People Also Search For: How to Mine PASF for SEO in 2026

People Also Search For is the most useful SERP feature nobody talks about.

While the SEO industry obsesses over AI Overviews, PASF quietly tells you the one thing every keyword tool tries to infer: what did the user actually want, and did the page they just clicked give it to them? The data sits on the SERP, free, and most teams never extract it.

PASF appears beneath an organic result after a user clicks through and bounces back. Google reads that bounce as a signal of dissatisfaction and surfaces 6-8 alternative queries based on what other dissatisfied users searched next. It’s a behavioral keyword research tool Google built for itself and then accidentally exposed to the public.

What People Also Search For actually is

People Also Search For is a Google SERP feature that renders a block of 6-8 alternative search queries directly beneath a clicked organic result, after the user returns to the SERP. The trigger is what SEOs call pogo-sticking. Semrush defines it as a searcher who “quickly navigates between webpages and search engine results pages (SERPs), only spending a few seconds on the pages.”

The mechanics matter. From Shopify’s PASF guide:

When Google detects this behavior, it displays a box containing 6 to 8 alternative search terms below the result the user just abandoned.

Semrush’s teardown of the feature counts the same six to eight alternatives and confirms the box “usually appears underneath the search result the user previously clicked on.” Two independent guides describe one behavior: a block scoped to the specific result that just failed a searcher.

The alternatives aren’t predicted by a language model. They’re aggregated from the actual next searches that other dissatisfied users ran after bouncing off the same result. In other words: PASF is a crowdsourced “what did I really mean?” suggestion, scoped to a specific page that failed a specific user.

Two consequences fall out of this:

1. PASF is per-result, not per-query. The same SERP shows different PASF blocks beneath different organic results, because each result has its own failure profile.
2. PASF only appears when Google has enough data on dissatisfaction. Long-tail queries with low click volume don’t trigger PASF at all. The blocks show up reliably on commercial head terms.

Where PASF shows up on the page

Placement is not identical across devices, which trips up manual auditors. On desktop, the block renders after you click a result and return to the SERP, stacked beneath that result. On mobile, Semrush notes that PASF often “shows up as you scroll down the search results — before you’ve clicked on a result” at all. If your tracking assumes the desktop click-back pattern everywhere, mobile blocks will look like they’re missing when they’re simply rendered differently. Log the device with every capture so the two surfaces never get averaged together.

PASF vs People Also Ask

PASF and PAA get conflated constantly. They are not the same feature.

	People Also Ask (PAA)	People Also Search For (PASF)
What it shows	Questions	Alternative queries
Where it appears	Near the top of the SERP	Beneath a clicked result, bottom of SERP
When it triggers	On most informational queries	After pogo-sticking off a result
Source	Indexed pages cited as answers	Aggregated post-click user behavior
Citation surface?	Yes — your page can be cited	No — PASF entries are searches, not links
Intent type	Broadening (“what else might I want?”)	Refinement (“what should I have searched?”)
Optimization play	FAQ schema + question-shaped H2s	Capture the PASF queries with new pages

The most common mistake is treating PASF queries as PAA questions and trying to “rank in the PASF box.” There is no box to rank in. PASF entries are clickable searches that take the user to a new SERP. The win is on the destination SERP, not the PASF block itself.

If you need the long version on PAA specifically, see our SERP features tracking guide.

PASF vs related searches

PASF also gets confused with the related searches block, and they are different signals. Related searches sit at the very bottom of every SERP and reflect query patterns adjacent to your original search, regardless of which result you clicked. PASF only appears after a click-back, tied to the specific result the searcher abandoned. Related searches answer “what else is in this topic?”; PASF answers “what did this page fail to deliver?”

For content planning, the distinction is practical. Related searches help you size a topic and find adjacent head terms. PASF tells you where one specific competitor page leaks demand — a sharper, per-result signal you can act on directly.

What PASF data tells you

PASF is a refinement-intent signal. It answers a question keyword tools can only guess at: when this query fails, where do users go next?

That makes PASF uniquely valuable for three SEO jobs:

1. Keyword research that maps real failure modes

Standard keyword tools surface queries that exist. PASF surfaces queries that exist because users ran them after another query disappointed them. The difference is enormous for commercial intent.

If “best CRM” surfaces PASF entries like “best CRM for small business”, “Salesforce alternatives”, and “free CRM tools”, you’ve learned things keyword volume alone won’t tell you:

• The original query is too broad for a meaningful percentage of searchers.
• Those searchers are willing to run a refined query. They’re not done shopping.
• The refined queries are commercially valuable (alternatives, free, segment-specific).

2. Competitor mapping

When PASF appears beneath a competitor’s organic result, every alternative query is a place that competitor’s page failed to satisfy. Stacked across multiple competitor URLs on the same SERP, you get a map of unmet demand in your category, plus a hit list of pages to write.

Picture the SERP for “project management software.” Beneath the incumbent’s result, PASF surfaces “project management software for small teams” and “free project management software.” Beneath a second competitor, it surfaces “project management software with time tracking.” Each block is a confession: that page pulled the click but didn’t close the intent.

Read together, those blocks tell you three pages are missing from the category — small-team, free-tier, and time-tracking angles — and they tell you which incumbent is weakest on each. That’s a content brief and a competitive read in one pull, sourced from behavior rather than a keyword tool’s guess.

3. Intent classification

PASF is the cleanest signal available that a query has multiple sub-intents. If a single SERP shows PASF blocks with wildly different refinements (“python tutorial” → “python tutorial for beginners”, “python tutorial pdf”, “python vs javascript”), the head term is fragmented. Your content needs to address the splits explicitly, not bury them.

This matters because many head terms carry more than one intent at once. Ahrefs points to “best air fryer” as informational, commercial, and transactional simultaneously. PASF is field evidence of that split — the refinements users actually ran, rather than a guess at what they might have meant.

Turn PASF output into a content map

Raw PASF output is a pile of queries. To make it plannable, cluster it. Keyword clustering — grouping search terms that “share the same search intent … and targeting them together on a single page,” in Semrush’s definition — turns dozens of PASF entries into a handful of page briefs.

A workable clustering pass has four steps:

1. Normalize. Lowercase, strip location modifiers, and dedupe near-identical phrasings.
2. Group. Bucket by shared head-bigram, then merge buckets whose intent is identical even when the wording differs.
3. Name by intent. Label each cluster by its dominant intent, not its highest-volume phrase.
4. Keep the context. Attach the source competitor URL(s) to every cluster, so you retain the failure evidence PASF gave you.

Each surviving cluster is one page. The competitor URLs attached to it are your proof that the page is winnable.

How to extract PASF at scale

The manual workflow is brutal: search the query, click a result, wait, click back, screenshot the PASF block, repeat. PASF doesn’t appear on every visit (Google rate-limits it by IP and session), so a single check produces noisy data.

The programmatic workflow is two API calls per query:

1. Issue the search.
2. Issue a “click + return” SERP request that triggers PASF rendering.

cloro’s SERP API handles both in a single request and returns PASF data alongside organic results, PAA, AI Overviews, knowledge panels, and related searches in one structured JSON response. Per-result, parsed, location- and device-aware. No browser automation, no proxy rotation, no DOM scraping.

Two collection habits keep the data clean. First, sample repeatedly: because PASF renders probabilistically, union the blocks from five to ten pulls per query rather than trusting a single snapshot. Second, dedupe on normalized text, not raw strings, so “crm for small business” and “small business CRM” don’t inflate your counts as two separate findings.

A reasonable tracking pipeline:

1. Define a tracked-query list: your category’s commercial head terms, plus the top 10 organic competitors per query.
2. Issue daily SERP requests with PASF expansion enabled.
3. Store snapshots keyed by (keyword, competitor_url, location, device, date).
4. Aggregate weekly: which PASF queries appear most often, beneath which competitors, with what consistency?
5. Route high-consistency PASF queries into your content backlog.

For the broader tracking architecture covering AI Overviews, PAA, featured snippets, and knowledge panels, see our SERP features tracking guide.

People Also Search For in the age of AI search

AI Overviews changed what happens after a search, not what happens before it. When Google rolled AI Overviews out to everyone in the U.S., more queries started resolving on the results page itself. That makes the pre-click refinement signal PASF captures more valuable, not less — it’s one of the few behavioral traces left when the click never happens.

Refinement intent also explains why keyword tools keep missing demand. Google has said 15 percent of the searches it sees every day are new — queries nobody has run before. Volume databases are built from historical logs, so they’re structurally blind to that slice. PASF isn’t: it surfaces the phrasing a real person reached for in the moment, including refinements no keyword tool has indexed yet.

For teams optimizing for both classic search and AI answers, PASF doubles as an AI-visibility input. The refined queries it exposes are exactly the follow-ups an AI engine is likely to synthesize next. Our GEO playbook covers how to structure the resulting pages so AI engines cite them, an increasingly important second order on every commercial query.

Putting PASF to work

A practical 30-day workflow:

1. Week 1, pull PASF for your top 20 head terms. Expand each query’s PASF block, then expand the PASF entries’ own PASF blocks (two levels deep gets you ~100 queries).
2. Week 2, filter for commercial intent and volume. Drop anything under 50 monthly searches or that’s obviously informational-only. Tag the survivors by sub-intent.
3. Week 3, cluster. Group PASF queries by shared head-bigram. Each cluster is a candidate page.
4. Week 4, score by competitor weakness. For each cluster, run a SERP check on the top 3 queries. Where the existing top results are weak (thin content, off-topic, outdated), prioritize that cluster.

This pipeline reliably surfaces 5-15 net-new page ideas per month for a category-leading site. Net-new because PASF queries are by definition the queries that competitors’ pages already failed at. You’re not fighting for terms competitors have locked down. You’re claiming terms competitors have measurably failed to serve.

The limits of PASF data

People Also Search For is a strong signal, not a complete one. Three limits are worth stating plainly.

It carries no volume of its own. PASF tells you a refinement happened, not how often — you still have to attach search-volume and difficulty data before you commit a page to the backlog.

It’s biased toward head terms. Blocks appear where Google has enough dissatisfaction data, so thin long-tail queries rarely trigger PASF at all. The tail you most want to map is the tail PASF is quietest about.

It lags reality. The aggregate behind a block reflects past sessions, so a brand-new product term can be live demand well before PASF ever shows it. Treat PASF as one input in a triangulation — alongside volume data and live SERP checks — not as the single source of truth.

Common mistakes

• Treating PASF as PAA. They’re different features with different optimization plays. Confusing them produces incoherent content briefs and wasted FAQ schema.
• Trying to “rank” in PASF. PASF entries are searches, not links. There’s nothing to rank in. The opportunity is the destination SERP each entry points to.
• Snapshotting once. PASF is rate-limited and doesn’t appear on every visit. A single snapshot is a sample, not a measurement. Pull 5-10 snapshots over a week and union the queries.
• Ignoring per-result variance. PASF differs beneath every clicked result on the same SERP. If you only check beneath your own result, you’re missing your competitors’ failure data, which is more valuable than yours.
• Skipping location and device. PASF is geo- and device-segmented. US desktop and UK mobile return different blocks for the same query. Tag every snapshot, or the data won’t aggregate cleanly.

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