Google Trends Scraper

Extract comprehensive data from Google Trends — interest over time, related queries, related topics, trending searches, and geographic breakdowns. Compare up to 5 keywords simultaneously with full historical data.

What does Google Trends Scraper do?

This actor scrapes Google Trends to extract keyword trend data that marketers, SEO professionals, content creators, and researchers use for data-driven decision making. It uses multiple scraping strategies (API endpoints + HTML parsing) for maximum reliability.

Key Features

• Interest Over Time — Get the full timeline of search interest (0–100 scale) for any keyword, with granularity from minute-level to monthly depending on time range
• Multi-Keyword Comparison — Compare up to 5 keywords side by side to see which terms dominate and how they trend relative to each other
• Related Queries — Discover "Top" queries (highest volume) and "Rising" queries (fastest growing) related to your keywords — goldmine for SEO keyword research
• Related Topics — Find broader topic associations including entity types (Person, Place, Company, etc.) for content strategy planning
• Daily Trending Searches — See what's trending right now in any country with traffic estimates and related news articles
• Real-Time Trending Stories — Get breaking trend stories with associated entities and news coverage
• Interest by Region — Geographic breakdown showing where keywords are most popular (state/province level)
• Seasonality Detection — Automatic analysis of seasonal patterns with peak/low months and strength score
• Trend Direction Analysis — Automated rising/declining/stable classification with percentage change calculations

Use Cases

Use Case	How This Actor Helps
SEO Keyword Research	Find rising search terms before competitors. Discover related keywords you haven't targeted yet. Prioritize keywords by actual search trend data.
Content Strategy	Identify trending topics for blog posts, videos, and social media. Time content publication to match seasonal search peaks.
Market Research	Track brand awareness over time. Compare competitor brand search interest. Identify emerging markets and consumer interests.
Product Development	Validate product ideas by checking search demand trends. Spot declining vs. growing product categories.
Academic Research	Study public interest in topics over time. Analyze geographic variation in topic popularity. Export clean datasets for analysis.
Competitive Analysis	Compare your brand vs. competitors in search interest. Identify geographic markets where competitors are stronger.
Investment Research	Track consumer interest in companies, products, and sectors. Identify breakout trends before they go mainstream.
News & Media Monitoring	Real-time trending stories with traffic estimates. Daily trending searches by country.

Input Configuration

Field	Type	Default	Description
searchTerms	string[]	required	Keywords to look up (e.g., ["AI", "machine learning"])
geo	string	"US"	Country code (ISO 3166-1 alpha-2). Empty = worldwide
timeRange	enum	"past_year"	Time period: past_hour, past_day, past_week, past_month, past_year, past_5_years
category	integer	0	Google Trends category ID (0 = all). See category list
maxResults	integer	100	Max keywords to process from list
includeRelatedQueries	boolean	true	Extract top + rising related queries
includeRelatedTopics	boolean	true	Extract top + rising related topics
includeTrendingSearches	boolean	false	Scrape daily + real-time trending searches
includeInterestByRegion	boolean	false	Geographic interest breakdown
outputFormat	enum	"summary"	summary (one record/keyword) or flat (one record/datapoint)
proxyConfiguration	object	—	Apify proxy config (residential recommended)
maxConcurrency	integer	2	Concurrent requests (keep low: 1–3)
customTimeRange	string	""	Custom date range: "2024-01-01 2024-12-31"

Example Input

{
    "searchTerms": ["artificial intelligence", "machine learning", "deep learning"],
    "geo": "US",
    "timeRange": "past_year",
    "category": 0,
    "includeRelatedQueries": true,
    "includeRelatedTopics": true,
    "includeTrendingSearches": false,
    "outputFormat": "summary",
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyGroups": ["RESIDENTIAL"]
    }
}

Output

Summary Format (default)

Each keyword produces one record with all data nested:

{
    "keyword": "artificial intelligence",
    "geo": "US",
    "timeRange": "past_year",
    "category": "All Categories",
    "averageInterest": 72,
    "peakInterest": 100,
    "peakDate": "2025-11-15T00:00:00.000Z",
    "troughInterest": 45,
    "trend": "rising",
    "trendChangePercent": 23.5,
    "volatility": 12.3,
    "dataPoints": 52,
    "seasonality": {
        "hasSeasonality": false,
        "peakMonth": "November",
        "lowMonth": "July",
        "seasonalityStrength": 18
    },
    "interestOverTime": [
        {
            "date": "2025-03-09T00:00:00.000Z",
            "interest": 65,
            "isPartial": false
        }
    ],
    "relatedQueries": {
        "top": [
            { "query": "ai tools", "value": 100 },
            { "query": "chatgpt", "value": 95 }
        ],
        "rising": [
            { "query": "claude ai", "value": 5000, "formattedValue": "Breakout" },
            { "query": "ai agents", "value": 2400, "formattedValue": "+2,400%" }
        ]
    },
    "relatedTopics": {
        "top": [
            { "title": "ChatGPT", "type": "Software", "value": 100 }
        ],
        "rising": [
            { "title": "Claude", "type": "AI assistant", "value": 5000, "formattedValue": "Breakout" }
        ]
    },
    "relatedQueriesCount": 45,
    "relatedTopicsCount": 30,
    "scrapedAt": "2026-03-01T12:00:00.000Z"
}

Flat Format

One record per data point — ideal for CSV export and spreadsheet analysis:

{
    "keyword": "artificial intelligence",
    "dataType": "interest_over_time",
    "geo": "US",
    "date": "2025-03-09T00:00:00.000Z",
    "interest": 65,
    "averageInterest": 72,
    "trend": "rising"
}

Trending Searches Output

{
    "dataType": "daily_trending",
    "title": "Super Bowl",
    "formattedTraffic": "5M+",
    "date": "2026-02-08",
    "geo": "US",
    "articlesCount": 12,
    "articles": [
        {
            "title": "Super Bowl 2026: Everything You Need to Know",
            "url": "https://...",
            "source": "ESPN"
        }
    ]
}

Pricing

This actor uses Pay Per Event pricing:

Event	Price	Description
trend-scraped	$0.005	Charged per keyword trend successfully extracted

Example costs:

• 10 keywords, summary format: ~$0.05
• 100 keywords, summary format: ~$0.50
• 10 keywords with trending searches: ~$0.06

Tips for Best Results

1. Use residential proxies — Google Trends aggressively blocks datacenter IPs. Residential proxies dramatically improve success rates.

2. Keep concurrency low — Set maxConcurrency to 1 or 2. Google rate-limits aggressive scraping.

3. Batch your keywords — The actor automatically batches keywords into groups of 5 (Google's comparison limit). Larger keyword lists take proportionally longer.

4. Choose the right time range — Shorter ranges (past_hour, past_day) give finer granularity but less historical context. past_year (weekly data) is the best balance for most SEO use cases.

5. Use custom date ranges — For precise historical analysis, use the customTimeRange field with format "2024-01-01 2024-12-31".

6. Category filtering — Narrow results with category IDs to avoid ambiguous keywords. "Python" in "Programming" (184) vs "All" gives very different results.

7. Flat format for analysis — Use outputFormat: "flat" when exporting to spreadsheets, databases, or data visualization tools.

Limitations

• Google Trends interest values are relative (0–100 scale), not absolute search volumes
• Maximum 5 keywords per comparison (the actor handles batching automatically)
• Real-time trending data is not available for all countries
• Very new or low-volume keywords may return no data
• Google may change their API format — the actor uses multiple fallback strategies to stay resilient

Changelog

v1.0 (2026-03-01)

• Initial release
• Multi-strategy scraping (API + HTML fallback)
• Interest over time, related queries, related topics
• Daily and real-time trending searches
• Interest by region (geographic breakdown)
• Seasonality detection and trend analysis
• Summary and flat output formats
• PPE billing at $0.005/keyword

Integration — Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("sovereigntaylor/google-trends-scraper").call(run_input={
    "searchTerm": "google trends",
    "maxResults": 50
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item.get('title', item.get('name', 'N/A'))}")

Integration — JavaScript

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('sovereigntaylor/google-trends-scraper').call({
    searchTerm: 'google trends',
    maxResults: 50
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach(item => console.log(item.title || item.name || 'N/A'));