📈 Google Trends Scraper

Scrape Google Trends data at scale — interest over time, peak dates, top and rising related queries, and interest by region, for up to 500 keywords per run. Built for SEO professionals, content strategists, and market researchers.

💰 Pricing

$0.005 per keyword, charged only when Google returns data for it. Keywords with no data (too little search volume) are listed in the results with a note and cost nothing.

🎯 What You Get

For each keyword, the actor returns:

• Interest Over Time — Daily/weekly/monthly search interest (0–100 scale)
• Summary columns — average, peak value, peak date and latest value of interest, top related query, number of rising queries
• Related Queries — Top and rising related search terms
• Interest by Region — Geographic breakdown by country or sub-region

Related topics are not included: Google does not show them to automated visitors (it returns an empty list), so this actor does not pretend to have them.

🚀 Use Cases

SEO & Content Strategy

• Find trending keywords before your competitors
• Identify seasonal search patterns for content planning
• Discover related queries to expand your keyword clusters

Market Research

• Compare brand awareness across regions
• Track industry trend shifts over time
• Identify emerging markets for product launches

Competitive Intelligence

• Monitor competitor brand search trends
• Compare multiple products or technologies
• Track market share shifts via search interest

Academic & Data Journalism

• Study public interest in events, policies, or phenomena
• Build datasets for research papers
• Visualize trend data for articles and reports

📥 Input

Parameter	Type	Default	Description
keywords	string[]	required	Keywords to scrape (up to 500 per run; each one charged only if Google returns data)
timeRange	string	"today 12-m"	Time range for data
geo	string	"" (worldwide)	Country code (US, FR, GB, etc.)
category	integer	0	Google Trends category ID

Time Range Options

Value	Description
now 1-H	Past hour
now 4-H	Past 4 hours
now 1-d	Past day
now 7-d	Past 7 days
today 1-m	Past month
today 3-m	Past 3 months
today 12-m	Past 12 months
today 5-y	Past 5 years
all	All time (2004–present)

Example Input

{
    "keywords": ["artificial intelligence", "machine learning", "deep learning"],
    "timeRange": "today 12-m",
    "geo": "US",
    "category": 0
}

📤 Output

Each keyword produces one dataset item:

{
    "keyword": "artificial intelligence",
    "timeRange": "today 12-m",
    "geo": "US",
    "scrapedAt": "2026-03-28T21:00:00.000Z",
    "interestOverTime": [
        { "date": "Mar 31 – Apr 6, 2025", "value": 71 },
        { "date": "Apr 7 – Apr 13, 2025", "value": 68 },
        { "date": "Apr 14 – Apr 20, 2025", "value": 75 },
        "..."
    ],
    "relatedQueries": {
        "top": [
            { "query": "ai artificial intelligence", "value": "100" },
            { "query": "chatgpt", "value": "85" },
            { "query": "openai", "value": "62" }
        ],
        "rising": [
            { "query": "claude ai", "value": "+4,500%" },
            { "query": "gemini ai", "value": "+2,800%" }
        ]
    },
    "interestByRegion": [
        { "geoName": "District of Columbia", "value": 100 },
        { "geoName": "Washington", "value": 89 },
        { "geoName": "California", "value": 85 },
        { "geoName": "Massachusetts", "value": 82 }
    ]
}

⚙️ How It Works

1. Fetches widget tokens from Google Trends explore API
2. Uses tokens to request each data type (time series, geo, related searches)
3. Parses and normalizes the response data
4. Random delays (1–3s) between requests to respect rate limits
5. Exponential backoff on rate limiting (HTTP 429)

🔧 Technical Details

• HTTP/2 for faster, more efficient connections
• Session cookies for proper authentication
• Exponential backoff with jitter on rate limits
• Graceful error handling — partial results are still saved

💡 Tips

• Up to 500 keywords per run, each fetched on its own. Keywords Google has no data for are listed in the results but not charged
• Use country codes (ISO 3166-1 alpha-2) for regional filtering
• Longer time ranges return weekly/monthly aggregates; shorter ranges give hourly/daily data
• Category filtering narrows results — find category IDs on Google Trends
• Schedule runs to track trends over time and build historical datasets

📊 Integrations

Export your data to:

• Google Sheets / Excel for visualization
• BigQuery / Snowflake for analytics pipelines
• Slack / Email via Apify webhooks for alerts on trending topics
• Custom dashboards via the Apify API

🏷️ Tags

google-trends seo keyword-research market-research trending search-trends content-strategy competitive-intelligence

🔗 Quick Integration

Python

from apify_client import ApifyClient
client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("joyouscam35875/google-trends-scraper").call(run_input={...})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });
const run = await client.actor('joyouscam35875/google-trends-scraper').call({...});
const { items } = await client.dataset(run.defaultDatasetId).listItems();

No-code: Make / Zapier / n8n

Search for this actor in the Apify connector. No code needed.

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