture. In a semantic world, we track visibility and presence. I explain it to stakeholders like this: “We aren’t just trying to be number one for a word; we are trying to take up more pixels on the screen for the topic.”
A simple KPI dashboard for beginners (table)
If I can only track three things to prove my entity strategy is working, I start here:
| Metric | What it indicates | How to track |
|---|---|---|
| Rich Result Impressions | Google is understanding your schema and content structure. | GSC > Search Appearance > Rich Results |
| Knowledge Panel Appearance | You are recognized as a trusted entity. | Manual checks / SERP tracking tools |
| Non-Branded Query Diversity | You are ranking for concepts, not just the one phrase you optimized. | GSC > Performance (look for new long-tail queries) |
Note on AI Citations: While tools are emerging to track how often you appear in AI overviews, currently the best proxy is high-ranking informational content combined with valid schema. There is no “AI Analytics” yet, but appearing in Featured Snippets is often a precursor to AI citations.
Common mistakes, FAQs, and next steps for semantic SEO (beginner-friendly)
Common mistakes (and quick fixes) when targeting search entities
- Mixing Intents: Trying to cover definitions, sales pitches, and news on one page.
Fix: Split them. One page for “What is X,” another for “Buy X.” - Orphaned Clusters: Writing great cluster content but failing to link it back to the pillar page.
Fix: Audit your internal links. Every spoke must link to the hub. - Ignoring Local Entities: For US local businesses, failing to mention neighborhoods or landmarks.
Fix: Add specific ‘areaServed’ locations in your footer and content. - Schema Spam: Marking up content that isn’t visible to the user.
Fix: Only markup what is on the page. Don’t hide FAQs in code. - Ambiguous Writing: Using “it,” “this,” and “that” too often.
Fix: Re-read your H2s. Do they make sense out of context? If not, rewrite them with specific nouns.
FAQs: semantic SEO, entity SEO, GEO/AEO, schema, and local entity tactics
What is the difference between semantic SEO and entity-based SEO?
Practically speaking, they are the same. Semantic SEO is the broader concept of optimizing for meaning, while entity-based SEO is the tactical execution of focusing on distinct people, places, and things.
What are GEO and AEO?
GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) are strategies to ensure your content is cited in AI-generated summaries (like ChatGPT or Google AI Overviews) rather than just ranking in a list.
Which schema type is most important?
It depends on your business. For a local shop, LocalBusiness is non-negotiable. For a blog, Article and FAQPage are critical. Start there.
How do I do hyper-local entity targeting?
Don’t just say “New York.” Mention specific neighborhoods, nearby landmarks, and zip codes. Use structured data to define your service radius explicitly.
Conclusion: my 3-point recap + next actions
We’ve covered a lot of ground, but semantic SEO doesn’t have to be overwhelming. If you take nothing else away, remember these three rules:
- Think in graphs, not lists: Your site is a web of connected topics, not a pile of keywords.
- Define, don’t just describe: Be explicit about what entities are. Ambiguity kills rankings.
- Structure is your signal: Use schema and internal linking to show Google how your content fits together.
Your next actions for this week:
- Audit your core service page. Does it answer the “what,” “how,” and “who” clearly?
- Create one entity map. Pick one topic and map out 5 supporting sub-topics.
- Implement Schema. Add Organization schema to your homepage if you haven’t already.
If you need to scale this process, you might explore tools like an SEO content generator to turn your entity maps into structured drafts faster. Remember, the goal isn’t to replace your expertise, but to operationalize it. Using an AI SEO tool to handle the heavy lifting of structure and briefing allows you to spend more time on the creative connections that actually build authority.
