YouTube Channel Finder: Influencer Category (Nano/Micro/Macro)

YouTube Channel Finder — Influencer Tier, Reach and Niche

YouTube Channel Finder: Influencer Category (Nano/Micro/Macro) searches YouTube by keyword or direct URL and returns each channel's real, parsed numeric subscriber/view/video counts, automatically classified into an influencer tier (nano to mega) with reach-efficiency metrics. It's built for influencer marketing teams sourcing creators by audience size, agencies segmenting a niche's creator landscape, and researchers who need real numbers, not raw display text like "1.2M subscribers."

What is YouTube Channel Finder: Influencer Category (Nano/Micro/Macro)?

YouTube Channel Finder: Influencer Category (Nano/Micro/Macro) is an Apify Actor that finds YouTube channels by keyword search or direct URL and converts their displayed subscriber/view/video counts into real numeric values, then classifies each into an influencer tier. Key capabilities:

• Search by keyword, or paste direct channel/video URLs to analyze specific channels
• Get real numeric subscriber, view, and video counts — parsed from text like "1.2M subscribers" into 1200000, not left as an unusable string
• Automatic influencer tier classification: nano (under 10K), micro (10K-100K), mid (100K-1M), macro (1M-10M), mega (10M+)
• Reach-efficiency metrics: views-per-subscriber and average views-per-video
• Filter by minimum/maximum real subscriber count to target one specific tier
• Filter by channel join date range and search region/language
• Optional LLM-based niche classification from the channel's description

What data can I extract with YouTube Channel Finder: Influencer Category (Nano/Micro/Macro)?

Field	Example Value	Use Case
channelName / userName	"MKBHD" / "@mkbhd"	Identify the channel
subscriberCount / subscriberCountInt	"18.2M subscribers" / 18200000	Raw display text and the real parsed number
influencerTier	"macro"	The core classification — nano/micro/mid/macro/mega
channel_views / channelViewsInt	"3.1B views" / 3100000000	Total channel views, raw and parsed
videos_uploaded / videosUploadedInt	"1,240 videos" / 1240	Total video count, raw and parsed
viewsPerSubscriber / avgViewsPerVideo	170.3 / 2500000	Reach-efficiency signals beyond raw subscriber count
description / email / location / links	channel bio / contact email / location / external links	Outreach and context data
joined_date / joinedDateIso	"Jan 12, 2008" / "2008-01-12"	Channel age and history
niche	"Tech & Electronics"	LLM-classified content category (opt-in)
error	null	Confirm whether this row resolved without issue

Real numeric parsing, not raw display text

subscriberCountInt, channelViewsInt, and videosUploadedInt are genuinely parsed from YouTube's own displayed abbreviated text (e.g., "1.2M subscribers" → 1200000) — this is what makes minSubscribers/maxSubscribers filtering and influencerTier classification possible at all. When a count can't be parsed, the channel is honestly excluded from subscriber-range filtering rather than assigned a guessed value.

Influencer tier and reach-efficiency metrics

influencerTier buckets channels using a fixed, disclosed threshold: nano under 10K subscribers, micro 10K-100K, mid 100K-1M, macro 1M-10M, and mega 10M+. viewsPerSubscriber and avgViewsPerVideo are computed directly from the real parsed counts, giving a reach-efficiency signal that raw subscriber count alone doesn't show — two channels with the same subscriber count can have very different average engagement per video.

Optional AI niche classification

niche is populated only when aiNicheClassification is on and a valid AI provider key is supplied — an LLM classifies the channel's content niche from its real description text. On any failure or missing key, niche is left null, never guessed.

Why not build this yourself?

YouTube's official Data API does expose channel statistics, but requires API key management, quota limits, and doesn't include influencer-tier classification, reach-efficiency scoring, or the min/max subscriber filtering this Actor provides out of the box. Building an equivalent tool yourself means handling YouTube's own subscriber-count text parsing (which varies by locale and abbreviation format) and building the tier-classification and reach-metric logic from scratch. All of that is already implemented in this Actor's source.

How to use data extracted from YouTube?

Influencer marketing and creator sourcing

Search your target niche keyword with minSubscribers/maxSubscribers set to your campaign's target tier (e.g. 10,000-100,000 for micro-influencers), then use viewsPerSubscriber to compare reach efficiency across candidates before outreach.

Agencies segmenting a niche's creator landscape

Run a set of niche keywords and export influencerTier/channelViewsInt to map how many creators exist at each audience-size tier in a given content category, informing campaign budget allocation across tiers.

Market and content research

Compare avgViewsPerVideo/joined_date across established versus newer channels in the same niche to see how quickly newer entrants are catching up in reach.

AI agents and automated pipelines

An agent can call this Actor to pre-screen creator candidates before triggering an outreach workflow, filtering on influencerTier and niche (when enabled) to focus only on relevant, appropriately-sized channels.

🔼 Input sample

Parameter	Required	Type	Description	Example Value
keywords	No	array	Keywords to search YouTube for channels	["fitness coach"]
urls	No	array	Direct channel/video URLs to analyze (skips search)	["https://www.youtube.com/@mkbhd"]
minSubscribers / maxSubscribers	No	integer (min 0)	Real numeric subscriber range; 0 = no limit	10000, 100000
channelCount	No	integer (1-50)	Max channels fetched per keyword; clamped to 50	10 (default)
searchRegion	No	string (enum)	YouTube search region/country code	"US" (default)
language	No	string (enum)	Interface/result language code	"en" (default)
fromDate / toDate	No	string (date)	Channel join-date range filter	"2015-01-01" / ""
aiNicheClassification	No	boolean	LLM-based niche classification from description	false (default)
aiModel	No	string (enum)	AI provider/model for niche classification	"claude-haiku-4-5" (default)
aiApiKey	No	string (secret)	Your own AI provider API key; falls back to a provider env var	"(your API key)"
proxyConfiguration	No	object	Optional proxy; falls back to residential on blocks	{"useApifyProxy": true}

{
  "keywords": ["fitness coach"],
  "minSubscribers": 10000,
  "maxSubscribers": 100000,
  "channelCount": 20
}

Common pitfall: channelCount is hard-capped at 50 by the underlying engine — values above 50 are silently clamped to 50, matching the schema's own maximum, so requesting more won't return more per keyword.

🔽 Output sample

Output is typed, normalized JSON — one row per found or directly-analyzed channel (exportable as JSON, CSV, or Excel).

{
  "keyword": "fitness coach",
  "channelId": "UCBJycsmduvYEL83R_U4JriQ",
  "channelName": "MKBHD",
  "userName": "@mkbhd",
  "canonicalChannelUrl": "https://www.youtube.com/@mkbhd",
  "url": "https://www.youtube.com/@mkbhd",
  "subscriberCount": "18.2M subscribers",
  "subscriberCountInt": 18200000,
  "influencerTier": "macro",
  "channel_views": "3.1B views",
  "channelViewsInt": 3100000000,
  "videos_uploaded": "1,240 videos",
  "videosUploadedInt": 1240,
  "viewsPerSubscriber": 170.3,
  "avgViewsPerVideo": 2500000,
  "description": "Videos about tech...",
  "email": null,
  "location": "United States",
  "links": ["https://twitter.com/mkbhd"],
  "joined_date": "Mar 21, 2008",
  "joinedDateIso": "2008-03-21",
  "niche": null,
  "scrapedAt": "2026-07-26T14:02:11Z",
  "error": null
}

All 24 fields shown above are the Actor's real dataset columns — this is the full output shape, not a subset.

How do you filter and target specific channels?

keywords versus urls is the discovery-vs-direct axis — search broadly or analyze exact channels you already know. minSubscribers/maxSubscribers is the genuine quality/tier threshold, letting you target one influencer tier precisely (e.g. 10,000-100,000 for micro-influencers) using real parsed counts, not raw display text. fromDate/toDate scopes by channel age. searchRegion/language narrow discovery-mode results to a specific market. Volume is controlled by channelCount (per keyword, capped at 50).

{ "keywords": ["tech reviews"], "minSubscribers": 1000000, "maxSubscribers": 10000000, "searchRegion": "US" }

{ "urls": ["https://www.youtube.com/@mkbhd", "https://www.youtube.com/@mrbeast"], "aiNicheClassification": true, "aiApiKey": "(your key)" }

{ "keywords": ["cooking channel"], "minSubscribers": 10000, "maxSubscribers": 100000, "fromDate": "2018-01-01" }

▶️ Want to try other YouTube scrapers?

Scraper Name	What it extracts
YouTube Comments Scraper With Commenter Lead Enrichment	Video comments with commenter lead data
YouTube Scraper: Channel, Playlist & Community Data Insights	Deeper channel/playlist/community data
YouTube Transcript Scraper & Chapter Extractor	Video transcripts and chapter markers
Instagram Profile Scraper & Account Privacy Checker	Comparable account-verification data for Instagram
Instagram Followers: With Bot Profile checker	Comparable audience-quality scoring for Instagram
TikTok Trending Hashtags With Momentum Analytics Scraper	Comparable creator/trend discovery for TikTok

How to extract YouTube data programmatically

Every run is a standard Apify Actor call — one API request with your token, JSON input, JSON results back.

Python example

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_API_TOKEN>")

run_input = {
    "keywords": ["fitness coach"],
    "minSubscribers": 10000,
    "maxSubscribers": 100000,
}

run = client.actor("<YOUR_USERNAME>/youtube-channel-finder-influencer-category-nano-micro-macro").call(run_input=run_input)

for channel in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(channel["channelName"], channel["influencerTier"], channel["subscriberCountInt"])

Export to spreadsheets or CRM

Export the dataset directly as CSV or Excel, then map channelName/email to your CRM's contact fields and influencerTier/viewsPerSubscriber to a scoring column for outreach prioritization.

Is it legal to scrape YouTube channel data?

Channel names, descriptions, and contact emails (when a creator lists one publicly) can be personal data for individual creators, so GDPR and CCPA obligations apply to storing and using this data. Public YouTube channel pages are visible to any logged-out visitor. Consult legal counsel for commercial applications involving bulk storage of personal data.

❓ FAQ

How accurate are the subscriber/view counts?

They're real, parsed numeric values from YouTube's own displayed text — not estimates. When a count can't be parsed, the channel is excluded from subscriber-range filtering rather than assigned a guessed number.

What are the exact influencer tier boundaries?

Nano: under 10,000 subscribers. Micro: 10,000-100,000. Mid: 100,000-1,000,000. Macro: 1,000,000-10,000,000. Mega: 10,000,000+.

How accurate is the AI niche classification?

It's genuine LLM classification from the channel's real description text, but it's entirely optional, requires your own API key, and returns niche: null on any failure rather than a guessed category.

How many channels can I get per keyword?

channelCount (1-50, default 10) caps this — the underlying engine hard-caps at 50 regardless of a higher requested value.

Do I need a YouTube account to use this?

No — the Actor reads YouTube's public channel and search pages, requiring no login or API key.

Does YouTube Channel Finder: Influencer Category work with Claude, ChatGPT, and AI agent frameworks?

It has no dedicated MCP server registration in this Actor's current setup, but it is callable as a standard Apify API endpoint by any agent framework that can make an authenticated HTTP request and parse JSON.

How does this compare to other YouTube channel-finder scrapers?

No independently verifiable competitor listing could be confirmed at the time of writing, so no specific comparison is made here. This Actor's distinguishing feature is the real numeric parsing plus automatic influencer-tier classification and reach-efficiency scoring, computed at no extra request cost.

Can I target a specific influencer tier directly?

Yes — combine minSubscribers and maxSubscribers to bound a specific tier's real subscriber range (e.g. 10,000-100,000 for micro-influencers).

Conclusion

YouTube Channel Finder: Influencer Category (Nano/Micro/Macro) turns a keyword search or URL list into real, numerically-parsed channel data — genuine influencer-tier classification and reach-efficiency metrics, not raw display text. It's built for influencer marketing teams, agencies, and researchers who need accurate audience-size segmentation. Start a run with your target keywords or URLs and a subscriber range to get tier-classified channels back in one pass.