Block 1 - Loop Over Items
- Type / Role
- n8n-nodes-base.splitInBatches - splitInBatches
- Config choices
- Version 3
Youtube outlier detector (find trending content based on your competitors)
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Important notice
This workflow is provided as-is. Please review and test before using in production.
1. Workflow Overview
Video explanation This n8n workflow helps you identify trending videos within your niche by detecting outlier videos that significantly outperform a c...
Best for
- Market Research automation workflows
- advanced n8n builders looking for reusable templates
Tools used
n8n-nodes-base.splitinbatches, n8n-nodes-base.httprequest, n8n-nodes-base.manualtrigger, n8n-nodes-base.executeworkflowtrigger, n8n-nodes-base.postgres, n8n-nodes-base.youtube, n8n-nodes-base.if, n8n-nodes-base.code
Source and attribution
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Leonardo Grigorio.
Original n8n.io source1.1 Workflow description
- Title
- Youtube outlier detector (find trending content based on your competitors)
- Workflow name
- Youtube outlier detector (find trending content based on your competitors)
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.
1.2 Logical Blocks
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
2. Block-by-Block Analysis
Block 2 - find_video_data1
- Type / Role
- n8n-nodes-base.httpRequest - httpRequest
- Config choices
- Version 4.2
Block 3 - When clicking ‘Test workflow’
- Type / Role
- n8n-nodes-base.manualTrigger - manualTrigger
- Config choices
- Version 1
Block 4 - Execute Workflow Trigger
- Type / Role
- n8n-nodes-base.executeWorkflowTrigger - executeWorkflowTrigger
- Config choices
- Version 1
Block 5 - fetch_last_registered
- Type / Role
- n8n-nodes-base.postgres - postgres
- Config choices
- Version 2.5
Block 6 - get_videos
- Type / Role
- n8n-nodes-base.youTube - youTube
- Config choices
- Version 1
Block 7 - if_is_empty
- Type / Role
- n8n-nodes-base.if - if
- Config choices
- Version 2.2
Block 8 - Postgres
- Type / Role
- n8n-nodes-base.postgres - postgres
- Config choices
- Version 2.5
Block 9 - insert_items
- Type / Role
- n8n-nodes-base.postgres - postgres
- Config choices
- Version 2.5
Block 10 - create_table
- Type / Role
- n8n-nodes-base.postgres - postgres
- Config choices
- Version 2.5
Block 11 - remove_shorts
- Type / Role
- n8n-nodes-base.code - code
- Config choices
- Version 2
Block 12 - create_query
- Type / Role
- n8n-nodes-base.code - code
- Config choices
- Version 2
Block 13 - structure_data
- Type / Role
- n8n-nodes-base.code - code
- Config choices
- Version 2
Block 14 - if_empty
- Type / Role
- n8n-nodes-base.if - if
- Config choices
- Version 2.2
Block 15 - already_populated
- Type / Role
- n8n-nodes-base.set - set
- Config choices
- Version 3.4
Block 16 - map_data
- Type / Role
- n8n-nodes-base.set - set
- Config choices
- Version 3.4
Block 17 - sanitize_data
- Type / Role
- n8n-nodes-base.code - code
- Config choices
- Version 2
Block 18 - Sticky Note
- Type / Role
- n8n-nodes-base.stickyNote - stickyNote
- Config choices
- Version 1
Block 19 - Sticky Note1
- Type / Role
- n8n-nodes-base.stickyNote - stickyNote
- Config choices
- Version 1
Block 20 - see table
- Type / Role
- n8n-nodes-base.postgres - postgres
- Config choices
- Version 2.5
Block 21 - drop table
- Type / Role
- n8n-nodes-base.postgres - postgres
- Config choices
- Version 2.5
3. Summary Table
| Workflow | Youtube outlier detector (find trending content based on your competitors) |
|---|---|
| Complexity | advanced |
| Nodes | 21 |
| Categories | Market Research |
| Author | Leonardo Grigorio |
| Published | 14 Feb 2025 |
4. Reproducing the Workflow from Scratch
-
1. Download the workflow JSON
Use the JSON export at /data/workflows/2903/2903.json as the source template for this automation.
-
2. Import the template into n8n
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
-
3. Configure credentials and variables
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
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4. Test with sample data
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
-
5. Activate and monitor
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
5. General Notes & Resources
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
Frequently asked questions
What does Youtube outlier detector (find trending content based on your competitors) do?
Video explanation This n8n workflow helps you identify trending videos within your niche by detecting outlier videos that significantly outperform a c...
What do I need before importing this workflow?
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Can I customize this workflow?
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Market Research use case.