Google Trends Scraper

Google Trends Scraper

The Google Trends Scraper is an Apify actor that extracts structured “interest over time” data from Google Trends for one or more keywords. It solves the challenge of manual trend tracking by programmatically pulling time-series interest values with configurable time ranges, geography, and categories. Built for marketers, developers, data analysts, and researchers, this Google Trends data scraper delivers clean, chart-ready outputs for trend analysis at scale. Whether you’re building dashboards or automating keyword research, this Google Trends API scraper and Google Trends Python scraper lets you scrape Google Trends data reliably and repeatedly. 🚀

What data / output can you get?

This actor outputs a single, structured record per run with a timeline of interest values for your chosen keywords. Each timeline record contains a date and one numeric field per keyword.

• Exports are available from the Apify dataset in JSON, CSV, or Excel.

Data type	Description	Example value
inputUrlOrTerm	Comma-separated list of input keywords for reference	"chatgpt, AI, python"
searchTerm	Comma-separated list of keywords used to query	"chatgpt, AI, python"
interestOverTime_timelineData	Array of time-series entries, one per date	[ { ... }, { ... } ]
interestOverTime_timelineData[].date	ISO date for the timeline entry	"2025-08-24"
interestOverTime_timelineData[].chatgpt	Interest value for keyword “chatgpt” on that date	87
interestOverTime_timelineData[].AI	Interest value for keyword “AI” on that date	65
interestOverTime_timelineData[].python	Interest value for keyword “python” on that date	42

Notes:

• The set of fields inside interestOverTime_timelineData is dynamic and matches your input keywords.
• Data granularity depends on timeRange: daily for “today 1-m” and “today 3-m”, weekly for “today 12-m”, monthly for “today 5-y”, and custom for specific date ranges.

Key features

• 🔁 Robust proxy fallback Automatically starts with no proxy and falls back to datacenter and then residential proxies if blocked. Helps maintain reliability for high-frequency or sensitive runs.

• 🧠 Smart retry with backoff Built-in retry logic and exponential backoff handle transient errors and rate limiting gracefully to keep your scrapes stable.

• 🗂️ Bulk keyword comparisons Provide multiple keywords to compare in a single run; each keyword becomes a column in the timeline output for side-by-side analysis.

• 🕒 Time-range aware granularity Supports Google Trends time frames like “today 1-m”, “today 3-m”, “today 12-m”, and “today 5-y” with appropriate daily/weekly/monthly granularity.

• 🌍 Geo and category filters Configure geo (e.g., "US") and Google Trends category ID (e.g., 0 for all) to narrow results to your target market.

• 📤 Flexible exports Download your results from the Apify dataset as JSON, CSV, or Excel for BI tools and further analysis.

• 👨‍💻 Developer-friendly foundation Implemented in Python using pytrends under the hood and deployable via Apify’s API — ideal for Google Trends programmatic access and pipeline automation.

• 🧱 Production-ready infrastructure Runs on Apify with proxy configuration support, detailed logs, and scalable execution to support both ad hoc pulls and scheduled monitoring.

How to use Google Trends Scraper - step by step

1. Create or log in to your Apify account
   Access the Apify Console to run the Google Trends scraping tool.

2. Open the Google Trends Scraper actor
   Find “Google Trends Scraper” and click Try for free.

3. Add your input data

   • Paste a list of keywords in keywords (supports bulk input).
   • Optionally set timeRange (e.g., "today 3-m"), geo (e.g., "US"), and category (0 for all categories).
   • Advanced: Configure proxyConfiguration or leave it unset to start with a direct connection.

4. Configure time range and scope

   • timeRange options: "today 1-m", "today 3-m", "today 12-m", "today 5-y", or a custom "YYYY-MM-DD YYYY-MM-DD".
   • Note: Granularity varies by range (daily/weekly/monthly).

5. Start the run
   Click Start. The actor will fetch the Google Trends interest over time data for your keywords.

6. Monitor logs
   Watch logs for messages like expected data points and date range. The actor will automatically retry and handle proxy fallback if needed.

7. Download your results
   Open the run’s Dataset tab and export as JSON, CSV, or Excel for further analysis in your stack.

Pro Tip: Use the Apify API to trigger runs on a schedule and feed outputs into dashboards, analytics notebooks, or marketing workflows for a Google Trends data downloader pipeline.

Use cases

Use case name	Description
SEO & content planning	Identify when interest peaks and prioritize topics by extracting historical “interest over time” for target keywords.
Market research & trend tracking	Monitor how consumer interest shifts across months or years to inform product and campaign timing.
PPC and keyword management	Compare multiple keywords to refine bidding strategies and spot rising opportunities.
Demand forecasting	Use time-series signals to anticipate seasonality and trend directions for inventory and production planning.
Regional targeting (geo-filtered)	Filter by geo (e.g., “US”) to focus on specific markets for localized campaigns and reporting.
Data science time-series modeling	Feed clean, date-indexed interest values into forecasting models or anomaly detection pipelines.
Academic research	Retrieve reproducible historical trend series for studies in economics, media, and social sciences.
API pipeline automation	Orchestrate scheduled, programmatic pulls using Apify’s API to power reports and dashboards.

Why choose Google Trends Scraper?

Built for precision, automation, and reliability, this Google Trends scraping tool focuses on clean, structured “interest over time” data you can trust.

• 🎯 Accurate, structured outputs — Clean timeline arrays with one numeric field per keyword and standardized dates.
• 📈 Scales with your workload — Supports bulk keywords and long ranges without manual fiddling.
• 👨‍💻 Developer access — Integrates smoothly with Apify’s API for Google Trends programmatic access.
• 🛡️ Safe and responsible — Operates on publicly available, aggregated data only.
• 🔄 Resilient by design — Retry logic and proxy fallback (none → datacenter → residential) help avoid blocks.
• 💾 Easy exports — Download as JSON, CSV, or Excel directly from the Apify dataset.
• 🏗️ Production-ready infrastructure — Apify platform reliability beats fragile browser extensions and ad hoc scripts.

In short, it’s a focused Google Trends data extractor that delivers dependable time-series outputs without the maintenance headaches.

Is it legal / ethical to use Google Trends Scraper?

Yes — when done responsibly. This actor collects publicly available, aggregated Google Trends information and does not access private or personal data.

Recommended guidelines:

• Use the tool ethically and respect Google’s terms of service.
• Avoid excessive request rates; rely on built-in retries and proxy management.
• Comply with applicable data protection laws (e.g., GDPR, CCPA) and internal policies.
• Consult your legal team for edge cases or large-scale enterprise deployments.

Input parameters & output format

Example input JSON

{
  "keywords": ["chatgpt", "AI", "python"],
  "timeRange": "today 3-m",
  "geo": "US",
  "category": 0,
  "sortOrder": "",
  "maxComments": 100,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}

Parameter reference:

Field	Type	Description	Default	Required
keywords	array	List of keywords or search terms to analyze trends for (supports bulk input).	(none)	Yes
timeRange	string	Time range for the trends data. Granularity varies by range: • 'today 1-m' → ~30 daily points • 'today 3-m' → ~90-93 daily points • 'today 12-m' → ~52 weekly points • 'today 5-y' → ~60 monthly points • Custom: 'YYYY-MM-DD YYYY-MM-DD'	"today 3-m"	No
geo	string	Geographic location code (e.g., 'BD' for Bangladesh, 'US' for United States). Leave empty for global.	""	No
category	integer	Google Trends category ID (0 for all categories).	0	No
sortOrder	string	Sort order for results (optional).	""	No
maxComments	integer	Maximum number of comments to retrieve (optional).	100	No
proxyConfiguration	object	Configure proxy settings. Actor will start with no proxy and fallback to datacenter/residential if blocked.	(not set; starts with direct connection)	No

Notes:

• Only keywords is required by the schema.
• The actor starts with a direct connection if proxyConfiguration is not provided and will attempt proxy fallback if needed.

Example output JSON

{
  "inputUrlOrTerm": "chatgpt, AI, python",
  "searchTerm": "chatgpt, AI, python",
  "interestOverTime_timelineData": [
    {
      "date": "2025-08-24",
      "chatgpt": 87,
      "AI": 65,
      "python": 42
    },
    {
      "date": "2025-08-25",
      "chatgpt": 96,
      "AI": 72,
      "python": 48
    }
  ]
}

Output notes:

• The keys inside interestOverTime_timelineData match your input keywords.
• The “isPartial” column returned by Google Trends is removed for clarity.

FAQ

Is there a free tier or trial?

Yes. You can run this actor on Apify with trial minutes, then choose a plan that fits your workload. This makes it a practical Google Trends data downloader for testing and small projects.

Can I scrape Google Trends with Python?

Yes. This actor is implemented in Python using pytrends under the hood, and you can integrate results into your Python workflows — perfect for a Google Trends Python scraper setup.

What data does this actor return?

It returns “interest over time” for the keywords you provide, as a timeline of date/value records. Each keyword appears as its own numeric field per date, making it ideal to scrape Google Trends data for comparisons.

Can I compare multiple keywords at once?

Yes. Add multiple entries to keywords and the output will include one column per keyword in each timeline record — a convenient Google Trends bulk keyword scraper capability.

Does it support regional data?

Yes, via the geo input. Set a two-letter country code like "US" to scope results. The output is still a single timeline for the specified region rather than a regional breakdown.

How far back can I get historical data?

Use timeRange to choose from "today 1-m", "today 3-m", "today 12-m", "today 5-y", or pass a custom range like "YYYY-MM-DD YYYY-MM-DD". Granularity adapts automatically (daily/weekly/monthly).

How does it handle blocking or rate limits?

The actor includes retry logic with exponential backoff and can switch proxies from none → datacenter → residential when needed, improving reliability for a Google Trends scraping tool in production.

How do I export results to CSV?

Open the run’s Dataset in Apify and use the built-in export options (JSON, CSV, Excel). This supports quick Google Trends export to CSV for BI tools and spreadsheets.

Closing CTA / Final thoughts

The Google Trends Scraper is built to deliver clean, reliable “interest over time” data for your keywords. With bulk keyword support, configurable ranges, geo filters, and robust proxy fallback, it streamlines Google Trends keyword research at scale for marketers, developers, analysts, and researchers. Export to JSON/CSV/Excel from the Apify dataset or automate via the Apify API to power your Google Trends programmatic access workflows. Start extracting smarter, structured trend insights today.