Search intent

What are the types of search intent?#

Search intent is usually sorted into four types — informational (learn), navigational (reach a site), commercial (compare options) and transactional (buy or sign up) — though many queries blend two, like “best CRM for startups”, which is comparison research with a purchase behind it.

The idea goes back to Andrei Broder’s 2002 paper “A taxonomy of web search,” which split queries into navigational, informational and transactional. Most SEO tools now use four types, separating comparison research from the purchase itself, and Google’s search quality rater guidelines use a parallel set: Know, Do, Website and Visit-in-person.

Google puts the principle plainly in its explanation of how ranking works: “To return relevant results, we first need to establish what you’re looking for — the intent behind your query.” The format of the pages that rank is Google’s answer to that question, which is why a strong page in the wrong format rarely breaks in.

How does search intent change in AI search?#

In AI search, intent decides whether the engine searches at all, what shape the answer takes and whether anyone clicks: learn-something questions are often answered outright, while comparison, verification and buying intents send the engine to shortlists, official pages and product feeds.

• Informational intent is answered in place. Pew found people clicked a classic result on 8% of visits when an AI summary appeared, against 15% without. You can still be cited here, but expect fewer visits.
• Verification intent goes to official pages. ChatGPT’s site: searches aim at brand, vendor and .gov domains, and “official” became one of the most common words in its sub-queries. A buyer checking your price is now served by your own clearly titled pricing page.
• Commercial intent produces shortlists. Answers name a handful of brands, and list-style pages feed them: listicles were 36.4% of the pages Claude cited in Profound’s 2026 data, against 19.7% for ChatGPT.
• Transactional intent moves to feeds. ChatGPT’s shopping results come from merchants’ structured product data, and AI Mode shops from Google’s Shopping Graph — so a buying query may never reach an article.

One prompt can also carry several intents at once. “Which CRM should a 10-person startup use?” is research, comparison and purchase in one sentence, and query fan-out splits it into sub-searches that each have a single intent. You compete for those one page at a time.

How do you identify search intent?#

Identify search intent by reading what already wins: the modifiers in the query, the page types in Google’s top 10, and the shape of the AI answer to the same question — then build the format all three point to.

1. Read the modifiers. “How” and “what” signal learning; “best”, “vs” and “alternatives” signal comparison; “pricing”, “buy” and “free trial” signal a purchase; a brand name signals navigation.
2. Read the SERP. If eight of the top 10 are comparison pages, Google has decided the query is commercial, and a how-to guide won’t break in. Note the features too — a video carousel, a local pack or shopping results each say something about the goal. See SERP.
3. Read the AI answer. Ask ChatGPT, Perplexity or Google AI Mode the question the way a buyer would. A definition, a numbered process, a shortlist of brands and a comparison table are four different intents.
4. Check for mixed intent. When the results split between two formats, the query has two intents. Serve the dominant one on this page and link to a separate page for the other.

Common mistakes with search intent#

The most common search intent mistakes are writing a blog post for a query that wants a product or comparison page, stuffing several intents into one page, and treating AI prompts as if they were short keywords with a single goal.

Related terms#

• Content & relevanceLong-tail keywordsSpecific search phrases that each draw few searches but together make up the vast majority of distinct queries, and because they signal precise intent they are the closest classic-SEO match to the conversational prompts people type into AI.Read the entry
• How LLMs answerQuery fan-outAn AI search technique in which the engine rewrites one user question into several narrower sub-queries, runs them in parallel and builds its answer from the combined results — which is why a page can be cited for a prompt it doesn’t rank for.Read the entry
• Content & relevanceAnswer-first contentA writing structure that puts the direct answer to a section’s question in its opening sentence and adds context and evidence after it, so readers and AI retrieval systems can take the answer without reading further.Read the entry
• Content & relevanceKeyword cannibalizationWhen two or more pages on the same site target the same query and intent, splitting links and relevance so search engines rotate between them or rank neither as well as one consolidated page would.Read the entry
• MeasurementSERPThe page a search engine returns for a query, which today mixes classic blue links with AI Overviews, ads, featured snippets, videos, forum threads and other features competing for the same attention.Read the entry
• Content & relevanceTopic clusterA group of interlinked pages on one subject — a broad pillar page linked to and from narrower pages on each subtopic — built to show search engines and AI retrieval systems complete coverage of the topic rather than a single article.Read the entry