Build a People Also Ask Research Workflow with SERP API Data

Build a People Also Ask Research Workflow with SERP API DataElowen

People Also Ask data is easy to screenshot and hard to reuse. A few questions in a browser tab can...

People Also Ask data is easy to screenshot and hard to reuse. A few questions in a browser tab can help with brainstorming, but they do not become a reliable research asset until they are collected, normalized, deduplicated, and grouped by intent.

This walkthrough shows a small workflow for turning People Also Ask results into a content research table. The output is a list of user questions with source query, location, intent group, and review notes.

The output we want

The final table should answer these questions:

  • Which user questions appear across several related searches?
  • Which questions are informational, commercial, comparative, or troubleshooting-oriented?
  • Which questions already map to existing content?
  • Which questions deserve a new section, FAQ, article, or product page update?

A minimal output can look like this:

question
normalized_question
source_query
location
intent_group
content_action
notes
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The important part is that the question is not just copied into a document. It is attached to enough context for later review.

Request SERP data with PAA included

TalorData SERP API can return structured Google SERP data, including people_also_ask. A request can be made with the same SERP endpoint used for other Google result checks.

curl -X POST 'https://serpapi.talordata.net/serp/v1/request' \
  -H 'Authorization: Bearer <TALORDATA_TOKEN>' \
  -H 'Content-Type: application/x-www-form-urlencoded' \
  -d 'engine=google' \
  -d 'q=serp api pricing' \
  -d 'gl=us' \
  -d 'hl=en' \
  -d 'device=desktop' \
  -d 'json=2'
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For research, run this across a cluster of related queries instead of a single keyword. A single SERP can be noisy, but repeated patterns across multiple SERPs are usually more useful.

Start with a query set 

QUERIES = [
    "serp api pricing",
    "google search api for seo",
    "serp api for ai agents",
    "search results api comparison",
]

CONTEXT = {
    "gl": "us",
    "hl": "en",
    "device": "desktop",
    "location": "United States",
}
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Keep this query set focused. If the set mixes unrelated topics, the deduped PAA list will become difficult to interpret.

Extract and normalize questions

Different response shapes can be handled defensively. The goal is to collect question-like text from the people_also_ask section without assuming every item has the same shape.

import re


def normalize_question(text: str) -> str:
    text = text.strip().lower()
    text = re.sub(r"\s+", " ", text)
    text = re.sub(r"[?!.]+$", "", text)
    return text


def extract_paa_questions(response_json, source_query, context):
    rows = []

    for item in response_json.get("people_also_ask", []):
        question = item.get("question") or item.get("title") or ""
        question = question.strip()
        if not question:
            continue

        rows.append({
            "question": question,
            "normalized_question": normalize_question(question),
            "source_query": source_query,
            "location": context.get("location", ""),
            "gl": context.get("gl", ""),
            "hl": context.get("hl", ""),
            "device": context.get("device", ""),
        })

    return rows
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This keeps the extraction step small. Do not assign intent yet. First collect the evidence.

Deduplicate across queries

A question may appear with small wording differences. Start with exact normalization, then review close duplicates manually or with a separate similarity step.

def dedupe_questions(rows):
    grouped = {}

    for row in rows:
        key = row["normalized_question"]
        if key not in grouped:
            grouped[key] = {
                "question": row["question"],
                "normalized_question": key,
                "source_queries": set(),
                "locations": set(),
            }

        grouped[key]["source_queries"].add(row["source_query"])
        grouped[key]["locations"].add(row["location"])

    output = []
    for item in grouped.values():
        output.append({
            "question": item["question"],
            "normalized_question": item["normalized_question"],
            "source_queries": ", ".join(sorted(item["source_queries"])),
            "locations": ", ".join(sorted(item["locations"])),
            "query_count": len(item["source_queries"]),
        })

    return sorted(output, key=lambda row: row["query_count"], reverse=True)
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query_count is useful because repeated questions are usually better candidates for content review than one-off questions.

Add intent groups

A lightweight intent classifier is enough for the first pass. It does not need to be perfect; it needs to make the review faster.

def classify_intent(question: str) -> str:
    q = question.lower()

    if any(term in q for term in ["pricing", "cost", "free", "paid"]):
        return "commercial"
    if any(term in q for term in ["vs", "compare", "alternative", "best"]):
        return "comparison"
    if any(term in q for term in ["how", "what is", "why", "guide"]):
        return "informational"
    if any(term in q for term in ["not working", "error", "fix", "limit"]):
        return "troubleshooting"

    return "mixed_or_review"
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This is deliberately simple. The output should be reviewed by a human before it becomes a content plan.

Map questions to content actions

The final step is to decide what to do with each question.

def suggest_action(intent_group, query_count):
    if query_count >= 3 and intent_group == "commercial":
        return "review product or pricing page"
    if query_count >= 2 and intent_group == "comparison":
        return "consider comparison section or article"
    if intent_group == "informational":
        return "map to FAQ or educational article"
    if intent_group == "troubleshooting":
        return "map to docs or support content"
    return "manual review"
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The goal is not to automate editorial judgment. The goal is to make the research table structured enough that editorial judgment can happen faster.

Example final row 

Question: How do I get Google search results from an API?
Source queries: google search api for seo, serp api for ai agents
Query count: 2
Intent group: informational
Content action: map to FAQ or educational article
Note: Review whether existing docs explain authentication, parameters, and output fields clearly.
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That row is much more useful than a raw PAA export. It tells the team why the question matters and where it might belong.

If you are building a PAA research workflow, TalorData SERP API can provide structured SERP data that includes people_also_ask. New accounts can use the included 500 responses to test a small query cluster before expanding the research workflow.