Keyword Research Scraper — Google, YouTube, Amazon, Bing & More

Turn one seed keyword into hundreds of real long-tail keywords that actual people are searching for — pulled live from the autocomplete of Google, YouTube, Bing, Amazon, eBay, DuckDuckGo, Wikipedia, Google Images, Google News and Google Shopping. Every keyword is enriched with search-intent classification, SEO metrics and a cross-platform demand signal, so you get a research-ready dataset, not just a list of strings.

No login, no API key, no browser — fast JSON extraction straight from the suggest endpoints, with 99%+ reliability.

Why this keyword scraper?

Most keyword scrapers cover a single platform — you run one for Google, another for YouTube, another for Amazon, then stitch the CSVs together yourself. This actor does all of them in one run and merges the results, so you instantly see which keywords appear on multiple platforms (the strongest demand signal there is).

Feature	Single-platform scrapers	This actor
Google autocomplete	✅	✅
YouTube, Bing, DuckDuckGo	separate actors	✅ one run
Amazon & eBay (product / ASO keywords)	separate actors	✅ one run
Wikipedia, Google Images / News / Shopping	❌	✅
Alphabet · question · preposition · comparison · commercial expansion	partial	✅ all
Recursive deep expansion	❌	✅
Search-intent classification (informational / commercial / transactional / navigational)	❌	✅
SEO metrics (word count, long-tail flag, contains-seed)	❌	✅
Google relevance score per suggestion	partial	✅
Cross-platform demand (platformCount + full sources)	❌	✅
Monitor mode — only new keywords	❌	✅

Platforms covered

Platform	What it tells you
Google (web)	Core SEO & content keyword demand
YouTube	Video SEO — what viewers search for
Bing	Second search engine, often different long-tail
DuckDuckGo	Privacy-audience search behaviour
Amazon	Product & buyer keywords (Amazon SEO / PPC)
eBay	Marketplace & resale product keywords
Wikipedia	Entities, topics and disambiguation terms
Google Images	Visual-intent queries
Google News	Trending / newsworthy angles on a topic
Google Shopping	High-commercial-intent product queries

Use cases

• SEO & content strategy — discover the long-tail and question keywords to build topic clusters, blog briefs and FAQ / People-Also-Ask sections.
• PPC & Amazon/eBay ads — find buyer-intent keywords (transactional / commercial) and negative keywords for Google Ads and marketplace campaigns.
• App Store Optimization (ASO) — pair this with the Google Play / App Store scrapers to research the terms users search for in stores.
• E-commerce product research — Amazon & eBay autocomplete reveals what shoppers actually type, including brands, sizes and modifiers.
• YouTube & video — surface video titles and topics your audience is already searching for.
• Trend monitoring — schedule the actor in monitor mode to capture only newly emerging long-tail keywords for a topic over time.
• Market & competitor research — see which keywords show demand across multiple platforms, and in any country/language.

How to use

1. Sign up for Apify — the free plan is enough to try this actor.
2. Open the Keyword Research Scraper, enter your seed keywords, pick the platforms and expansions, and click Start.
3. Watch keywords stream into the dataset table.
4. Export as JSON, CSV, Excel, XML, or RSS — or pull results programmatically via the Apify API.

Input

{
    "keywords": ["coffee maker", "crm software"],
    "platforms": ["google", "youtube", "bing", "amazon"],
    "expansions": ["alphabet", "questions", "prepositions"],
    "country": "US",
    "language": "en",
    "recursiveDepth": 0,
    "maxResults": 1000,
    "dedupeAcrossPlatforms": true,
    "includeQuestionsOnly": false,
    "monitorMode": false
}

• keywords (required) — one or more seed keywords; each is expanded across every selected platform.
• platforms — any of google, youtube, bing, amazon, ebay, duckduckgo, wikipedia, google_images, google_news, google_shopping.
• expansions — alphabet (a–z), numbers (0–9), questions (how/what/why…), prepositions (for/with/near…), comparisons (vs/alternative…), commercial (best/buy/price…). Empty = only the bare seed.
• country / language — localise suggestions (Google gl/hl, Amazon/eBay marketplace, Bing market, Wikipedia edition).
• recursiveDepth (default 0) — re-query discovered keywords to dig deeper into the long tail (bounded by maxResults).
• maxResults (default 1000) — hard cap on unique keywords (cost control).
• dedupeAcrossPlatforms (default true) — one row per keyword with a platforms list + sources array, or one row per keyword-per-platform when off.
• includeQuestionsOnly / minWordCount / mustIncludeTerms / excludeTerms — filters.
• monitorMode (default false) — return ONLY keywords not seen in previous runs (see below).

Output

One record per unique keyword (or per keyword-per-platform when dedupeAcrossPlatforms is off):

{
    "keyword": "coffee maker with grinder",
    "seed": "coffee maker",
    "seeds": ["coffee maker"],
    "platform": "google",
    "platforms": ["google", "bing", "amazon"],
    "platformLabels": ["Google", "Bing", "Amazon"],
    "platformCount": 3,
    "country": "US",
    "language": "en",
    "searchIntent": "commercial",
    "isQuestion": false,
    "isCommercial": false,
    "isTransactional": false,
    "isComparison": false,
    "isLocal": false,
    "wordCount": 4,
    "charCount": 25,
    "isLongTail": true,
    "containsSeed": true,
    "bestRank": 1,
    "bestRelevanceScore": 601,
    "expansionTypes": ["base", "prepositions"],
    "discoveryDepth": 0,
    "entity": null,
    "sources": [
        { "platform": "google", "platformLabel": "Google", "rank": 1, "relevanceScore": 601, "suggestionType": "QUERY", "subtypes": [512, 433], "modifierQuery": "coffee maker", "expansionType": "base", "depth": 0 },
        { "platform": "amazon", "platformLabel": "Amazon", "rank": 5, "relevanceScore": null, "modifierQuery": "coffee maker with", "expansionType": "prepositions", "depth": 0 }
    ],
    "scrapedAt": "2026-06-16T12:00:00.000Z"
}

Every keyword keeps its full provenance in sources — exactly which platform, rank, relevance score and modifier query surfaced it — so nothing the suggest APIs return is thrown away.

Monitor mode — only new keywords

Turn on monitorMode and the actor remembers every keyword it has emitted (in a named key-value store keyed by monitorKey). On the next run it returns only keywords it has never seen before — ideal for spotting emerging long-tail terms and trends without re-processing the same list.

This is fully compatible with Apify Schedules: the schedule starts the run on your cron (e.g. daily), and monitor mode deduplicates against all prior runs. Use different monitorKey values to keep separate watch-lists per topic or market.

Automate & schedule

Run this actor on autopilot and pull results into your own stack:

• Apify API — start runs, fetch datasets, and manage schedules over REST.
• apify-client for JavaScript and apify-client for Python — official SDKs.
• Schedules — run it daily/weekly with monitorMode to track new keywords for a topic.
• Webhooks — trigger downstream actions (push to a sheet, Slack alert, content pipeline) the moment a run finishes.

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'MY_APIFY_TOKEN' });

const run = await client.actor('scrapesage/keyword-research-scraper').call({
    keywords: ['running shoes'],
    platforms: ['google', 'youtube', 'amazon'],
    expansions: ['alphabet', 'questions'],
    maxResults: 500,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Got ${items.length} keywords`);

Integrate with any app

Connect the dataset to 5,000+ apps — no code required:

• Make — multi-step automation scenarios.
• Zapier — push new keywords straight into your content or ads workflow.
• Slack — get notified when monitored keywords appear.
• Google Drive / Sheets — auto-export every run to a spreadsheet.
• Airbyte — pipe results into your data warehouse.
• GitHub — trigger runs from commits or releases.

Use with AI assistants (MCP)

The output is clean, LLM-ready JSON. You can call this actor from Claude, ChatGPT, or any agent framework through the Apify MCP server — ask your assistant to "research long-tail keywords for coffee maker on Google, YouTube and Amazon" and let it run this scraper for you.

More scrapers from scrapesage

Build a complete content, SEO & e-commerce research stack:

• Google News Scraper — articles, publishers and trending coverage for content angles.
• YouTube Scraper — channels, videos and stats for video SEO.
• Google Play Scraper — Android app listings, ratings and reviews for ASO.
• App Store Scraper — iOS app metadata and reviews for ASO.
• Chrome Web Store Scraper — extension listings, installs and reviews.
• Shopify Store Scraper — products, prices and inventory from any Shopify store.
• Product Hunt Scraper — product launches, makers and traction.
• Google Ads Transparency Scraper — who's advertising what on Google.
• Facebook Ad Library Scraper — competitor ad creative & messaging.

Tips

• Start small, then widen. Begin with a couple of platforms and ["alphabet","questions"]; add prepositions, comparisons, commercial and more platforms once you see the shape of the data.
• Cross-platform = demand. Sort by platformCount to find keywords that show up on several platforms — those are your safest bets.
• Filter for intent. Use searchIntent to split content keywords (informational) from money keywords (commercial / transactional).
• Question content. Set includeQuestionsOnly to build FAQ and People-Also-Ask style pages.
• Cost control. maxResults is a hard cap; recursiveDepth multiplies coverage fast, so raise it together with maxResults.

FAQ

Where does the data come from? From the public autocomplete / suggest endpoints each platform uses to power its own search box — the same suggestions you see when you start typing. No API key or login required.

Do you provide monthly search volume and CPC? No — those numbers are not exposed by autocomplete, and this actor does not fabricate them. Instead it gives you real signals: Google's relevance score, the suggestion rank, and how many platforms surface each keyword (platformCount) — an honest, cross-platform demand indicator.

Which platforms can I scrape at once? Any combination of the 10 supported platforms in a single run. With dedupeAcrossPlatforms on, the same keyword found on several platforms is merged into one row with a sources breakdown.

Can I research keywords in other countries and languages? Yes — set country and language. Amazon/eBay switch to the right marketplace, Google uses gl/hl, Bing uses the matching market and Wikipedia uses the matching edition.

How do I get only new keywords over time? Turn on monitorMode and run on a Schedule. Each run returns only keywords not seen in previous runs.

Can I export to Google Sheets, CSV, or Excel? Yes — one click in the dataset view, or automatically on every run via the Google Drive integration.

Is scraping autocomplete legal? This actor collects publicly available suggestion data only. You are responsible for using the data in compliance with applicable laws and each platform's terms.

Need help?

Open an issue on the actor's Issues tab, or visit the Apify help center. Feature requests are welcome — this actor is actively maintained.