Search intent
Definition
Search intent is the underlying goal behind a query — what the user is actually trying to accomplish when they search. Classifying intent is the foundation of modern SEO and AI search optimization because the right answer for an informational query ("what is share of voice") is structurally different from the right answer for a transactional query ("buy AI visibility tracking software").
The four search intent types
Most SEO frameworks classify intent into four categories:
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Informational: "what is X", "how does Y work". User wants to learn. Wins: definition pages, deep guides, FAQ schema.
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Navigational: "Indexly login", "GitHub docs". User wants to reach a specific destination. Wins: branded pages, well-known landing pages.
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Commercial investigation: "best AI visibility tools", "Indexly vs competitors". User is evaluating options pre-purchase. Wins: comparison pages, listicles, case studies.
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Transactional: "buy AI visibility software", "Indexly pricing". User is ready to act. Wins: product pages, pricing pages, calculators.
Some queries blend intents (e.g., "best CRM" is partly informational, partly commercial), and AI search increasingly handles blended intent in a single response. But the four-type framework remains the practical baseline for content planning.
Search intent vs keyword
A keyword is the literal string a user types. The intent is the goal underneath. Two queries can share keywords and have different intents ("apple" → fruit vs the company); two queries can share intent and use different keywords ("best CRM" vs "top CRM software" both express commercial-investigation intent).
Modern algorithms (Google BERT, MUM, Gemini, and AI assistants generally) read intent rather than keywords. Pages that match intent rank and get cited; pages that match keywords without matching intent don't.
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Practical intent classifications — informational, navigational, commercial investigation, transactional
Industry standard
Multi-intent
Most conversational AI prompts blend two or more intents in a single query
Indexly observation, 2026
Top 10
Reading the organic top 10 is the fastest reliable way to read Google's intent classification for a query
Indexly best practice
Why it matters
Three concrete payoffs from getting intent right:
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Ranking position depends on it. Google ranks pages whose format matches the dominant intent on the SERP. A definition page won't rank in a SERP full of comparison pages, no matter how good it is.
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AI citation rate depends on it too. AI engines cite from sources that match the prompt's intent. A blog post that's structurally a "best X tools" comparison won't get cited for "what is X" prompts.
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Conversion follows intent. Driving traffic to a transactional page from informational intent yields low conversion; the visitor isn't ready to buy. Matching content to intent keeps conversion rates honest.
How to classify search intent
Five practices for accurate intent classification:
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Read the SERP. The top 10 organic results are Google's answer to "what intent does this query have." If the SERP is all listicles, the query is commercial-investigation; if all definitions, informational.
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Check the AI Overview. AI Overviews and AI Mode citations also encode intent classification. A query that triggers an AI Overview with comparison-heavy citations is signaling commercial intent.
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Use linguistic markers. "What", "how", "why" → informational. "Best", "vs", "alternatives" → commercial investigation. "Buy", "pricing", "free trial" → transactional. Brand names alone → navigational.
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Sample real prompts in AI assistants. Conversational AI prompts often blend intents in a single query ("I'm trying to pick between X and Y for [scenario] — what should I know?"). Classify these as multi-intent and plan content that addresses each layer.
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Re-classify periodically. A query's intent can shift as the category matures. A query that was informational a year ago can move to commercial intent as buyers move from research to decision. Quarterly re-checks catch the drift.
Frequently asked questions
How do I classify search intent for a query?
Read the top 10 organic results and the AI Overview citations. The format that dominates is Google's answer for that query's intent. Layer in linguistic markers ("best", "vs", "buy", "what is") and sample real AI prompts to catch multi-intent queries.
Can a single query have multiple intents?
Yes — and conversational AI prompts increasingly do. A prompt like "I'm picking between X and Y for [scenario] — what should I know?" blends commercial investigation with informational intent. Plan content that addresses each layer explicitly.
Does Google really care about intent?
Materially. Modern Google ranking (BERT, MUM, Gemini) reads intent rather than keywords. Pages that match intent rank and get cited in AI surfaces; pages that match keywords without matching intent don't.
How does intent affect AI search visibility?
AI engines cite from sources that match the prompt's intent. A query asking for definitions cites definition pages; a query asking for comparisons cites comparison pages. Matching content format to intent is the single highest- leverage GEO lever after schema and atomic openings.
Should I rewrite content if I misclassified intent?
Yes — and it's often the cheapest content win. A page with strong content but the wrong intent- matching format underperforms forever. Rewriting it to match the dominant SERP format on its target query typically lifts rankings and AI citations within a quarter.
Keyword research
Keyword research is the practice of identifying the queries your audience actually types into Google, Bing, and AI assistants — with their volume, intent, difficulty, and competitive landscape — to ground content investment in real demand. In 2026, modern keyword research extends beyond head-term and long-tail keywords to include *prompts*: the conversational queries buyers send to ChatGPT, Claude, Perplexity, and AI Mode.
Keyword clustering
Keyword clustering is the practice of grouping related queries into topical clusters that map to a single page or content asset — instead of building one page per individual keyword. Clustering is what turns a 5,000-keyword research dump into a 20-cluster content roadmap and is foundational to both modern SEO and Generative Engine Optimization (GEO).
SERP analysis
SERP analysis is the systematic study of a search engine results page for a target query — the ranked links, AI Overviews, People Also Ask boxes, knowledge panels, video carousels, and ads — to understand what Google thinks the user wants and what content format is winning. In 2026, SERP analysis has expanded to include AI Mode citations and AI Overview source lists alongside the traditional ten blue links.
Content gap analysis
Content gap analysis is the systematic comparison of your site's content coverage against competitors and against the queries your audience actually searches — surfacing topics where competitors rank or earn AI citations and you don't. In 2026 it expands beyond Google rankings to include AI search gaps — topics where ChatGPT, Claude, Perplexity, and AI Overviews cite competitors but never mention you.
Google BERT algorithm
The Google BERT algorithm is a natural-language model — Bidirectional Encoder Representations from Transformers — that Google rolled into Search in October 2019 to better interpret the full context of a query rather than reading it word-by-word. BERT is now part of the foundation that AI Overviews and AI Mode build on, making it the bridge between traditional SEO and 2026's generative search.
AI citation optimization
AI citation optimization is the practice of structuring web content so AI assistants — ChatGPT, Claude, Perplexity, Gemini, Bing Chat, and Google AI Overviews — choose to cite it as a source in their generated answers. It is the citation-layer counterpart to traditional SEO link building and a core discipline within Generative Engine Optimization (GEO).