Finding Buyer Intent Keywords for Google and AI Search in 2026

Buyer intent keywords — the queries people type or speak when they’re ready to purchase — have always been the highest-conversion category in SEO. Now they’re also the battleground for an entirely new channel: AI search. Semrush published a comprehensive guide on May 15, 2026 covering how buyer intent keyword research has expanded to cover not just Google organic rankings but also how brands surface when users prompt ChatGPT, Gemini, and other LLMs with purchase-ready questions. The mechanics of finding these keywords have changed, the surfaces you need to track have multiplied, and the cost of ignoring the intent tier has never been higher.

What Happened

Semrush released a detailed, methodology-first breakdown of buyer intent keyword research this week, covering everything from the foundational Google Keyword Planner workflow to a genuinely new category of research: prompt research for AI search systems. It’s the clearest statement yet from a major SEO platform that buyer intent keyword strategy now requires a parallel AI-search workflow — not as a future consideration, but as a current operational requirement.

The core taxonomy, while familiar, is worth stating precisely because it drives every downstream decision. Buyer intent keywords split into two tiers:

Low-intent (Commercial intent) targets users in the middle of a buying journey — still comparing options, reading reviews, and evaluating products or services. These queries use modifiers like “review,” “best,” “top,” or “vs.” A user searching “best project management software for remote teams” is in commercial intent territory. They’re going to buy something. They haven’t decided what yet. The content these users need is evaluative and trust-building: comparison pages, roundups, and use-case-specific guides that help them narrow from a category to a choice.

High-intent (Transactional intent) signals that a user is ready to act now. These queries include terms like “buy,” “deal,” “discount,” “coupon,” or incorporate specific brand names and product SKUs. A query like “Asana discount code” or “buy standing desk under $500” is firmly in transactional territory. The decision is made or nearly made — the user is looking for the final step: a price, a button, a checkout. The content these users need is conversion-optimized: pricing visibility, clear CTAs, trust signals, and minimal friction.

The four methods Semrush covers for finding buyer intent keywords represent a genuinely evolved research workflow:

Method 1: Google Keyword Planner remains the foundational free tool. Running broad category terms through the Planner surfaces modifiers, monthly search volume ranges, competition levels, and suggested bid data. High suggested bids are a reliable proxy for commercial value — advertisers bid aggressively on terms they’ve empirically validated as converting. If a keyword carries a high cost-per-click, there’s a direct line between that keyword and revenue, and organic teams should treat that signal as actionable.

Method 2: Google Search Features offers real-time intent signals that Keyword Planner misses. Autocomplete suggestions surface how actual users are phrasing purchase-oriented queries right now. People Also Search For (PASF) boxes reveal buyer-ready variants with different modifiers, expanding the keyword cluster without requiring paid tool access. People Also Ask (PAA) boxes generate question-based keywords using “which,” “where,” and “what” — high-intent framing that works especially well for content targeting comparison and decision-stage queries. These features are free, always current, and reflect actual search behavior at the moment of research.

Method 3: Keyword Research Tools — specifically Semrush’s Keyword Overview and Keyword Magic Tool — bring scale and systematic intent classification that manual SERP review can’t match. The Keyword Magic Tool surfaces thousands of query variants and lets you filter specifically by “Commercial” and “Transactional” intent, eliminating the manual triage work that consumes research time without adding strategic value. Keyword difficulty and volume data allow prioritization by effort-to-return ratio: not just “does this keyword have buyer intent?” but “is the ranking opportunity worth the content investment?”

Method 4: Prompt Research Tools is where the guide breaks new ground. Semrush’s Prompt Research Tool shows how users phrase commercial questions to AI systems specifically. The patterns are categorically distinct from traditional Google search queries: instead of “best standing desk under $500,” the AI query becomes “recommend me a standing desk under $500 with storage for a home office.” Conversational framing, specific qualifiers, outcome-focused requests, contextual constraints. These prompts are the buyer intent keywords of the LLM era, and if your content isn’t structured to be cited as an answer to them, you’re invisible on a channel growing faster than any other search surface.

Semrush’s Position Tracking tool now monitors daily ranking visibility across Google, Gemini, and ChatGPT simultaneously — a platform update that reflects how seriously marketers need to treat AI search as a parallel surface, not a side experiment.

Why This Matters

The reason to specifically target buyer intent keywords — rather than high-volume keywords broadly — is conversion efficiency. Semrush identifies four business benefits: driving conversions, earning trust, increasing visibility across both organic and AI channels, and reducing ad spend waste by reaching purchase-ready audiences instead of early-stage browsers. Every one of these benefits is more measurable — and more urgent — now than it was two or three years ago.

The dual-channel shift makes intent research non-negotiable for several distinct reasons.

Google organic and AI search are now serving separate audience behaviors. A user who types “best CRM for real estate agents” into Google and a user who asks ChatGPT “what’s the best CRM for a solo real estate agent managing under 200 contacts?” are expressing the same underlying intent but behaving differently. The Google user will click through and evaluate multiple results — they’re open to persuasion across several sources. The ChatGPT user is likely to act on whatever the LLM recommends directly — the evaluation has already been delegated to the AI. If your content ranks on Google but isn’t structured to be cited by AI systems, you’re capturing only part of the intent-driven audience, and the part you’re missing is growing.

The funnel conversion differential is not marginal — it’s an order of magnitude. According to Search Engine Land’s comprehensive guide on BOFU keywords, bottom-of-funnel pages convert at an average rate of 4.78% — nearly 25x higher than top-of-funnel content. One documented case study found pages targeting BOFU keywords converting up to 2,400% better than awareness-stage content. The content marketers leaving the most revenue on the table are those who have built extensive informational content libraries without an equally developed strategy for buyer-stage queries. Informational content earns traffic. Buyer intent content earns revenue. The gap between those two outcomes is measurable and directly addressable.

Intent alignment affects AI visibility directly, not just traditional rankings. According to Search Engine Land’s analysis of search intent alignment vs. technical SEO, “recognition, not rankings” is emerging as SEO’s primary goal. Google’s systems evaluate whether brands appear in AI recommendations alongside traditional ranking position — meaning the quality of your intent alignment directly affects your AI search visibility. Content that precisely matches the framing of a buyer’s question is more likely to be cited by AI systems. Generic content that broadly covers a category without reflecting how buyers actually phrase their decision-stage queries gets passed over by both Google’s AI Overviews and external LLMs.

Keyword intent is frequently multi-layered, not binary. As Semrush’s keyword intent guide notes, many queries carry multiple simultaneous intent signals. “Make coffee at home” is both informational and commercial. “Google Analytics alternatives free” is both commercial and navigational. Treating intent as a single linear decision — informational vs. transactional — misses the nuanced overlaps that separate adequate keyword research from precise audience mapping. The most effective buyer intent strategies account for these overlaps by building content that serves the dominant intent while anticipating the secondary one.

Conversational query patterns are expanding the effective keyword universe. The structural shift Semrush’s guide highlights — that purchase queries are becoming significantly more verbose and specific in the LLM era — has direct research implications. The traditional “buy best standing desk” gives way to “where can I buy the best standing desk under $500 with storage?” on Google, and then to even more contextually rich formulations on ChatGPT and Gemini. This matters because the long-tail intent queries that previously showed minimal Google search volume are now being asked of AI systems at meaningful scale — and AI systems don’t require search volume thresholds to surface and cite your content.

The populations most directly affected span multiple practitioner types: e-commerce operators who run SEO alongside paid campaigns and need to maximize organic conversion capture; B2B SaaS marketers running content programs targeting decision-stage prospects; agencies managing keyword strategy across multiple client verticals; and in-house content teams whose leadership is asking why traffic is strong but pipeline is weak. The answer is consistently the same: the content program is over-indexed on informational intent and under-indexed on the intent tiers that drive revenue.

The Data

The conversion rate differential between intent tiers is stark and well-documented. The table below combines data from Search Engine Land’s BOFU keyword analysis, Semrush’s intent classification guide, and Semrush’s search intent framework:

Per Search Engine Land, the 4.78% average conversion rate for BOFU pages represents roughly 25x the performance of top-funnel informational content. The same analysis cites case studies of BOFU-targeted pages outperforming informational content by 2,400% — a figure that reframes the ROI argument for buyer intent keyword strategy entirely.

The key modifiers that signal buyer intent, per Semrush’s buyer keyword guide, differ systematically between traditional Google queries and AI prompt formulations:

The CPC signal deserves separate emphasis. Semrush’s methodology treats Google Keyword Planner’s suggested bid data as empirical validation of commercial value. When advertisers bid competitively on a keyword, they’ve tested and confirmed that the traffic converts — they’re paying based on actual performance data, not assumptions. Organic teams who use paid bid data as a research input are borrowing the expensive validation work already done by the paid channel and applying it to free traffic acquisition. This is one of the most practical shortcuts in buyer intent research and it’s systematically underutilized by content-first teams.

High cost-per-click is also a signal worth monitoring as AI search evolves. As more purchase-stage traffic migrates to LLM responses, advertisers competing for the same buyer intent audience in paid search will likely intensify bidding on traditional transactional keywords — creating an organic opportunity in terms that competitors may deprioritize in paid while leaning harder into AI optimization. Watching CPC trends on your core buyer intent keywords is an early indicator of how fast AI search is absorbing intent traffic in your category.

Real-World Use Cases

Use Case 1: B2B SaaS — Building a Competitor Comparison Keyword Cluster

Scenario: A SaaS company selling project management software has a blog driving significant informational traffic but producing minimal demo requests. Leadership wants to understand why sessions are up but pipeline is flat.

Implementation: Using Semrush’s Keyword Magic Tool filtered to “Commercial” intent, the marketer identifies a cluster of “[competing brand] alternatives,” “[brand] vs [competitor],” and “best project management software for [specific use case]” queries with meaningful search volume and manageable difficulty scores. They run the highest-volume commercial queries through the Prompt Research Tool to understand how the same intent translates in LLM prompts — typically longer and more context-specific formulations like “best project management tool for a 10-person remote agency that needs client-facing views.” They build individual head-to-head comparison pages for each key competitor, a master “alternatives” page, and three use-case-specific “best for [vertical]” pages. Each page opens with a direct, extractable answer statement, includes transparent feature comparisons in markdown table format, incorporates third-party review citations, and features a prominent trial CTA.

Expected Outcome: Commercial intent pages in B2B SaaS consistently generate 3–5x the demo request rate of informational blog content. The structured format — direct answers, comparison tables, qualifier-specific sections — also positions the pages for citation in LLM responses when users ask equivalent purchase-ready questions. Both channels benefit from the same content investment.

Use Case 2: E-Commerce — Capturing Deal-Seeking Transactional Traffic

Scenario: A direct-to-consumer home goods brand runs paid search on broad category terms and wants to reduce ad spend dependency by building organic transactional traffic for high-margin products.

Implementation: The team runs their top product categories through Google Keyword Planner, filtering for “discount,” “deal,” “cheap,” and “coupon” modifiers. They identify that “[product category] sale” and “[brand name] discount code” carry meaningful monthly search volume with organic competition lower than their domain authority can handle. They build dedicated sale and discount landing pages that are SEO-optimized and kept evergreen — updated seasonally rather than recreated — ensuring they accumulate ranking authority over time. They also use Google Autocomplete and PASF to find more specific transactional variants: “best [product] under $200,” “[product type] free shipping.” Separately, they pull Google Shopping campaign data to identify which product-specific queries carry the highest paid conversion rates — those queries become priority targets for product detail page optimization, where intent-matched CTAs and pricing transparency are built in from the start.

Expected Outcome: Organic transactional traffic captured through discount and deal keywords carries conversion rates consistently above 4%, per Search Engine Land’s BOFU analysis. Reducing reliance on broad paid terms for discovery-stage queries also improves blended campaign efficiency — paid spend concentrates on terms where the intent is certain, and organic handles the long-tail transactional volume.

Use Case 3: Local Service Business — Targeting High-Intent Geo Queries

Scenario: A home renovation contractor wants to generate more qualified leads from organic search. They currently rank for informational terms like “kitchen renovation ideas” but the traffic doesn’t produce quote requests.

Implementation: The marketer shifts focus entirely to commercial and transactional intent geo queries: “kitchen remodel contractors [city],” “how much does a bathroom renovation cost in [city],” and “get a kitchen remodel quote [city].” Using People Also Ask boxes for local renovation queries, they identify that the decision-stage questions revolve around cost transparency, timeline, and social proof — exactly what conversion-focused content must address. They build service-area landing pages targeting transactional geo-specific queries, each including a cost estimator, client testimonials with project photos, timeline expectations, and a prominent quote request CTA above the fold. They also optimize for AI search by ensuring each page answers “how much does [service] cost in [city]” directly and specifically, so Gemini and Google AI Overviews can extract a clean answer when buyers prompt for it.

Expected Outcome: Local transactional queries convert at high rates because both intent and geography are narrowly specified. Intent alignment analysis from Search Engine Land confirms that precision matching between query intent and page content is the primary driver of both rankings and conversion performance. The dual optimization — for Google and AI citation — requires no additional content investment; it requires structural precision in the content already being written.

Use Case 4: Content Agency — Conducting a Buyer Intent Audit for a Client

Scenario: A content marketing agency needs to demonstrate concrete ROI on its SEO program for a B2B client. Leadership wants to understand why traffic is up 40% year-over-year but leads are flat.

Implementation: The agency pulls every keyword the client’s site ranks for and runs each through Semrush Keyword Overview to classify by intent type. They build a simple dashboard showing the split: what percentage of ranking keywords are informational vs. commercial vs. transactional. For most content-first brands, the audit reveals a distribution of roughly 75% informational, 20% commercial, and 5% transactional — a ratio that directly explains the traffic-to-leads gap. The audit identifies the highest-volume commercial and transactional queries where the client has no or minimal visibility, and generates a prioritized gap list with effort scores. Position Tracking is reconfigured to monitor Google, Gemini, and ChatGPT visibility simultaneously for all commercial and transactional target keywords, giving leadership a dashboard that shows AI search visibility alongside traditional rank data.

Expected Outcome: The intent audit creates a defensible roadmap showing precisely why the content strategy needs to shift, backed by conversion data rather than strategic opinions. Tracking AI visibility separately from Google rankings surfaces opportunities that traditional rank tracking misses entirely, and positions the agency as more sophisticated than competitors still reporting rank-only metrics.

Use Case 5: In-House SEO Team — Systematizing AI Citation Capture

Scenario: A software company’s in-house SEO team notices growing referral traffic from AI sources (ChatGPT, Perplexity, Gemini) but it’s unpredictable and untracked. They want to systematically increase their share of AI-driven buyer intent traffic.

Implementation: The team uses Semrush’s Prompt Research Tool to identify the specific prompt patterns buyers use when asking AI systems about their product category. They find that AI users consistently add specific qualifiers — budget ranges, company size, use case specifics, integration requirements — that appear only rarely in Google search data. They restructure their highest-value comparison and evaluation pages using these findings: each page opens with a direct, extractable summary statement answering the most common AI prompt formulation, uses tables for feature comparisons that AI systems can easily parse and cite, and includes dedicated subsections answering the qualifying questions buyers ask in prompts (“is this suitable for a 50-person team?”, “what’s the typical implementation timeline?”, “how does pricing change at scale?”). Position tracking for all target keywords is configured across Google, Gemini, and ChatGPT in a unified weekly dashboard.

Expected Outcome: Content restructured for AI extractability sees measurably improved citation rates in LLM responses. The structured format — direct answers, comparison tables, qualifier-specific sections — also improves Google featured snippet capture, creating a dual benefit from a single optimization effort. Teams that consistently track AI citation rates alongside organic rankings build empirical knowledge over time about what content structures drive LLM citations in their specific category.

The Bigger Picture

The expansion of buyer intent keyword research to include AI search surfaces is not an incremental methodology update — it’s a structural shift in how search-driven demand generation works, and the shift is accelerating faster than most marketing teams have adjusted for.

For the past decade, “search intent” in SEO meant aligning page content with what Google’s ranking algorithm determined the user wanted. As Semrush’s search intent framework traces, this maps to Google’s own Search Quality Evaluator Guidelines, which classify intent into Know, Do, Website, and Visit-in-Person categories. The SEO industry translated that framework into the standard Informational / Navigational / Commercial / Transactional model. The goal was to match content format and depth to the dominant intent behind a query.

What has changed fundamentally is the surface. Google’s AI Overviews now appear for commercial and transactional queries — the highest-value searches — directly on the results page, before organic listings. ChatGPT processes substantial search volume for product and service recommendations. Gemini handles purchase queries with inline citations. The buyer who used to click through three organic results and evaluate them against each other is increasingly reading an AI-generated synthesis and clicking one cited source, if any. The content that gets cited wins the buyer — and that content is chosen by the AI’s evaluation logic, not by the searcher browsing a results page.

Search Engine Land’s analysis frames this as a shift from “rankings” to “recognition” — the goal is no longer purely to rank in position 1-3 on a results page but to be the source that AI systems recommend in response to purchase-ready questions. This changes the optimization target: it’s no longer only about the on-page signals that Google’s ranking algorithm weights, but about structural clarity, answer precision, and the formatting that makes content machine-extractable and citation-worthy.

The competitive implications are significant. Buyer intent keywords are contested — every player in a category wants the “best [product] for [use case]” rankings. The AI search layer adds a competitive dimension that is harder to game than traditional link-building. A brand with genuinely better-structured comparison content, more direct answers, and more precise qualifier matching will outperform a brand with more backlinks in AI citation rates. This shifts competitive advantage toward content quality and structural precision rather than domain authority alone — a meaningful change for brands that have historically been outranked by better-linked competitors despite having better products.

The convergence with paid search research is also a practical reality worth embracing. Semrush’s methodology explicitly treats paid bid data as a proxy for organic commercial value. Search Engine Land notes that sales call transcripts, support ticket data, and chatbot logs are rich sources of BOFU keyword language — the precise words buyers use when they’re close to purchasing show up in sales conversations before they show up in keyword research tools. The brands winning on buyer intent keywords treat keyword research as a cross-functional exercise: SEO, paid search, sales, and customer success all contributing to an increasingly complete picture of how buyers actually communicate when they’re ready to buy.

What Smart Marketers Should Do Now

1. Run a full intent audit on your current keyword portfolio.

Pull every keyword your site ranks for using Google Search Console or your position tracking tool. Run each through Semrush Keyword Overview to classify by intent type, then build a simple spreadsheet showing the distribution: what percentage are informational, commercial, and transactional? Most content-heavy brands will find 70–80% informational — which directly explains why traffic doesn’t convert to revenue. The audit generates a defensible roadmap for where to invest next and a baseline to measure against as you shift the intent mix over time. Per Semrush’s buyer keyword methodology, the highest-value shift is toward commercial and transactional terms that reflect real purchase decision points. Run this audit quarterly — intent ratios shift as your content program evolves, and monitoring that shift is how you quantify strategic progress in concrete terms.

2. Use Prompt Research to map the AI query layer for your top commercial topics.

Semrush’s Prompt Research Tool surfaces how buyers phrase commercial questions to AI systems — and as Semrush makes clear, this data is categorically different from traditional keyword research. LLM prompts are longer, more conversational, and include qualifiers that rarely surface in Google autocomplete or keyword tools. Run your top 10 commercial keywords through the Prompt Research Tool and document the specific prompt patterns buyers use when they’re close to purchasing. These patterns become the structural blueprint for how to write and organize your highest-value pages — not just what topics to cover, but how to frame answers, what qualifiers to address directly, and what comparison dimensions buyers are actively working through.

3. Build separate pages for commercial and transactional intent — stop trying to serve both with one piece of content.

Semrush’s keyword intent analysis is explicit: keywords can carry multiple intent signals, but the page that serves each intent best is structured for the primary intent of that specific query type. A page targeting “best CRM for real estate agents” (commercial intent) should be organized as a comparison and evaluation resource: clear criteria, third-party evidence, social proof, transparent trade-offs. A page targeting “buy CRM for real estate agents” or “[brand] CRM pricing” (transactional intent) should lead with conversion: pricing visibility, a direct CTA, trust signals, and a frictionless path to demo or purchase. Trying to serve both intents with a single hybrid page typically means serving neither well. The conversion data from Search Engine Land confirms this: intent-specific BOFU pages convert dramatically better than hybrid content attempting to cover multiple funnel stages at once.

4. Set up AI visibility tracking alongside traditional rank tracking immediately.

Semrush’s Position Tracking now monitors rankings across Google, Gemini, and ChatGPT simultaneously, per their buyer keyword guide. If you’re not tracking AI visibility for your highest-value buyer intent keywords, you have a growing blind spot in your performance reporting — one that will become more consequential as AI search share increases. Configure a Position Tracking project with your top 20–30 commercial and transactional target keywords, enable AI visibility monitoring across all three surfaces, and review it weekly alongside traditional ranking data. The divergence between Google rankings and AI citation rates surfaces specific structural content gaps: pages that rank organically but aren’t being cited by AI systems almost always lack direct answer statements, comparison tables, or the qualifier-specific sections that make content machine-extractable.

5. Optimize your highest-value pages for structured AI extractability.

The content optimization framework in Semrush’s guide translates directly into AI citation performance: answer questions directly at the opening of each section, use descriptive headings that mirror how buyers phrase their questions, keep paragraphs to a single idea, and use tables and numbered lists for comparisons and feature breakdowns. These aren’t cosmetic readability improvements — they’re the formatting signals that both Google’s featured snippet system and LLM citation logic use to identify content as a reliable, extractable answer to buyer queries. Search Engine Land’s intent alignment analysis identifies six reinforcing signals: CTR, engagement metrics, Core Web Vitals, schema type, internal anchor text, and URL structure. Apply Product, FAQ, or Review schema to buyer intent pages to send additional intent-matching signals to search systems, and ensure internal links to those pages use anchor text that reflects the commercial or transactional intent you’re targeting.

What to Watch Next

AI search share of commercial queries is the metric to track most closely over the next two quarters. ChatGPT’s search volume has grown consistently through early 2026, and Gemini’s integration into Google Search means that traditional Google queries increasingly surface AI-generated summaries before organic listings for high-intent terms. The brands with buyer intent keyword strategies built for AI extractability will see compounding advantages as this share grows — and the brands still optimizing exclusively for traditional Google rankings will see performance declines they can’t easily attribute.

Google AI Overviews expansion for commercial and transactional queries is the near-term development with the most direct impact on ranking strategy. Google has been selective about which intent types trigger AI Overviews — expanding them broadly to transactional queries would significantly change the value of position-1 organic rankings for purchase-stage searches. Watch Google Search Console for impressions data on your transactional keywords alongside AI Overview appearance rates throughout Q2 and Q3 2026.

Prompt research tooling is an emerging category. Semrush’s Prompt Research Tool is among the first dedicated platforms for mapping AI query patterns at scale, but expect additional tools to build this capability in Q3–Q4 2026. Any platform that provides structured data on how buyers phrase commercial and transactional queries to LLMs is worth evaluating — this data will become a core input to keyword research as AI search share grows, and teams that build prompt research into their standard workflow early will have a structural advantage.

LLM-specific ranking factors remain largely undocumented. The precise weighting mechanisms that determine AI citation rates are not publicly defined by any major LLM provider. Track your AI citation rates for individual pages alongside deliberate content changes to build your own empirical dataset on what drives LLM citations in your specific category. This institutional knowledge compounds over time and will be a competitive advantage as the AI search ranking ecosystem matures.

Voice search query patterns are converging with LLM query patterns — both are more conversational and context-rich than typed Google searches. The same content structures that perform well in LLM citation contexts — direct answers, specific qualifier matching, comparison tables, structured data — also perform well in voice search. Over the next 12 months, voice and AI search will increasingly be treated as a unified “conversational search” optimization category rather than separate strategies requiring different approaches.

Bottom Line

Buyer intent keywords have always been the highest-ROI category in organic search, and the data is unambiguous: Search Engine Land documents BOFU pages converting at 4.78% on average — nearly 25x the rate of informational content — with documented case studies showing 2,400% conversion differentials between intent tiers. What Semrush’s May 2026 guide makes operationally clear is that buyer intent keyword research now requires a parallel workflow for AI search: the same purchase intent manifests in longer, more conversational, qualifier-rich prompts on LLMs that are structurally different from traditional Google queries and demand different content architecture to capture. The brands that treat buyer intent as a multi-surface strategy — auditing keyword portfolios by intent, mapping AI prompt patterns with dedicated tools, building intent-specific pages rather than hybrid content, and tracking visibility across Google, Gemini, and ChatGPT simultaneously — will compound advantages in both conversion rates and AI search citation rates. The brands still treating keyword research as a single-surface, volume-first exercise will increasingly find that their traffic growth doesn’t explain why their revenue metrics don’t move.