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A 2026 guide to using “People Also Search For” insights to expand keywords and improve SEO performance.
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"People Also Search For" (PASF) is a feature on Google's search engine results page that shows related queries users commonly explore after clicking a result and bouncing back to the search results page. Introduced in 2018, PASF typically appears beneath a result you clicked once Google detects signs of dissatisfaction with that result.
Where traditional keyword research starts with what users search for initially, PASF reveals the refinement journey — what users search for next when the first result didn't fully meet their needs. This "bounce-triggered" query data captures a fundamentally different intent signal than search volume metrics or keyword difficulty scores.
Think of PASF as Google's real-time user satisfaction feedback mechanism. When users bounce from a result and search again, Google captures the pattern and surfaces the most common follow-up queries for similar behavior — providing a window into the semantic neighborhood around any search topic.
| Feature | People Also Search For (PASF) | People Also Ask (PAA) |
|---|---|---|
| Trigger | Appears after user bounces from a result | Appears proactively on the initial SERP |
| Format | Clickable search queries with search icons | Questions with expandable direct answers |
| Intent signal | Follow-up search behavior | Related question intent |
| Best use | Keyword discovery, content gaps | FAQ content, featured snippet optimization |
| Data type | Refinement queries | Question-answer pairs |
PASF terms are often long-tail variants of your seed keyword — more specific, lower competition, and often higher conversion intent. When you search a broad term and study the PASF results, you discover the specific angles and qualifiers that real users actually care about.
For example: searching "project management software" might surface PASF terms like "project management software for remote teams with time tracking" — a long-tail keyword with high purchase intent that a generic keyword research tool might underweight.
Implementation: Take your top 10–20 target keywords, run each through Google, and collect their PASF terms. Group related PASF terms by intent and identify clusters that currently lack dedicated pages on your site.
PASF analysis reveals what users expect to find when searching in your topic area. If PASF terms for your target keyword consistently include angles your existing content doesn't address, that's a content gap — and likely why users bounce from your page to refine their search.
Compare your content coverage against the PASF landscape for your primary keywords. Every PASF term that relates to your topic but doesn't have a matching page on your site represents a potential ranking opportunity or a content addition that would reduce bounce rates.
PASF reveals the intent journey behind searches — what users actually wanted when they refined their query. A search for "best CRM software" with PASF showing "best CRM for small business under $50/month" and "best CRM for freelancers" tells you users are highly price and scale-sensitive.
This intent intelligence should shape your content's angle, your product page's messaging, and your comparison content's frame of reference — beyond what search volume data alone can reveal.
PASF naturally maps the semantic relationships between queries in your topic area — which keywords cluster together in user intent, which are near-synonyms, and which represent distinct subtopics worth dedicated pages.
Use PASF data to identify your pillar page topics (the broad terms with many related PASF variants) and your cluster page topics (the specific PASF variants that deserve individual coverage). This produces topical authority architecture grounded in real user behavior rather than SEO theory.
PASF relationships reveal which pages on your site should be internally linked — because they represent the queries users naturally flow between. If PASF shows "project management for remote teams" as a follow-up to "project management software," your remote teams page should internally link from your main software page and vice versa.
Internal linking built from PASF patterns mirrors the intent flow of real users, improving time-on-site and reducing the bounce behavior that PASF itself tracks.
Many PASF terms are phrased as questions or comparison queries — exactly the format that earns featured snippets. A PASF term like "what is the difference between CRM and ERP?" represents a featured snippet opportunity: write a direct, structured answer to that exact question, and you may win the snippet for users who refine their search in that direction.
Combining PASF discovery with featured snippet optimization creates a compounding advantage: you capture the user who bounces to the PASF query and present them with a featured snippet that satisfies their intent before they need to click anywhere.
The most forward-looking use of PASF in 2026: mapping the semantic neighborhood around your keywords to understand what associated queries users bring to AI platforms.
Users who search Google, bounce, and encounter PASF are experiencing the same intent dissatisfaction that drives AI search adoption. Many of those same users are increasingly taking their "what I really wanted" query directly to ChatGPT or Perplexity instead of refining on Google. PASF data is a window into the intent gap that AI search is designed to fill.
PASF is Google's mechanism for surfacing what users searched for next when a result didn't satisfy them. It's a window into the intent gap between what users initially queried and what they actually needed.
In AI search, there's an equivalent phenomenon — but it happens invisibly inside ChatGPT, Perplexity, and Google AI Mode rather than on a Google SERP where you can observe it. Users who don't find what they need through Google increasingly take their refined, specific queries directly to AI platforms. These "dark queries" — the real prompts users type into AI search tools — represent the full intent landscape that PASF data only partially maps.
Traditional keyword research tools and PASF analysis cannot surface these dark queries because they happen in AI platforms, not on Google. They're invisible to Google Search Console, Google Keyword Planner, and any tool that relies on Google's search data.
Dageno AI provides the AI search equivalent of PASF data through its Intent Insights capability, powered by 120M+ real AI conversation data. Intent Insights surfaces the actual prompts that users type into ChatGPT, Perplexity, and other AI platforms in your category — including the specific, refined queries that represent your highest-value AI search opportunities.
For SEO teams that use PASF to discover what users really wanted when Google's results didn't satisfy them, Intent Insights provides the same intelligence for AI search: what users are really asking AI platforms when they want direct, synthesized answers about your category. This is the prompt research that traditional keyword tools cannot access — and the foundation of a GEO (Generative Engine Optimization) strategy grounded in actual user behavior rather than guesswork.
Explore Dageno's Intent Insights and AI search monitoring, and the Dageno research hub for data on real AI prompt patterns. Free plan at dageno.ai.
"People Also Search For" data is one of the most underutilized intent intelligence sources in traditional SEO — revealing the refinement journey of real users, exposing content gaps, surfacing long-tail opportunities, and mapping topical clusters that keyword volume data alone misses.
In 2026, PASF has an AI search equivalent: the dark queries users type into ChatGPT, Perplexity, and Google AI Mode that represent the same "what I really wanted" intent that PASF captures in Google. Dageno's Intent Insights provides this AI-side intent intelligence — completing the map of user intent that PASF begins but cannot finish.
Updated by
Updated on Apr 09, 2026
Updated by
Ye Faye
Ye Faye is an SEO and AI growth executive with extensive experience spanning leading SEO service providers and high-growth AI companies, bringing a rare blend of search intelligence and AI product expertise. As a former Marketing Operations Director, he has led cross-functional, data-driven initiatives that improve go-to-market execution, accelerate scalable growth, and elevate marketing effectiveness. He focuses on Generative Engine Optimization (GEO), helping organizations adapt their content and visibility strategies for generative search and AI-driven discovery, and strengthening authoritative presence across platforms such as ChatGPT and Perplexity
