YouTube Channel Monetization Detector

Scan a YouTube channel's recent videos and detect which brands sponsor it, which affiliate programs it runs, and how aggressively it monetizes — by parsing every video description for sponsor mentions, affiliate-link fingerprints, promo codes, and creator-side monetization platforms (Patreon / Ko-fi / Buy Me a Coffee / Shopify / etc.). Returns a per-channel summary plus a per-video breakdown.

This is Wappalyzer for YouTube channels — a complementary tool to (not a replacement for) classic comment / transcript scrapers. Built for brand marketers doing competitive intel, creator agencies sourcing deals, advertisers vetting channels before sponsorship spend, and deal databases building a creator-economy data layer.

Powered by yt-dlp (no scraping fragility, no API key required) with optional YouTube Data API key for higher quota.

What you get

Two row types in the output dataset:

Channel summary (one per run)

Field	Type	Notes
result_type	string	Always "channel_summary"
channel_id	string	YouTube channel ID (UC...)
channel_name	string	Channel display name
channel_url	string	Canonical channel URL
videos_analyzed	integer	How many videos we scanned
videos_with_monetization_signal	integer	Subset that had any sponsor/affiliate/monetization marker
monetization_density_score	number	0-100, share of videos with monetization signals
total_links_in_descriptions	integer	Raw link count across all scanned descriptions
sponsors_detected	array	[{brand, mention_count, first_seen_upload_date}] — brands named in classic sponsor language
top_affiliate_programs	array	[{program, video_count, sample_links}] — Amazon Associates, ShareASale, NordVPN, BetterHelp, etc.
top_monetization_platforms	array	[{platform, video_count}] — Patreon, Ko-fi, Buy Me A Coffee, Shopify, etc.
promo_codes_detected	array	Branded promo codes (e.g. MRBEAST10, SAVE20)

Per-video record (one per analyzed video)

Field	Type	Notes
result_type	string	Always "video"
channel_id	string	YouTube channel ID
video_id	string	YouTube video ID
video_url	string	Full URL
title	string	Video title
upload_date	string	YYYYMMDD
view_count / duration	number	Standard metadata
affiliate_links	array	[{url, program}]
monetization_links	array	[{url, platform}]
sponsor_mentions	array	Names captured from sponsor-language patterns
promo_codes	array	Promo codes captured
all_links_count	integer	Raw link count for this video

Use cases

• Brand competitive intel — see every channel sponsoring Magic Spoon / NordVPN / BetterHelp this month, sized by monetization density.
• Creator-agency deal sourcing — identify under-monetized channels (high views, low sponsor density) ripe for representation.
• Advertiser channel vetting — before booking a $50K integration, verify the channel actually runs sponsorships and which competing brands it's already partnered with.
• Creator-economy databases — populate Patreon-style sponsor-deal tables programmatically.
• Competitive analyst — track which sponsors a competing creator just landed (first-seen date) to time your own outreach.
• Brand safety — surface every channel that runs Raid Shadow Legends sponsorships before approving them for a brand-safe campaign.

Quick start

Input JSON:

{
  "channel_url": "https://www.youtube.com/@MrBeast",
  "max_videos": 50
}

Sample summary output (truncated):

{
  "result_type": "channel_summary",
  "channel_id": "UCX6OQ3DkcsbYNE6H8uQQuVA",
  "channel_name": "MrBeast",
  "videos_analyzed": 50,
  "videos_with_monetization_signal": 47,
  "monetization_density_score": 94.0,
  "sponsors_detected": [
    {"brand": "Bass Pro Shops", "mention_count": 1, "first_seen_upload_date": "20260418"},
    {"brand": "Shopify", "mention_count": 8, "first_seen_upload_date": "20260101"}
  ],
  "top_affiliate_programs": [
    {"program": "Amazon Associates", "video_count": 12, "sample_links": ["https://amzn.to/abc123"]}
  ],
  "top_monetization_platforms": [
    {"platform": "Shopify Storefront", "video_count": 50}
  ],
  "promo_codes_detected": ["BEAST10", "MRBEAST"]
}

Python SDK example

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("nexgendata/youtube-channel-monetization-detector").call(run_input={
    "channel_url": "https://www.youtube.com/@MKBHD",
    "max_videos": 30,
})
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
summary = next(i for i in items if i["result_type"] == "channel_summary")
print(f"Density: {summary['monetization_density_score']}%")
for s in summary["sponsors_detected"][:10]:
    print(f"  {s['brand']:40} mentioned in {s['mention_count']} videos")

cURL example

curl -X POST "https://api.apify.com/v2/acts/nexgendata~youtube-channel-monetization-detector/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"channel_url":"@MarquesBrownlee","max_videos":25}'

Integrations

• Zapier / Make.com / n8n — schedule a weekly scan of your competitor's channel and alert when a new sponsor first appears.
• Postgres / Snowflake / BigQuery — sync the dataset for sponsor-trends dashboards.
• CRM — auto-enrich creator records in HubSpot / Salesforce with detected sponsor lists.

Pricing

Pay-per-event:

• Actor start: $0.00005 (one-time per run)
• Per result row (channel summary or per-video record): $0.005

Cost calculator (each run = 1 channel summary + N per-video records):

max_videos	rows pushed	Approx. cost
10	11	$0.06
25	26	$0.13
50	51	$0.26
100	101	$0.51
200	201	$1.01

Standard pricing — this is a competitive-intel tool that you typically run once per channel per week, not a high-volume scrape.

FAQ

Q: Does this need a YouTube Data API key?
A: No — uses yt-dlp to extract everything. (A future toggle will allow swapping to YouTube Data API v3 for users who already have a key.)

Q: Are sponsor mentions detected reliably?
A: Detection covers the most common sponsor-language patterns ("sponsored by X", "thanks to X", "head to X.com", "use code Y"). Brands that hide sponsorships in pure visuals or music drops will be missed — that's a fundamental limit of description-based detection. Affiliate links and promo codes are detected with high precision because they have machine-readable fingerprints.

Q: How is monetization_density_score calculated?
A: 100 × (videos with any monetization signal) / (videos analyzed). A score of 80+ means the channel monetizes nearly every video; <20 means very lightly monetized.

Q: What's the relationship to YouTube Comments / Transcript scrapers?
A: Complementary. The transcript scraper extracts spoken content; the comments scraper pulls audience reactions. This actor focuses on the description block — which is where the actual deal flow (sponsorship attribution, affiliate links) lives.

Q: Are unlisted / private videos covered?
A: No — only public videos accessible via yt-dlp's channel feed.

Q: Will YouTube rate-limit / block this?
A: yt-dlp's rate of access is mild (channel listing + per-video metadata, no actual downloads). For very large max_videos values, expect ~1-2 minutes of runtime as we space requests politely.

Related actors from nexgendata

• YouTube Comments Scraper — pair monetization detection with comment-sentiment analysis.
• YouTube Transcript Scraper — full transcripts of every analyzed video.
• Shopify Store Detector — when monetization detection surfaces a creator's Shopify storefront, this actor probes the store stack.

About nexgendata

Built and maintained by nexgendata — a portfolio of 160+ specialized scrapers and MCP servers. Need higher volume, custom output, or a private fork? Email steve_corbeil@hotmail.com.