In SEO practice, search intent is usually divided into five types
- informational
- navigational
- commercial
- transactional
- local
Google itself also differentiates between Know, Do, Website and In-Person Visit.
Regardless of the subdivision, the purpose behind search intent is always to align the content format, page structure, and conversion path with the actual intention. .
Therefore, it gains its relevance for classic search engine optimization, in content strategy, and for optimizing for generative search systems like ChatGPT, Perplexity, or Google AI Overviews.
Three terms or concepts are regularly confused here:
- The Keyword the typed string itself. However, the same string can have different search intentions. Example: „käfer“ (beetle/bug). Is it about the insect or the car?
- The User intent whereas the overarching motivation of the entire Research or buying trip; the search intent is the respective excerpt of it per request.
- Buyer Intent Data again are company-level behavioral signals. They show, which companies research, not What intention behind a query.
The Five Types of Search Intent at a Glance
Today, SEO practice distinguishes five types. Each can be recognized by typical keyword patterns and requires a different content format.
Informational: The user wants to know something. Recognizable by W-questions and knowledge keywords: „What is...“, „how does... work“, „how it works“, „difference between...“. Example: „Servo drive how it works“. Suitable format: Glossary, Guide, How-to, Explainer video.
Navigation: The user wants to go to a specific page. Recognizable by brand, product, or URL names: „webraketen“ or „web.de Login“. The user already knows the destination and uses search only as a shortcut. Suitable format: Brand homepage, product overview, clearly named subpage.
Commercial Investigation: the user compares before buying. Recognizable by comparison and evaluation keywords: „best,“ „test,“ „comparison,“ „alternative to...,“ „selection criteria,“ „provider for....“. Example: „Servo drive selection criteria.“ Suitable format: Comparison table, solution page, selection guide, case study.
Transactional: the user wants to transact. Recognizable by action keywords and specific product designations: „buy,“ „order,“ „download,“ „request quote,“ but also pure part numbers and SKUs. Example: „6204-2RS SKF“ or „Servo drive XYZ-1234 datasheet.“ Suitable format: product page, configurator page, or inquiry page.
Local: The user is searching in their vicinity. Recognizable by location references and proximity keywords: „...near me,“ „... [city],“ „opening hours,“ „directions.“ Example: „Hairdresser Munich“ or „Rent construction equipment nearby.“ Expected are Google Maps results, opening hours, and correct location data. Suitable format: Location or branch page with consistent NAP data (Name, Address, Phone) and a well-maintained Google Business Profile. This type corresponds to Google's own „Visit-in-Person“ category (see below) and is relevant for B2B for manufacturers with factory, service, or sales locations, less so for purely supra-regional providers.
The first three types can be traced back to academic research; commercial investigation and the separate Local category have augmented SEO practice. Both strands are explained in the next section.
What types of search intent lead to which decisions
Typology is not an end in itself: It determines which content format a page must carry.
An informational query belongs on a glossary or how-to post, a navigational one on a brand or product overview page, a commercial investigation query on a comparison or solution page, and a transactional one on a product, configurator, or request page.
Whoever places a datasheet where the SERP expects a comparison will not rank, regardless of the optimization effort.
So, anyone searching for „food packaging“ usually doesn't immediately need a Online store, who sells him machines. The user first needs information: What do I need to pay attention to? What options are there?
The Classic Three-Part Structure According to Broder and the Expansions of SEO Practice
Andrei Broder in 2002 „A Taxonomy of Web Search (SIGIR Forum) three categories were introduced: informational (wanting to know something), navigational (going to a specific page), and transactional (performing a transaction such as a purchase, download, or map retrieval).
In his log file analysis of 1,000 AltaVista queries at the time (Google wasn't always #1), the queries were broken down into approximately 20 % navigational, 48 % informational, and 30 % transactional queries.
Rose and Levinson refined the model in 2004 hierarchically, replacing „transactional“ with a broader „resource“ category; they identified approximately 62 % informational, 13 % navigational, and 24 % resource.
A subsequent large-scale log file analysis by Jansen et al. (2008, Penn State University) found over 80 % informational queries and approximately 10 % navigational and transactional queries each. The figures vary depending on the method and classification scheme—but one thing remains clear: informational queries account for the largest share.
A recent tool analysis by SE Ranking (2025) confirms this picture, albeit with shifted weightings: approximately 70 % of the keywords examined were informational, 22 % are commercial, 7 % are navigational, and 1 % is transactional—the commercial share has grown significantly since the early studies, while transactional individual queries are rare, which aligns with the part number logic discussed below.
The SEO practice has supplemented the academic model with two categories not present in the original studies: commercial investigation — the phase where someone compares providers, products, or solutions before purchasing or inquiring — and local, location-based search.
The narrower four-type model (informational, navigational, commercial, transactional) without the local category remains prevalent; the five-type model used here cleanly separates local because location-based queries require their own content format and optimization signals. Neither of these extensions has a unified scientific origin.
In a B2B industrial context, the majority of substantial inquiries fall into commercial investigation and informational categories. „Servo drive selection criteria“ is commercial, „Servo drive operating principle“ is informational, „Servo drive food industry manufacturer“ is commercial with a strong supplier research component, and „Servo drive XYZ-1234 datasheet“ is transactional in a narrower industrial sense (see part number searches below).
Google's Classification: Know, Do, Website, Visit-in-Person
Google itself classifies in the Search Quality Rater Guidelines four categories: Know (informational searches), Do (actions including purchasing), Website (direct brand or URL searches), and Visit-in-Person (directions, physical business locations).
Know-Queries are further divided into Know and Know Simple. The latter are factual short answers that mostly appear directly in the Knowledge Graph. Do-Queries include „Device Action“ queries, such as voice commands.
At their core, both models lead to the same view: the SEO view classifies by content purpose, the Google view by expected user outcome. The categories largely overlap.
Know corresponds to informational, Do encompasses transactional and commercial, Website corresponds to navigational, and Visit-in-Person is Google's equivalent to the local category from the SEO model.
The Google interpretation becomes practically relevant through the „Needs Met“ rating: According to Google's own definition, how useful a result is depends on how completely it fulfills the intention interpreted from the query.
This leads to the Rule of thumbThe SERP shows how Google interpreted the intent. So it's best to just search for the keyword, then it's usually quickly apparent what intent is being asked for. And then just cater to that.
A page that formally contains all the keywords but doesn't meet the interpreted intent will not rank.
The same principle is evident in user behavior as "pogo sticking": if someone clicks a result, immediately returns to the SERP, and selects the next one, it signals a gap between search intent and page content.
Whether Google uses this signal directly as a ranking factor is debated, but it is considered likely.
How to reliably determine the search intent of a keyword
Key takeaway: The most reliable source is the SERP (Search Engine Results Page) itself, not your own assessment.
Google has already formed an intent hypothesis for each keyword and made it visible in the top 10 results. If you primarily see glossary posts in the top ten organic results for a keyword, you will not rank with a product page—regardless of what the keyword suggests at first glance.
Three signals structure SERP analysis:
- SERP Features as Intent Indicators Featured Snippets and People Also Ask indicate informational intent; Shopping boxes, product carousels, and pricing-Display transactional; Knowledge Panels for navigational brand searches or know-simple queries. AI Overviews signal that Google classifies the query as summarizable — this reduces the click-through rate for classic results.
- Dominant Content Formats in the Top 10: Do guides, comparison tables, product pages, manufacturer data sheets, or forum discussions prevail? The format pattern is the strongest single signal.
- SERP Overlap as a Comparison Measure The more identical URLs Google shows in the top 10 for two search queries, the more similar Google judges the underlying intent. In practice, this means: Keywords with a high URL overlap do not need two separate pages; they can—and generally should—be served on the same page. If the overlap is close to zero, the keywords, despite their semantic closeness, belong on separate pages.
The method breaks down at one point: For long-tail queries with very low search volume, Google sometimes delivers SERPs that are created more out of a lack of matching results than out of intent interpretation. In an industrial context, this affects many highly specific technical inquiries—here, analyzing neighboring, higher-volume keywords is the more viable approach.
What to do when a term has multiple search intents
Search queries like „beetle“ or „CNC machine“ generate mixed intents in the SERP: the top 10 simultaneously include manufacturer product pages, Wikipedia/glossary entries, comparison portals, and application guides. This is not a classification error, but Google's admission that different user groups with different intentions are behind the term.
The temptation to cover everything on one page regularly leads to content that lacks the right depth for any intention. A tiered content architecture along the Buyer's JourneyA hub page that explains the term and interlinks the sub-intentions (how it works, selection criteria, applications, products/series, inquiry).
Each sub-page then targets a narrower, more specific query: „frequency converter function,“ „frequency converter sizing,“ „frequency converter food industry.“ This way, each term can rank cleanly, while the hub page covers the ambiguous term itself.
Which sub-pages are necessary will again be decided by SERP overlap: If two narrower queries provide largely the same URLs, one page is sufficient; if the SERPs diverge, they belong separately.
How generative search changes search intent
Generative search systems (like ChatGPT, Perplexity, Google AI Overviews, or Gemini) process queries differently than classic SERPs. They break down a query into sub-questions (query fan-out), use user context, and predict the actual task behind the words, instead of primarily delivering keyword matches. The SEO industry is talking about a sixth intent type, the so-called generative search intent: queries that aim directly for a summarizing, synthesizing answer instead of a list of sources.
For practical purposes, two shifts are relevant. First: Classic informational requests are increasingly ending in an AI response, without the user clicking on a source.
For glossary content, this means that the traffic-driven value decreases, while the citation value (being mentioned in an AI answer) increases. Second, according to industry observations, the remaining clicks from AI search are more intent-driven. This is because the user has already seen a recommendation and is specifically looking for confirmation, details, or providers.
Consequence for B2B prioritization: Informational top-of-funnel content will be geared towards citability by LLMs – clear definitions, unambiguous sources, precise facts, structured statements. Commercial investigation and transactional content will retain their classic SEO value because clicks will continue here: comparisons, configurators, spec sheets, inquiry paths. Whether content is geared more towards AI citation or SERP clicks thus becomes an intent question in itself – no longer pertaining solely to the user, but to the system that processes the query first.