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Weekly Google Trends Keyword Tracker with Apify & Sheets (n8n)

Weekly Google Trends Keyword Tracker with Apify & Sheets (n8n)

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Weekly Google Trends Keyword Tracker with Apify & Sheets (n8n)

Weekly Google Trends Keyword Tracker with Apify & Sheets (n8n)

Regular price £59.99
Regular price £59.99 Sale price
SAVE Sold out

Weekly Google Trends keyword insights in Google Sheets—automatically

This n8n workflow runs every Monday and pulls weekly Google Trends data for your chosen keywords via Apify, then summarizes the key metrics and appends one row per keyword to your Google Sheets spreadsheet.

What this workflow does

  • Scheduled run: Uses an n8n Schedule Trigger to run every Monday at 8am.
  • Fetch Google Trends data with Apify: Calls Apify’s Google Keyword Trends actor using the keyword list and parameters you provide (including location, time range, category, and search type).
  • Summarize results per keyword: Transforms the dataset into one summary row per keyword, including:
    • run date
    • interest average
    • top queries
    • breakout rising queries
    • (plus relevant configured fields such as location/window as available)
  • Append to Google Sheets: Writes the summarized rows into your selected worksheet by adding new rows with auto-mapping based on matching column names.

Use cases

  • SEO and content planning: Track weekly interest and rising search terms for target keywords and share updates with your team in Google Sheets.
  • SaaS marketing operations: Monitor demand signals by location and category over time, then use the sheet as your reporting source of truth.
  • Automation engineers: Deploy a repeatable “trends-to-spreadsheet” workflow for keyword research pipelines.

Technical details

  • n8n nodes: Schedule Trigger, Apify (via apifyn8n-nodes-apify), Code, and a Google Sheets append step.
  • Apify setup: Provide an Apify API token plus the actor input JSON (keywords + location/time range/category/search type).
  • Google Sheets setup: Ensure your sheet has matching columns (e.g., run_date, keyword, location, window, resolution, interest_average, top_queries, breakout_rising_queries) so data appends correctly.
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