2026-09-17, Keyword Research · Google

People Also Ask for SEO: turn PAA into a content strategy

People Also Ask (PAA) is Google’s list of related questions on a results page, each paired with the page or AI-generated answer Google picked for it. For SEO it is most useful as three things at once: a list of questions in searchers’ own phrasing, a record of which page currently owns each answer, and a signal of how often Google answers the topic with generated text instead of a link.

The sections below cover intent mapping, page-versus-section decisions, answer ownership, the AIOVERVIEW versus LINK split and refresh cadence. Extracting and expanding PAA level by level is covered in mining People Also Ask for keyword research.

Definitions

• PAA question: one entry in the list, returned as peopleAlsoAsk[].question.
• Answer type: LINK (a web page answers it, with title, link, snippet), AIOVERVIEW (an AI-generated overview answers it) or UNKNOWN.
• Answer owner: the domain in link for a LINK item, or the domains in sources[] for a hydrated AIOVERVIEW item.
• AI answer share: the share of PAA items in a set of results whose type is AIOVERVIEW.
• Owner concentration: the share of LINK answers in a cluster held by the single most frequent domain.

Google’s own name for the block is a “related questions group”, described in its visual elements gallery as “a cluster of questions related to the user’s initial search” (checked 2026-09-17).

Step 1: map questions to intent

A PAA list mixes intents. Sorting questions by what the searcher is trying to do tells you what kind of content answers them and where it belongs.

A simple rule-based classifier on the first words of each question sorts most of a list; review the rest by hand. Intent matters more than wording: “Is a standing desk worth it” and “Are standing desks good for you” are both evaluation questions and usually belong on the same page.

Page or section?

Decide per cluster, not per question:

1. Own page when the cluster has several distinct sub-questions, shows up across many of your seed queries, and its current owners are dedicated pages.
2. Section on an existing page when the questions are follow-ups to a topic you already cover, and the owners are sections of longer pages.
3. FAQ block for short factual questions (eligibility, limits, yes or no) that do not justify a heading of their own.
4. Skip when the intent does not match what you sell or know, even if the question is popular.

Step 2: FAQ sections without the rich-result assumption

One PAA tactic was to add an FAQ section with FAQPage markup and hope for an FAQ rich result. That no longer applies. Google’s documentation changelog records that in September 2023 the FAQ documentation was updated to say the feature is only shown for well-known, authoritative government and health websites, and a May 2026 entry adds a deprecation notice because “this feature will no longer appear in Google Search starting May 7, 2026” (Latest Google Search documentation updates, checked 2026-09-17).

FAQ sections are still worth writing when they help readers, because the structure itself answers PAA questions cleanly:

• Use the PAA question, or a close natural variant, as the heading.
• Put the direct answer in the first sentence under it. Qualifications come after.
• Keep one question per heading. Merged questions produce vague answers.
• Answer only what you can answer accurately. Google’s guidance on helpful content says content made “primarily to attract search engine visits” is “not aligned with what our systems seek to reward” (checked 2026-09-17).

Do not pad pages with every PAA question you collected. A focused page that answers its cluster completely is more useful than a long page that touches forty loosely related questions.

Step 3: analyze answer owners

The owner of a PAA answer is the page you need to beat, and the pattern of owners across a cluster tells you how hard that is.

For each cluster, compute from your stored results:

• Owner concentration: the top domain’s share of LINK answers.
• Owner type mix: publishers, brands, forums, government or academic sites, marketplaces. Classify domains once and reuse the mapping.
• Your share: how many answers in the cluster your domain holds.
• Stability: how often each question’s owner changed between the last few runs.

Then read the pattern:

Look at the owning pages themselves before you brief a writer. The snippet on a LINK item shows the passage Google showed for that question, which is a practical hint about the answer length and format that currently wins.

Step 4: AIOVERVIEW versus LINK share as a signal

Each PAA item carries a type. Measured over a cluster and over time, the split is a useful signal about how a topic is answered in your results.

• Mostly LINK: questions are answered by individual pages. Classic on-page work applies: the right heading, a direct answer, a strong page.
• Mostly AIOVERVIEW: questions are answered by generated text built from several sources. There is no single slot to win; the goal is to be among the cited sources[], and to be cited consistently.
• Rising AIOVERVIEW share: a topic moving from links to generated answers. Report it early; it changes what “owning” the question means for your team.

Two cautions apply. The share describes your own queries, markets and devices, not Google as a whole. And without include.paaAioverview an AIOVERVIEW item has no answer data, so it looks ownerless; hydrate at least a sample before calling a cluster an easy win.

Google’s documentation on AI features says pages need to be indexed and eligible to be shown with a snippet to appear in AI Overviews, and that “there are no additional requirements” (AI features and your website, checked 2026-09-17). So the page-level work for AIOVERVIEW clusters is the same as for LINK clusters; what changes is how you measure success. How AI engines choose citations and featured snippets and AI Overviews go further.

Step 5: set a refresh cadence

PAA lists and owners change. How often to re-run depends on what a change would trigger:

Keep every run comparable: same query text, country or gl, hl, location if used, and device. Diff each run against the previous one and report three lists: new questions, disappeared questions, and changed owners. Monitoring cadence discusses choosing intervals, and SERP feature change tracking shows the diffing pattern.

Collecting PAA at scale

A Google Search request returns peopleAlsoAsk[] with the rest of the page, so no separate call is needed:

curl -X POST https://api.answerline.dev/v1/monitor/google \
  -H "Authorization: Bearer $ANSWERLINE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "query": "standing desk benefits", "country": "US", "hl": "en", "include": { "paaAioverview": true } }'

For a cluster report, compute the signals above from a set of stored results:

from collections import Counter, defaultdict
from urllib.parse import urlsplit

def host(url):
    return (urlsplit(url or "").hostname or "").lower().removeprefix("www.")

def cluster_report(results: list[dict], cluster_of, my_domain: str) -> dict:
    """results: stored `result` objects; cluster_of: function mapping a question to a cluster name."""
    types = defaultdict(Counter)
    owners = defaultdict(Counter)
    cited = defaultdict(Counter)
    for result in results:
        for item in result.get("peopleAlsoAsk") or []:
            c = cluster_of(item["question"])
            types[c][item.get("type", "UNKNOWN")] += 1
            if item.get("type") == "LINK" and item.get("link"):
                owners[c][host(item["link"])] += 1
            for s in item.get("sources") or []:
                cited[c][host(s["url"])] += 1
    report = {}
    for c, t in types.items():
        links = sum(owners[c].values())
        top = owners[c].most_common(1)
        report[c] = {
            "items": sum(t.values()),
            "ai_answer_share": round(t["AIOVERVIEW"] / sum(t.values()), 2),
            "owner_concentration": round(top[0][1] / links, 2) if links else None,
            "top_owner": top[0][0] if top else None,
            "my_link_answers": owners[c][my_domain],
            "my_citations": cited[c][my_domain],
        }
    return report

In TypeScript, the per-item logic is the same:

type Paa = { question: string; type: "AIOVERVIEW" | "LINK" | "UNKNOWN"; link?: string; sources?: { url: string }[] };

const host = (u?: string) => (u ? new URL(u).hostname.toLowerCase().replace(/^www\./, "") : "");

export async function paaFor(query: string): Promise<Paa[]> {
  const res = await fetch("https://api.answerline.dev/v1/monitor/google", {
    method: "POST",
    headers: {
      Authorization: `Bearer ${process.env.ANSWERLINE_API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({ query, country: "US", hl: "en", include: { paaAioverview: true } }),
  });
  if (!res.ok) throw new Error(`HTTP ${res.status}: ${await res.text()}`);
  const { result } = await res.json();
  return result.peopleAlsoAsk ?? [];
}

export function owners(items: Paa[]): string[] {
  return items.flatMap((i) => (i.type === "LINK" ? [host(i.link)] : (i.sources ?? []).map((s) => host(s.url))));
}

For more than a handful of queries, submit GOOGLE tasks through POST /v1/async/task/batch with deterministic idempotency keys and a webhook, and expand level by level with deduplication. That code, including the expansion loop and cost walk-through, is in PAA keyword research; the batch receiver is in the async batch pipeline.

Cost

From the current credit prices: a Google Search async task costs 3 credits, include.paaAioverview adds 2 once per request (not per item), and synchronous calls add 2. Requests with include.paaAioverview take longer to return.

A practical split: collect the whole map without hydration, and hydrate only the clusters where the AI answer share is high enough to matter.

From PAA to AI assistants

PAA questions read like prompts, which makes a PAA cluster map a good starting point for an AI-answer prompt set. Run the cluster heads on ChatGPT or Gemini and compare who is cited there with who owns the PAA answers on Google. Prompt set design covers building that set, and GEO vs SEO metrics how to report both sides.

Related reading on the same results page: related searches and “people also search for”, what is a SERP, and the keyword research use case.

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