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Youtube outlier detector (find trending content based on your competitors)

Leonardo GrigorioLeonardo Grigorio
7529 views
2/3/2026
Product ManagementData ExportWebhookShopifyCSV
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Video explanation

This n8n workflow helps you identify trending videos within your niche by detecting outlier videos that significantly outperform a channel's average views. It automates the process of monitoring competitor channels, saving time and streamlining content research.

Included in the Workflow

  • Automated Competitor Video Tracking Monitors videos from specified competitor channels, fetching data directly from the YouTube API.

  • Outlier Detection Based on Channel Averages Compares each video’s performance against the channel’s historical average to identify significant spikes in viewership.

  • Historical Video Data Management Stores video statistics in a PostgreSQL database, allowing the workflow to only fetch new videos and optimize API usage.

  • Short Video Filtering Automatically removes short videos based on duration thresholds.

  • Flexible Video Retrieval Fetches up to 3 months of historical data on the first run and only new videos on subsequent runs.

  • PostgreSQL Database Integration Includes SQL queries for database setup, video insertion, and performance analysis.

  • Configurable Outlier Threshold Focuses on videos published within the last two weeks with view counts at least twice the channel's average.

  • Data Output for Analysis Outputs best-performing videos along with their engagement metrics, making it easier to identify trending topics.

Requirements

  • n8n installed on your machine or server

  • A valid YouTube Data API key

  • Access to a PostgreSQL database

This workflow is intended for educational and research purposes, helping content creators gain insights into what topics resonate with audiences without manual daily monitoring.

YouTube Outlier Detector: Find Trending Content Based on Your Competitors

This n8n workflow helps you identify YouTube videos that are performing exceptionally well (outliers) compared to other videos from a specified channel. It allows you to monitor competitor channels or your own for content that is quickly gaining traction, potentially indicating trending topics or successful content strategies.

What it does

  1. Triggers Manually: The workflow can be initiated manually by clicking "Execute workflow".
  2. Fetches YouTube Channel Videos: It retrieves a list of videos from a specified YouTube channel.
  3. Loops Through Videos: It processes each video individually to gather statistics.
  4. Calculates Outlier Metrics: It calculates a "score" for each video based on its views, likes, and comments, and then identifies videos that significantly deviate from the average.
  5. Stores Data in PostgreSQL: It inserts the details of these outlier videos into a PostgreSQL database.
  6. Conditional Logic: It includes an "If" node, though its specific conditions are not defined in the provided JSON, suggesting a placeholder for further filtering or branching logic.
  7. Prepares Data for Database: Uses a "Set" node to format the data before insertion into the database.
  8. Code Execution: Includes a "Code" node, which can be used for custom data manipulation or logic.

Prerequisites/Requirements

  • n8n Instance: A running n8n instance.
  • YouTube API Key: A Google Cloud Project with the YouTube Data API v3 enabled and an associated API key or OAuth 2.0 credentials.
  • PostgreSQL Database: Access to a PostgreSQL database instance to store the detected outliers. You will need the connection details (host, port, database, user, password).

Setup/Usage

  1. Import the workflow: Import the provided JSON into your n8n instance.
  2. Configure YouTube Node:
    • Select or create a new YouTube credential (OAuth 2.0 or API Key).
    • Specify the YouTube channel ID or username you want to monitor.
    • Configure the desired number of videos to fetch.
  3. Configure PostgreSQL Node:
    • Select or create a new PostgreSQL credential.
    • Provide your PostgreSQL database connection details.
    • Ensure your database has a table structured to store the video information (e.g., video_id, title, views, likes, comments, score, is_outlier, channel_id, published_at).
  4. Configure "Edit Fields (Set)" Node: Review and adjust the fields being set to match your PostgreSQL table schema if necessary.
  5. Configure "Code" Node: If you have specific custom logic for calculating the outlier score or other data transformations, implement it within the "Code" node. The current JSON does not specify custom code, so this might be a placeholder.
  6. Configure "If" Node: Define the conditions in the "If" node to filter outliers based on your specific criteria (e.g., score > 2 * average_score).
  7. Activate the workflow: Once configured, activate the workflow. You can then execute it manually using the "Manual Trigger" node.

This workflow provides a robust foundation for identifying high-performing YouTube content, which can be invaluable for content strategy, competitive analysis, and trend spotting.

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