Entity-based keywords: keyword research for answer engines
For twenty years, keyword research meant collecting a list of phrases and their search volumes. That list still matters, but it no longer describes how people find you. A growing share of searches end in an answer, and the systems producing those answers do not think in phrases. They think in things: businesses, places, products, concepts, and the questions that connect them. Research has to follow.
Entity-based keyword research organizes keyword research around entities, the people, places, products, and concepts a search engine recognizes, and the questions asked about them, rather than around a flat list of phrases. For answer engine optimization, it means finding the topics, subtopics, and intents where an AI answer, not ten blue links, is the likely destination for a query.
What are entity-based keywords, and how do they differ from a keyword list?
An entity-based keyword is a search term understood in terms of the thing it refers to and its relationships, not just its letters. A traditional list treats "Naples personal injury lawyer" and "car accident attorney Naples" as two separate rows. An entity lens sees one concept, a legal service in a place, with many phrasings and many questions clustered around it.
The shift is from strings to things, a phrase Google itself used when it launched the Knowledge Graph. A keyword list is a collection of strings ranked by volume. An entity model maps the real-world things those strings point to and how they connect: a business belongs to a category, sits in a location, offers services. Research organized this way survives the endless variation in how people phrase a query.
Search engines and AI assistants resolve a query to an entity before they decide what to show. If your research is a spreadsheet of isolated phrases, you are optimizing for the surface. If it is a map of entities and the questions around them, you are optimizing for the layer where the decision actually happens.
None of this retires classic keyword research. Volume, difficulty, and phrasing still tell you what language your audience uses and where the demand is. The change is in how you organize that raw material: not as a ranked list to pick from, but as evidence about entities and intents. The mechanics of gathering keyword data are covered on our keyword research services page.
Why does answer engine optimization change how you do keyword research?
Because the destination is changing. On more and more queries the result is a synthesized answer that names a few sources, not a page of links you can climb. Research has to identify where that is happening, what the underlying question is, and whether your business is even a candidate to be named, before you spend effort competing for a position that no longer exists in its old form.
Consumer behavior has moved faster than most keyword tools. In BrightLocal's 2026 survey, the share of consumers using ChatGPT and other generative AI tools for local recommendations rose to 45%, up from 6% a year earlier (BrightLocal, 2026). When nearly half your prospects may ask an assistant, the phrases they use and the answers they receive belong in your research.
The catch is that appearing in a generative answer is far more selective than ranking. SOCi found that ChatGPT recommends only about 1.2% of business locations, while those same brands appear in Google's local 3-pack 35.9% of the time (SOCi, 2026). A phrase can have healthy volume and a page-one ranking and still never surface when someone asks an assistant. Research has to account for that gap rather than assume ranking equals visibility.
So the job expands. Alongside "what do people search and how hard is it to rank," you now ask "does this query return an answer, what question is really being asked, and is our business a plausible source for it." That is a research question, answered before any content is written, and the optimization tactics that follow are the domain of our GEO services page.
How do you map a topic into entities and the questions around them?
Start from the core entity, usually your service in your market, then branch outward into the subtopics and related entities that surround it, and finally into the questions people ask at each branch. The output is not a list, it is a small map: a hub concept, its neighboring concepts, and the real questions clustered on each, ready to be matched to content.
Begin by naming the hub. For a Naples roofing company, the hub entity is roofing services in Naples. Around it sit related entities: roof types, materials, storm and hurricane damage, insurance claims, permits, inspection, repair versus replacement. Each is a thing an engine already understands, with its own body of questions, and listing them draws on domain knowledge as much as any tool.
Now attach questions to each branch. Under insurance claims: does a new roof lower my premium, will insurance cover a twenty-year-old roof. Under materials: how long does a tile roof last in a coastal climate. These are the answer-shaped keywords, the ones where an assistant is likely to respond directly, and they rarely appear as tidy rows in a volume tool.
The result is a topic map: a hub, its neighboring entities, and the questions on each. This map is what you plan content against, and the deliverable is the research artifact itself, not the content built on it later.
How do you use "people also ask" and query fan-out to expand a topic?
Treat every seed question as a doorway. "People also ask" boxes, related searches, and autocomplete reveal the adjacent questions a real audience asks, and each answer typically spawns more. Query fan-out is the same idea from the engine's side: a single prompt is silently expanded into many sub-questions. Mapping those branches shows you the full shape of a topic, not just its entrance.
The manual version is simple and still valuable. Search your seed question, read the "people also ask" entries, note the "related searches" at the foot of the page, and watch what autocomplete suggests as you type. Each is Google telling you which questions cluster around your topic, and expanding a few quickly surfaces sub-questions you would never have brainstormed from a keyword tool alone.
Query fan-out is the machine-side counterpart. Modern AI search does not answer your literal words, it decomposes them into several underlying questions, gathers sources for each, and synthesizes a response. A single high-level query is really a bundle of narrower ones. If your research maps that bundle, you can plan content that answers the sub-questions an engine will actually go looking for, rather than only the headline phrase.
Practically, this turns one seed into a branching tree. "How much does local SEO cost" fans out into what is included, what affects price, monthly versus project pricing, how to tell if it is worth it. Each branch is a candidate for its own answer-shaped section. The research skill is knowing which branches carry genuine intent and which are noise.
How do you match search intent to answer-shaped content?
Read what the query wants before deciding what to make. Some questions want a direct definition or number, ideal for a concise, liftable answer. Others want comparison, a step-by-step process, or a considered opinion. Matching each mapped question to the shape of answer it deserves is the bridge between research and content, and it is where entity research earns its keep.
Intent is not a single axis. The familiar informational, navigational, commercial, and transactional buckets still help, but answer engines reward a finer read. A "what is" question wants a crisp definition an assistant can quote. A "best" or "versus" question wants a fair comparison. A "how to" wants ordered steps. A "should I" wants judgment with reasoning. Mislabeling the intent wastes the effort.
Answer-shaped means the content leads with the answer. Where a generative engine is the likely destination, the passages that get pulled tend to state the answer plainly and early, then support it. Your research should tag each mapped question with the answer form it calls for, so the writer knows whether they are producing a sixty-word definition, a comparison, or a walkthrough. That tagging is a planning act, done before drafting.
This is also where you protect against thin, redundant coverage. If three mapped questions really share one intent, they belong in one strong answer, not three weak pages. Matching intent to answer shape keeps the eventual content map lean, which is what our content strategy work builds on once the research is settled.
Which terms should you prioritize when an AI answer is the destination?
Prioritize questions where you can plausibly be the named source, where the intent is commercial enough to matter, and where a clear answer exists to be given. De-prioritize high-volume phrases that return a generic summary naming no one, and vanity terms with no path to your business. The best targets are specific, answerable, and close to a real decision.
Not every answer-engine query is worth chasing. A broad "what is SEO" prompt returns a textbook summary that cites large publishers and helps you little. A narrower question, "how much should a local business pay for SEO in Florida," is both answerable and close to a buying decision, and a local specialist is a credible source for it. The skill is telling those two apart during research, not after publishing.
Being a plausible source is its own filter. Ahrefs, studying roughly 75,000 brands, found that branded web mentions correlate with AI visibility at about 0.66 to 0.71, while link metrics correlate only very weakly (Ahrefs, 2026). Engines tend to name entities that are already recognized and discussed, so favor questions your business has standing to answer.
There is upside to getting this right. Controlled testing of generative engine optimization tactics found they can raise a source's visibility in generative responses by up to 40%, varying by domain (Aggarwal et al., KDD 2024). We make no promise of a citation, because no one controls what an engine names, but concentrating on specific, answerable, decision-adjacent questions is where the effort pays back.
How is this different from building your entity or tracking rankings?
Research is the planning step: finding and organizing the entities, questions, and intents worth pursuing. Building your entity is making engines recognize your business as a thing, a separate job. Structuring a page to answer cleanly is another. Tracking whether you get named comes after publishing. This post owns only the first: producing the map everything else is built on.
It helps to see the whole sequence so the pieces do not blur. First you research: map the entities and questions, match intents, and prioritize the terms where an answer is the destination. Then come the adjacent disciplines. Establishing your business as a recognized entity, so engines are confident who and where you are, is entity building. Shaping an individual page so its answer is clean and liftable is content structure. Watching whether your pages get cited or ranked over time is tracking. Each depends on good research, but none of them is research, and conflating them tends to weaken all four.
Keeping the research step distinct is what makes it rigorous. The deliverable here is a defensible plan: these entities, these questions, this intent, this priority, and why. What you do with that plan is where our keyword research and GEO services take over. We would rather talk you out of chasing a term with no path to your business than sell you effort against it.
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LAST UPDATED July 10, 2026 · WRITTEN BY JAMIE KLONCZ, FOUNDER · SEO ELITE AGENCY, NAPLES FL
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