Google Trends Scraper

Pull the full Google Trends dataset for any keyword, country, and time range — interest over time, regional breakdowns (country / state / metro / city), and the top and rising related queries and topics. Built for marketers, analysts, investors, and product teams who need trend data at scale without clicking through Google Trends one search at a time.

Why This Scraper?

• Every Google Trends panel in one record — timeline, regional breakdown, related queries (top + rising), and related topics (top + rising), all for a single search term in a single dataset item
• Compare up to 5 terms side-by-side — put "coffee, tea, matcha" in one entry and get a multi-series timeline with each term's relative interest
• Worldwide or country-level detail — pick "Worldwide" to see which countries are most interested, or pick a country to drill into states, metros, and cities
• Paste Google Trends URLs directly — already applied filters in the browser? Copy the URL and we preserve every filter (geo, time range, category) automatically
• Human-readable inputs — pick "United States" and "Arts & Entertainment" from dropdowns instead of decoding ISO codes and numeric category IDs
• 9 time-range presets plus custom date ranges — from "Past hour" to "2004 – present", or supply any exact YYYY-MM-DD YYYY-MM-DD window
• All 25 Google Trends categories — filter to Finance, Health, Shopping, Travel, and 21 more to cut noise from unrelated searches
• Choose where Trends is viewed from — Google Trends results vary by viewer location. Pick the country matching your audience for geographically-accurate data
• $2 per 1,000 results — one of the lowest prices on Apify for Google Trends data

Use Cases

SEO & Content Strategy

• Discover rising search queries around any topic to write content people are actively looking for
• Find long-tail keyword opportunities from the "Top" related queries panel
• Validate whether a keyword's interest is seasonal, trending up, or declining before investing in content

Market Research & Trend Forecasting

• Track interest in product categories, brands, or cultural topics across any time window
• Compare competing brands, products, or terms head-to-head on a normalized interest scale
• Identify which regions, states, or cities are the hottest market for a given product or service

Finance & Investment Signal

• Monitor retail interest in tickers, cryptocurrencies, and asset classes as a sentiment leading indicator
• Pull historical trend data back to 2004 for backtesting and time-series analysis
• Watch "Rising" related queries to catch narrative shifts early

Advertising & Campaign Planning

• Match ad creative and launch timing to when interest is peaking in each region
• Size the opportunity in specific DMAs (designated market areas) before allocating budget
• Compare branded vs unbranded search interest over time

Product & Brand Monitoring

• Track share of interest between your brand and competitors week over week
• Catch emerging competitor names in the "Rising" related queries panel
• Feed trend data into dashboards alongside sales, reviews, and social mentions

Getting Started

Simple Search

Enter a single term — everything else defaults to sensible values (Worldwide, past 12 months, all categories):

{
    "searchTerms": ["bitcoin"]
}

Multiple Independent Searches

Each entry runs as its own search. Great for building a keyword universe:

{
    "searchTerms": ["bitcoin", "ethereum", "solana", "dogecoin"],
    "geo": "US",
    "timeRange": "today 5-y"
}

Side-by-Side Comparison

Separate terms with commas inside a single entry to get one multi-series timeline on the same chart. Up to 5 terms per comparison:

{
    "searchTerms": ["coffee, tea, matcha"],
    "geo": "JP",
    "timeRange": "today 12-m",
    "category": "71"
}

Custom Date Range

Pull an exact time window — useful for pre/post analysis (product launch, news event, seasonal campaign):

{
    "searchTerms": ["black friday deals"],
    "customTimeRange": "2024-11-01 2024-12-15",
    "geo": "US"
}

Paste Google Trends URLs

Already dialed in your filters in the browser? Paste the URL — every filter is preserved:

{
    "startUrls": [
        "https://trends.google.com/trends/explore?date=today%2012-m&geo=US-CA&q=electric%20vehicles&cat=47"
    ]
}

Worldwide Country Breakdown

Leave geo empty to see interest broken down by country worldwide:

{
    "searchTerms": ["world cup"],
    "timeRange": "today 3-m"
}

Input Reference

What to Scrape

Parameter	Type	Default	Description
searchTerms	string[]	["bitcoin"]	Topics to analyze. Each entry is an independent search. Separate with commas in a single entry to compare up to 5 terms side-by-side
startUrls	string[]	[]	Paste Google Trends URLs to reuse filters you've already applied in the browser. Overrides Time range, Geo, and Category when provided

Filters

Parameter	Type	Default	Description
timeRange	string	"today 12-m" (Past 12 months)	Time window preset — "now 1-H", "now 4-H", "now 1-d", "now 7-d", "today 1-m", "today 3-m", "today 12-m", "today 5-y", "all" (2004–present)
customTimeRange	string	null	Exact date range YYYY-MM-DD YYYY-MM-DD. Overrides the Time range preset
geo	string	"" (Worldwide)	Country to analyze interest in. Worldwide shows country-level breakdown; picking a country shows state / metro / city breakdown
category	string	"0" (All categories)	One of 26 Google Trends category filters (All, Arts & Entertainment, Business, Finance, Health, Shopping, Travel, …)

Proxy

Parameter	Type	Default	Description
viewedFrom	string	"us"	Country to fetch Google Trends from. Google Trends results vary by viewer location — pick the country matching your end users for accurate data

Advanced

Parameter	Type	Default	Description
maxItems	integer	0 (unlimited)	Hard cap on dataset items returned

Output

One record per search term, with every Google Trends panel filled in:

{
    "inputUrlOrTerm": "bitcoin",
    "searchTerm": "bitcoin",
    "interestOverTime_timelineData": [
        {
            "time": 1712188800,
            "formattedTime": "Apr 4, 2024",
            "formattedAxisTime": "Apr 4",
            "value": [72],
            "hasData": [true],
            "formattedValue": ["72"]
        }
    ],
    "interestOverTime_averages": [],
    "interestBySubregion": [
        {
            "geoCode": "US-WY",
            "geoName": "Wyoming",
            "value": [100],
            "formattedValue": ["100"],
            "maxValueIndex": 0,
            "hasData": [true]
        },
        {
            "geoCode": "US-NV",
            "geoName": "Nevada",
            "value": [99],
            "formattedValue": ["99"],
            "maxValueIndex": 0,
            "hasData": [true]
        }
    ],
    "interestByCity": [],
    "interestByMetro": [],
    "interestBy": [],
    "relatedQueries_top": [
        {
            "query": "bitcoin price",
            "value": 100,
            "formattedValue": "100",
            "hasData": true,
            "link": "/trends/explore?q=bitcoin+price&date=today+12-m&geo=US"
        },
        {
            "query": "bitcoin stock",
            "value": 17,
            "formattedValue": "17",
            "hasData": true,
            "link": "/trends/explore?q=bitcoin+stock&date=today+12-m&geo=US"
        }
    ],
    "relatedQueries_rising": [
        {
            "query": "bitcoin etf",
            "value": 250,
            "formattedValue": "+250%",
            "hasData": true,
            "link": "/trends/explore?q=bitcoin+etf&date=today+12-m&geo=US"
        }
    ],
    "relatedTopics_top": [
        {
            "topic": { "mid": "/m/05p0rrx", "title": "Bitcoin", "type": "Cryptocurrency" },
            "value": 100,
            "formattedValue": "100",
            "hasData": true,
            "link": "/trends/explore?q=%2Fm%2F05p0rrx&date=today+12-m&geo=US"
        }
    ],
    "relatedTopics_rising": []
}

All Available Fields

Field	Type	Description
inputUrlOrTerm	string	The original term or URL you provided that produced this record
searchTerm	string	Normalized search term (decoded from a URL when startUrls was used)
interestOverTime_timelineData	object[]	Timeline points with time, formattedTime, formattedAxisTime, value, formattedValue, hasData, and isPartial when the last point is still forming
interestOverTime_averages	number[]	Average interest per compared term (populated only when comparing multiple terms)
interestBy	object[]	Country-level interest (when Geo is set to Worldwide)
interestBySubregion	object[]	State / province-level interest (when Geo is a country that exposes subregions)
interestByMetro	object[]	Metro / DMA-level interest (when Geo is a US state or similar)
interestByCity	object[]	City-level interest (when the target geo exposes cities)
relatedQueries_top	object[]	Most-searched related queries — query, value (0–100 normalized), formattedValue, hasData, link
relatedQueries_rising	object[]	Fastest-growing related queries — same shape, with growth % in formattedValue
relatedTopics_top	object[]	Most-searched related topics — topic.mid, topic.title, topic.type, value, formattedValue, hasData, link
relatedTopics_rising	object[]	Fastest-growing related topics — same shape

Each geo / region / city row carries a value array that is N-long for an N-term comparison, so a 2-term compare puts both terms' values on every row.

Tips for Best Results

Geo Granularity

• Worldwide — returns country-level interest (interestBy field filled in)
• A single country — returns state / province (interestBySubregion) and, for countries that support it, city (interestByCity) and metro (interestByMetro)
• A URL with geo=US-CA (a state) — drills down to city and metro level

Comparing Terms

• Put up to 5 terms in one entry separated by commas to get one multi-series chart with normalized values
• Put terms in separate entries to run each as its own independent search — best when you want each term to anchor its own 0–100 scale
• The interestOverTime_averages field is populated only in multi-term comparisons — it gives you each term's average interest on the shared scale

Time Ranges

• "Past hour" and "Past 4 hours" use Google Trends' realtime data (different backend) — great for news monitoring
• "2004 – present" is the longest window — ideal for long-horizon backtests
• Custom date ranges must be YYYY-MM-DD YYYY-MM-DD — the second date must be today or earlier

View Location

• Google Trends results vary by where the viewer is located. If your audience is in Germany, set View Trends from = Germany to get data that reflects what a German user would see

Bulk Runs

• Build a keyword list in Google Sheets and feed it into the actor via the Apify Sheets integration or the API — the actor will loop through each term automatically

Pricing

$2 per 1,000 results — one of the lowest prices on Apify for Google Trends data.

Results	Cost
100	$0.20
1,000	$2.00
10,000	$20.00
100,000	$200.00

Platform fees (compute, proxy, storage) are additional and depend on your Apify plan.

Integrations

Export data in JSON, CSV, Excel, XML, or RSS. Connect to 1,500+ apps via:

• Zapier / Make / n8n — Workflow automation
• Google Sheets — Direct spreadsheet export or keyword-list import
• Slack / Email — Notifications on run completion
• Webhooks — Custom API integrations
• Apify API — Full programmatic access

Legal & Ethical Use

This actor is designed for legitimate market research, SEO, content strategy, and business intelligence. Users are responsible for complying with applicable laws and Google's Terms of Service. The output contains aggregated, anonymized interest data — no personal information is collected.